Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

Robust Kalman filter design for predictive wind shear detection

NASA Technical Reports Server (NTRS)

Stratton, Alexander D.; Stengel, Robert F.

1991-01-01

Severe, low-altitude wind shear is a threat to aviation safety. Airborne sensors under development measure the radial component of wind along a line directly in front of an aircraft. In this paper, optimal estimation theory is used to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. This paper presents statistical analysis methods to refine wind shear detection algorithm robustness. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.

Longitudinal driver model and collision warning and avoidance algorithms based on human driving databases

NASA Astrophysics Data System (ADS)

Lee, Kangwon

Intelligent vehicle systems, such as Adaptive Cruise Control (ACC) or Collision Warning/Collision Avoidance (CW/CA), are currently under development, and several companies have already offered ACC on selected models. Control or decision-making algorithms of these systems are commonly evaluated under extensive computer simulations and well-defined scenarios on test tracks. However, they have rarely been validated with large quantities of naturalistic human driving data. This dissertation utilized two University of Michigan Transportation Research Institute databases (Intelligent Cruise Control Field Operational Test and System for Assessment of Vehicle Motion Environment) in the development and evaluation of longitudinal driver models and CW/CA algorithms. First, to examine how drivers normally follow other vehicles, the vehicle motion data from the databases were processed using a Kalman smoother. The processed data was then used to fit and evaluate existing longitudinal driver models (e.g., the linear follow-the-leader model, the Newell's special model, the nonlinear follow-the-leader model, the linear optimal control model, the Gipps model and the optimal velocity model). A modified version of the Gipps model was proposed and found to be accurate in both microscopic (vehicle) and macroscopic (traffic) senses. Second, to examine emergency braking behavior and to evaluate CW/CA algorithms, the concepts of signal detection theory and a performance index suitable for unbalanced situations (few threatening data points vs. many safe data points) are introduced. Selected existing CW/CA algorithms were found to have a performance index (geometric mean of true-positive rate and precision) not exceeding 20%. To optimize the parameters of the CW/CA algorithms, a new numerical optimization scheme was developed to replace the original data points with their representative statistics. A new CW/CA algorithm was proposed, which was found to score higher than 55% in the performance index. This dissertation provides a model of how drivers follow lead-vehicles that is much more accurate than other models in the literature. Furthermore, the data-based approach was used to confirm that a CW/CA algorithm utilizing lead-vehicle braking was substantially more effective than existing algorithms, leading to collision warning systems that are much more likely to contribute to driver safety.

Traffic sign recognition by color segmentation and neural network

NASA Astrophysics Data System (ADS)

Surinwarangkoon, Thongchai; Nitsuwat, Supot; Moore, Elvin J.

2011-12-01

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.

PubMed

Botwey, Ransford Henry; Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G

2014-01-01

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Real-Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

NASA Astrophysics Data System (ADS)

Kodera, Yuki

2018-01-01

Large earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late-onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku-oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

Small-target leak detection for a closed vessel via infrared image sequences

NASA Astrophysics Data System (ADS)

Zhao, Ling; Yang, Hongjiu

2017-03-01

This paper focus on a leak diagnosis and localization method based on infrared image sequences. Some problems on high probability of false warning and negative affect for marginal information are solved by leak detection. An experimental model is established for leak diagnosis and localization on infrared image sequences. The differential background prediction is presented to eliminate the negative affect of marginal information on test vessel based on a kernel regression method. A pipeline filter based on layering voting is designed to reduce probability of leak point false warning. A synthesize leak diagnosis and localization algorithm is proposed based on infrared image sequences. The effectiveness and potential are shown for developed techniques through experimental results.

Implementing a C++ Version of the Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Guillemot, C.; Murray, J. R.

2015-12-01

The earthquake early warning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.

Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Lee, C.

2013-12-01

The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

NASA Astrophysics Data System (ADS)

Intrieri, Emanuele; Bardi, Federica; Fanti, Riccardo; Gigli, Giovanni; Fidolini, Francesco; Casagli, Nicola; Costanzo, Sandra; Raffo, Antonio; Di Massa, Giuseppe; Capparelli, Giovanna; Versace, Pasquale

2017-10-01

A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

Lane change warning threshold based on driver perception characteristics.

PubMed

Wang, Chang; Sun, Qinyu; Fu, Rui; Li, Zhen; Zhang, Qiong

2018-08-01

Lane Change Warning system (LCW) is exploited to alleviate driver workload and improve the safety performance of lane changes. Depending on the secure threshold, the lane change warning system could transmit caution to drivers. Although the system possesses substantial benefits, it may perturb the conventional operating of the driver and affect driver judgment if the warning threshold does not conform to the driver perception of safety. Therefore, it is essential to establish an appropriate warning threshold to enhance the accuracy rate and acceptability of the lane change warning system. This research aims to identify the threshold that conforms to the driver perception of the ability to safely change lanes with a rear vehicle fast approaching. We propose a theoretical warning model of lane change based on a safe minimum distance and deceleration of the rear vehicle. For the purpose of acquiring the different safety levels of lane changes, 30 licensed drivers are recruited and we obtain the extreme moments represented by driver perception characteristics from a Front Extremity Test and a Rear Extremity Test implemented on the freeway. The required deceleration of the rear vehicle corresponding to the extreme time is calculated according to the proposed model. In light of discrepancies in the deceleration in these extremity experiments, we determine two levels of a hierarchical warning system. The purpose of the primary warning is to remind drivers of the existence of potentially dangerous vehicles and the second warning is used to warn the driver to stop changing lanes immediately. We use the signal detection theory to analyze the data. Ultimately, we confirm that the first deceleration threshold is 1.5 m/s 2 and the second deceleration threshold is 2.7 m/s 2 . The findings provide the basis for the algorithm design of LCW and enhance the acceptability of the intelligent system. Copyright © 2018 Elsevier Ltd. All rights reserved.

Lightning Initiation Forecasting: An Operational Dual-Polarimetric Radar Technique

NASA Technical Reports Server (NTRS)

Woodard, Crystal J.; Carey, L. D.; Petersen, W. A.; Roeder, W. P.

2011-01-01

The objective of this NASA MSFC and NOAA CSTAR funded study is to develop and test operational forecast algorithms for the prediction of lightning initiation utilizing the C-band dual-polarimetric radar, UAHuntsville's Advanced Radar for Meteorological and Operational Research (ARMOR). Although there is a rich research history of radar signatures associated with lightning initiation, few studies have utilized dual-polarimetric radar signatures (e.g., Z(sub dr) columns) and capabilities (e.g., fuzzy-logic particle identification [PID] of precipitation ice) in an operational algorithm for first flash forecasting. The specific goal of this study is to develop and test polarimetric techniques that enhance the performance of current operational radar reflectivity based first flash algorithms. Improving lightning watch and warning performance will positively impact personnel safety in both work and leisure environments. Advanced warnings can provide space shuttle launch managers time to respond appropriately to secure equipment and personnel, while they can also provide appropriate warnings for spectators and players of leisure sporting events to seek safe shelter. Through the analysis of eight case dates, consisting of 35 pulse-type thunderstorms and 20 non-thunderstorm case studies, lightning initiation forecast techniques were developed and tested. The hypothesis is that the additional dual-polarimetric information could potentially reduce false alarms while maintaining high probability of detection and increasing lead-time for the prediction of the first lightning flash relative to reflectivity-only based techniques. To test the hypothesis, various physically-based techniques using polarimetric variables and/or PID categories, which are strongly correlated to initial storm electrification (e.g., large precipitation ice production via drop freezing), were benchmarked against the operational reflectivity-only based approaches to find the best compromise between forecast skill and lead-time. Forecast skill is determined by statistical analysis of probability of detection (POD), false alarm ratio (FAR), Operational Utility Index (OUI), and critical success index (CSI).

The pathway to earthquake early warning in the US

NASA Astrophysics Data System (ADS)

Allen, R. M.; Given, D. D.; Heaton, T. H.; Vidale, J. E.; West Coast Earthquake Early Warning Development Team

2013-05-01

The development of earthquake early warning capabilities in the United States is now accelerating and expanding as the technical capability to provide warning is demonstrated and additional funding resources are making it possible to expand the current testing region to the entire west coast (California, Oregon and Washington). Over the course of the next two years we plan to build a prototype system that will provide a blueprint for a full public system in the US. California currently has a demonstrations warning system, ShakeAlert, that provides alerts to a group of test users from the public and private sector. These include biotech companies, technology companies, the entertainment industry, the transportation sector, and the emergency planning and response community. Most groups are currently in an evaluation mode, receiving the alerts and developing protocols for future response. The Bay Area Rapid Transit (BART) system is the one group who has now implemented an automated response to the warning system. BART now stops trains when an earthquake of sufficient size is detected. Research and development also continues to develop improved early warning algorithms to better predict the distribution of shaking in large earthquakes when the finiteness of the source becomes important. The algorithms under development include the use of both seismic and GPS instrumentation and integration with existing point source algorithms. At the same time, initial testing and development of algorithms in and for the Pacific Northwest is underway. In this presentation we will review the current status of the systems, highlight the new research developments, and lay out a pathway to a full public system for the US west coast. The research and development described is ongoing at Caltech, UC Berkeley, University of Washington, ETH Zurich, Southern California Earthquake Center, and the US Geological Survey, and is funded by the Gordon and Betty Moore Foundation and the US Geological Survey.

Health management system for rocket engines

NASA Technical Reports Server (NTRS)

Nemeth, Edward

1990-01-01

The functional framework of a failure detection algorithm for the Space Shuttle Main Engine (SSME) is developed. The basic algorithm is based only on existing SSME measurements. Supplemental measurements, expected to enhance failure detection effectiveness, are identified. To support the algorithm development, a figure of merit is defined to estimate the likelihood of SSME criticality 1 failure modes and the failure modes are ranked in order of likelihood of occurrence. Nine classes of failure detection strategies are evaluated and promising features are extracted as the basis for the failure detection algorithm. The failure detection algorithm provides early warning capabilities for a wide variety of SSME failure modes. Preliminary algorithm evaluation, using data from three SSME failures representing three different failure types, demonstrated indications of imminent catastrophic failure well in advance of redline cutoff in all three cases.

Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

PubMed

Huang, Ping-Tzan; Jong, Tai-Lang; Li, Chien-Ming; Chen, Wei-Ling; Lin, Chia-Hung

2017-08-01

Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

An algorithm for power line detection and warning based on a millimeter-wave radar video.

PubMed

Ma, Qirong; Goshi, Darren S; Shih, Yi-Chi; Sun, Ming-Ting

2011-12-01

Power-line-strike accident is a major safety threat for low-flying aircrafts such as helicopters, thus an automatic warning system to power lines is highly desirable. In this paper we propose an algorithm for detecting power lines from radar videos from an active millimeter-wave sensor. Hough Transform is employed to detect candidate lines. The major challenge is that the radar videos are very noisy due to ground return. The noise points could fall on the same line which results in signal peaks after Hough Transform similar to the actual cable lines. To differentiate the cable lines from the noise lines, we train a Support Vector Machine to perform the classification. We exploit the Bragg pattern, which is due to the diffraction of electromagnetic wave on the periodic surface of power lines. We propose a set of features to represent the Bragg pattern for the classifier. We also propose a slice-processing algorithm which supports parallel processing, and improves the detection of cables in a cluttered background. Lastly, an adaptive algorithm is proposed to integrate the detection results from individual frames into a reliable video detection decision, in which temporal correlation of the cable pattern across frames is used to make the detection more robust. Extensive experiments with real-world data validated the effectiveness of our cable detection algorithm. © 2011 IEEE

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Stano, Geoffrey T.; Gatlin, Patrick N.

2013-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. In order to become a viable option for operational forecasters to incorporate into their severe storm monitoring process, the total lightning jump must be placed into the framework of several severe storm conceptual models (e.g., radar evolution, storm morphology) which forecasters have built through training and experience. Thus, one of the goals of this study is to examine and relate the lightning jump concept to often used radar parameters (e.g., dBZ vertical structure, VIL, MESH, MESO/shear) in the warning environment. Tying lightning trends and lightning jump occurrences to these radar based parameters will provide forecasters with an additional tool that they can use to build an accurate realtime depiction as to what is going on in a given environment. Furthermore, relating the lightning jump concept to these parameters could also increase confidence in a warning decision they have already made, help tip the scales on whether or not to warn on a given storm, or to draw the forecaster s attention to a particular storm that is rapidly developing. Furthermore the lightning information will add vital storm scale information in regions that are not well covered by radar, or when radar failures occur. The physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relation to updraft strength, updraft volume, precipitation -sized ice mass, etc.; however, very few have related the concept of the lightning jump and manifestation of severe weather to storm dynamics and microphysics using multi -Doppler and polarimetric radar techniques. Therefore, the second half of this study will combine the lightning jump algorithm and these radar techniques in order to place the lightning jump concept into a physical and dynamical framework. This analysis includes examining such parameters as mixed phase precipitation volume, charging zone, updraft strength and updraft volume. Such a study should provide increased understanding of and confidence in the strengths and limitations of the lightning jump algorithm in the storm warning process.

Multiple-Threshold Event Detection and Other Enhancements to the Virtual Seismologist (VS) Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Fischer, M.; Caprio, M.; Cua, G. B.; Heaton, T. H.; Clinton, J. F.; Wiemer, S.

2009-12-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to earthquake early warning (EEW) being implemented by the Swiss Seismological Service at ETH Zurich. The application of Bayes’ theorem in earthquake early warning states that the most probable source estimate at any given time is a combination of contributions from a likelihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS algorithm was one of three EEW algorithms involved in the California Integrated Seismic Network (CISN) real-time EEW testing and performance evaluation effort. Its compelling real-time performance in California over the last three years has led to its inclusion in the new USGS-funded effort to develop key components of CISN ShakeAlert, a prototype EEW system that could potentially be implemented in California. A significant portion of VS code development was supported by the SAFER EEW project in Europe. We discuss recent enhancements to the VS EEW algorithm. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to be declared an event to reduce false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and it requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) to a hybrid on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Offline analysis on Swiss and California waveform datasets indicate that the multiple-threshold approach is faster and more reliable for larger events than the earlier version of the VS codes. This multiple-threshold approach is well-suited for implementation on a wide range of devices, from embedded processor systems installed at a seismic stations, to small autonomous networks for local warnings, to large-scale regional networks such as the CISN. In addition, we quantify the influence of systematic use of prior information and Vs30-based corrections for site amplification on VS magnitude estimation performance, and describe how components of the VS algorithm will be integrated into non-EEW standard network processing procedures at CHNet, the national broadband / strong motion network in Switzerland. These enhancements to the VS codes will be transitioned from off-line to real-time testing at CHNet in Europe in the coming months, and will be incorporated into the development of key components of CISN ShakeAlert prototype system in California.

Tsunami Detection by High-Frequency Radar Beyond the Continental Shelf

NASA Astrophysics Data System (ADS)

Grilli, Stéphan T.; Grosdidier, Samuel; Guérin, Charles-Antoine

2016-12-01

Where coastal tsunami hazard is governed by near-field sources, such as submarine mass failures or meteo-tsunamis, tsunami propagation times may be too small for a detection based on deep or shallow water buoys. To offer sufficient warning time, it has been proposed to implement early warning systems relying on high-frequency (HF) radar remote sensing, that can provide a dense spatial coverage as far offshore as 200-300 km (e.g., for Diginext Ltd.'s Stradivarius radar). Shore-based HF radars have been used to measure nearshore currents (e.g., CODAR SeaSonde® system; http://www.codar.com/), by inverting the Doppler spectral shifts, these cause on ocean waves at the Bragg frequency. Both modeling work and an analysis of radar data following the Tohoku 2011 tsunami, have shown that, given proper detection algorithms, such radars could be used to detect tsunami-induced currents and issue a warning. However, long wave physics is such that tsunami currents will only rise above noise and background currents (i.e., be at least 10-15 cm/s), and become detectable, in fairly shallow water which would limit the direct detection of tsunami currents by HF radar to nearshore areas, unless there is a very wide shallow shelf. Here, we use numerical simulations of both HF radar remote sensing and tsunami propagation to develop and validate a new type of tsunami detection algorithm that does not have these limitations. To simulate the radar backscattered signal, we develop a numerical model including second-order effects in both wind waves and radar signal, with the wave angular frequency being modulated by a time-varying surface current, combining tsunami and background currents. In each "radar cell", the model represents wind waves with random phases and amplitudes extracted from a specified (wind speed dependent) energy density frequency spectrum, and includes effects of random environmental noise and background current; phases, noise, and background current are extracted from independent Gaussian distributions. The principle of the new algorithm is to compute correlations of HF radar signals measured/simulated in many pairs of distant "cells" located along the same tsunami wave ray, shifted in time by the tsunami propagation time between these cell locations; both rays and travel time are easily obtained as a function of long wave phase speed and local bathymetry. It is expected that, in the presence of a tsunami current, correlations computed as a function of range and an additional time lag will show a narrow elevated peak near the zero time lag, whereas no pattern in correlation will be observed in the absence of a tsunami current; this is because surface waves and background current are uncorrelated between pair of cells, particularly when time-shifted by the long-wave propagation time. This change in correlation pattern can be used as a threshold for tsunami detection. To validate the algorithm, we first identify key features of tsunami propagation in the Western Mediterranean Basin, where Stradivarius is deployed, by way of direct numerical simulations with a long wave model. Then, for the purpose of validating the algorithm we only model HF radar detection for idealized tsunami wave trains and bathymetry, but verify that such idealized case studies capture well the salient tsunami wave physics. Results show that, in the presence of strong background currents, the proposed method still allows detecting a tsunami with currents as low as 0.05 m/s, whereas a standard direct inversion based on radar signal Doppler spectra fails to reproduce tsunami currents weaker than 0.15-0.2 m/s. Hence, the new algorithm allows detecting tsunami arrival in deeper water, beyond the shelf and further away from the coast, and providing an early warning. Because the standard detection of tsunami currents works well at short range, we envision that, in a field situation, the new algorithm could complement the standard approach of direct near-field detection by providing a warning that a tsunami is approaching, at larger range and in greater depth. This warning would then be confirmed at shorter range by a direct inversion of tsunami currents, from which the magnitude of the tsunami would also estimated. Hence, both algorithms would be complementary. In future work, the algorithm will be applied to actual tsunami case studies performed using a state-of-the-art long wave model, such as briefly presented here in the Mediterranean Basin.

Usage of Wireless Sensor Networks in a service based spatial data infrastructure for Landslide Monitoring and Early Warning

NASA Astrophysics Data System (ADS)

Arnhardt, C.; Fernandez-Steeger, T. M.; Walter, K.; Kallash, A.; Niemeyer, F.; Azzam, R.; Bill, R.

2007-12-01

The joint project Sensor based Landslide Early Warning System (SLEWS) aims at a systematic development of a prototyping alarm- and early warning system for the detection of mass movements by application of an ad hoc wireless sensor network (WSN). Next to the development of suitable sensor setups, sensor fusion and network fusion are applied to enhance data quality and reduce false alarm rates. Of special interest is the data retrieval, processing and visualization in GI-Systems. Therefore a suitable serviced based Spatial Data Infrastructure (SDI) will be developed with respect to existing and upcoming Open Geospatial Consortium (OGC) standards.The application of WSN provides a cheap and easy to set up solution for special monitoring and data gathering in large areas. Measurement data from different low-cost transducers for deformation observation (acceleration, displacement, tilting) is collected by distributed sensor nodes (motes), which interact separately and connect each other in a self-organizing manner. Data are collected and aggregated at the beacon (transmission station) and further operations like data pre-processing and compression can be performed. The WSN concept provides next to energy efficiency, miniaturization, real-time monitoring and remote operation, but also new monitoring strategies like sensor and network fusion. Since not only single sensors can be integrated at single motes either cross-validation or redundant sensor setups are possible to enhance data quality. The planned monitoring and information system will include a mobile infrastructure (information technologies and communication components) as well as methods and models to estimate surface deformation parameters (positioning systems). The measurements result in heterogeneous observation sets that have to be integrated in a common adjustment and filtering approach. Reliable real-time information will be obtained using a range of sensor input and algorithms, from which early warnings and prognosis may be derived. Implementation of sensor algorithms is an important task to form the business logic. This will be represented in self-contained web-based processing services (WPS). In the future different types of sensor networks can communicate via an infrastructure of OGC services using an interoperable way by standardized protocols as the Sensor Markup Language (SensorML) and Observations & Measurements Schema (O&M). Synchronous and asynchronous information services as the Sensor Alert Service (SAS) and the Web Notification Services (WNS) will provide defined users and user groups with time-critical readings from the observation site. Techniques using services for visualizing mapping data (WMS), meta data (CSW), vector (WFS) and raster data (WCS) will range from high detailed expert based output to fuzzy graphical warning elements.The expected results will be an advancement regarding classical alarm and early warning systems as the WSN are free scalable, extensible and easy to install.

Air quality early-warning system for cities in China

NASA Astrophysics Data System (ADS)

Xu, Yunzhen; Yang, Wendong; Wang, Jianzhou

2017-01-01

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.

A triangular climate-based decision model to forecast crop anomalies in Kenya

NASA Astrophysics Data System (ADS)

Guimarães Nobre, G.; Davenport, F.; Veldkamp, T.; Jongman, B.; Funk, C. C.; Husak, G. J.; Ward, P.; Aerts, J.

2017-12-01

By the end of 2017, the world is expected to experience unprecedented demands for food assistance where, across 45 countries, some 81 million people will face a food security crisis. Prolonged droughts in Eastern Africa are playing a major role in these crises. To mitigate famine risk and save lives, government bodies and international donor organisations are increasingly building up efforts to resolve conflicts and secure humanitarian relief. Disaster-relief and financing organizations traditionally focus on emergency response, providing aid after an extreme drought event, instead of taking actions in advance based on early warning. One of the reasons for this approach is that the seasonal risk information provided by early warning systems is often considered highly uncertain. Overcoming the reluctance to act based on early warnings greatly relies on understanding the risk of acting in vain, and assessing the cost-effectiveness of early actions. This research develops a triangular climate-based decision model for multiple seasonal time-scales to forecast strong anomalies in crop yield shortages in Kenya using Casual Discovery Algorithms and Fast and Frugal Decision Trees. This Triangular decision model (1) estimates the causality and strength of the relationship between crop yields and hydro climatological predictors (extracted from the Famine Early Warning Systems Network's data archive) during the crop growing season; (2) provides probabilistic forecasts of crop yield shortages in multiple time scales before the harvesting season; and (3) evaluates the cost-effectiveness of different financial mechanisms to respond to early warning indicators of crop yield shortages obtained from the model. Furthermore, we reflect on how such a model complements and advances the current state-of-art FEWS Net system, and examine its potential application to improve the management of agricultural risks in Kenya.

Improving Early Warning Systems with Categorized Course Resource Usage

ERIC Educational Resources Information Center

Waddington, R. Joseph; Nam, SungJin; Lonn, Steven; Teasley, Stephanie D.

2016-01-01

Early Warning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviors into the algorithms underlying EWSs to improve predictors of students' success or failure. Many EWSs currently…

A lightning-based nowcast-warning approach for short-duration rainfall events: Development and testing over Beijing during the warm seasons of 2006-2007

NASA Astrophysics Data System (ADS)

Wu, Fan; Cui, Xiaopeng; Zhang, Da-Lin

2018-06-01

Nowcasting short-duration (i.e., <6 h) rainfall (SDR) events is examined using total [i.e., cloud-to-ground (CG) and intra-cloud (IC)] lightning observations over the Beijing Metropolitan Region (BMR) during the warm seasons of 2006-2007. A total of 928 moderate and 554 intense SDR events, i.e., with the respective hourly rainfall rates (HRR) of 10-20 and ≥20 mm h-1, are utilized to estimate sharp-increasing rates in rainfall and lightning flash, termed as rainfall and lightning jumps, respectively. By optimizing the parameters in a lightning jump and a rainfall jump algorithm, their different jump intensity grades are verified for the above two categories of SDR events. Then, their corresponding graded nowcast-warning models are developed for the moderate and intense SDR events, respectively, with a low-grade warning for hitting more SDR events and a high-grade warning for reducing false alarms. Any issued warning in the nowcast-warning models is designed to last for 2 h after the occurrence of a lightning jump. It is demonstrated that the low-grade warnings can have the probability of detection (POD) of 67.8% (87.0%) and the high-grade warnings have the false alarms ratio (FAR) of 27.0% (22.2%) for the moderate (intense) SDR events, with an averaged lead time of 36.7 (52.0) min. The nowcast-warning models are further validated using three typical heavy-rain-producing storms that are independent from those used to develop the models. Results show that the nowcast-warning models can provide encouraging early warnings for the associated SDR events from the regional to meso-γ scales, indicating that they have a great potential in being applied to the other regions where high-resolution total lightning observations are available.

Integrated SeismoGeodetic Systsem with High-Resolution, Real-Time GNSS and Accelerometer Observation For Earthquake Early Warning Application.

NASA Astrophysics Data System (ADS)

Passmore, P. R.; Jackson, M.; Zimakov, L. G.; Raczka, J.; Davidson, P.

2014-12-01

The key requirements for Earthquake Early Warning and other Rapid Event Notification Systems are: Quick delivery of digital data from a field station to the acquisition and processing center; Data integrity for real-time earthquake notification in order to provide warning prior to significant ground shaking in the given target area. These two requirements are met in the recently developed Trimble SG160-09 SeismoGeodetic System, which integrates both GNSS and acceleration measurements using the Kalman filter algorithm to create a new high-rate (200 sps), real-time displacement with sufficient accuracy and very low latency for rapid delivery of the acquired data to a processing center. The data acquisition algorithm in the SG160-09 System provides output of both acceleration and displacement digital data with 0.2 sec delay. This is a significant reduction in the time interval required for real-time transmission compared to data delivery algorithms available in digitizers currently used in other Earthquake Early Warning networks. Both acceleration and displacement data are recorded and transmitted to the processing site in a specially developed Multiplexed Recording Format (MRF) that minimizes the bandwidth required for real-time data transmission. In addition, a built in algorithm calculates the τc and Pd once the event is declared. The SG160-09 System keeps track of what data has not been acknowledged and re-transmits the data giving priority to current data. Modified REF TEK Protocol Daemon (RTPD) receives the digital data and acknowledges data received without error. It forwards this "good" data to processing clients of various real-time data processing software including Earthworm and SeisComP3. The processing clients cache packets when a data gap occurs due to a dropped packet or network outage. The cache packet time is settable, but should not exceed 0.5 sec in the Earthquake Early Warning network configuration. The rapid data transmission algorithm was tested with different communication media, including Internet, DSL, Wi-Fi, GPRS, etc. The test results show that the data latency via most communication media do not exceed 0.5 sec nominal from a first sample in the data packet. Detailed acquisition algorithm and results of data transmission via different communication media are presented.

Advancement and results in hostile fire indication using potassium line missile warning sensors

NASA Astrophysics Data System (ADS)

Montgomery, Joel; Montgomery, Marjorie; Hardie, Russell

2014-06-01

M&M Aviation has been developing and conducting Hostile Fire Indication (HFI) tests using potassium line emission sensors for the Air Force Visible Missile Warning System (VMWS) to advance both algorithm and sensor technologies for UAV and other airborne systems for self protection and intelligence purposes. Work began in 2008 as an outgrowth of detecting and classifying false alarm sources for the VMWS using the same K-line spectral discrimination region but soon became a focus of research due to the high interest in both machine-gun fire and sniper geo-location via airborne systems. Several initial tests were accomplished in 2009 using small and medium caliber weapons including rifles. Based on these results, the Air Force Research Laboratory (AFRL) funded the Falcon Sentinel program in 2010 to provide for additional development of both the sensor concept, algorithm suite changes and verification of basic phenomenology including variance based on ammunition type for given weapons platform. Results from testing over the past 3 years have showed that the system would be able to detect and declare a sniper rifle at upwards of 3km, medium machine gun at 5km, and explosive events like hand-grenades at greater than 5km. This paper will outline the development of the sensor systems, algorithms used for detection and classification, and test results from VMWS prototypes as well as outline algorithms used for the VMWS. The Falcon Sentinel Program will be outlined and results shown. Finally, the paper will show the future work for ATD and transition efforts after the Falcon Sentinel program completed.

Tensor Fukunaga-Koontz transform for small target detection in infrared images

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Wang, Jingzhuo; Yang, Huizhen; Gong, Chenglong; Zhou, Yuanshen; Liu, Lipeng; Zhang, Zhen; Shen, Shuli

2016-09-01

Infrared small targets detection plays a crucial role in warning and tracking systems. Some novel methods based on pattern recognition technology catch much attention from researchers. However, those classic methods must reshape images into vectors with the high dimensionality. Moreover, vectorizing breaks the natural structure and correlations in the image data. Image representation based on tensor treats images as matrices and can hold the natural structure and correlation information. So tensor algorithms have better classification performance than vector algorithms. Fukunaga-Koontz transform is one of classification algorithms and it is a vector version method with the disadvantage of all vector algorithms. In this paper, we first extended the Fukunaga-Koontz transform into its tensor version, tensor Fukunaga-Koontz transform. Then we designed a method based on tensor Fukunaga-Koontz transform for detecting targets and used it to detect small targets in infrared images. The experimental results, comparison through signal-to-clutter, signal-to-clutter gain and background suppression factor, have validated the advantage of the target detection based on the tensor Fukunaga-Koontz transform over that based on the Fukunaga-Koontz transform.

Adapting ElarmS Earthquake Early Warnings for Cascadia: Development and Testing of ShakeAlerts in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Hartog, J. R.; Kress, V. C.; Thomas, T.; Malone, S. D.; Henson, I. H.; Neuhauser, D. S.

2013-12-01

As a first step in establishing an earthquake early warning system in Cascadia, we have installed the ElarmS component of the ShakeAlert system at the Pacific Northwest Seismic Network. In Cascadia our initial focus is primarily on the development of a seismo-geodetic-based real-time finite fault rupture algorithm to detect and characterize a large plate-boundary rupture in progress (see Crowell et. al., this session). In this regard the goal of the purely seismic-data-based ElarmS implementation is to 'trigger' the finite fault rupture algorithm. At the same time, however, the Cascadian ElarmS will also produce warnings for smaller onshore crustal earthquakes. While warnings from these smaller and closer earthquakes will provide shorter warning times for communities, and for less dramatic earthquakes, we intend to use them for educational purposes, and to coordinate with our regional and collaborating partners. They will also help to guide us to shorten data latencies and learn where additional instrumentation is most needed to increase performance. The accuracy of ElarmS in Cascadia is another major concern, because the current ElarmS model presumes an initial focal depth for earthquakes of 8 km based on California experience, while in Cascadia earthquakes of major concern may be as deep as 50 km, and/or occur beyond the western fringe of the seismic network. To this purpose our testing protocol is aimed at determining what changes are required to ensure top performance of an ElarmS-based warning system in Cascadia. Because of Cascadia's relatively low seismicity rate, and the paucity of data from plate boundary earthquakes there of any size, we have prioritized the development of a test system. The test system permits us to: 1) replay segments of actual seismic waveform data recorded from the PNSN and contributing seismic network stations to represent both earthquakes and noise conditions, and 2) broadcast synthetic data into the system to simulate signals we anticipate from earthquakes for which we have no actual ground motion recordings. The test system lets us also simulate various error conditions (latent and/or out-of-sequence data, telemetry drop-outs, etc.) to explore how to protect the system from them. We have also been testing the ElarmS system on real-time seismic network data for about 6 months as of the time of writing of this abstract. Using 268 channels of streaming strong motion and broad-band data, the system has produced very few false alarms and generally performed well for earthquakes between about magnitudes 2.5 and 4.5. Warning times are shorter (and the 'blind zone' smaller) in parts of the network where station density is higher and/or telemetry more fleet. One significant problem we find is that the discriminant used in northern California to differentiate local earthquake signals from teleseisms often fails in Cascadia. We are working to produce a valid teleseism detector.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699

Lane Detection on the iPhone

NASA Astrophysics Data System (ADS)

Ren, Feixiang; Huang, Jinsheng; Terauchi, Mutsuhiro; Jiang, Ruyi; Klette, Reinhard

A robust and efficient lane detection system is an essential component of Lane Departure Warning Systems, which are commonly used in many vision-based Driver Assistance Systems (DAS) in intelligent transportation. Various computation platforms have been proposed in the past few years for the implementation of driver assistance systems (e.g., PC, laptop, integrated chips, PlayStation, and so on). In this paper, we propose a new platform for the implementation of lane detection, which is based on a mobile phone (the iPhone). Due to physical limitations of the iPhone w.r.t. memory and computing power, a simple and efficient lane detection algorithm using a Hough transform is developed and implemented on the iPhone, as existing algorithms developed based on the PC platform are not suitable for mobile phone devices (currently). Experiments of the lane detection algorithm are made both on PC and on iPhone.

Research and application of a novel hybrid air quality early-warning system: A case study in China.

PubMed

Li, Chen; Zhu, Zhijie

2018-06-01

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency. Copyright © 2018 Elsevier B.V. All rights reserved.

Combining Multiple Rupture Models in Real-Time for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Minson, S. E.; Wu, S.; Beck, J. L.; Heaton, T. H.

2015-12-01

The ShakeAlert earthquake early warning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user's location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user's distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.

An on-board pedestrian detection and warning system with features of side pedestrian

NASA Astrophysics Data System (ADS)

Cheng, Ruzhong; Zhao, Yong; Wong, ChupChung; Chan, KwokPo; Xu, Jiayao; Wang, Xin'an

2012-01-01

Automotive Active Safety(AAS) is the main branch of intelligence automobile study and pedestrian detection is the key problem of AAS, because it is related with the casualties of most vehicle accidents. For on-board pedestrian detection algorithms, the main problem is to balance efficiency and accuracy to make the on-board system available in real scenes, so an on-board pedestrian detection and warning system with the algorithm considered the features of side pedestrian is proposed. The system includes two modules, pedestrian detecting and warning module. Haar feature and a cascade of stage classifiers trained by Adaboost are first applied, and then HOG feature and SVM classifier are used to refine false positives. To make these time-consuming algorithms available in real-time use, a divide-window method together with operator context scanning(OCS) method are applied to increase efficiency. To merge the velocity information of the automotive, the distance of the detected pedestrian is also obtained, so the system could judge if there is a potential danger for the pedestrian in the front. With a new dataset captured in urban environment with side pedestrians on zebra, the embedded system and its algorithm perform an on-board available result on side pedestrian detection.

Using Machine Learning for Advanced Anomaly Detection and Classification

NASA Astrophysics Data System (ADS)

Lane, B.; Poole, M.; Camp, M.; Murray-Krezan, J.

2016-09-01

Machine Learning (ML) techniques have successfully been used in a wide variety of applications to automatically detect and potentially classify changes in activity, or a series of activities by utilizing large amounts data, sometimes even seemingly-unrelated data. The amount of data being collected, processed, and stored in the Space Situational Awareness (SSA) domain has grown at an exponential rate and is now better suited for ML. This paper describes development of advanced algorithms to deliver significant improvements in characterization of deep space objects and indication and warning (I&W) using a global network of telescopes that are collecting photometric data on a multitude of space-based objects. The Phase II Air Force Research Laboratory (AFRL) Small Business Innovative Research (SBIR) project Autonomous Characterization Algorithms for Change Detection and Characterization (ACDC), contracted to ExoAnalytic Solutions Inc. is providing the ability to detect and identify photometric signature changes due to potential space object changes (e.g. stability, tumble rate, aspect ratio), and correlate observed changes to potential behavioral changes using a variety of techniques, including supervised learning. Furthermore, these algorithms run in real-time on data being collected and processed by the ExoAnalytic Space Operations Center (EspOC), providing timely alerts and warnings while dynamically creating collection requirements to the EspOC for the algorithms that generate higher fidelity I&W. This paper will discuss the recently implemented ACDC algorithms, including the general design approach and results to date. The usage of supervised algorithms, such as Support Vector Machines, Neural Networks, k-Nearest Neighbors, etc., and unsupervised algorithms, for example k-means, Principle Component Analysis, Hierarchical Clustering, etc., and the implementations of these algorithms is explored. Results of applying these algorithms to EspOC data both in an off-line "pattern of life" analysis as well as using the algorithms on-line in real-time, meaning as data is collected, will be presented. Finally, future work in applying ML for SSA will be discussed.

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Shultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Stano, Geoffrey T.; Blakeslee, Richard J.; Goodman, Steven J.

2014-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. Currently, the lightning jump algorithm is being tested in two separate but important efforts. Schultz et al. (2014; AMS 10th Satellite Symposium) is exploring the transition of the algorithm from its research based formulation to a fully objective algorithm that includes storm tracking, Geostationary Lightning Mapper (GLM) Proxy data and the lightning jump algorithm. Chronis et al. (2014; this conference) provides context for the transition to current operational forecasting using lightning mapping array based products. However, what remains is an end to end physical and dynamical basis for relating lightning rates to severe storm manifestation, so the forecaster has a reason beyond simple correlation to utilize the lightning jump algorithm within their severe storm conceptual models. Therefore, the physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relation to updraft strength, updraft volume, precipitation-sized ice mass, etc.; however, relation specifically to lightning jumps is fragmented within the literature. Thus the goal of this study is to use multiple Doppler techniques to resolve the physical and dynamical storm characteristics specifically around the time of the lightning jump. This information will help forecasters anticipate lightning jump occurrence, or even be of use to determine future characteristics of a given storm (e.g., development of a mesocyclone, downdraft, or hail signature on radar), providing additional lead time/confidence in the severe storm warning paradigm.

Cascade reservoir flood control operation based on risk grading and warning in the Upper Yellow River

NASA Astrophysics Data System (ADS)

Xuejiao, M.; Chang, J.; Wang, Y.

2017-12-01

Flood risk reduction with non-engineering measures has become the main idea for flood management. It is more effective for flood risk management to take various non-engineering measures. In this paper, a flood control operation model for cascade reservoirs in the Upper Yellow River was proposed to lower the flood risk of the water system with multi-reservoir by combining the reservoir flood control operation (RFCO) and flood early warning together. Specifically, a discharge control chart was employed to build the joint RFCO simulation model for cascade reservoirs in the Upper Yellow River. And entropy-weighted fuzzy comprehensive evaluation method was adopted to establish a multi-factorial risk assessment model for flood warning grade. Furthermore, after determining the implementing mode of countermeasures with future inflow, an intelligent optimization algorithm was used to solve the optimization model for applicable water release scheme. In addition, another model without any countermeasure was set to be a comparative experiment. The results show that the model developed in this paper can further decrease the flood risk of water system with cascade reservoirs. It provides a new approach to flood risk management by coupling flood control operation and flood early warning of cascade reservoirs.

Vision-based measurement for rotational speed by improving Lucas-Kanade template tracking algorithm.

PubMed

Guo, Jie; Zhu, Chang'an; Lu, Siliang; Zhang, Dashan; Zhang, Chunyu

2016-09-01

Rotational angle and speed are important parameters for condition monitoring and fault diagnosis of rotating machineries, and their measurement is useful in precision machining and early warning of faults. In this study, a novel vision-based measurement algorithm is proposed to complete this task. A high-speed camera is first used to capture the video of the rotational object. To extract the rotational angle, the template-based Lucas-Kanade algorithm is introduced to complete motion tracking by aligning the template image in the video sequence. Given the special case of nonplanar surface of the cylinder object, a nonlinear transformation is designed for modeling the rotation tracking. In spite of the unconventional and complex form, the transformation can realize angle extraction concisely with only one parameter. A simulation is then conducted to verify the tracking effect, and a practical tracking strategy is further proposed to track consecutively the video sequence. Based on the proposed algorithm, instantaneous rotational speed (IRS) can be measured accurately and efficiently. Finally, the effectiveness of the proposed algorithm is verified on a brushless direct current motor test rig through the comparison with results obtained by the microphone. Experimental results demonstrate that the proposed algorithm can extract accurately rotational angles and can measure IRS with the advantage of noncontact and effectiveness.

Integrated Land- and Underwater-Based Sensors for a Subduction Zone Earthquake Early Warning System

NASA Astrophysics Data System (ADS)

Pirenne, B.; Rosenberger, A.; Rogers, G. C.; Henton, J.; Lu, Y.; Moore, T.

2016-12-01

Ocean Networks Canada (ONC — oceannetworks.ca/ ) operates cabled ocean observatories off the coast of British Columbia (BC) to support research and operational oceanography. Recently, ONC has been funded by the Province of BC to deliver an earthquake early warning (EEW) system that integrates offshore and land-based sensors to deliver alerts of incoming ground shaking from the Cascadia Subduction Zone. ONC's cabled seismic network has the unique advantage of being located offshore on either side of the surface expression of the subduction zone. The proximity of ONC's sensors to the fault can result in faster, more effective warnings, which translates into more lives saved, injuries avoided and more ability for mitigative actions to take place.ONC delivers near real-time data from various instrument types simultaneously, providing distinct advantages to seismic monitoring and earthquake early warning. The EEW system consists of a network of sensors, located on the ocean floor and on land, that detect and analyze the initial p-wave of an earthquake as well as the crustal deformation on land during the earthquake sequence. Once the p-wave is detected and characterized, software systems correlate the data streams of the various sensors and deliver alerts to clients through a Common Alerting Protocol-compliant data package. This presentation will focus on the development of the earthquake early warning capacity at ONC. It will describe the seismic sensors and their distribution, the p-wave detection algorithms selected and the overall architecture of the system. It will further overview the plan to achieve operational readiness at project completion.

GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application

PubMed Central

Cheng, Qi; Xue, Dabin; Wang, Guanyu; Ochieng, Washington Yotto

2017-01-01

The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision. PMID:29186851

GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application.

PubMed

Sun, Rui; Cheng, Qi; Xue, Dabin; Wang, Guanyu; Ochieng, Washington Yotto

2017-11-25

The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.

Technical note: Efficient online source identification algorithm for integration within a contamination event management system

NASA Astrophysics Data System (ADS)

Deuerlein, Jochen; Meyer-Harries, Lea; Guth, Nicolai

2017-07-01

Drinking water distribution networks are part of critical infrastructures and are exposed to a number of different risks. One of them is the risk of unintended or deliberate contamination of the drinking water within the pipe network. Over the past decade research has focused on the development of new sensors that are able to detect malicious substances in the network and early warning systems for contamination. In addition to the optimal placement of sensors, the automatic identification of the source of a contamination is an important component of an early warning and event management system for security enhancement of water supply networks. Many publications deal with the algorithmic development; however, only little information exists about the integration within a comprehensive real-time event detection and management system. In the following the analytical solution and the software implementation of a real-time source identification module and its integration within a web-based event management system are described. The development was part of the SAFEWATER project, which was funded under FP 7 of the European Commission.

Evaluating the Real-time and Offline Performance of the Virtual Seismologist Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G.; Fischer, M.; Heaton, T.; Wiemer, S.

2009-04-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to regional, network-based earthquake early warning (EEW). Bayes' theorem as applied in the VS algorithm states that the most probable source estimates at any given time is a combination of contributions from relatively static prior information that does not change over the timescale of earthquake rupture and a likelihood function that evolves with time to take into account incoming pick and amplitude observations from the on-going earthquake. Potentially useful types of prior information include network topology or station health status, regional hazard maps, earthquake forecasts, and the Gutenberg-Richter magnitude-frequency relationship. The VS codes provide magnitude and location estimates once picks are available at 4 stations; these source estimates are subsequently updated each second. The algorithm predicts the geographical distribution of peak ground acceleration and velocity using the estimated magnitude and location and appropriate ground motion prediction equations; the peak ground motion estimates are also updated each second. Implementation of the VS algorithm in California and Switzerland is funded by the Seismic Early Warning for Europe (SAFER) project. The VS method is one of three EEW algorithms whose real-time performance is being evaluated and tested by the California Integrated Seismic Network (CISN) EEW project. A crucial component of operational EEW algorithms is the ability to distinguish between noise and earthquake-related signals in real-time. We discuss various empirical approaches that allow the VS algorithm to operate in the presence of noise. Real-time operation of the VS codes at the Southern California Seismic Network (SCSN) began in July 2008. On average, the VS algorithm provides initial magnitude, location, origin time, and ground motion distribution estimates within 17 seconds of the earthquake origin time. These initial estimate times are dominated by the time for 4 acceptable picks to be available, and thus are heavily influenced by the station density in a given region; these initial estimate times also include the effects of telemetry delay, which ranges between 6 and 15 seconds at the SCSN, and processing time (~1 second). Other relevant performance statistics include: 95% of initial real-time location estimates are within 20 km of the actual epicenter, 97% of initial real-time magnitude estimates are within one magnitude unit of the network magnitude. Extension of real-time VS operations to networks in Northern California is an on-going effort. In Switzerland, the VS codes have been run on offline waveform data from over 125 earthquakes recorded by the Swiss Digital Seismic Network (SDSN) and the Swiss Strong Motion Network (SSMS). We discuss the performance of the VS algorithm on these datasets in terms of magnitude, location, and ground motion estimation.

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Chris; Carey, Larry; Schultz, Elise V.; Stano, Geoffrey; Gatlin, Patrick N.; Kozlowski, Danielle M.; Blakeslee, Rich J.; Goodman, Steve

2013-01-01

Key points this analysis will address: 1) What physically is going on in the cloud when there is a jump in lightning? -- Updraft variations, Ice fluxes 2) How do these processes fit in with severe storm conceptual models? 3) What would this information provide an end user? --Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi -Doppler derived physical relationships

Novel Algorithms Enabling Rapid, Real-Time Earthquake Monitoring and Tsunami Early Warning Worldwide

NASA Astrophysics Data System (ADS)

Lomax, A.; Michelini, A.

2012-12-01

We have introduced recently new methods to determine rapidly the tsunami potential and magnitude of large earthquakes (e.g., Lomax and Michelini, 2009ab, 2011, 2012). To validate these methods we have implemented them along with other new algorithms within the Early-est earthquake monitor at INGV-Rome (http://early-est.rm.ingv.it, http://early-est.alomax.net). Early-est is a lightweight software package for real-time earthquake monitoring (including phase picking, phase association and event detection, location, magnitude determination, first-motion mechanism determination, ...), and for tsunami early warning based on discriminants for earthquake tsunami potential. In a simulation using archived broadband seismograms for the devastating M9, 2011 Tohoku earthquake and tsunami, Early-est determines: the epicenter within 3 min after the event origin time, discriminants showing very high tsunami potential within 5-7 min, and magnitude Mwpd(RT) 9.0-9.2 and a correct shallow-thrusting mechanism within 8 min. Real-time monitoring with Early-est givess similar results for most large earthquakes using currently available, real-time seismogram data. Here we summarize some of the key algorithms within Early-est that enable rapid, real-time earthquake monitoring and tsunami early warning worldwide: >>> FilterPicker - a general purpose, broad-band, phase detector and picker (http://alomax.net/FilterPicker); >>> Robust, simultaneous association and location using a probabilistic, global-search; >>> Period-duration discriminants TdT0 and TdT50Ex for tsunami potential available within 5 min; >>> Mwpd(RT) magnitude for very large earthquakes available within 10 min; >>> Waveform P polarities determined on broad-band displacement traces, focal mechanisms obtained with the HASH program (Hardebeck and Shearer, 2002); >>> SeisGramWeb - a portable-device ready seismogram viewer using web-services in a browser (http://alomax.net/webtools/sgweb/info.html). References (see also: http://alomax.net/pub_list.html): Lomax, A. and A. Michelini (2012), Tsunami early warning within 5 minutes, Pure and Applied Geophysics, 169, nnn-nnn, doi: 10.1007/s00024-012-0512-6. Lomax, A. and A. Michelini (2011), Tsunami early warning using earthquake rupture duration and P-wave dominant period: the importance of length and depth of faulting, Geophys. J. Int., 185, 283-291, doi: 10.1111/j.1365-246X.2010.04916.x. Lomax, A. and A. Michelini (2009b), Tsunami early warning using earthquake rupture duration, Geophys. Res. Lett., 36, L09306, doi:10.1029/2009GL037223. Lomax, A. and A. Michelini (2009a), Mwpd: A Duration-Amplitude Procedure for Rapid Determination of Earthquake Magnitude and Tsunamigenic Potential from P Waveforms, Geophys. J. Int.,176, 200-214, doi:10.1111/j.1365-246X.2008.03974.x

CISN ShakeAlert: Improving the Virtual Seismologist (VS) earthquake early warning framework to provide faster, more robust warning information

NASA Astrophysics Data System (ADS)

Meier, M.; Cua, G. B.; Wiemer, S.; Fischer, M.

2011-12-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) that uses observed phase arrivals, ground motion amplitudes and selected prior information to estimate earthquake magnitude, location and origin time, and predict the distribution of peak ground motion throughout a region using envelope attenuation relationships. Implementation of the VS algorithm in California is an on-going effort of the Swiss Seismological Service (SED) at ETH Zürich. VS is one of three EEW algorithms - the other two being ElarmS (Allen and Kanamori, 2003) and On-Site (Wu and Kanamori, 2005; Boese et al., 2008) - that form the basis of the California Integrated Seismic Network ShakeAlert system, a prototype end-to-end EEW system that could potentially be implemented in California. The current prototype version of VS in California requires picks at 4 stations to initiate an event declaration. On average, taking into account data latency, variable station distribution, and processing time, this initial estimate is available about 20 seconds after the earthquake origin time, corresponding to a blind zone of about 70 km around the epicenter which would receive no warning, but where it would be the most useful. To increase the available warning time, we want to produce EEW estimates faster (with less than 4 stations). However, working with less than 4 stations with our current approach would increase the number of false alerts, for which there is very little tolerance in a useful EEW system. We explore the use of back-azimuth estimations and the Voronoi-based concept of not-yet-arrived data for reducing false alerts of the earliest VS estimates. The concept of not-yet-arrived data was originally used to provide evolutionary location estimates in EEW (Horiuchi, 2005; Cua and Heaton, 2007; Satriano et al. 2008). However, it can also be applied in discriminating between earthquake and non-earthquake signals. For real earthquakes, the constraints on earthquake location from the not-yet-arrived data and the back-azimuth estimations are consistent with location constraints from the available picks. For non-earthquake signals, these different location constraints are in most cases inconsistent. We use archived event data from the Northern and Southern California Seismic Networks as well as archived continuous waveform data from where the current VS codes erroneously declared events to quantify how using a combination of pick-based and not-yet-arrived data constraints can reduce VS false alert rates while providing faster warning information. The consistency of the pick-based and not-yet-arrived data constraints are mapped into the VS likelihood parameter, which reflects the degree of believe that the signals come from a real earthquake. This approach contributes towards improving the robustness of the Virtual Seismologist Multiple Threshold Event Detection (VS-MTED), which allows for single-station event declarations, when signal amplitudes are large enough.

Railway obstacle detection algorithm using neural network

NASA Astrophysics Data System (ADS)

Yu, Mingyang; Yang, Peng; Wei, Sen

2018-05-01

Aiming at the difficulty of detection of obstacle in outdoor railway scene, a data-oriented method based on neural network to obtain image objects is proposed. First, we mark objects of images(such as people, trains, animals) acquired on the Internet. and then use the residual learning units to build Fast R-CNN framework. Then, the neural network is trained to get the target image characteristics by using stochastic gradient descent algorithm. Finally, a well-trained model is used to identify an outdoor railway image. if it includes trains and other objects, it will issue an alert. Experiments show that the correct rate of warning reached 94.85%.

Edge-directed inference for microaneurysms detection in digital fundus images

NASA Astrophysics Data System (ADS)

Huang, Ke; Yan, Michelle; Aviyente, Selin

2007-03-01

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

Assessment of early warning system performance and improvements since it is in operational phase in Romania

NASA Astrophysics Data System (ADS)

Ionescu, Constantin; Marmureanu, Alexandru; Marmureanu, Gheorghe; Ortansa Cioflan, Carmen

2017-04-01

Earthquake represents a major natural disaster for Romanian territory. The main goal following the occurrence of a strong earthquake is to minimize the total number of fatalities. A rapid early warning system (REWS) was developed in Romania in order to provide 25-35 seconds warning time to Bucharest facilities for the earthquakes with M>5.0. The system consists of four components: a network of strong motion sensors installed in the epicentral area, a redundant communication network, an automatic analyzing system located in the Romanian Data Centre and an alert distribution system. The detection algorithm is based on the magnitude computation using strong motion data and rapid evaluation and scaling relation between the maximum P-wave acceleration measured in the epicentral area and the higher ground motion amplitude recorded in Bucharest. In order to reduce the damages caused by earthquakes, the exploitation of the up to date technology is very important. The information is the key point in the disaster management, and the internet is one of the most used instrument, implying also low costs. The Rapid Early Warning System was expanded to cover all countries affected by major earthquakes originating in the Vrancea seismic area and reduce their impact on existing installations of national interest in neighbouring Romania and elsewhere. REWS provides an efficient instrument for prevention and reaction based on the integrated system for seismic detection in South-Eastern Europe. REWS has been operational since 2013 and sends alert the authorities, hazardous facilities in Romania and Bulgaria (NPP, emergency response agencies etc.) and to public via twitter and some smartphone applications developed in the house. Also, NIEP is part of the UNESCO initiative case on developing a platform on earthquake early warning systems (IP-MEP) that aims to promote and strengthen the development of earthquake early warning systems in earthquake-prone regions of the world by sharing scientific knowledge, capacity building and international cooperation.

A Neutral Network based Early Eathquake Warning model in California region

NASA Astrophysics Data System (ADS)

Xiao, H.; MacAyeal, D. R.

2016-12-01

Early Earthquake Warning systems could reduce loss of lives and other economic impact resulted from natural disaster or man-made calamity. Current systems could be further enhanced by neutral network method. A 3 layer neural network model combined with onsite method was deployed in this paper to improve the recognition time and detection time for large scale earthquakes.The 3 layer neutral network early earthquake warning model adopted the vector feature design for sample events happened within 150 km radius of the epicenters. Dataset used in this paper contained both destructive events and small scale events. All the data was extracted from IRIS database to properly train the model. In the training process, backpropagation algorithm was used to adjust the weight matrices and bias matrices during each iteration. The information in all three channels of the seismometers served as the source in this model. Through designed tests, it was indicated that this model could identify approximately 90 percent of the events' scale correctly. And the early detection could provide informative evidence for public authorities to make further decisions. This indicated that neutral network model could have the potential to strengthen current early warning system, since the onsite method may greatly reduce the responding time and save more lives in such disasters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

Reducing process delays for real-time earthquake parameter estimation - An application of KD tree to large databases for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Yin, Lucy; Andrews, Jennifer; Heaton, Thomas

2018-05-01

Earthquake parameter estimations using nearest neighbor searching among a large database of observations can lead to reliable prediction results. However, in the real-time application of Earthquake Early Warning (EEW) systems, the accurate prediction using a large database is penalized by a significant delay in the processing time. We propose to use a multidimensional binary search tree (KD tree) data structure to organize large seismic databases to reduce the processing time in nearest neighbor search for predictions. We evaluated the performance of KD tree on the Gutenberg Algorithm, a database-searching algorithm for EEW. We constructed an offline test to predict peak ground motions using a database with feature sets of waveform filter-bank characteristics, and compare the results with the observed seismic parameters. We concluded that large database provides more accurate predictions of the ground motion information, such as peak ground acceleration, velocity, and displacement (PGA, PGV, PGD), than source parameters, such as hypocenter distance. Application of the KD tree search to organize the database reduced the average searching process by 85% time cost of the exhaustive method, allowing the method to be feasible for real-time implementation. The algorithm is straightforward and the results will reduce the overall time of warning delivery for EEW.

Enhanced chemical weapon warning via sensor fusion

NASA Astrophysics Data System (ADS)

Flaherty, Michael; Pritchett, Daniel; Cothren, Brian; Schwaiger, James

2011-05-01

Torch Technologies Inc., is actively involved in chemical sensor networking and data fusion via multi-year efforts with Dugway Proving Ground (DPG) and the Defense Threat Reduction Agency (DTRA). The objective of these efforts is to develop innovative concepts and advanced algorithms that enhance our national Chemical Warfare (CW) test and warning capabilities via the fusion of traditional and non-traditional CW sensor data. Under Phase I, II, and III Small Business Innovative Research (SBIR) contracts with DPG, Torch developed the Advanced Chemical Release Evaluation System (ACRES) software to support non real-time CW sensor data fusion. Under Phase I and II SBIRs with DTRA in conjunction with the Edgewood Chemical Biological Center (ECBC), Torch is using the DPG ACRES CW sensor data fuser as a framework from which to develop the Cloud state Estimation in a Networked Sensor Environment (CENSE) data fusion system. Torch is currently developing CENSE to implement and test innovative real-time sensor network based data fusion concepts using CW and non-CW ancillary sensor data to improve CW warning and detection in tactical scenarios.

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawerence D.; Schultz, Elise V.; Stano, Geoffery T.; Kozlowski, Danielle M.; Goodman, Steven

2012-01-01

Key points that this analysis will begin to address are: 1)What physically is going on in the cloud when there is a jump in lightning? - Updraft variations, ice fluxes. 2)How do these processes fit in with severe storm conceptual models? 3)What would this information provide an end user (i.e., the forecaster)? - Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi-Doppler derived physical relationships 4) How do we best transistionthis algorithm into the warning decision process. The known relationship between lightning updraft strength/volume and precipitation ice mass production can be extended to the concept of the lightning jump. Examination of the first lightning jump times from 329 storms in Schultz et al. shows an increase in the mean reflectivity profile and mixed phase echo volume during the 10 minutes prior to the lightning jump. Limited dual-Doppler results show that the largest lightning jumps are well correlated in time with increases in updraft strength/volume and precipitation ice mass production; however, the smaller magnitude lightning jumps appear to have more subtle relationships to updraft and ice mass characteristics.

G-FAST Early Warning Potential for Great Earthquakes in Chile

NASA Astrophysics Data System (ADS)

Crowell, B.; Schmidt, D. A.; Baker, B. I.; Bodin, P.; Vidale, J. E.

2016-12-01

The importance of GNSS-based earthquake early warning for modeling large earthquakes has been studied extensively over the past decade and several such systems are currently under development. In the Pacific Northwest, we have developed the G-FAST GNSS-based earthquake early warning module for eventual inclusion in the US West-Coast wide ShakeAlert system. We have also created a test system that allows us to replay past and synthetic earthquakes to identify problems with both the network architecture and the algorithms. Between 2010 and 2016, there have been seven M > 8 earthquakes across the globe, of which three struck offshore Chile; the 27 February 2010 Mw 8.8 Maule, the 1 April 2014 Mw 8.2 Iquique, and the 16 September 2015 Mw 8.3 Illapel. Subsequent to these events, the Chilean national GNSS network operated by the Centro Sismologico Nacional (http://www.sismologia.cl/) greatly expanded to over 150 continuous GNSS stations, providing the best recordings of great earthquakes with GNSS outside of Japan. Here we report on retrospective G-FAST performance for those three great earthquakes in Chile. We discuss the interplay of location errors, latency, and data completeness with respect to the precision and timing of G-FAST earthquake source alerts as well as the computational demands of the system.

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Brown, A.; Brown, J.

2010-09-01

We develop and evaluate the performance of advanced algorithms which provide significantly improved capabilities for automated detection and tracking of ballistic and flying dim objects in the presence of highly structured intense clutter. Applications include ballistic missile early warning, midcourse tracking, trajectory prediction, and resident space object detection and tracking. The set of algorithms include, in particular, adaptive spatiotemporal clutter estimation-suppression and nonlinear filtering-based multiple-object track-before-detect. These algorithms are suitable for integration into geostationary, highly elliptical, or low earth orbit scanning or staring sensor suites, and are based on data-driven processing that adapts to real-world clutter backgrounds, including celestial, earth limb, or terrestrial clutter. In many scenarios of interest, e.g., for highly elliptic and, especially, low earth orbits, the resulting clutter is highly nonstationary, providing a significant challenge for clutter suppression to or below sensor noise levels, which is essential for dim object detection and tracking. We demonstrate the success of the developed algorithms using semi-synthetic and real data. In particular, our algorithms are shown to be capable of detecting and tracking point objects with signal-to-clutter levels down to 1/1000 and signal-to-noise levels down to 1/4.

Development of an algorithm for an EEG-based driver fatigue countermeasure.

PubMed

Lal, Saroj K L; Craig, Ashley; Boord, Peter; Kirkup, Les; Nguyen, Hung

2003-01-01

Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability. Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue. The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01). This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system. The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.

Lightning jump as a nowcast predictor: Application to severe weather events in Catalonia

NASA Astrophysics Data System (ADS)

Farnell, C.; Rigo, T.; Pineda, N.

2017-01-01

Several studies reported sudden increases in the total lightning flash rate (intra-cloud+cloud-to-ground) preceding the occurrence of severe weather (large hail, wind gusts associated to thunderstorms and/or tornadoes). Named ;Lightning Jump;, this pattern has demonstrated to be of operational applicability in the forecasting of severe weather phenomena. The present study introduces the application of a lightning jump algorithm, with an identification of cells based solely on total lightning data, revealing that there is no need of radar data to trigger severe weather warnings. The algorithm was validated by means of a dataset severe weather events occurred in Catalonia in the period 2009-2014. Results obtained revealed very promising.

Runway Safety Monitor Algorithm for Single and Crossing Runway Incursion Detection and Alerting

NASA Technical Reports Server (NTRS)

Green, David F., Jr.

2006-01-01

The Runway Safety Monitor (RSM) is an aircraft based algorithm for runway incursion detection and alerting that was developed in support of NASA's Runway Incursion Prevention System (RIPS) research conducted under the NASA Aviation Safety and Security Program's Synthetic Vision System project. The RSM algorithm provides warnings of runway incursions in sufficient time for pilots to take evasive action and avoid accidents during landings, takeoffs or when taxiing on the runway. The report documents the RSM software and describes in detail how RSM performs runway incursion detection and alerting functions for NASA RIPS. The report also describes the RIPS flight tests conducted at the Reno/Tahoe International Airport (RNO) and the Wallops Flight Facility (WAL) during July and August of 2004, and the RSM performance results and lessons learned from those flight tests.

Exploring radar and lightning variables associated with the Lightning Jump. Can we predict the size of the hail?

NASA Astrophysics Data System (ADS)

Farnell, C.; Rigo, T.; Pineda, N.

2018-04-01

Severe weather regularly hits the Lleida Plain (western part of Catalonia, NE of Iberian Peninsula), causing important damage to the local agriculture. In order to help severe weather surveillance tasks, the Meteorological Service of Catalonia (SMC) implemented in 2016 the Lightning Jump (LJ) algorithm as operative warning tool after an exhaustive validation phase of several months. The present study delves into the analysis of the relationship between Lightning Jump alerts and hail occurrence, through the analysis of lightning and radar variables in the moment when the warning is issued. Overall, the study has consisted of the analysis of 149 cases, grouping them into two categories according to hail size: small and large hail, with a threshold of 2 cm of diameter. The thunderstorms related to big sized hail presented remarkable differences in some of the variables analysed that could help forecast the size of hail when the LJ alert is triggered. Moreover, other variables have been allowed to observe and to corroborate how the LJ algorithm works during the 13 min before the warning is triggered.

Development of Algorithms and Error Analyses for the Short Baseline Lightning Detection and Ranging System

NASA Technical Reports Server (NTRS)

Starr, Stanley O.

1998-01-01

NASA, at the John F. Kennedy Space Center (KSC), developed and operates a unique high-precision lightning location system to provide lightning-related weather warnings. These warnings are used to stop lightning- sensitive operations such as space vehicle launches and ground operations where equipment and personnel are at risk. The data is provided to the Range Weather Operations (45th Weather Squadron, U.S. Air Force) where it is used with other meteorological data to issue weather advisories and warnings for Cape Canaveral Air Station and KSC operations. This system, called Lightning Detection and Ranging (LDAR), provides users with a graphical display in three dimensions of 66 megahertz radio frequency events generated by lightning processes. The locations of these events provide a sound basis for the prediction of lightning hazards. This document provides the basis for the design approach and data analysis for a system of radio frequency receivers to provide azimuth and elevation data for lightning pulses detected simultaneously by the LDAR system. The intent is for this direction-finding system to correct and augment the data provided by LDAR and, thereby, increase the rate of valid data and to correct or discard any invalid data. This document develops the necessary equations and algorithms, identifies sources of systematic errors and means to correct them, and analyzes the algorithms for random error. This data analysis approach is not found in the existing literature and was developed to facilitate the operation of this Short Baseline LDAR (SBLDAR). These algorithms may also be useful for other direction-finding systems using radio pulses or ultrasonic pulse data.

Birth, growth and progresses through the last twelve years of a regional scale landslide warning system

NASA Astrophysics Data System (ADS)

Fanti, Riccardo; Segoni, Samuele; Rosi, Ascanio; Lagomarsino, Daniela; Catani, Filippo

2017-04-01

SIGMA is a regional landslide warning system that operates in the Emilia Romagna region (Italy). In this work, we depict its birth and the continuous development process, still ongoing, after over a decade of operational employ. Traditionally, landslide rainfall thresholds are defined by the empirical correspondence between a rainfall database and a landslide database. However, in the early stages of the research, a complete catalogue of dated landslides was not available. Therefore, the prototypal version of SIGMA was based on rainfall thresholds defined by means of a statistical analysis performed over the rainfall time series. SIGMA was purposely designed to take into account both shallow and deep seated landslides and it was based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering. The statistical distribution of the rainfall series was analyzed, and multiples of the standard deviation (σ) were used as thresholds to discriminate between ordinary and extraordinary rainfall events. In the warning system, the measured and the forecasted rainfall are compared with these thresholds. Since the response of slope stability to rainfall may be complex, SIGMA is based on a decision algorithm aimed at identifying short but exceptionally intense rainfalls and mild but exceptionally prolonged rains: while the former are commonly associated with shallow landslides, the latter are mainly associated with deep-seated landslides. In the first case, the rainfall threshold is defined by high σ values and short durations (i.e. a few days); in the second case, σ values are lower but the decision algorithm checks long durations (i.e. some months). The exact definition of "high" and "low" σ values and of "short" and "long" duration varied through time according as it was adjusted during the evolution of the model. Indeed, since 2005, a constant work was carried out to gather and organize newly available data (rainfall recordings and landslides occurred) and to use them to define more robust relationships between rainfalls and landslide triggering, with the final aim to increase the forecasting effectiveness of the warning system. The updated rainfall and landslide database were used to periodically perform a quantitative validation and to analyze the errors affecting the system forecasts. The errors characterization was used to implement a continuous process of updating and modification of SIGMA, that included: - Main model upgrades (generalization from a pilot test site to the whole Emilia Romagna region; calibration against well documented landslide events to define specific σ levels for each territorial units; definition of different alert levels according to the number of expected - Ordinary updates (periodically, the new landslide and rainfall data were used to re-calibrate the thresholds, taking into account a more robust sample). - Model tuning (set up of the optimal version of the decisional algorithm, including different definitions of "long" and "short" periods; selection of the optimal reference rain gauge for each Territorial Unit; modification of the boundaries of some territorial - Additional features (definition of a module that takes into account the effect of snow melt and snow accumulation; coupling with a landslide susceptibility model to improve the spatial accuracy of the model). - Various performance tests (including the comparison with alternate versions of SIGMA or with thresholds based on rainfall intensity and duration). This process has led to an evolution of the warning system and to a documented improvement of its forecasting effectiveness. Landslide forecasting at regional scale is a very complex task, but as time passes by and with the systematic gathering of new substantial data and the continuous progresses of research, uncertainties can be progressively reduced and a warning system can be set that increases its performances and reliability with time.

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

CISN ShakeAlert: Accounting for site amplification effects and quantifying time and spatial dependence of uncertainty estimates in the Virtual Seismologist earthquake early warning algorithm

NASA Astrophysics Data System (ADS)

Caprio, M.; Cua, G. B.; Wiemer, S.; Fischer, M.; Heaton, T. H.; CISN EEW Team

2011-12-01

The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system being tested in real-time in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network (SCSN) since July 2008, and at the Northern California Seismic Network (NCSN) since February 2009. With the aim of improving the convergence of real-time VS magnitude estimates to network magnitudes, we evaluate various empirical and Vs30-based approaches to accounting for site amplification. Empirical station corrections for SCSN stations are derived from M>3.0 events from 2005 through 2009. We evaluate the performance of the various approaches using an independent 2010 dataset. In addition, we analyze real-time VS performance from 2008 to the present to quantify the time and spatial dependence of VS uncertainty estimates. We also summarize real-time VS performance for significant 2011 events in California. Improved magnitude and uncertainty estimates potentially increase the utility of EEW information for end-users, particularly those intending to automate damage-mitigating actions based on real-time information.

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.

PubMed

Lin, Chin-Teng; Chen, Yu-Chieh; Huang, Teng-Yi; Chiu, Tien-Ting; Ko, Li-Wei; Liang, Sheng-Fu; Hsieh, Hung-Yi; Hsu, Shang-Hwa; Duann, Jeng-Ren

2008-05-01

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

DOE PAGES

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.; ...

2015-12-17

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

PPP Sliding Window Algorithm and Its Application in Deformation Monitoring.

PubMed

Song, Weiwei; Zhang, Rui; Yao, Yibin; Liu, Yanyan; Hu, Yuming

2016-05-31

Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts.

Alcohol Warning Label Perceptions: Do Warning Sizes and Plain Packaging Matter?

PubMed

Al-Hamdani, Mohammed; Smith, Steven M

2017-01-01

There is a dearth of research on the effectiveness of stringent alcohol warning labels. Our experiment tested whether increasing the size of an alcohol health warning lowers product-based ratings. We examined whether plain packaging lowers ratings of alcohol products and the consumers who use them, increases ratings of bottle "boringness," and enhances warning recognition compared with branded packaging. A total of 440 adults (51.7% female) viewed one of three warning sizes (50%, 75%, or 90% of label surface) on either a plain or branded bottle of distilled spirits, wine, and beer. Participants also rated alcohol bottles on product-based (assessing the product itself), consumer-based (assessing perceptions of consumers of the product), and bottle boringness ratings, and then attempted to recognize the correct warning out of four choices. As expected, the size of warning labels lowered product-based ratings. Similarly, plain packaging lowered product-based and consumer-based ratings and increased bottle boringness but only for wine bottles. Further, plain packaging increased the odds of warning recognition on bottles of distilled spirits. This study shows that plain packaging and warning size (similar to the graphic warnings on cigarette packages) affect perceptions about alcohol bottles. It also shows that plain packaging increases the likelihood for correct health warning recognition, which builds the case for alcohol warning and packaging research and policy.

Earthquake Early Warning ShakeAlert System: Testing and certification platform

USGS Publications Warehouse

Cochran, Elizabeth S.; Kohler, Monica D.; Given, Douglas; Guiwits, Stephen; Andrews, Jennifer; Meier, Men-Andrin; Ahmad, Mohammad; Henson, Ivan; Hartog, Renate; Smith, Deborah

2017-01-01

Earthquake early warning systems provide warnings to end users of incoming moderate to strong ground shaking from earthquakes. An earthquake early warning system, ShakeAlert, is providing alerts to beta end users in the western United States, specifically California, Oregon, and Washington. An essential aspect of the earthquake early warning system is the development of a framework to test modifications to code to ensure functionality and assess performance. In 2016, a Testing and Certification Platform (TCP) was included in the development of the Production Prototype version of ShakeAlert. The purpose of the TCP is to evaluate the robustness of candidate code that is proposed for deployment on ShakeAlert Production Prototype servers. TCP consists of two main components: a real‐time in situ test that replicates the real‐time production system and an offline playback system to replay test suites. The real‐time tests of system performance assess code optimization and stability. The offline tests comprise a stress test of candidate code to assess if the code is production ready. The test suite includes over 120 events including local, regional, and teleseismic historic earthquakes, recentering and calibration events, and other anomalous and potentially problematic signals. Two assessments of alert performance are conducted. First, point‐source assessments are undertaken to compare magnitude, epicentral location, and origin time with the Advanced National Seismic System Comprehensive Catalog, as well as to evaluate alert latency. Second, we describe assessment of the quality of ground‐motion predictions at end‐user sites by comparing predicted shaking intensities to ShakeMaps for historic events and implement a threshold‐based approach that assesses how often end users initiate the appropriate action, based on their ground‐shaking threshold. TCP has been developed to be a convenient streamlined procedure for objectively testing algorithms, and it has been designed with flexibility to accommodate significant changes in development of new or modified system code. It is expected that the TCP will continue to evolve along with the ShakeAlert system, and the framework we describe here provides one example of how earthquake early warning systems can be evaluated.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-05-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-09-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Technical Note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2015-04-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

Use of a novel electronic maternal surveillance system to generate automated alerts on the labor and delivery unit.

PubMed

Klumpner, Thomas T; Kountanis, Joanna A; Langen, Elizabeth S; Smith, Roger D; Tremper, Kevin K

2018-06-26

Maternal early warning systems reduce maternal morbidity. We developed an electronic maternal surveillance system capable of visually summarizing the labor and delivery census and identifying changes in clinical status. Automatic page alerts to clinical providers, using an algorithm developed at our institution, were incorporated in an effort to improve early detection of maternal morbidity. We report the frequency of pages generated by the system. To our knowledge, this is the first time such a system has been used in peripartum care. Alert criteria were developed after review of maternal early warning systems, including the Maternal Early Warning Criteria (MEWC). Careful consideration was given to the frequency of pages generated by the surveillance system. MEWC notification criteria were liberalized and a paging algorithm was created that triggered paging alerts to first responders (nurses) and then managing services due to the assumption that paging all clinicians for each vital sign triggering MEWC would generate an inordinate number of pages. For preliminary analysis, to determine the effect of our automated paging algorithm on alerting frequency, the paging frequency of this system was compared to the frequency of vital signs meeting the Maternal Early Warning Criteria (MEWC). This retrospective analysis was limited to a sample of 34 patient rooms uniquely capable of storing every vital sign reported by the bedside monitor. Over a 91-day period, from April 1 to July 1, 2017, surveillance was conducted from 64 monitored beds, and the obstetrics service received one automated page every 2.3 h. The most common triggers for alerts were for hypertension and tachycardia. For the subset of 34 patient rooms uniquely capable of real-time recording, one vital sign met the MEWC every 9.6 to 10.3 min. Anecdotally, the system was well-received. This novel electronic maternal surveillance system is designed to reduce cognitive bias and improve timely clinical recognition of maternal deterioration. The automated paging algorithm developed for this software dramatically reduces paging frequency compared to paging for isolated vital sign abnormalities alone. Long-term, prospective studies will be required to determine its impact on patient outcomes.

Automated recognition and tracking of aerosol threat plumes with an IR camera pod

NASA Astrophysics Data System (ADS)

Fauth, Ryan; Powell, Christopher; Gruber, Thomas; Clapp, Dan

2012-06-01

Protection of fixed sites from chemical, biological, or radiological aerosol plume attacks depends on early warning so that there is time to take mitigating actions. Early warning requires continuous, autonomous, and rapid coverage of large surrounding areas; however, this must be done at an affordable cost. Once a potential threat plume is detected though, a different type of sensor (e.g., a more expensive, slower sensor) may be cued for identification purposes, but the problem is to quickly identify all of the potential threats around the fixed site of interest. To address this problem of low cost, persistent, wide area surveillance, an IR camera pod and multi-image stitching and processing algorithms have been developed for automatic recognition and tracking of aerosol plumes. A rugged, modular, static pod design, which accommodates as many as four micro-bolometer IR cameras for 45deg to 180deg of azimuth coverage, is presented. Various OpenCV1 based image-processing algorithms, including stitching of multiple adjacent FOVs, recognition of aerosol plume objects, and the tracking of aerosol plumes, are presented using process block diagrams and sample field test results, including chemical and biological simulant plumes. Methods for dealing with the background removal, brightness equalization between images, and focus quality for optimal plume tracking are also discussed.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

Never Use the Complete Search Space: a Concept to Enhance the Optimization Procedure for Monitoring Networks

NASA Astrophysics Data System (ADS)

Bode, F.; Reuschen, S.; Nowak, W.

2015-12-01

Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Enhanced ICP for the Registration of Large-Scale 3D Environment Models: An Experimental Study

PubMed Central

Han, Jianda; Yin, Peng; He, Yuqing; Gu, Feng

2016-01-01

One of the main applications of mobile robots is the large-scale perception of the outdoor environment. One of the main challenges of this application is fusing environmental data obtained by multiple robots, especially heterogeneous robots. This paper proposes an enhanced iterative closest point (ICP) method for the fast and accurate registration of 3D environmental models. First, a hierarchical searching scheme is combined with the octree-based ICP algorithm. Second, an early-warning mechanism is used to perceive the local minimum problem. Third, a heuristic escape scheme based on sampled potential transformation vectors is used to avoid local minima and achieve optimal registration. Experiments involving one unmanned aerial vehicle and one unmanned surface vehicle were conducted to verify the proposed technique. The experimental results were compared with those of normal ICP registration algorithms to demonstrate the superior performance of the proposed method. PMID:26891298

CISN ShakeAlert: Faster Warning Information Through Multiple Threshold Event Detection in the Virtual Seismologist (VS) Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G. B.; Fischer, M.; Caprio, M.; Heaton, T. H.; Cisn Earthquake Early Warning Project Team

2010-12-01

The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system that could potentially be implemented in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network since July 2008, and at the Northern California Seismic Network since February 2009. We discuss recent enhancements to the VS EEW algorithm that are being integrated into CISN ShakeAlert. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to initiate an event declaration, with the goal of reducing false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and the requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) into an on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Real-time and offline analysis on Swiss and California waveform datasets indicate that the multiple-threshold approach is faster and more reliable for larger events than the earlier version of the VS codes. In addition, we provide evolutionary estimates of the probability of false alarms (PFA), which is an envisioned output stream of the CISN ShakeAlert system. The real-time decision-making approach envisioned for CISN ShakeAlert users, where users specify a threshhold PFA in addition to thresholds on peak ground motion estimates, has the potential to increase the available warning time for users with high tolerance to false alarms without compromising the needs of users with lower tolerances to false alarms.

Tsunami early warning in the Mediterranean: role, structure and tricks of pre-computed tsunami simulation databases and matching/forecasting algorithms

NASA Astrophysics Data System (ADS)

Armigliato, Alberto; Pagnoni, Gianluca; Tinti, Stefano

2014-05-01

The general idea that pre-computed simulated scenario databases can play a key role in conceiving tsunami early warning systems is commonly accepted by now. But it was only in the last decade that it started to be applied to the Mediterranean region, taking special impulse from initiatives like the GDACS and from recently concluded EU-funded projects such as TRIDEC and NearToWarn. With reference to these two projects and with the possibility of further developing this research line in the frame of the FP7 ASTARTE project, we discuss some results we obtained regarding two major topics, namely the strategies applicable to the tsunami scenario database building and the design and performance assessment of a timely and "reliable" elementary-scenario combination algorithm to be run in real-time. As for the first theme, we take advantage of the experience gained in the test areas of Western Iberia, Rhodes (Greece) and Cyprus to illustrate the criteria with which a "Matching Scenario Database" (MSDB) can be built. These involve 1) the choice of the main tectonic tsunamigenic sources (or areas), 2) their tessellation with matrices of elementary faults whose dimension heavily depend on the particular studied area and must be a compromise between the needs to represent the tsunamigenic area in sufficient detail and of limiting the number of scenarios to be simulated, 3) the computation of the scenarios themselves, 4) the choice of the relevant simulation outputs and the standardisation of their formats. Regarding the matching/forecast algorithm, we want it to select and combine the MSDB elements based on the initial earthquake magnitude and location estimate, and to produce a forecast of (at least) the tsunami arrival time, amplitude and period at the closest tide-level sensors and in all needed forecast points. We discuss the performance of the algorithm in terms of the time needed to produce the forecast after the earthquake is detected. In particular, we analyse the different contributions of a number of factors such as the efficient code development and availability of cutting-edge hardware to run the code itself, the wise selection of the MSDB outputs to be combined, the choice of the forecast points where water elevation time series must be taken into account, and few others.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

PubMed

Tamibmaniam, Jayashamani; Hussin, Narwani; Cheah, Wee Kooi; Ng, Kee Sing; Muninathan, Prema

2016-01-01

WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

PubMed Central

Jang, Jeong Ah; Kim, Hyun Suk; Cho, Han Byeog

2011-01-01

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use. PMID:22164025

Development of algorithms for tsunami detection by High Frequency Radar based on modeling tsunami case studies in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Grilli, Stéphan; Guérin, Charles-Antoine; Grosdidier, Samuel

2015-04-01

Where coastal tsunami hazard is governed by near-field sources, Submarine Mass Failures (SMFs) or earthquakes, tsunami propagation times may be too small for a detection based on deep or shallow water buoys. To offer sufficient warning time, it has been proposed by others to implement early warning systems relying on High Frequency Surface Wave Radar (HFSWR) remote sensing, that has a dense spatial coverage far offshore. A new HFSWR, referred to as STRADIVARIUS, has been recently deployed by Diginext Inc. to cover the "Golfe du Lion" (GDL) in the Western Mediterranean Sea. This radar, which operates at 4.5 MHz, uses a proprietary phase coding technology that allows detection up to 300 km in a bistatic configuration (with a baseline of about 100 km). Although the primary purpose of the radar is vessel detection in relation to homeland security, it can also be used for ocean current monitoring. The current caused by an arriving tsunami will shift the Bragg frequency by a value proportional to a component of its velocity, which can be easily obtained from the Doppler spectrum of the HFSWR signal. Using state of the art tsunami generation and propagation models, we modeled tsunami case studies in the western Mediterranean basin (both seismic and SMFs) and simulated the HFSWR backscattered signal that would be detected for the entire GDL and beyond. Based on simulated HFSWR signal, we developed two types of tsunami detection algorithms: (i) one based on standard Doppler spectra, for which we found that to be detectable within the environmental and background current noises, the Doppler shift requires tsunami currents to be at least 10-15 cm/s, which typically only occurs on the continental shelf in fairly shallow water; (ii) to allow earlier detection, a second algorithm computes correlations of the HFSWR signals at two distant locations, shifted in time by the tsunami propagation time between these locations (easily computed based on bathymetry). We found that this second method allowed detection for currents as low as 5 cm/s, i.e., in deeper water, beyond the shelf and further away from the coast, thus allowing an earlier detection.

Main components and characteristics of landslide early warning systems operational worldwide

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Cepeda, José

2017-04-01

During the last decades the number of victims and economic losses due to natural hazards are dramatically increased worldwide. The reason can be mainly ascribed to climate changes and urbanization in areas exposed at high level of risk. Among the many mitigation measures available for reducing the risk to life related to natural hazards, early warning systems certainly constitute a significant cost-effective option available to the authorities in charge of risk management and governance. The aim is to help and protect populations exposed to natural hazards, reducing fatalities when major events occur. Landslide is one of the natural hazards addressed by early warning systems. Landslide early warning systems (LEWSs) are mainly composed by the following four components: set-up, correlation laws, decisional algorithm and warning management. Within this framework, the set-up includes all the preliminary actions and choices necessary for designing a LEWS, such as: the area covered by the system, the types of landslides and the monitoring instruments. The monitoring phase provides a series of important information on different variables, considered as triggering factors for landslides, in order to define correlation laws and thresholds. Then, a decisional algorithm is necessary for defining the: number of warning levels to be employed in the system, decision making procedures, and everything else system managers may need for issuing warnings in different warning zones. Finally the warning management is composed by: monitoring and warning strategy; communication strategy; emergency plan and, everything connected to the social sphere. Among LEWSs operational worldwide, two categories can be defined as a function of the scale of analysis: "local" and "territorial" systems. The scale of analysis influences several actions and aspects connected to the design and employment of the system, such as: the actors involved, the monitoring systems, type of landslide phenomena addressed and variables to be considered for correlations. The characteristics of LEWSs at local scale are strongly affected by numerous constraints and factors, from time to time different, related to the characteristics of the problem they address. Monitoring measures, variables and correlation laws considered for the design and employment of local LEWSs, strongly depends on the type of landslide to be addressed. On the other hand, territorial LEWSs mainly deals with rainfall-induced landslides characterized by fast slope movement. These systems have become a risk management approach, employed worldwide over areas of relevant extension. Before 2005 only few experiences of LEWSs at a regional scale were carried out, such as in: Hong Kong, China; Zhejiang Province, China; San Francisco Bay, California, USA; Appalachians, USA; Oregon, USA; Rio de Janeiro, Brazil. Since the beginning of the XXI century, increased knowledge on rainfall-landslide correlations and upgraded technologies in weather forecast have promoted the development and improvement of territorial LEWSs around the world.

Operational warning of interplanetary shock arrivals using energetic particle data from ACE: Real-time Upstream Monitoring System

NASA Astrophysics Data System (ADS)

Donegan, M.; Vandegriff, J.; Ho, G. C.; Julia, S. J.

2004-12-01

We report on an operational system which provides advance warning and predictions of arrival times at Earth of interplanetary (IP) shocks that originate at the Sun. The data stream used in our prediction algorithm is real-time and comes from the Electron, Proton, and Alpha Monitor (EPAM) instrument on NASA's Advanced Composition Explorer (ACE) spacecraft. Since locally accelerated energetic storm particle (ESP) events accompany most IP shocks, their arrival can be predicted using ESP event signatures. We have previously reported on the development and implementation of an algorithm which recognizes the upstream particle signature of approaching IP shocks and provides estimated countdown predictions. A web-based system (see (http://sd-www.jhuapl.edu/UPOS/RISP/index.html) combines this prediction capability with real-time ACE/EPAM data provided by the NOAA Space Environment Center. The most recent ACE data is continually processed and predictions of shock arrival time are updated every five minutes when an event is impending. An operational display is provided to indicate advisories and countdowns for the event. Running the algorithm on a test set of historical events, we obtain a median error of about 10 hours for predictions made 24-36 hours before actual shock arrival and about 6 hours when the shock is 6-12 hours away. This system can provide critical information to mission planners, satellite operations controllers, and scientists by providing significant lead-time for approaching events. Recently, we have made improvements to the triggering mechanism as well as re-training the neural network, and here we report prediction results from the latest system.

Geostationary Lightning Mapper for GOES-R

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2007-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Inappropriate Alarm Rates and Driver Annoyance

DOT National Transportation Integrated Search

1996-02-01

Future in-vehicle crash avoidance warning systems will inevitably deliver : inappropriate alarms from time to time, caused for example, by situations where : algorithms have correctly identified an object but pose no threat or danger to : the driver....

Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals

PubMed Central

Yin, Jinghai; Mu, Zhendong

2016-01-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors’ main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue. PMID:28529761

Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.

PubMed

Yin, Jinghai; Hu, Jianfeng; Mu, Zhendong

2017-02-01

The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.

Incorporating Geodectic Processing Modules into a Real-Time Earthworm Environment to Enhance NOAA's Tsunami Warning Capability

NASA Astrophysics Data System (ADS)

Macpherson, K. A.

2017-12-01

The National Oceanographic and Atmospheric Administration's National and Pacific Tsunami Warning Centers currently rely on traditional seismic data in order to detect and evaluate potential tsunamigenic earthquakes anywhere on the globe. The first information products disseminated by the centers following a significant seismic event are based solely on seismically-derived earthquake locations and magnitudes, and are issued within minutes of the earthquake origin time. Thus, the rapid and reliable determination of the earthquake magnitude is a critical piece of information needed by the centers to generate the appropriate alert levels. However, seismically-derived magnitudes of large events are plagued by well-known problems, particularly during the first few minutes following the origin time; near-source broad-band instruments may go off scale, and magnitudes tend to saturate until sufficient teleseismic data arrive to represent the long-period signal that characterizes large events. However, geodetic data such as high-rate Global Positioning System (hGPS) displacements and seismogeodetic data that is a combination of collocated hGPS and accelerometer data do not suffer from these limitations. These sensors stay on scale, even for large events, and they record both dynamic and static displacements that may be used to estimate magnitude without saturation. Therefore, there is an ongoing effort to incorporate these data streams into the operations of the tsunami warning centers to enhance current magnitude determination capabilities, and eventually, to invert the geodetic displacements for mechanism and finite-fault information. These later quantities will be useful for tsunami modeling and forecasting. The tsunami warning centers rely on the Earthworm system for real-time data acquisition, so we have developed Earthworm modules for the Magnitude from Peak Ground Displacement (MPGD) algorithm, developed at the University of Washington and the University of California, Berkeley, and a module for a Static Offset Estimator algorithm that was developed by the NASA Jet Propulsion Laboratory. In this presentation we will discuss module architecture and show output computed by replaying both synthetic and historical scenarios in a simulated real-time Earthworm environment.

Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection

NASA Astrophysics Data System (ADS)

Gruber, Thomas; Grim, Larry; Fauth, Ryan; Tercha, Brian; Powell, Chris; Steinhardt, Kristin

2011-05-01

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. Sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e.g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused / un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

Early Warning/Track-and-Trigger Systems to Detect Deterioration and Improve Outcomes in Hospitalized Patients.

PubMed

Shiloh, Ariel L; Lominadze, George; Gong, Michelle N; Savel, Richard H

2016-02-01

As a global effort toward improving patient safety, a specific area of focus has been the early recognition and rapid intervention in deteriorating ward patients. This focus on "failure to rescue" has led to the construction of early warning/track-and-trigger systems. In this review article, we present a description of the data behind the creation and implementation of such systems, including multiple algorithms and strategies for deployment. Additionally, the strengths and weaknesses of the various systems and their evaluation in the literature are emphasized. Despite the limitations of the current literature, the potential benefit of these early warning/track-and-trigger systems to improve patient outcomes remains significant. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

A visually guided collision warning system with a neuromorphic architecture.

PubMed

Okuno, Hirotsugu; Yagi, Tetsuya

2008-12-01

We have designed a visually guided collision warning system with a neuromorphic architecture, employing an algorithm inspired by the visual nervous system of locusts. The system was implemented with mixed analog-digital integrated circuits consisting of an analog resistive network and field-programmable gate array (FPGA) circuits. The resistive network processes the interaction between the laterally spreading excitatory and inhibitory signals instantaneously, which is essential for real-time computation of collision avoidance with a low power consumption and a compact hardware. The system responded selectively to approaching objects of simulated movie images at close range. The system was, however, confronted with serious noise problems due to the vibratory ego-motion, when it was installed in a mobile miniature car. To overcome this problem, we developed the algorithm, which is also installable in FPGA circuits, in order for the system to respond robustly during the ego-motion.

Feasibility study of earthquake early warning (EEW) in Hawaii

USGS Publications Warehouse

Thelen, Weston A.; Hotovec-Ellis, Alicia J.; Bodin, Paul

2016-09-30

The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquake early warning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises.The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking.The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations.The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than when using a regional network of seismometers. Given the current network, a single-station approach provides more warning for damaging earthquakes that occur close to the station, but it would have limited benefit compared to a fully implemented ShakeAlert system. For Honolulu, for example, the single-station approach provides an advantage over ShakeAlert only for earthquakes that occur in a narrow zone extending northeast and southwest of O‘ahu. Instrumentation and alarms associated with the single-station approach are typically maintained and assessed within the target facility, and thus no outside connectivity is required. A single-station approach, then, is unlikely to help broader populations beyond the individuals at the target facility, but they have the benefit of being commercially available for relatively little cost. The USGS Hawaiian Volcano Observatory (HVO) is the Advanced National Seismic System (ANSS) regional seismic network responsible for locating and characterizing earthquakes across the State of Hawaii. During 2014 and 2015, HVO tested a network-based EEW algorithm within the current seismic network in order to assess the suitability for building a full EEW system. Using the current seismic instrumentation and processing setup at HVO, it is possible for a network approach to release an alarm a little more than 3 seconds after the earthquake is recorded on the fourth seismometer. Presently, earthquakes having M≥3 detected with the ElarmS algorithm have an average location error of approximately 4.5 km and an average magnitude error of -0.3 compared to the reviewed catalog locations from the HVO. Additional stations and upgrades to existing seismic stations would serve to improve solution precision and warning times and additional staffing would be required to provide support for a robust, network-based EEW system. For a critical facility on the Island of Hawaiʻi, such as the telescopes atop Mauna Kea, one phased approach to mitigate losses could be to immediately install a single station system to establish some level of warning. Subsequently, supporting the implementation of a full network-based EEW system on the Island of Hawaiʻi would provide additional benefit in the form of improved warning times once the system is fully installed and operational, which may take several years. Distributed populations across the Hawaiian Islands, including those outside the major cities and far from the likely earthquake source areas, would likely only benefit from a network approach such as ShakeAlert to provide warnings of strong shaking.

Development of a self-made framework for the acquisition and communication of real-time precipitation data

NASA Astrophysics Data System (ADS)

Pedrozo-Acuña, A.; Magos-Hernández, J. A.; Sánchez-Peralta, J. A.; Blanco-Figueroa, J.; Breña-Naranjo, J. A.

2017-12-01

This contribution presents a real-time system for issuing warnings of intense precipitation events during major storms, developed for Mexico City, Mexico. The system is based on high-temporal resolution (Dt=1min) measurements of precipitation in 10 different points within the city, which report variables such as intensity, number of raindrops, raindrop size, kinetic energy, fall velocity, etc. Each one of these stations, is comprised of an optical disdrometer to measure size and fall velocity of hydrometeors, a solar panel to guarantee an uninterrupted power supply, a wireless broadband access to internet, and a resource constrained device known as Raspberry Pi3 for the processing, storage and sharing of the sensor data over the world wide web. The self-made developed platform follows a component-based system paradigm allowing users to implement custom algorithms and models depending on application requirements. The system is in place since July 2016, and continuous measurements of rainfall in real-time are published over the internet through the webpage www.oh-iiunam.mx. Additionally, the developed platform for the data collection and management interacts with the social network known as Twitter to enable real-time warnings of precipitation events. Key contribution of this development is the design and implementation of a scalable, easy to use, interoperable platform that facilitates the development of real-time precipitation sensor networks and warnings. The system is easy to implement and could be used as a prototype for systems in other regions of the world.

Evaluation of Real-Time and Off-Line Performance of the Virtual Seismologist Earthquake Early Warning Algorithm in Switzerland

NASA Astrophysics Data System (ADS)

Behr, Yannik; Clinton, John; Cua, Georgia; Cauzzi, Carlo; Heimers, Stefan; Kästli, Philipp; Becker, Jan; Heaton, Thomas

2013-04-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) originally formulated by Cua and Heaton (2007). Implementation of VS into real-time EEW codes has been an on-going effort of the Swiss Seismological Service at ETH Zürich since 2006, with support from ETH Zürich, various European projects, and the United States Geological Survey (USGS). VS is one of three EEW algorithms that form the basis of the California Integrated Seismic Network (CISN) ShakeAlert system, a USGS-funded prototype end-to-end EEW system that could potentially be implemented in California. In Europe, VS is currently operating as a real-time test system in Switzerland. As part of the on-going EU project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction), VS installations in southern Italy, western Greece, Istanbul, Romania, and Iceland are planned or underway. In Switzerland, VS has been running in real-time on stations monitored by the Swiss Seismological Service (including stations from Austria, France, Germany, and Italy) since 2010. While originally based on the Earthworm system it has recently been ported to the SeisComp3 system. Besides taking advantage of SeisComp3's picking and phase association capabilities it greatly simplifies the potential installation of VS at networks in particular those already running SeisComp3. We present the architecture of the new SeisComp3 based version and compare its results from off-line tests with the real-time performance of VS in Switzerland over the past two years. We further show that the empirical relationships used by VS to estimate magnitudes and ground motion, originally derived from southern California data, perform well in Switzerland.

The effects of in-vehicle and infrastructure-based collision warnings at signalized intersections

DOT National Transportation Integrated Search

2009-12-01

The potential effectiveness of warnings to drivers of the imminent threat of a collision with a red light violator was evaluated in an experiment that used a driving simulator. Three warnings were tested: (1) an infrastructure-based warning that imme...

Application of the Risk-Based Early Warning Method in a Fracture-Karst Water Source, North China.

PubMed

Guo, Yongli; Wu, Qing; Li, Changsuo; Zhao, Zhenhua; Sun, Bin; He, Shiyi; Jiang, Guanghui; Zhai, Yuanzheng; Guo, Fang

2018-03-01

  The paper proposes a risk-based early warning considering characteristics of fracture-karst aquifer in North China and applied it in a super-large fracture-karst water source. Groundwater vulnerability, types of land use, water abundance, transmissivity and spatial temporal variation of groundwater quality were chosen as indexes of the method. Weights of factors were obtained by using AHP method based on relative importance of factors, maps of factors were zoned by GIS, early warning map was conducted based on extension theory with the help of GIS, ENVI+IDL. The early warning map fused five factors very well, serious and tremendous warning areas are mainly located in northwest and east with high or relatively high transmissivity and groundwater pollutant loading, and obviously deteriorated or deteriorated trend of petroleum. The early warning map warns people where more attention should be paid, and the paper guides decision making to take appropriate protection actions in different warning levels areas.

Development of new tsunami detection algorithms for high frequency radars and application to tsunami warning in British Columbia, Canada

NASA Astrophysics Data System (ADS)

Grilli, S. T.; Guérin, C. A.; Shelby, M. R.; Grilli, A. R.; Insua, T. L.; Moran, P., Jr.

2016-12-01

A High-Frequency (HF) radar was installed by Ocean Networks Canada in Tofino, BC, to detect tsunamis from far- and near-field seismic sources; in particular, from the Cascadia Subduction Zone. This HF radar can measure ocean surface currents up to a 70-85 km range, depending on atmospheric conditions, based on the Doppler shift they cause in ocean waves at the Bragg frequency. In earlier work, we showed that tsunami currents must be at least 0.15 m/s to be directly detectable by a HF radar, when considering environmental noise and background currents (from tide/mesoscale circulation). This limits a direct tsunami detection to shallow water areas where currents are sufficiently strong due to wave shoaling and, hence, to the continental shelf. It follows that, in locations with a narrow shelf, warning times using a direct inversion method will be small. To detect tsunamis in deeper water, beyond the continental shelf, we proposed a new algorithm that does not require directly inverting currents, but instead is based on observing changes in patterns of spatial correlations of the raw radar signal between two radar cells located along the same wave ray, after time is shifted by the tsunami propagation time along the ray. A pattern change will indicate the presence of a tsunami. We validated this new algorithm for idealized tsunami wave trains propagating over a simple seafloor geometry in a direction normally incident to shore. Here, we further develop, extend, and validate the algorithm for realistic case studies of seismic tsunami sources impacting Vancouver Island, BC. Tsunami currents, computed with a state-of-the-art long wave model are spatially averaged over cells aligned along individual wave rays, located within the radar sweep area, obtained by solving the wave geometric optic equation; for long waves, such rays and tsunami propagation times along those are only function of the seafloor bathymetry, and hence can be precalculated for different incident tsunami directions. A model simulating the radar backscattered signal in space and time as a function of simulated tsunami currents is applied to the sweep area. Numerical experiments show that the new algorithm can detect a realistic tsunami further offshore than a direct detection method. Correlation thresholds for tsunami detection will be derived from the results.

The Accelerometric Networks in Istanbul

NASA Astrophysics Data System (ADS)

Zulfikar, Can; Alcik, Hakan; Mert, Aydin; Tahtasizoglu, Bahar; Kafadar, Nafiz; Korkmaz, Ahmet; Ozel, Oguz; Erdik, Mustafa

2010-05-01

In recent years several strong motion networks have been established in Istanbul with a preparation purpose for future probable earthquake. This study addresses the introduction of current seismic networks and presentation of some recent results recorded in these networks. Istanbul Earthquake Early Warning System Istanbul Earthquake Early Warning System has ten strong motion stations which were installed as close as possible to Marmara Sea main fault zone. Continuous on-line data from these stations via digital radio modem provide early warning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust Early Warning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The current algorithm compares the band-pass filtered accelerations and the cumulative absolute velocity (CAV) with specified threshold levels. The bracketed CAV window values that will be put into practice are accepted as to be 0.20, 0.40 and 0.70 m/s for three alarm levels, respectively. Istanbul Earthquake Rapid Response System Istanbul Earthquake Rapid Response System has one hundred 18 bit-resolution strong motion accelerometers which were placed in quasi-free field locations (basement of small buildings) in the populated areas of the city, within an area of approximately 50x30km, to constitute a network that will enable early damage assessment and rapid response information after a damaging earthquake. Early response information is achieved through fast acquisition and analysis of processed data obtained from the network. The stations are routinely interrogated on regular basis by the main data center. After triggered by an earthquake, each station processes the streaming strong motion data to yield the spectral accelerations at specific periods and sends these parameters in the form of SMS messages at every 20s directly to the main data center through a designated GSM network and through a microwave system. A shake map and damage distribution map (using aggregate building inventories and fragility curves) will then be automatically generated using the algorithm developed for this purpose. Loss assessment studies are complemented by a large citywide digital database on the topography, geology, soil conditions, building, infrastructure and lifeline inventory. The shake and damage maps will be conveyed to the governor's and mayor's offices and army headquarters within 3 minutes using radio modem and GPRS communication. Self Organizing Seismic Early Warning Information Network (SOSEWIN) in Atakoy District SOSEWIN sensors were developed by GFZ and Humbold University as part of SAFER project and EDIM project, and with cooperation of KOERI, the sensors were installed in Atakoy district of Istanbul city with Early Warning purpose. The main features of the SOSEWIN system are each sensing unit is comprised of low-cost components, undertakes its own seismological data processing, analysis and archiving, and its self-organizing capability with wireless mesh network communication. Seismic Network in Important Structures Some of the critical structures located in Istanbul city such as Fatih Sultan Mehmet Suspension Bridge which is connecting Asian and European sides of the city, Hagia Sophia Museum and Suleymaniye Mosque which are historical structures with an age of over 1000 years and 450 years respectively, . Kanyon Tower&Mall, Trakya Elektrik (formerly ENRON) and Isbank Tower (ISKULE) are monitorized to observe their seismic behaviors.

Preventing Heat Injuries by Predicting Individualized Human Core Temperature

DTIC Science & Technology

2015-10-14

hardware/software warning system of an impending rise in TC and generate alerts to potentially prevent heat injuries. PREVENTING HEAT INJURIES BY...TC estimates, provides ahead-of-time alerts about an impending rise in TC and 2) an individualized model that uses non-invasive measurements of AC...PREDICTION AND ALERT ALGORITHMS Here, we detail the development of an algorithm that uses a time series of recent-past TC measurements to provide

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornado activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William; Petersen, Walt; Buechler, Dennis; Krehbiel, Paul; Gatlin, Patrick; Zubrick, Steven

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational.The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) sate]lite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications. Abstract for the 3 rd Conference on Meteorological

Prioritizing earthquake and tsunami alerting efforts

NASA Astrophysics Data System (ADS)

Allen, R. M.; Allen, S.; Aranha, M. A.; Chung, A. I.; Hellweg, M.; Henson, I. H.; Melgar, D.; Neuhauser, D. S.; Nof, R. N.; Strauss, J. A.

2015-12-01

The timeline of hazards associated with earthquakes ranges from seconds for the strong shaking at the epicenter, to minutes for strong shaking at more distant locations in big quakes, to tens of minutes for a local tsunami. Earthquake and tsunami warning systems must therefore include very fast initial alerts, while also taking advantage of available time in bigger and tsunami-generating quakes. At the UC Berkeley Seismological Laboratory we are developing a suite of algorithms to provide the fullest possible information about earthquake shaking and tsunami inundation from seconds to minutes after a quake. The E-larmS algorithm uses the P-wave to rapidly detect an earthquake and issue a warning. It is currently issuing alerts to test users in as little as 3 sec after the origin time. Development of a new waveform detector may lead to even faster alerts. G-larmS uses permanent deformation estimates from GNSS stations to estimate the geometry and extent of rupture underway providing more accurate ground shaking estimates in big (M>~7) earthquakes. It performed well in the M6.0 2014 Napa earthquake. T-larmS is a new algorithm designed to extend alert capabilities to tsunami inundation. Rapid estimates of source characteristics for subduction zones event can not only be used to warn of the shaking hazard, but also the local tsunami inundation hazard. These algorithms are being developed, implemented and tested with a focus on the western US, but are also now being tested in other parts of the world including Israel, Turkey, Korea and Chile. Beta users in the Bay Area are receiving the alerts and beginning to implement automated actions. They also provide feedback on users needs, which has led to the development of the MyEEW smartphone app. This app allows beta users to receive the alerts on their cell phones. All these efforts feed into our ongoing assessment of directions and priorities for future development and implementation efforts.

Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

PubMed Central

Liu, Yan; Xu, Zhen-Jun

2013-01-01

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established. PMID:24191134

Safety early warning research for highway construction based on case-based reasoning and variable fuzzy sets.

PubMed

Liu, Yan; Yi, Ting-Hua; Xu, Zhen-Jun

2013-01-01

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

Cable Overheating Risk Warning Method Based on Impedance Parameter Estimation in Distribution Network

NASA Astrophysics Data System (ADS)

Yu, Zhang; Xiaohui, Song; Jianfang, Li; Fei, Gao

2017-05-01

Cable overheating will lead to the cable insulation level reducing, speed up the cable insulation aging, even easy to cause short circuit faults. Cable overheating risk identification and warning is nessesary for distribution network operators. Cable overheating risk warning method based on impedance parameter estimation is proposed in the paper to improve the safty and reliability operation of distribution network. Firstly, cable impedance estimation model is established by using least square method based on the data from distribiton SCADA system to improve the impedance parameter estimation accuracy. Secondly, calculate the threshold value of cable impedance based on the historical data and the forecast value of cable impedance based on the forecasting data in future from distribiton SCADA system. Thirdly, establish risks warning rules library of cable overheating, calculate the cable impedance forecast value and analysis the change rate of impedance, and then warn the overheating risk of cable line based on the overheating risk warning rules library according to the variation relationship between impedance and line temperature rise. Overheating risk warning method is simulated in the paper. The simulation results shows that the method can identify the imedance and forecast the temperature rise of cable line in distribution network accurately. The result of overheating risk warning can provide decision basis for operation maintenance and repair.

Quantitative Analysis of Transnasal Anterior Skull Base Approach: Report of Technology for Intraoperative Assessment of Instrument Motion.

PubMed

Berens, Angelique M; Harbison, Richard Alex; Li, Yangming; Bly, Randall A; Aghdasi, Nava; Ferreira, Manuel; Hannaford, Blake; Moe, Kris S

2017-08-01

To develop a method to measure intraoperative surgical instrument motion. This model will be applicable to the study of surgical instrument kinematics including surgical training, skill verification, and the development of surgical warning systems that detect aberrant instrument motion that may result in patient injury. We developed an algorithm to automate derivation of surgical instrument kinematics in an endoscopic endonasal skull base surgery model. Surgical instrument motion was recorded during a cadaveric endoscopic transnasal approach to the pituitary using a navigation system modified to record intraoperative time-stamped Euclidian coordinates and Euler angles. Microdebrider tip coordinates and angles were referenced to the cadaver's preoperative computed tomography scan allowing us to assess surgical instrument kinematics over time. A representative cadaveric endoscopic endonasal approach to the pituitary was performed to demonstrate feasibility of our algorithm for deriving surgical instrument kinematics. Technical feasibility of automatically measuring intraoperative surgical instrument motion and deriving kinematics measurements was demonstrated using standard navigation equipment.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

PubMed

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-03-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

Istanbul Earthquake Early Warning and Rapid Response System

NASA Astrophysics Data System (ADS)

Erdik, M. O.; Fahjan, Y.; Ozel, O.; Alcik, H.; Aydin, M.; Gul, M.

2003-12-01

As part of the preparations for the future earthquake in Istanbul a Rapid Response and Early Warning system in the metropolitan area is in operation. For the Early Warning system ten strong motion stations were installed as close as possible to the fault zone. Continuous on-line data from these stations via digital radio modem provide early warning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust Early Warning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The band-pass filtered accelerations and the cumulative absolute velocity (CAV) are compared with specified threshold levels. When any acceleration or CAV (on any channel) in a given station exceeds specific threshold values it is considered a vote. Whenever we have 2 station votes within selectable time interval, after the first vote, the first alarm is declared. In order to specify the appropriate threshold levels a data set of near field strong ground motions records form Turkey and the world has been analyzed. Correlations among these thresholds in terms of the epicenter distance the magnitude of the earthquake have been studied. The encrypted early warning signals will be communicated to the respective end users by UHF systems through a "service provider" company. The users of the early warning signal will be power and gas companies, nuclear research facilities, critical chemical factories, subway system and several high-rise buildings. Depending on the location of the earthquake (initiation of fault rupture) and the recipient facility the alarm time can be as high as about 8s. For the rapid response system one hundred 18 bit-resolution strong motion accelerometers were placed in quasi-free field locations (basement of small buildings) in the populated areas of the city, within an area of approximately 50x30km, to constitute a network that will enable early damage assessment and rapid response information after a damaging earthquake. Early response information is achieved through fast acquisition and analysis of processed data obtained from the network. The stations are routinely interrogated on regular basis by the main data center. After triggered by an earthquake, each station processes the streaming strong motion data to yield the spectral accelerations at specific periods, 12Hz filtered PGA and PGV and will send these parameters in the form of SMS messages at every 20s directly to the main data center through a designated GSM network and through a microwave system. A shake map and damage distribution map (using aggregate building inventories and fragility curves) will be automatically generated using the algorithm developed for this purpose. Loss assessment studies are complemented by a large citywide digital database on the topography, geology, soil conditions, building, infrastructure and lifeline inventory. The shake and damage maps will be conveyed to the governor's and mayor's offices, fire, police and army headquarters within 3 minutes using radio modem and GPRS communication. An additional forty strong motion recorders were placed on important structures in several interconnected clusters to monitor the health of these structures after a damaging earthquake.

Future of Earthquake Early Warning: Quantifying Uncertainty and Making Fast Automated Decisions for Applications

NASA Astrophysics Data System (ADS)

Wu, Stephen

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications. Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake. To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.

Impact of Graphic and Text Warnings on Cigarette Packs: Findings from Four Countries over Five Years

PubMed Central

Borland, Ron; Wilson, Nick; Fong, Geoffrey T.; Hammond, David; Cummings, K. Michael; Yong, Hua-Hie; Hosking, Warwick; Hastings, Gerard; Thrasher, James; McNeill, Ann

2015-01-01

Objectives To examine the impact of health warnings on smokers by comparing the short-term impact of new graphic (2006) Australian warnings with: (i) earlier (2003) United Kingdom (UK) larger text-based warnings; (ii) and Canadian graphic warnings (late 2000); and secondarily, to extend our understanding of warning wear-out. Methods The International Tobacco Control Policy Evaluation Survey (ITC Project) follows prospective cohorts (with replenishment) of adult smokers annually (5 waves: 2002–2006), in Canada, United States, UK, and Australia (around 2000 per country per wave; total n=17,773). Measures were of pack warning salience (reading and noticing); cognitive responses (thoughts of harm and quitting); and two behavioural responses: forgoing cigarettes and avoiding the warnings. Results All four indicators of impact increased markedly among Australian smokers following the introduction of graphic warnings. Controlling for date of introduction, they stimulated more cognitive responses than the UK (text-only) changes, and were avoided more, did not significantly increase forgoing cigarettes, but were read and noticed less. The findings also extend previous work showing partial wear-out of both graphic and text-only warnings, but the Canadian warnings have more sustained effects than UK ones. Conclusions Australia’s new health warnings increased reactions that are prospectively predictive of cessation activity. Warning size increases warning effectiveness and graphic warnings may be superior to text-based warnings. While there is partial wear-out in the initial impact associated with all warnings, stronger warnings tend to sustain their effects for longer. These findings support arguments for governments to exceed minimum FCTC requirements on warnings. PMID:19561362

Detection of severe respiratory disease epidemic outbreaks by CUSUM-based overcrowd-severe-respiratory-disease-index model.

PubMed

Polanco, Carlos; Castañón-González, Jorge Alberto; Macías, Alejandro E; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.

Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model

PubMed Central

Castañón-González, Jorge Alberto; Macías, Alejandro E.; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts. PMID:24069063

Earthquake Early Warning: Real-time Testing of an On-site Method Using Waveform Data from the Southern California Seismic Network

NASA Astrophysics Data System (ADS)

Solanki, K.; Hauksson, E.; Kanamori, H.; Wu, Y.; Heaton, T.; Boese, M.

2007-12-01

We have implemented an on-site early warning algorithm using the infrastructure of the Caltech/USGS Southern California Seismic Network (SCSN). We are evaluating the real-time performance of the software system and the algorithm for rapid assessment of earthquakes. In addition, we are interested in understanding what parts of the SCSN need to be improved to make early warning practical. Our EEW processing system is composed of many independent programs that process waveforms in real-time. The codes were generated by using a software framework. The Pd (maximum displacement amplitude of P wave during the first 3sec) and Tau-c (a period parameter during the first 3 sec) values determined during the EEW processing are being forwarded to the California Integrated Seismic Network (CISN) web page for independent evaluation of the results. The on-site algorithm measures the amplitude of the P-wave (Pd) and the frequency content of the P-wave during the first three seconds (Tau-c). The Pd and the Tau-c values make it possible to discriminate between a variety of events such as large distant events, nearby small events, and potentially damaging nearby events. The Pd can be used to infer the expected maximum ground shaking. The method relies on data from a single station although it will become more reliable if readings from several stations are associated. To eliminate false triggers from stations with high background noise level, we have created per station Pd threshold configuration for the Pd/Tau-c algorithm. To determine appropriate values for the Pd threshold we calculate Pd thresholds for stations based on the information from the EEW logs. We have operated our EEW test system for about a year and recorded numerous earthquakes in the magnitude range from M3 to M5. Two recent examples are a M4.5 earthquake near Chatsworth and a M4.7 earthquake near Elsinore. In both cases, the Pd and Tau-c parameters were determined successfully within 10 to 20 sec of the arrival of the P-wave at the station. The Tau-c values predicted the magnitude within 0.1 and the predicted average peak-ground-motion was 0.7 cm/s and 0.6 cm/s. The delays in the system are caused mostly by the packetizing delay because our software system is based on processing miniseed packets. Most recently we have begun reducing the data latency using new qmaserv2 software for the Q330 Quanterra datalogger. We implemented qmaserv2 based multicast receiver software to receive the native 1 sec packets from the dataloggers. The receiver reads multicast packets from the network and writes them into shared memory area. This new software will fully take advantage of the capabilities of the Q330 datalogger and significantly reduce data latency for EEW system. We have also implemented a new EEW sub-system that compliments the currently running EEW system by associating Pd and Tau-c values from multiple stations. So far, we have implemented a new trigger generation algorithm for real-time processing for the sub-system, and are able to routinely locate events and determine magnitudes using the Pd and Tau-c values.

Reality Check Algorithm for Complex Sources in Early Warning

NASA Astrophysics Data System (ADS)

Karakus, G.; Heaton, T. H.

2013-12-01

In almost all currently operating earthquake early warning (EEW) systems, presently available seismic data are used to predict future shaking. In most cases, location and magnitude are estimated. We are developing an algorithm to test the goodness of that prediction in real time. We monitor envelopes of acceleration, velocity, and displacement; if they deviate significantly from the envelope predicted by Cua's envelope gmpe's then we declare an overfit (perhaps false alarm) or an underfit (possibly a larger event has just occurred). This algorithm is designed to provide a robust measure and to work as quickly as possible in real-time. We monitor the logarithm of the ratio between the envelopes of the ongoing observed event and the envelopes derived from the predicted envelopes of channels of ground motion of the Virtual Seismologist (VS) (Cua, G. and Heaton, T.). Then, we recursively filter this result with a simple running median (de-spiking operator) to minimize the effect of one single high value. Depending on the result of the filtered value we make a decision such as if this value is large enough (e.g., >1), then we would declare, 'that a larger event is in progress', or similarly if this value is small enough (e.g., <-1), then we would declare a false alarm. We design the algorithm to work at a wide range of amplitude scales; that is, it should work for both small and large events.

NOAA/West coast and Alaska Tsunami warning center Atlantic Ocean response criteria

USGS Publications Warehouse

Whitmore, P.; Refidaff, C.; Caropolo, M.; Huerfano-Moreno, V.; Knight, W.; Sammler, W.; Sandrik, A.

2009-01-01

West Coast/Alaska Tsunami Warning Center (WCATWC) response criteria for earthquakesoccurring in the Atlantic and Caribbean basins are presented. Initial warning center decisions are based on an earthquake's location, magnitude, depth, distance from coastal locations, and precomputed threat estimates based on tsunami models computed from similar events. The new criteria will help limit the geographical extent of warnings and advisories to threatened regions, and complement the new operational tsunami product suite. Criteria are set for tsunamis generated by earthquakes, which are by far the main cause of tsunami generation (either directly through sea floor displacement or indirectly by triggering of sub-sea landslides).The new criteria require development of a threat data base which sets warning or advisory zones based on location, magnitude, and pre-computed tsunami models. The models determine coastal tsunami amplitudes based on likely tsunami source parameters for a given event. Based on the computed amplitude, warning and advisory zones are pre-set.

Graphic tobacco health warnings: which genre to choose?

PubMed

Sobani, Z; Nizami, S; Raza, E; ul Ain Baloch, N; Khan, J A

2010-03-01

Tobacco prevention studies show that graphic health warnings are more effective than text warnings, but there are no data on the effectiveness of different types of graphic health warnings in a Pakistani population. Even marginal differences in the effectiveness of genres can be of potential significance for public health. To study the effectiveness of different types of graphic tobacco warnings in a Pakistani population. We presented ten anti-smoking warnings to randomly selected volunteers (n = 170) and recorded their opinion on the effectiveness of each warning. The warnings were based on a range of images aimed at the diverse population interviewed. A grading scale based on appeal, application, educational potential and motivation towards cessation was used to produce a composite grade of perceived effectiveness of the warning. Our results indicate that graphic warnings reach a greater proportion of the population than text warnings. Those appealing to logic, and those inculcating a sense of fear by showing a deleterious outcome of smoking, were judged likely to be most effective in motivating smokers to quit and preventing experimental smokers from forming a habit.

Development of algorithms for tsunami detection by High Frequency Radar based on modeling tsunami case studies in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Grilli, S. T.; Guérin, C. A.; Grosdidier, S.

2014-12-01

Where coastal tsunami hazard is governed by near-field sources, Submarine Mass Failures (SMFs) or earthquakes, tsunami propagation times may be too small for a detection based on deep or shallow water buoys. To offer sufficient warning time, it has been proposed by others to implement early warning systems relying on High Frequency Radar (HFR) remote sensing, that has a dense spatial coverage far offshore. A new HFR, referred to as STRADIVARIUS, is being deployed by Diginext Inc. (in Fall 2014), to cover the "Golfe du Lion" (GDL) in the Western Mediterranean Sea. This radar uses a proprietary phase coding technology that allows detection up to 300 km, in a bistatic configuration (for which radar and antennas are separated by about 100 km). Although the primary purpose of the radar is vessel detection in relation to homeland security, the 4.5 MHz HFR will provide a strong backscattered signal for ocean surface waves at the so-called Bragg frequency (here, wavelength of 30 m). The current caused by an arriving tsunami will shift the Bragg frequency, by a value proportional to the current magnitude (projected on the local radar ray direction), which can be easily obtained from the Doppler spectrum of the HFR signal. Using state of the art tsunami generation and propagation models, we modeled tsunami case studies in the western Mediterranean basin (both seismic and SMFs) and simulated the HFR backscattered signal that would be detected for the entire GDL and beyond. Based on simulated HFR signal, we developed two types of tsunami detection algorithms: (i) one based on standard Doppler spectra, for which we found that to be detectable within the environmental and background current noises, the Doppler shift requires tsunami currents to be at least 10-15 cm/s, which typically only occurs on the continental shelf in fairly shallow water; (ii) to allow earlier detection, a second algorithm computes correlations of the HFR signals at two distant locations, shifted in time by the tsunami propagation time between these locations (easily computed based on bathymetry). We found that this second method allowed detection for currents as low as 5 cm/s, i.e., in deeper water, beyond the shelf and further away from the coast, thus allowing an earlier detection.

Probabilistic and Evolutionary Early Warning System: concepts, performances, and case-studies

NASA Astrophysics Data System (ADS)

Zollo, A.; Emolo, A.; Colombelli, S.; Elia, L.; Festa, G.; Martino, C.; Picozzi, M.

2013-12-01

PRESTo (PRobabilistic and Evolutionary early warning SysTem) is a software platform for Earthquake Early Warning that integrates algorithms for real-time earthquake location, magnitude estimation and damage assessment into a highly configurable and easily portable package. In its regional configuration, the software processes, in real-time, the 3-component acceleration data streams coming from seismic stations, for P-waves arrival detection and, in the case a quite large event is occurring, can promptly performs event detection and location, magnitude estimation and peak ground-motion prediction at target sites. The regional approach has been integrated with a threshold-based early warning method that allows, in the very first seconds after a moderate-to-large earthquake, to identify the most Probable Damaged Zone starting from the real-time measurement at near-source stations located at increasing distances from the earthquake epicenter, of the peak displacement (Pd) and predominant period of P-waves (τc), over a few-second long window after the P-wave arrival. Thus, each recording site independently provides an evolutionary alert level, according to the Pd and τc it measured, through a decisional table. Since 2009, PRESTo has been under continuous real-time testing using data streaming from the Iripinia Seismic Network (Southern Italy) and has produced a bulletin of some hundreds low magnitude events, including all the M≥2.5 earthquakes occurred in that period in Irpinia. Recently, PRESTo has been also implemented at the accelerometric network and broad-band networks in South Korea and in Romania, and off-line tested in Iberian Peninsula, in Turkey, in Israel, and in Japan. The feasibility of an Early Warning System at national scale, is currently under testing by studying the performances of the PRESTo platform for the Italian Accelerometric Network. Moreover, PRESTo is under experimentation in order to provide alert in a high-school located in the neighborhood of Naples at about 100 km from the Irpinia region.

Forward collision warning based on kernelized correlation filters

NASA Astrophysics Data System (ADS)

Pu, Jinchuan; Liu, Jun; Zhao, Yong

2017-07-01

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

Forecasting infectious disease emergence subject to seasonal forcing.

PubMed

Miller, Paige B; O'Dea, Eamon B; Rohani, Pejman; Drake, John M

2017-09-06

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Laser development for optimal helicopter obstacle warning system LADAR performance

NASA Astrophysics Data System (ADS)

Yaniv, A.; Krupkin, V.; Abitbol, A.; Stern, J.; Lurie, E.; German, A.; Solomonovich, S.; Lubashitz, B.; Harel, Y.; Engart, S.; Shimoni, Y.; Hezy, S.; Biltz, S.; Kaminetsky, E.; Goldberg, A.; Chocron, J.; Zuntz, N.; Zajdman, A.

2005-04-01

Low lying obstacles present immediate danger to both military and civilian helicopters performing low-altitude flight missions. A LADAR obstacle detection system is the natural solution for enhancing helicopter safety and improving the pilot situation awareness. Elop is currently developing an advanced Surveillance and Warning Obstacle Ranging and Display (SWORD) system for the Israeli Air Force. Several key factors and new concepts have contributed to system optimization. These include an adaptive FOV, data memorization, autonomous obstacle detection and warning algorithms and the use of an agile laser transmitter. In the present work we describe the laser design and performance and discuss some of the experimental results. Our eye-safe laser is characterized by its pulse energy, repetition rate and pulse length agility. By dynamically controlling these parameters, we are able to locally optimize the system"s obstacle detection range and scan density in accordance with the helicopter instantaneous maneuver.

Acoustic signal detection of manatee calls

NASA Astrophysics Data System (ADS)

Niezrecki, Christopher; Phillips, Richard; Meyer, Michael; Beusse, Diedrich O.

2003-04-01

The West Indian manatee (trichechus manatus latirostris) has become endangered partly because of a growing number of collisions with boats. A system to warn boaters of the presence of manatees, that can signal to boaters that manatees are present in the immediate vicinity, could potentially reduce these boat collisions. In order to identify the presence of manatees, acoustic methods are employed. Within this paper, three different detection algorithms are used to detect the calls of the West Indian manatee. The detection systems are tested in the laboratory using simulated manatee vocalizations from an audio compact disc. The detection method that provides the best overall performance is able to correctly identify ~=96% of the manatee vocalizations. However the system also results in a false positive rate of ~=16%. The results of this work may ultimately lead to the development of a manatee warning system that can warn boaters of the presence of manatees.

Acoustic detection of manatee vocalizations

NASA Astrophysics Data System (ADS)

Niezrecki, Christopher; Phillips, Richard; Meyer, Michael; Beusse, Diedrich O.

2003-09-01

The West Indian manatee (trichechus manatus latirostris) has become endangered partly because of a growing number of collisions with boats. A system to warn boaters of the presence of manatees, that can signal to boaters that manatees are present in the immediate vicinity, could potentially reduce these boat collisions. In order to identify the presence of manatees, acoustic methods are employed. Within this paper, three different detection algorithms are used to detect the calls of the West Indian manatee. The detection systems are tested in the laboratory using simulated manatee vocalizations from an audio compact disk. The detection method that provides the best overall performance is able to correctly identify ~96% of the manatee vocalizations. However, the system also results in a false alarm rate of ~16%. The results of this work may ultimately lead to the development of a manatee warning system that can warn boaters of the presence of manatees.

THE DIFFERENTIAL ALGORITHM BETWEEN RHEUMATOLOGIC AND MALIGN DISEASES

PubMed Central

Këpuska, Arbnore Batalli; Spahiju, Lidvana; Bejiq, Ramush; Manqestena, Rufadije; Stavileci, Valbona; Ibraimi, Zana

2016-01-01

Objective: The aim of this study is to determine the differential algorithm between rheumatism and malignant diseases. For every pediatrician, to be warned when attending joint pain and child arthralgia and prevent and treat within time malignant diseases. Methods: Our case presented in Pediatric Clinic, was referred by Regional Hospital of Ferizaj with suspected diagnose of Febris Rheumatica and Arthralgia. The main complaint was joint pain. Initially the patient was admitted at Cardiology and Rheumatology department. Then after examinations was referred to Hemato-Oncology department. Hospitalized during the period from 12.12.2014 to 18.01.2015. Results: Bone marrow biopsy as terminal diagnostic tool reviled severe malignant hematologic disease, which was masked by clinical and lab findings as Febris Rheumatica. Conclusion: Arthralgia as one of child’s often complain, should have a special attention paid to, as it might be a warning sign for a lot of diseases. Steroid treatment should not be used before final diagnose of the disease and before rolling out hematologic etiology with peripheral blood smear. PMID:27147926

Backup Warning Signals: Driver Perception and Response

DOT National Transportation Integrated Search

1996-08-01

This report describes the findings of three experiments that concern driver reaction to acoustic signals that might be used for backup warning devices. Intelligent warning devices are under development that will use vehicle-based sensors to warn back...

GOES-R Geostationary Lightning Mapper Performance Specifications and Algorithms

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Petersen, William A.; Boldi, Robert A.; Carey, Lawrence D.; Bateman, Monte G.; Buchler, Dennis E.; McCaul, E. William, Jr.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series will carry a GLM that will provide continuous day and night observations of lightning. The mission objectives for the GLM are to: (1) Provide continuous, full-disk lightning measurements for storm warning and nowcasting, (2) Provide early warning of tornadic activity, and (2) Accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997- present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. The science data will consist of lightning "events", "groups", and "flashes". The algorithm is being designed to be an efficient user of the computational resources. This may include parallelization of the code and the concept of sub-dividing the GLM FOV into regions to be processed in parallel. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama, Oklahoma, Central Florida, and the Washington DC Metropolitan area) are being used to develop the prelaunch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution.

ADAPTIVE WATER SENSOR SIGNAL PROCESSING: EXPERIMENTAL RESULTS AND IMPLICATIONS FOR ONLINE CONTAMINANT WARNING SYSTEMS

EPA Science Inventory

A contaminant detection technique and its optimization algorithms have two principal functions. One is the adaptive signal treatment that suppresses background noise and enhances contaminant signals, leading to a promising detection of water quality changes at a false rate as low...

Assessing the performance of regional landslide early warning models: the EDuMaP method

NASA Astrophysics Data System (ADS)

Calvello, M.; Piciullo, L.

2016-01-01

A schematic of the components of regional early warning systems for rainfall-induced landslides is herein proposed, based on a clear distinction between warning models and warning systems. According to this framework an early warning system comprises a warning model as well as a monitoring and warning strategy, a communication strategy and an emergency plan. The paper proposes the evaluation of regional landslide warning models by means of an original approach, called the "event, duration matrix, performance" (EDuMaP) method, comprising three successive steps: identification and analysis of the events, i.e., landslide events and warning events derived from available landslides and warnings databases; definition and computation of a duration matrix, whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model performance by means of performance criteria and indicators applied to the duration matrix. During the first step the analyst identifies and classifies the landslide and warning events, according to their spatial and temporal characteristics, by means of a number of model parameters. In the second step, the analyst computes a time-based duration matrix with a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warning data from the municipal early warning system operating in Rio de Janeiro (Brazil).

GASAKe: forecasting landslide activations by a genetic-algorithms based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-02-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic-algorithms and allows to obtaining thresholds of landslide activation from the set of historical occurrences and from the rainfall series. GASAKe can be applied to either single landslides or set of similar slope movements in a homogeneous environment. Calibration of the model is based on genetic-algorithms, and provides for families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from these latter, the corresponding mobility functions (i.e. the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to hydro-geological complexity of the site. Generally, smaller values are expected for shallow slope instabilities with respect to large-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing recorded or forecasted rainfall series. Example of application of GASAKe to a medium-scale slope movement (the Uncino landslide at San Fili, in Calabria, Southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, Southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of landslide occurrence. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e. neither missing nor false alarms) has been achieved against 5 activations. As for temporal validation, the experiments performed by considering the extra dates of landslide activation have also proved satisfactory. In view of early-warning applications for civil protection purposes, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model obtained against different types of slope instabilities, characterized by several historical activations. Nevertheless, further refinements are still needed for applications to landslide risk mitigation within early-warning and decision-support systems.

Combining multiple earthquake models in real time for earthquake early warning

USGS Publications Warehouse

Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

DEVELOPING A METHOD TO IDENTIFY HORIZONTAL CURVE SEGMENTS WITH HIGH CRASH OCCURRENCES USING THE HAF ALGORITHM

DOT National Transportation Integrated Search

2018-04-01

Crashes occur every day on Utahs highways. Curves can be particularly dangerous as they require driver focus due to potentially unseen hazards. Often, crashes occur on curves due to poor curve geometry, a lack of warning signs, or poor surface con...

Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

NASA Astrophysics Data System (ADS)

Guérin, Charles-Antoine; Grilli, Stéphan T.; Moran, Patrick; Grilli, Annette R.; Insua, Tania L.

2018-05-01

The authors recently proposed a new method for detecting tsunamis using high-frequency (HF) radar observations, referred to as "time-correlation algorithm" (TCA; Grilli et al. Pure Appl Geophys 173(12):3895-3934, 2016a, 174(1): 3003-3028, 2017). Unlike standard algorithms that detect surface current patterns, the TCA is based on analyzing space-time correlations of radar signal time series in pairs of radar cells, which does not require inverting radial surface currents. This was done by calculating a contrast function, which quantifies the change in pattern of the mean correlation between pairs of neighboring cells upon tsunami arrival, with respect to a reference correlation computed in the recent past. In earlier work, the TCA was successfully validated based on realistic numerical simulations of both the radar signal and tsunami wave trains. Here, this algorithm is adapted to apply to actual data from a HF radar installed in Tofino, BC, for three test cases: (1) a simulated far-field tsunami generated in the Semidi Subduction Zone in the Aleutian Arc; (2) a simulated near-field tsunami from a submarine mass failure on the continental slope off of Tofino; and (3) an event believed to be a meteotsunami, which occurred on October 14th, 2016, off of the Pacific West Coast and was measured by the radar. In the first two cases, the synthetic tsunami signal is superimposed onto the radar signal by way of a current memory term; in the third case, the tsunami signature is present within the radar data. In light of these test cases, we develop a detection methodology based on the TCA, using a correlation contrast function, and show that in all three cases the algorithm is able to trigger a timely early warning.

Health warnings promote healthier dietary decision making: Effects of positive versus negative message framing and graphic versus text-based warnings.

PubMed

Rosenblatt, Daniel H; Bode, Stefan; Dixon, Helen; Murawski, Carsten; Summerell, Patrick; Ng, Alyssa; Wakefield, Melanie

2018-08-01

Food product health warnings have been proposed as a potential obesity prevention strategy. This study examined the effects of text-only and text-and-graphic, negatively and positively framed health warnings on dietary choice behavior. In a 2 × 5 mixed experimental design, 96 participants completed a dietary self-control task. After providing health and taste ratings of snack foods, participants completed a baseline measure of dietary self-control, operationalized as participants' frequency of choosing healthy but not tasty items and rejecting unhealthy yet tasty items to consume at the end of the experiment. Participants were then randomly assigned to one of five health warning groups and presented with 10 health warnings of a given form: text-based, negative framing; graphic, negative framing; text, positive framing; graphic, positive framing; or a no warning control. Participants then completed a second dietary decision making session to determine whether health warnings influenced dietary self-control. Linear mixed effects modeling revealed a significant interaction between health warning group and decision stage (pre- and post-health warning presentation) on dietary self-control. Negatively framed graphic health warnings promoted greater dietary self-control than other health warnings. Negatively framed text health warnings and positively framed graphic health warnings promoted greater dietary self-control than positively framed text health warnings and control images, which did not increase dietary self-control. Overall, HWs primed healthier dietary decision making behavior, with negatively framed graphic HWs being most effective. Health warnings have potential to become an important element of obesity prevention. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Tourism ecological security early warning of Zhangjiajie, China based on the improved TOPSIS method and the grey GM (1,1)model].

PubMed

Xu, Mei; Liu, Chun la; Li, Dan; Zhong, Xiao Lin

2017-11-01

Tourism ecological security early warning is of great significance both to the coordination of ecological environment protection and tourism industry rapid development in tourism destination, and the sustainable and healthy development of regional social and economy. Firstly, based on the DPSIR model, the tourism ecological security early warning index system of Zhangjiajie was constructed from 5 aspects, which were driving force, pressure, state, impact and response. Then, by using the improved TOPSIS method, the tourism ecological security situation of Zhangjiajie from 2001 to 2014 was analyzed. Lastly, by using the grey GM (1,1) model, the tourism ecological security evolution trend of 2015-2020 was predicted. The results indicated that, on the whole, the close degree of Zhangjiajie's tourism ecological security showed a slightly upward trend during 2001-2014, the warning degree was the moderate warning. In terms of each subsystem, warning degree of the driving force system and the pressure system of Zhangjiajie's tourism ecological secu-rity were on the rise, which evolved from light warning to heavy warning; warning degree of the state system and the impact system had not changed so much, and had been in the moderate warning; warning degree of the response system was on the decline, which changed from huge warning to no warning during 2001-2014. According to the current development trend, the close degree of Zhangjiajie's tourism ecological security would rise further in 2015-2020, and the warning degree would turn from moderate warning into light warning, but the task of coordinating the relationship between tourism development and ecological construction and environmental protection would be still arduous.

NASA Runway Incursion Prevention System (RIPS) Dallas-Fort Worth Demonstration Performance Analysis

NASA Technical Reports Server (NTRS)

Cassell, Rick; Evers, Carl; Esche, Jeff; Sleep, Benjamin; Jones, Denise R. (Technical Monitor)

2002-01-01

NASA's Aviation Safety Program Synthetic Vision System project conducted a Runway Incursion Prevention System (RIPS) flight test at the Dallas-Fort Worth International Airport in October 2000. The RIPS research system includes advanced displays, airport surveillance system, data links, positioning system, and alerting algorithms to provide pilots with enhanced situational awareness, supplemental guidance cues, a real-time display of traffic information, and warnings of runway incursions. This report describes the aircraft and ground based runway incursion alerting systems and traffic positioning systems (Automatic Dependent Surveillance - Broadcast (ADS-B) and Traffic Information Service - Broadcast (TIS-B)). A performance analysis of these systems is also presented.

Web-based Tsunami Early Warning System with instant Tsunami Propagation Calculations in the GPU Cloud

NASA Astrophysics Data System (ADS)

Hammitzsch, M.; Spazier, J.; Reißland, S.

2014-12-01

Usually, tsunami early warning and mitigation systems (TWS or TEWS) are based on several software components deployed in a client-server based infrastructure. The vast majority of systems importantly include desktop-based clients with a graphical user interface (GUI) for the operators in early warning centers. However, in times of cloud computing and ubiquitous computing the use of concepts and paradigms, introduced by continuously evolving approaches in information and communications technology (ICT), have to be considered even for early warning systems (EWS). Based on the experiences and the knowledge gained in three research projects - 'German Indonesian Tsunami Early Warning System' (GITEWS), 'Distant Early Warning System' (DEWS), and 'Collaborative, Complex, and Critical Decision-Support in Evolving Crises' (TRIDEC) - new technologies are exploited to implement a cloud-based and web-based prototype to open up new prospects for EWS. This prototype, named 'TRIDEC Cloud', merges several complementary external and in-house cloud-based services into one platform for automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The prototype in its current version addresses tsunami early warning and mitigation. The integration of GPU accelerated tsunami simulation computations have been an integral part of this prototype to foster early warning with on-demand tsunami predictions based on actual source parameters. However, the platform is meant for researchers around the world to make use of the cloud-based GPU computation to analyze other types of geohazards and natural hazards and react upon the computed situation picture with a web-based GUI in a web browser at remote sites. The current website is an early alpha version for demonstration purposes to give the concept a whirl and to shape science's future. Further functionality, improvements and possible profound changes have to implemented successively based on the users' evolving needs.

Urban Flood Prevention and Early Warning System in Jinan City

NASA Astrophysics Data System (ADS)

Feng, Shiyuan; Li, Qingguo

2018-06-01

The system construction of urban flood control and disaster reduction in China is facing pressure and challenge from new urban water disaster. Under the circumstances that it is difficult to build high standards of flood protection engineering measures in urban areas, it is particularly important to carry out urban flood early warning. In Jinan City, a representative inland area, based on the index system of early warning of flood in Jinan urban area, the method of fuzzy comprehensive evaluation was adopted to evaluate the level of early warning. Based on the cumulative rainfall of 3 hours, the CAflood simulation results based on cellular automaton model of urban flooding were used as evaluation indexes to realize the accuracy and integration of urban flood control early warning.

Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis.

PubMed

Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng

2014-08-01

The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.

Technical Note: An operational landslide early warning system at regional scale based on space-time variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2014-10-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b), makes use of LAMI rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain-gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult and it provides different outputs. Switching among different views, the system is able to focus both on monitoring of real time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a very straightforward view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain-gauges can be displayed and constantly compared with rainfall thresholds. To better account for the high spatial variability of the physical features, which affects the relationship between rainfall and landslides, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of 332 rain gauges, it allows monitoring each alert zone separately and warnings can be issued independently from an alert zone to another. An important feature of the warning system is the use of thresholds that may vary in time adapting at the conditions of the rainfall path recorded by the rain-gauges. Depending on when the starting time of the rainfall event is set, the comparison with the threshold may produce different outcomes. Therefore, a recursive algorithm was developed to check and compare with the thresholds all possible starting times, highlighting the worst scenario and showing in the WebGIS interface at what time and how much the rainfall path has exceeded or will exceed the most critical threshold. Besides forecasting and monitoring the hazard scenario over the whole region with hazard levels differentiated for 25 distinct alert zones, the system can be used to gather, analyze, visualize, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial resolution appropriate to the NWP system. We then demonstrate how these warning areas could eventually complement existing global systems such as the Global Flood Awareness System (GloFAS), to give warnings of flash floods. This work demonstrates the possibility of creating a global flash flood forecasting system based on forecasts from existing global NWP systems. Future developments, in post-processing for example, will need to address an under-prediction bias, for extreme point rainfall, that is innate to current-generation global models.

Effects of plain packaging, warning labels, and taxes on young people's predicted sugar-sweetened beverage preferences: an experimental study.

PubMed

Bollard, Tessa; Maubach, Ninya; Walker, Natalie; Ni Mhurchu, Cliona

2016-09-01

Consumption of sugar-sweetened beverages (SSBs) is associated with increased risk of obesity, diabetes, heart disease and dental caries. Our aim was to assess the effects of plain packaging, warning labels, and a 20 % tax on predicted SSB preferences, beliefs and purchase probabilities amongst young people. A 2 × 3 × 2 between-group experimental study was conducted over a one-week period in August 2014. Intervention scenarios were delivered, and outcome data collected, via an anonymous online survey. Participants were 604 New Zealand young people aged 13-24 years who consumed soft drinks regularly. Participants were randomly allocated using a computer-generated algorithm to view one of 12 experimental conditions, specifically images of branded versus plain packaged SSBs, with either no warning, a text warning, or a graphic warning, and with or without a 20 % tax. Participant perceptions of the allocated SSB product and of those who might consume the product were measured using seven-point Likert scales. Purchase probabilities were measured using 11-point Juster scales. Six hundred and four young people completed the survey (51 % female, mean age 18 (SD 3.4) years). All three intervention scenarios had a significant negative effect on preferences for SSBs (plain packaging: F (6, 587) = 54.4, p <0.001; warning label: F (6, 588) = 19.8, p <0.001; 20 % tax: F (6, 587) = 11.3, p <0.001). Plain packaging and warning labels also had a significant negative impact on reported likelihood of purchasing SSB's (p = <0.001). A 20 % tax reduced participants' purchase probability but the difference was not statistically significant (p = 0.2). Plain packaging and warning labels significantly reduce young people's predicted preferences for, and reported probability of purchasing, SSBs.

Development of guidance on the timeliness in response to acute kidney injury warning stage test results for adults in primary care: an appropriateness ratings evaluation

PubMed Central

Blakeman, Tom; Griffith, Kathryn; Lasserson, Dan; Lopez, Berenice; Tsang, Jung Y; Campbell, Stephen; Tomson, Charles

2016-01-01

Objectives Tackling the harm associated with acute kidney injury (AKI) is a global priority. In England, a national computerised AKI algorithm is being introduced across the National Health Service (NHS) to drive this change. The study sought to maximise its clinical utility and minimise the potential for burden on clinicians and patients in primary care. Design An appropriateness ratings evaluation using the RAND/UCLA Appropriateness Method. Setting Clinical scenarios were developed to test the timeliness in (1) communication of AKI warning stage test results from clinical pathology services to primary care, and (2) primary care clinician response to an AKI warning stage test result. Participants A 10-person panel was purposively sampled with representation from clinical biochemistry, acute and emergency medicine and general practice. General practitioners (GPs) represented typical practice in relation to rural and urban practice, out of hours care, GP commissioning and those interested in reducing the impact of medicalisation and ‘overdiagnosis’. Results There was agreement that delivery of AKI warning stage test results through interruptive methods of communication (ie, telephone) from laboratories to primary care was the appropriate next step for patients with an AKI warning stage 3 test result. In the context of acute illness, waiting up to 72 hours to respond to an AKI warning stage test result was deemed an inappropriate action in 62 out of the 65 (94.5%) cases. There was agreement that a clinician response was required within 6 hours, or less, in 39 out of 40 (97.5%) clinical cases relating AKI warning stage test results in the presence of moderate hyperkalaemia. Conclusions The study has informed national guidance to support a timely and calibrated response to AKI warning stage test results for adults in primary care. Further research is needed to support effective implementation, with a view to examine the effect on health outcomes and costs. PMID:27729353

Short-term volcano-tectonic earthquake forecasts based on a moving mean recurrence time algorithm: the El Hierro seismo-volcanic crisis experience

NASA Astrophysics Data System (ADS)

García, Alicia; De la Cruz-Reyna, Servando; Marrero, José M.; Ortiz, Ramón

2016-05-01

Under certain conditions, volcano-tectonic (VT) earthquakes may pose significant hazards to people living in or near active volcanic regions, especially on volcanic islands; however, hazard arising from VT activity caused by localized volcanic sources is rarely addressed in the literature. The evolution of VT earthquakes resulting from a magmatic intrusion shows some orderly behaviour that may allow the occurrence and magnitude of major events to be forecast. Thus governmental decision makers can be supplied with warnings of the increased probability of larger-magnitude earthquakes on the short-term timescale. We present here a methodology for forecasting the occurrence of large-magnitude VT events during volcanic crises; it is based on a mean recurrence time (MRT) algorithm that translates the Gutenberg-Richter distribution parameter fluctuations into time windows of increased probability of a major VT earthquake. The MRT forecasting algorithm was developed after observing a repetitive pattern in the seismic swarm episodes occurring between July and November 2011 at El Hierro (Canary Islands). From then on, this methodology has been applied to the consecutive seismic crises registered at El Hierro, achieving a high success rate in the real-time forecasting, within 10-day time windows, of volcano-tectonic earthquakes.

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

47 CFR 87.483 - Audio visual warning systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 5 2014-10-01 2014-10-01 false Audio visual warning systems. 87.483 Section 87... AVIATION SERVICES Stations in the Radiodetermination Service § 87.483 Audio visual warning systems. An audio visual warning system (AVWS) is a radar-based obstacle avoidance system. AVWS activates...

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study.

PubMed

Chen, Rong; Kusano, Kristofer D; Gabler, Hampton C

2015-01-01

Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human-machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data. The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips. Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40-50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed. This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.

Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients.

PubMed

Green, Malcolm; Lander, Harvey; Snyder, Ashley; Hudson, Paul; Churpek, Matthew; Edelson, Dana

2018-02-01

Traditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration. We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score. Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013. Cardiac arrest, ICU transfer or death within 24h of a score RESULTS: Overall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799-0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716-0.720]; 0.698 [0.696-0.700]; 0.663 [0.661-0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of >=4 and >=2, NEWS thresholds of >=5 and >=2, and eCART thresholds of >=12 and >=4, respectively. At those thresholds, eCART caught 22 more adverse events per 10,000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer. An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing the performance of regional landslide early warning models: the EDuMaP method

NASA Astrophysics Data System (ADS)

Calvello, M.; Piciullo, L.

2015-10-01

The paper proposes the evaluation of the technical performance of a regional landslide early warning system by means of an original approach, called EDuMaP method, comprising three successive steps: identification and analysis of the Events (E), i.e. landslide events and warning events derived from available landslides and warnings databases; definition and computation of a Duration Matrix (DuMa), whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model Performance (P) by means of performance criteria and indicators applied to the duration matrix. During the first step, the analyst takes into account the features of the warning model by means of ten input parameters, which are used to identify and classify landslide and warning events according to their spatial and temporal characteristics. In the second step, the analyst computes a time-based duration matrix having a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The proposed method is based on a framework clearly distinguishing between local and regional landslide early warning systems as well as among correlation laws, warning models and warning systems. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warnings data from the municipal early warning system operating in Rio de Janeiro (Brazil).

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

PubMed Central

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-01-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety. PMID:28257073

A new type of tri-axial accelerometers with high dynamic range MEMS for earthquake early warning

NASA Astrophysics Data System (ADS)

Peng, Chaoyong; Chen, Yang; Chen, Quansheng; Yang, Jiansi; Wang, Hongti; Zhu, Xiaoyi; Xu, Zhiqiang; Zheng, Yu

2017-03-01

Earthquake Early Warning System (EEWS) has shown its efficiency for earthquake damage mitigation. As the progress of low-cost Micro Electro Mechanical System (MEMS), many types of MEMS-based accelerometers have been developed and widely used in deploying large-scale, dense seismic networks for EEWS. However, the noise performance of these commercially available MEMS is still insufficient for weak seismic signals, leading to the large scatter of early-warning parameters estimation. In this study, we developed a new type of tri-axial accelerometer based on high dynamic range MEMS with low noise level using for EEWS. It is a MEMS-integrated data logger with built-in seismological processing. The device is built on a custom-tailored Linux 2.6.27 operating system and the method for automatic detecting seismic events is STA/LTA algorithms. When a seismic event is detected, peak ground parameters of all data components will be calculated at an interval of 1 s, and τc-Pd values will be evaluated using the initial 3 s of P wave. These values will then be organized as a trigger packet actively sent to the processing center for event combining detection. The output data of all three components are calibrated to sensitivity 500 counts/cm/s2. Several tests and a real field test deployment were performed to obtain the performances of this device. The results show that the dynamic range can reach 98 dB for the vertical component and 99 dB for the horizontal components, and majority of bias temperature coefficients are lower than 200 μg/°C. In addition, the results of event detection and real field deployment have shown its capabilities for EEWS and rapid intensity reporting.

Dengue Contingency Planning: From Research to Policy and Practice.

PubMed

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J; Sánchez Tejeda, Gustavo; Lloyd, Linda S; Hakim, Lokman; Bowman, Leigh R; Horstick, Olaf; Coelho, Giovanini

2016-09-01

Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan.

Severe Thunderstorm and Tornado Warnings at Raleigh, North Carolina.

NASA Astrophysics Data System (ADS)

Hoium, Debra K.; Riordan, Allen J.; Monahan, John; Keeter, Kermit K.

1997-11-01

The National Weather Service issues public warnings for severe thunderstorms and tornadoes when these storms appear imminent. A study of the warning process was conducted at the National Weather Service Forecast Office at Raleigh, North Carolina, from 1994 through 1996. The purpose of the study was to examine the decision process by documenting the types of information leading to decisions to warn or not to warn and by describing the sequence and timing of events in the development of warnings. It was found that the evolution of warnings followed a logical sequence beginning with storm monitoring and proceeding with increasingly focused activity. For simplicity, information input to the process was categorized as one of three types: ground truth, radar reflectivity, or radar velocity.Reflectivity, velocity, and ground truth were all equally likely to initiate the investigation process. This investigation took an average of 7 min, after which either a decision was made not to warn or new information triggered the warning. Decisions not to issue warnings were based more on ground truth and reflectivity than radar velocity products. Warnings with investigations of more than 2 min were more likely to be triggered by radar reflectivity, than by velocity or ground truth. Warnings with a shorter investigation time, defined here as "immediate trigger warnings," were less frequently based on velocity products and more on ground truth information. Once the decision was made to warn, it took an average of 2.1 min to prepare the warning text. In 85% of cases when warnings were issued, at least one contact was made to emergency management officials or storm spotters in the warned county. Reports of severe weather were usually received soon after the warning was transmitted-almost half of these within 30 min after issue. A total of 68% were received during the severe weather episode, but some of these storm reports later proved false according to Storm Data.Even though the WSR-88D is a sophisticated tool, ground truth information was found to be a vital part of the warning process. However, the data did not indicate that population density was statistically correlated either with the number of warnings issued or the verification rate.

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

Tornadogenesis via squall line and supercell interaction revisited - The 15 November 1989 Huntsville tornado

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Knupp, Kevin R.

1990-01-01

A case study analyzing the environmental setting and storm system morphology that provides observational evidence of a mechanism involving the interaction of a gust front with a preexisting mesocyclone is presented. This case serves to reemphasize the existence of a high conditional probability of tornado occurrence, given the merger of a gust front (or storm outflow) with a moderate to strong thunderstorm ahead of it. The resultant data serve to illustrate some important unresolved issues relevant to the severe weather warning system that involve the present and planned local warning and network radars, and future algorithms that might intelligently integrate other data sources and models with the radar data.

Early warning system for financially distressed hospitals via data mining application.

PubMed

Koyuncugil, Ali Serhan; Ozgulbas, Nermin

2012-08-01

The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health's public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation.

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

PubMed

Smith, D L; Kerns, J P; Walker, N R; Payne, A F; Horvath, B; Inguagiato, J C; Kaminski, J E; Tomaso-Peterson, M; Koch, P L

2018-01-01

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass

PubMed Central

Smith, D. L.; Kerns, J. P.; Walker, N. R.; Payne, A. F.; Horvath, B.; Inguagiato, J. C.; Kaminski, J. E.; Tomaso-Peterson, M.

2018-01-01

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass. PMID:29522560

Cooperative wireless network control based health and activity monitoring system.

PubMed

Prakash, R; Ganesh, A Balaji; Girish, Siva V

2016-10-01

A real-time cooperative communication based wireless network is presented for monitoring health and activity of an end-user in their environment. The cooperative communication offers better energy consumption and also an opportunity to aware the current location of a user non-intrusively. The link between mobile sensor node and relay node is dynamically established by using Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) based on adaptive relay selection scheme. The study proposes a Linear Acceleration based Transmission Power Decision Control (LA-TPDC) algorithm to further enhance the energy efficiency of cooperative communication. Further, the occurrences of false alarms are carefully prevented by introducing three stages of sequential warning system. The real-time experiments are carried-out by using the nodes, namely mobile sensor node, relay nodes and a destination node which are indigenously developed by using a CC430 microcontroller integrated with an in-built transceiver at 868 MHz. The wireless node performance characteristics, such as energy consumption, Signal-Noise ratio (SNR), Bit Error Rate (BER), Packet Delivery Ratio (PDR) and transmission offset are evaluated for all the participated nodes. The experimental results observed that the proposed linear acceleration based transmission power decision control algorithm almost doubles the battery life time than energy efficient conventional cooperative communication.

A bioinspired collision detection algorithm for VLSI implementation

NASA Astrophysics Data System (ADS)

Cuadri, J.; Linan, G.; Stafford, R.; Keil, M. S.; Roca, E.

2005-06-01

In this paper a bioinspired algorithm for collision detection is proposed, based on previous models of the locust (Locusta migratoria) visual system reported by F.C. Rind and her group, in the University of Newcastle-upon-Tyne. The algorithm is suitable for VLSI implementation in standard CMOS technologies as a system-on-chip for automotive applications. The working principle of the algorithm is to process a video stream that represents the current scenario, and to fire an alarm whenever an object approaches on a collision course. Moreover, it establishes a scale of warning states, from no danger to collision alarm, depending on the activity detected in the current scenario. In the worst case, the minimum time before collision at which the model fires the collision alarm is 40 msec (1 frame before, at 25 frames per second). Since the average time to successfully fire an airbag system is 2 msec, even in the worst case, this algorithm would be very helpful to more efficiently arm the airbag system, or even take some kind of collision avoidance countermeasures. Furthermore, two additional modules have been included: a "Topological Feature Estimator" and an "Attention Focusing Algorithm". The former takes into account the shape of the approaching object to decide whether it is a person, a road line or a car. This helps to take more adequate countermeasures and to filter false alarms. The latter centres the processing power into the most active zones of the input frame, thus saving memory and processing time resources.

Advanced algorithms for the identification of mixtures using condensed-phase FT-IR spectroscopy

NASA Astrophysics Data System (ADS)

Arnó, Josep; Andersson, Greger; Levy, Dustin; Tomczyk, Carol; Zou, Peng; Zuidema, Eric

2011-06-01

FT-IR spectroscopy is the technology of choice to identify solid and liquid phase unknown samples. Advances in instrument portability have made possible the use of FT-IR spectroscopy in emergency response and military field applications. The samples collected in those harsh environments are rarely pure and typically contain multiple chemical species in water, sand, or inorganic matrices. In such critical applications, it is also desired that in addition to broad chemical identification, the user is warned immediately if the sample contains a threat or target class material (i.e. biological, narcotic, explosive). The next generation HazMatID 360 combines the ruggedized design and functionality of the current HazMatID with advanced mixture analysis algorithms. The advanced FT-IR instrument allows effective chemical assessment of samples that may contain one or more interfering materials like water or dirt. The algorithm was the result of years of cumulative experience based on thousands of real-life spectra sent to our ReachBack spectral analysis service by customers in the field. The HazMatID 360 combines mixture analysis with threat detection and chemical hazard classification capabilities to provide, in record time, crucial information to the user. This paper will provide an overview of the software and algorithm enhancements, in addition to examples of improved performance in mixture identification.

Near-Field Tsunami Models with Rapid Earthquake Source Inversions from Land and Ocean-Based Observations: The Potential for Forecast and Warning

NASA Astrophysics Data System (ADS)

Melgar, D.; Bock, Y.; Crowell, B. W.; Haase, J. S.

2013-12-01

Computation of predicted tsunami wave heights and runup in the regions adjacent to large earthquakes immediately after rupture initiation remains a challenging problem. Limitations of traditional seismological instrumentation in the near field which cannot be objectively employed for real-time inversions and the non-unique source inversion results are a major concern for tsunami modelers. Employing near-field seismic, GPS and wave gauge data from the Mw 9.0 2011 Tohoku-oki earthquake, we test the capacity of static finite fault slip models obtained from newly developed algorithms to produce reliable tsunami forecasts. First we demonstrate the ability of seismogeodetic source models determined from combined land-based GPS and strong motion seismometers to forecast near-source tsunamis in ~3 minutes after earthquake origin time (OT). We show that these models, based on land-borne sensors only tend to underestimate the tsunami but are good enough to provide a realistic first warning. We then demonstrate that rapid ingestion of offshore shallow water (100 - 1000 m) wave gauge data significantly improves the model forecasts and possible warnings. We ingest data from 2 near-source ocean-bottom pressure sensors and 6 GPS buoys into the earthquake source inversion process. Tsunami Green functions (tGFs) are generated using the GeoClaw package, a benchmarked finite volume code with adaptive mesh refinement. These tGFs are used for a joint inversion with the land-based data and substantially improve the earthquake source and tsunami forecast. Model skill is assessed by detailed comparisons of the simulation output to 2000+ tsunami runup survey measurements collected after the event. We update the source model and tsunami forecast and warning at 10 min intervals. We show that by 20 min after OT the tsunami is well-predicted with a high variance reduction to the survey data and by ~30 minutes a model that can be considered final, since little changed is observed afterwards, is achieved. This is an indirect approach to tsunami warning, it relies on automatic determination of the earthquake source prior to tsunami simulation. It is more robust than ad-hoc approaches because it relies on computation of a finite-extent centroid moment tensor to objectively determine the style of faulting and the fault plane geometry on which to launch the heterogeneous static slip inversion. Operator interaction and physical assumptions are minimal. Thus, the approach can provide the initial conditions for tsunami simulation (seafloor motion) irrespective of the type of earthquake source and relies heavily on oceanic wave gauge measurements for source determination. It reliably distinguishes among strike-slip, normal and thrust faulting events, all of which have been observed recently to occur in subduction zones and pose distinct tsunami hazards.

Study of Earthquake Disaster Prediction System of Langfang city Based on GIS

NASA Astrophysics Data System (ADS)

Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei

2017-07-01

In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.

Earthquake Early Warning: New Strategies for Seismic Hardware

NASA Astrophysics Data System (ADS)

Allardice, S.; Hill, P.

2017-12-01

Implementing Earthquake Early Warning System (EEWS) triggering algorithms into seismic networks has been a hot topic of discussion for some years now. With digitizer technology now available, such as the Güralp Minimus, with on average 40-60ms delay time (latency) from earthquake origin to issuing an alert the next step is to provide network operators with a simple interface for on board parameter calculations from a seismic station. A voting mechanism is implemented on board which mitigates the risk of false positives being communicated. Each Minimus can be configured to with a `score' from various sources i.e. Z channel on seismometer, N/S E/W channels on accelerometer and MEMS inside Minimus. If the score exceeds the set threshold then an alert is sent to the `Master Minimus'. The Master Minimus within the network will also be configured as to when the alert should be issued i.e. at least 3 stations must have triggered. Industry standard algorithms focus around the calculation of Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD) and C. Calculating these single station parameters on-board in order to stream only the results could help network operators with possible issues, such as restricted bandwidth. Developments on the Minimus allow these parameters to be calculated and distributed through Common Alert Protocol (CAP). CAP is the XML based data format used for exchanging and describing public warnings and emergencies. Whenever the trigger conditions are met the Minimus can send a signed UDP packet to the configured CAP receiver which can then send the alert via SMS, e-mail or CAP forwarding. Increasing network redundancy is also a consideration when developing these features, therefore the forwarding CAP message can be sent to multiple destinations. This allows for a hierarchical approach by which the single station (or network) parameters can be streamed to another Minimus, or data centre, or both, so that there is no one single point of failure. Developments on the Guralp Minimus to calculate these on board parameters which are capable of streaming single station parameters, accompanied with the ultra-low latency is the next generation of EEWS and Güralps contribution to the community.

Earthquake Warning Performance in Vallejo for the South Napa Earthquake

NASA Astrophysics Data System (ADS)

Wurman, G.; Price, M.

2014-12-01

In 2002 and 2003, Seismic Warning Systems, Inc. installed first-generation QuakeGuardTM earthquake warning devices at all eight fire stations in Vallejo, CA. These devices are designed to detect the P-wave of an earthquake and initiate predetermined protective actions if the impending shaking is estimated at approximately Modifed Mercalli Intensity V or greater. At the Vallejo fire stations the devices were set up to sound an audio alert over the public address system and to command the equipment bay doors to open. In August 2014, after more than 11 years of operating in the fire stations with no false alarms, the five units that were still in use triggered correctly on the MW 6.0 South Napa earthquake, less than 16 km away. The audio alert sounded in all five stations, providing fire fighters with 1.5 to 2.5 seconds of warning before the arrival of the S-wave, and the equipment bay doors opened in three of the stations. In one station the doors were disconnected from the QuakeGuard device, and another station lost power before the doors opened completely. These problems highlight just a small portion of the complexity associated with realizing actionable earthquake warnings. The issues experienced in this earthquake have already been addressed in subsequent QuakeGuard product generations, with downstream connection monitoring and backup power for critical systems. The fact that the fire fighters in Vallejo were afforded even two seconds of warning at these epicentral distances results from the design of the QuakeGuard devices, which focuses on rapid false positive rejection and ground motion estimates. We discuss the performance of the ground motion estimation algorithms, with an emphasis on the accuracy and timeliness of the estimates at close epicentral distances.

Seizure Forecasting and the Preictal State in Canine Epilepsy.

PubMed

Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M; Worrell, Gregory A; Brinkmann, Benjamin H

2017-02-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

SEIZURE FORECASTING AND THE PREICTAL STATE IN CANINE EPILEPSY

PubMed Central

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent M.; Worrell, Gregory A.; Brinkmann, Benjamin H.

2017-01-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state. PMID:27464854

Sugar-Sweetened Beverage Warning Labels: Lessons Learned From the Tobacco Industry

PubMed Central

Popova, Lucy

2016-01-01

Tobacco warning labels effectively educate consumers about the harms of tobacco and reduce smoking behavior. Lessons from tobacco warning labels can be applied to developing and implementing warning labels for sugar-sweetened beverages (SSBs). Large pictorial rotating warnings are particularly effective. Dental professionals can be an important voice in countering the industry’s efforts to create controversy around the effects of SSBs and in advocating for effective warning labels based on the evidence from the tobacco warning labels. Sugar, rum and tobacco are commodities which are nowhere necessaries of life, which are become objects of almost universal consumption and which are therefore extremely proper subjects of taxation.1 PMID:28190943

Improving tractor safety warnings: readability is missing.

PubMed

Tebeaux, E

2010-07-01

Research on tractor safety has not focused on user manuals. This study focuses on tractor operator manuals, specifically safety warnings, selected from the files of the Tractor Test facility at University of Nebraska-Lincoln. Analysis of many common warnings, based on readability and legibility research, shows that many warnings contain excessive information, confusing visuals and safety icons, poor document design, and illegible typefaces. The result is unreadable warnings that do not communicate quickly and correctly, and discourage readers rather than clarify critical information. Many tractor operator warnings are cluttered, "over-written," and contain information needed to protect the manufacturer rather than to inform operators. What is needed is a careful analysis and revision of many safety warnings with the goal of encouraging operators to read the warnings and follow their message.

Study on warning radius of diffuse reflection laser warning based on fish-eye lens

NASA Astrophysics Data System (ADS)

Chen, Bolin; Zhang, Weian

2013-09-01

The diffuse reflection type of omni-directional laser warning based on fish-eye lens is becoming more and more important. As one of the key parameters of warning system, the warning radius should be put into investigation emphatically. The paper firstly theoretically analyzes the energy detected by single pixel of FPA detector in the system under complicated environment. Then the least energy detectable by each single pixel of the system is computed in terms of detector sensitivity, system noise, and minimum SNR. Subsequently, by comparison between the energy detected by single pixel and the least detectable energy, the warning radius is deduced from Torrance-Sparrow five-parameter semiempirical statistic model. Finally, a field experiment was developed to validate the computational results. It has been found that the warning radius has a close relationship with BRDF parameters of the irradiated target, propagation distance, angle of incidence, and detector sensitivity, etc. Furthermore, an important fact is shown that the experimental values of warning radius are always less than that of theoretical ones, due to such factors as the optical aberration of fish-eye lens, the transmissivity of narrowband filter, and the packing ratio of detector.

UNAVCO Real-Time GNSS Positioning: High-Precision Static and Kinematic Testing of the Next Generation GNSS network.

NASA Astrophysics Data System (ADS)

Berglund, H. T.; Hodgkinson, K. M.; Blume, F.; Mencin, D.; Phillips, D. A.; Meertens, C. M.; Mattioli, G. S.

2014-12-01

The GAGE Facility, managed by UNAVCO, operates a real-time GNSS (RT-GNSS) network of ~450 stations. The majority of the streaming stations are part of the EarthScope Plate Boundary Observatory (PBO). Following community input from a real-time GNSS data products and formats meeting hosted by UNAVCO in Spring of 2011, UNAVCO now provides real-time PPP positions, and network solutions where practical, for all available stations using Trimble's PIVOT RTX server software and TrackRT. The UNAVCO real-time system has the potential to enhance our understanding of earthquakes, seismic wave propagation, volcanic eruptions, magmatic intrusions, movement of ice, landslides, and the dynamics of the atmosphere. Beyond the ever increasing applications in science and engineering, RT-GNSS has the potential to provide early warning of hazards to emergency managers, utilities, other infrastructure managers, first responders and others. Upgrades to the network include eight Trimble NetR9 GNSS receivers with GLONASS and receiver-based RTX capabilities and sixteen new co-located MEMS based accelerometers. These new capabilities will allow integration of GNSS and strong motion data to produce broad-spectrum waveforms improving Earthquake Early Warning systems. Controlled outdoor kinematic and static experiments provide a useful method for evaluating and comparing real-time systems. UNAVCO has developed a portable low-cost antenna actuator to characterize the kinematic performance of receiver- and server-based real-time positioning algorithms and identify system limitations. We have performed tests using controlled 1-d antenna motions and will present comparisons between these and other post-processed kinematic algorithms including GIPSY-OASIS and TRACK. In addition to kinematic testing, long-term static testing of Trimble's RTX service is ongoing at UNAVCO and will be used to characterize the stability of the position time-series produced by RTX. In addition, with the goal of characterizing stability and improving software and higher level products based on real-time and high frequency GNSS time series, we present an overview of the UNAVCO RT-GPS system, a comparison of the UNAVCO generated real-time, static and community data products, and an overview of available common data sets.

Application of a Tsunami Warning Message Metric to refine NOAA NWS Tsunami Warning Messages

NASA Astrophysics Data System (ADS)

Gregg, C. E.; Johnston, D.; Sorensen, J.; Whitmore, P.

2013-12-01

In 2010, the U.S. National Weather Service (NWS) funded a three year project to integrate social science into their Tsunami Program. One of three primary requirements of the grant was to make improvements to tsunami warning messages of the NWS' two Tsunami Warning Centers- the West Coast/Alaska Tsunami Warning Center (WCATWC) in Palmer, Alaska and the Pacific Tsunami Warning Center (PTWC) in Ewa Beach, Hawaii. We conducted focus group meetings with a purposive sample of local, state and Federal stakeholders and emergency managers in six states (AK, WA, OR, CA, HI and NC) and two US Territories (US Virgin Islands and American Samoa) to qualitatively asses information needs in tsunami warning messages using WCATWC tsunami messages for the March 2011 Tohoku earthquake and tsunami event. We also reviewed research literature on behavioral response to warnings to develop a tsunami warning message metric that could be used to guide revisions to tsunami warning messages of both warning centers. The message metric is divided into categories of Message Content, Style, Order and Formatting and Receiver Characteristics. A message is evaluated by cross-referencing the message with the operational definitions of metric factors. Findings are then used to guide revisions of the message until the characteristics of each factor are met. Using findings from this project and findings from a parallel NWS Warning Tiger Team study led by T. Nicolini, the WCATWC implemented the first of two phases of revisions to their warning messages in November 2012. A second phase of additional changes, which will fully implement the redesign of messages based on the metric, is in progress. The resulting messages will reflect current state-of-the-art knowledge on warning message effectiveness. Here we present the message metric; evidence-based rational for message factors; and examples of previous, existing and proposed messages.

Depth-averaged instantaneous currents in a tidally dominated shelf sea from glider observations

NASA Astrophysics Data System (ADS)

Merckelbach, Lucas

2016-12-01

Ocean gliders have become ubiquitous observation platforms in the ocean in recent years. They are also increasingly used in coastal environments. The coastal observatory system COSYNA has pioneered the use of gliders in the North Sea, a shallow tidally energetic shelf sea. For operational reasons, the gliders operated in the North Sea are programmed to resurface every 3-5 h. The glider's dead-reckoning algorithm yields depth-averaged currents, averaged in time over each subsurface interval. Under operational conditions these averaged currents are a poor approximation of the instantaneous tidal current. In this work an algorithm is developed that estimates the instantaneous current (tidal and residual) from glider observations only. The algorithm uses a first-order Butterworth low pass filter to estimate the residual current component, and a Kalman filter based on the linear shallow water equations for the tidal component. A comparison of data from a glider experiment with current data from an acoustic Doppler current profilers deployed nearby shows that the standard deviations for the east and north current components are better than 7 cm s-1 in near-real-time mode and improve to better than 6 cm s-1 in delayed mode, where the filters can be run forward and backward. In the near-real-time mode the algorithm provides estimates of the currents that the glider is expected to encounter during its next few dives. Combined with a behavioural and dynamic model of the glider, this yields predicted trajectories, the information of which is incorporated in warning messages issued to ships by the (German) authorities. In delayed mode the algorithm produces useful estimates of the depth-averaged currents, which can be used in (process-based) analyses in case no other source of measured current information is available.

Perceptions of tobacco health warnings in China compared with picture and text-only health warnings from other countries: an experimental study.

PubMed

Fong, Geoffrey T; Hammond, David; Jiang, Yuan; Li, Qiang; Quah, Anne C K; Driezen, Pete; Yan, Mi

2010-10-01

To assess the perceived effectiveness of cigarette health warnings in China, compared with picture and text-only warnings from other countries. 1169 individuals (adult smokers, adult nonsmokers and youth) from four Chinese cities (Beijing, Shanghai, Kunming and Yinchuan) viewed 10 health warnings on cigarette packages, which included (a) the current Chinese text warnings covering 30% of the front/back of the pack (introduced October 2008); (b) the former Chinese text warning located on the side of the pack; (c) four picture warnings covering 50% of the front/back of the pack from Canada (lung cancer), Singapore (mouth disease), Hong Kong (gangrene) and European Union (clogged arteries); and (d) the same four warnings without the picture. Participants rated and ranked the 10 warnings on dimensions including how effective each would be in motivating smokers to quit and in convincing youth not to start smoking. Both Chinese warnings were consistently rated as least effective, with the new Chinese warning rated only slightly higher than the old warning. The picture warnings were consistently ranked or rated as most effective, with the text-only versions in the middle. Results were consistent across subject group, city and sex. (1) Picture warnings are rated as much more effective than the same warnings without pictures. (2) The revised health warnings in China, introduced in October 2008, are only marginally more effective than the previous warning and far less effective than even text warnings from other countries. These results, coupled with population-based evaluation studies, suggest that pictorial warnings would significantly increase the impact of health warnings in China.

A space-based concept for a collision warning sensor

NASA Technical Reports Server (NTRS)

Talent, David L.; Vilas, Faith

1990-01-01

This paper describes a concept for a space-based collision warning sensor experiment, the Debris Collision Warning Sensor (DCWS) experiment, in which the sensor will rely on passive sensing of debris in optical and IR passband. The DCWS experiment will be carried out under various conditions of solar phase angle and pass geometry; debris from 1.5 m to 1 mm diam will be observable. The mission characteristics include inclination in the 55-60 deg range and an altitude of about 500 km. The results of the DCWS experiment will be used to generate collision warning scenarios for the Space Station Freedom.

Development of guidance on the timeliness in response to acute kidney injury warning stage test results for adults in primary care: an appropriateness ratings evaluation.

PubMed

Blakeman, Tom; Griffith, Kathryn; Lasserson, Dan; Lopez, Berenice; Tsang, Jung Y; Campbell, Stephen; Tomson, Charles

2016-10-11

Tackling the harm associated with acute kidney injury (AKI) is a global priority. In England, a national computerised AKI algorithm is being introduced across the National Health Service (NHS) to drive this change. The study sought to maximise its clinical utility and minimise the potential for burden on clinicians and patients in primary care. An appropriateness ratings evaluation using the RAND/UCLA Appropriateness Method. Clinical scenarios were developed to test the timeliness in (1) communication of AKI warning stage test results from clinical pathology services to primary care, and (2) primary care clinician response to an AKI warning stage test result. A 10-person panel was purposively sampled with representation from clinical biochemistry, acute and emergency medicine and general practice. General practitioners (GPs) represented typical practice in relation to rural and urban practice, out of hours care, GP commissioning and those interested in reducing the impact of medicalisation and 'overdiagnosis'. There was agreement that delivery of AKI warning stage test results through interruptive methods of communication (ie, telephone) from laboratories to primary care was the appropriate next step for patients with an AKI warning stage 3 test result. In the context of acute illness, waiting up to 72 hours to respond to an AKI warning stage test result was deemed an inappropriate action in 62 out of the 65 (94.5%) cases. There was agreement that a clinician response was required within 6 hours, or less, in 39 out of 40 (97.5%) clinical cases relating AKI warning stage test results in the presence of moderate hyperkalaemia. The study has informed national guidance to support a timely and calibrated response to AKI warning stage test results for adults in primary care. Further research is needed to support effective implementation, with a view to examine the effect on health outcomes and costs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

NASA Technical Reports Server (NTRS)

Martin, Rodney A.; Santanu, Das; Janakiraman, Vijay Manikandan; Hosein, Stefan

2015-01-01

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

NASA Astrophysics Data System (ADS)

Brigandì, Giuseppina; Tito Aronica, Giuseppe; Bonaccorso, Brunella; Gueli, Roberto; Basile, Giuseppe

2017-09-01

The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall-streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall-runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002-2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

A Risk-Based Multi-Objective Optimization Concept for Early-Warning Monitoring Networks

NASA Astrophysics Data System (ADS)

Bode, F.; Loschko, M.; Nowak, W.

2014-12-01

Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources which cannot be eliminated, especially in urban regions. As matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs.In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations and the early warning time and to minimize the installation and operating costs of the monitoring network. A qualitative risk ranking is used to prioritize the known risk sources for monitoring. The unknown risk sources can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well.We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks which are valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrade) to also cover moderate, tolerable and unknown risk sources. Monitoring networks which are valid for the remaining risk also cover all other risk sources but the early-warning time suffers.The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. To avoid numerical dispersion during the transport simulations we use the particle-tracking random walk method.

Successful ShakeAlert Performance for the Napa Quake

NASA Astrophysics Data System (ADS)

Allen, R. M.; Given, D. D.; Heaton, T. H.; Vidale, J. E.

2014-12-01

ShakeAlert, the demonstration earthquake early warning system, developed by the USGS, UC Berkeley, Caltech, ETH, and the University of Washington, functioned as expected for the August 24, 2014, M6.0 Napa earthquake. The first ShakeAlert was generated by the ElarmS algorithm 5.1 sec after the origin time of the earthquake, and 3.3 sec after the P-wave arrived at the closest station 6.5 km from the epicenter. This initial alert, based on P-wave triggers from four stations, estimated the magnitude to be 5.7. The warning was received at the UC Berkeley Seismological Laboratory 5 seconds before the S-wave and about 10 sec prior to the onset of the strongest shaking. ShakeAlert beta-testers across the San Francisco Bay Area simultaneously received the alert, including the San Francisco 911 center with 8 sec warning, and the BART train system. BART has implemented an automated train-stopping system that was activated (although no trains were running at 3:20 am). With the available network geometry and communications, the blind zone of the first alert had a radius of 16 km. The four stations that contributed to the first alert all encapsulate data into 1-second packets, but the latency in transmitting data to the processing center ranged from 0.27 to 2.62 seconds. If all the stations were to deliver data in 0.27 seconds, then the alert would have been available 2.3 sec sooner and the blind zone would be reduced to about 8 km. This would also mean that the city of Napa would have received about 1 second of warning. The magnitude estimate and event location were accurate from the initial alert onwards. The magnitude estimate did first increase to 5.8 and then dip to 5.4 2.6 sec after the initial alert, stayed at that level for 2 sec, and then returned to 5.7. The final magnitude estimate was 6.0, consistent with the ANSS catalog.

Progress in Development of an Airborne Turbulence Detection System

NASA Technical Reports Server (NTRS)

Hamilton, David W.; Proctor, Fred H.

2006-01-01

Aircraft encounters with turbulence are the leading cause of in-flight injuries (Tyrvanas 2003) and have occasionally resulted in passenger and crew fatalities. Most of these injuries are caused by sudden and unexpected encounters with severe turbulence in and around convective activity (Kaplan et al 2005). To alleviate this problem, the Turbulence Prediction and Warning Systems (TPAWS) element of NASA s Aviation Safety program has investigated technologies to detect and warn of hazardous in-flight turbulence. This effort has required the numerical modeling of atmospheric convection: 1) for characterizing convectively induced turbulence (CIT) environments, 2) for defining turbulence hazard metrics, and 3) as a means of providing realistic three-dimensional data sets that can be used to test and evaluate turbulence detection sensors. The data sets are being made available to industry and the FAA for certification of future airborne turbulence-detection systems (ATDS) with warning capability. Early in the TPAWS project, a radar-based ATDS was installed and flight tested on NASA s research aircraft, a B-757. This ATDS utilized new algorithms and hazard metrics that were developed for use with existing airborne predictive windshear radars, thus avoiding the installation of new hardware. This system was designed to detect and warn of hazardous CIT even in regions with weak radar reflectivity (i.e. 5-15 dBz). Results from an initial flight test of the ATDS were discussed in Hamilton and Proctor (2002a; 2002b). In companion papers (Proctor et al 2002a; 2002b), a numerical simulation of the most significant encounter from that flight test was presented. Since the presentation of these papers a second flight test has been conducted providing additional cases for examination. In this paper, we will present results from NASA s flight test and a numerical model simulation of a turbulence environment encountered on 30 April 2002. Progress leading towards FAA certification of industry built ATDS will also be discussed.

Scientific Visualization and Simulation for Multi-dimensional Marine Environment Data

NASA Astrophysics Data System (ADS)

Su, T.; Liu, H.; Wang, W.; Song, Z.; Jia, Z.

2017-12-01

As higher attention on the ocean and rapid development of marine detection, there are increasingly demands for realistic simulation and interactive visualization of marine environment in real time. Based on advanced technology such as GPU rendering, CUDA parallel computing and rapid grid oriented strategy, a series of efficient and high-quality visualization methods, which can deal with large-scale and multi-dimensional marine data in different environmental circumstances, has been proposed in this paper. Firstly, a high-quality seawater simulation is realized by FFT algorithm, bump mapping and texture animation technology. Secondly, large-scale multi-dimensional marine hydrological environmental data is virtualized by 3d interactive technologies and volume rendering techniques. Thirdly, seabed terrain data is simulated with improved Delaunay algorithm, surface reconstruction algorithm, dynamic LOD algorithm and GPU programming techniques. Fourthly, seamless modelling in real time for both ocean and land based on digital globe is achieved by the WebGL technique to meet the requirement of web-based application. The experiments suggest that these methods can not only have a satisfying marine environment simulation effect, but also meet the rendering requirements of global multi-dimension marine data. Additionally, a simulation system for underwater oil spill is established by OSG 3D-rendering engine. It is integrated with the marine visualization method mentioned above, which shows movement processes, physical parameters, current velocity and direction for different types of deep water oil spill particle (oil spill particles, hydrates particles, gas particles, etc.) dynamically and simultaneously in multi-dimension. With such application, valuable reference and decision-making information can be provided for understanding the progress of oil spill in deep water, which is helpful for ocean disaster forecasting, warning and emergency response.

Unveiling the truth: warnings reduce the repetition-based truth effect.

PubMed

Nadarevic, Lena; Aßfalg, André

2017-07-01

Typically, people are more likely to consider a previously seen or heard statement as true compared to a novel statement. This repetition-based "truth effect" is thought to rely on fluency-truth attributions as the underlying cognitive mechanism. In two experiments, we tested the nature of the fluency-attribution mechanism by means of warning instructions, which informed participants about the truth effect and asked them to prevent it. In Experiment 1, we instructed warned participants to consider whether a statement had already been presented in the experiment to avoid the truth effect. However, warnings did not significantly reduce the truth effect. In Experiment 2, we introduced control questions and reminders to ensure that participants understood the warning instruction. This time, warning reduced, but did not eliminate the truth effect. Assuming that the truth effect relies on fluency-truth attributions, this finding suggests that warned participants could control their attributions but did not disregard fluency altogether when making truth judgments. Further, we found no evidence that participants overdiscount the influence of fluency on their truth judgments.

Warnings on alcohol containers and advertisements: international experience and evidence on effects.

PubMed

Wilkinson, Claire; Room, Robin

2009-07-01

In light of possible introduction of alcohol warning labels in Australia and New Zealand, this paper discusses the international experience with and evidence of effects of alcohol warning labels. The report describes international experience with providing information and warnings concerning the promotion or sale of alcoholic beverages, and considers the evidence on the effects of such information and warnings. The experience with and evaluations of the effects of tobacco warning labels are also considered. The most methodologically sound evaluations of alcohol warning labels are based on the US experience. Although these evaluations find little evidence that the introduction of the warning label in the USA had an impact on drinking behaviour, there is evidence that they led to an increase in awareness of the message they contained. In contrast, evaluations of tobacco warning labels find clear evidence of effects on behaviour. There is a need and opportunity for a rigorous evaluation of the impacts of introducing alcohol warning labels to add to the published work on their effectiveness. The experience with tobacco labels might guide the way for more effective alcohol warning labels. Alcohol warning labels are an increasingly popular alcohol policy initiative. It is clear that warning labels can be ineffective, but the tobacco experience suggests that effective warning labels are possible. Any introduction of alcohol warning labels should be evaluated in terms of effects on attitudes and behaviour.

Aircraft Cabin Turbulence Warning Experiment

NASA Technical Reports Server (NTRS)

Bogue, Rodney K.; Larcher, Kenneth

2006-01-01

New turbulence prediction technology offers the potential for advance warning of impending turbulence encounters, thereby allowing necessary cabin preparation time prior to the encounter. The amount of time required for passengers and flight attendants to be securely seated (that is, seated with seat belts fastened) currently is not known. To determine secured seating-based warning times, a consortium of aircraft safety organizations have conducted an experiment involving a series of timed secured seating trials. This demonstrative experiment, conducted on October 1, 2, and 3, 2002, used a full-scale B-747 wide-body aircraft simulator, human passenger subjects, and supporting staff from six airlines. Active line-qualified flight attendants from three airlines participated in the trials. Definitive results have been obtained to provide secured seating-based warning times for the developers of turbulence warning technology

Towards real-time risk mitigation for NPP in Switzerland: the potential role of EEW and OEF.

NASA Astrophysics Data System (ADS)

Cauzzi, Carlo; Wiemer, Stefan; Behr, Yannik; Clinton, John; Renault, Philippe; Le Guenan, Thomas; Douglas, John; Woessner, Jochen; Biro, Yesim; Caprio, Marta; Cua, Georgia

2014-05-01

Spurred by the research activities being carried out within the EC-funded project REAKT (Strategies and Tools for Real Time Earthquake Risk Reduction, FP7, contract no. 282862, 2011-2014, www.reaktproject.eu), we present herein the key elements to understanding the potential benefits of routinely using Earthquake Early Warning and Operational Earthquake Forecasting methods to mitigate the seismic risk at NPP in Switzerland. The advantages of using the aforementioned real-time risk reduction tools are critically discussed based on the limitations of the current scientific knowledge and technology, as well as on the costs associated to both system maintenance and machine- or human-triggered actions following an alert. Basic inputs to this discussion are, amongst others: a) the performances of the Swiss seismic network (http://www.seismo.ethz.ch/monitor, where SeisComP3 is used as earthquake monitoring software) and the selected EEW algorithm (the Virtual Seismologist, VS, http://www.seiscomp3.org/doc/seattle/2013.200/apps/vs.html), in terms of correct detections, false alerts, and missed events; b) the reliability of time-dependent hazard scenarios for the region of interest; c) a careful assessment of the frequency of occurrence of critical warnings based on the local and regional seismicity; d) the identification of the mitigation actions and their benefits and costs for the stakeholders.

PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

NASA Astrophysics Data System (ADS)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings.

An experimental study of the effects of electronic cigarette warnings on young adult nonsmokers' perceptions and behavioral intentions.

PubMed

Mays, Darren; Smith, Clayton; Johnson, Andrea C; Tercyak, Kenneth P; Niaura, Raymond S

2016-01-01

Electronic cigarette ("e-cigarette") manufacturers use warning labels on their advertising that vary widely in content and the U.S. Food and Drug Administration has issued a warning label requirement for e-cigarettes. There is limited data on the effects of these warnings on e-cigarette perceptions and other potential predictors of future tobacco use behavior in populations of interest to inform future regulatory requirements. This study examined the effects of e-cigarette warnings on perceptions of e-cigarettes and cigarettes and other cognitive precursors to tobacco use among young adult non-smokers. Non-smoking young adults ages 18 to 30 years (n = 436) were recruited through an internet-based crowdsourcing platform for an online experiment. Participants completed pre-exposure measures of demographics, tobacco use, and other relevant constructs and were randomized to view 1 of 9 e-cigarette stimuli in a 3 (Ad/Warning condition: Ad Only, Ad with Warning, Warning Only) x 3 (E-cigarette brand: Blu, MarkTen, Vuse) design. After viewing e-cigarette stimuli, participants reported perceptions of e-cigarettes and behavioral intentions to use e-cigarettes. Participants in the Ad Only and Ad with Warning conditions also completed a heat-mapping task assessing aspects of the ads that captured their attention. Then, participants were randomized to view cigarette ads from 1 of 3 major cigarette brands and reported perceptions of cigarettes and intentions to smoke cigarettes. Participants in the Warning Only condition reported significantly greater perceived harm and addictiveness of e-cigarettes and thoughts about not using e-cigarettes than the Ad Only and Ad with Warning conditions (p's < .05). The Ad Only and Ad with Warning conditions did not differ on these outcomes. Participants in the Warning Only condition also reported the harms of e-cigarettes were closer to those of cigarettes than the Ad Only condition (p < .05), but neither differed from the Ad with Warning condition. Visual inspection of heat-mapping task data indicate warnings drew few participants' attention. There were no significant differences across study conditions on perceptions of cigarettes or intentions to smoke. Text-based warning messages influenced young non-smokers' perceptions in a way that may dissuade e-cigarette use, but warnings appearing on advertisements had little impact.

TANDIR: projectile warning system using uncooled bolometric technology

NASA Astrophysics Data System (ADS)

Horovitz-Limor, Z.; Zahler, M.

2007-04-01

Following the demand for affordable, various range and light-weight protection against ATGM's, Elisra develops a cost-effective passive IR system for ground vehicles. The system is based on wide FOV uncooled bolometric sensors with full azimuth coverage and a lightweight processing & control unit. The system design is based on the harsh environmental conditions. The basic algorithm discriminates the target from its clutter and predicts the time to impact (TTI) and the target aiming direction with relation to vehicle. The current detector format is 320*240 pixels and frame rate is 60 Hz, Spectral response is on Far Infrared (8-14μ). The digital video output has 14bit resolution & wide dynamic range. Future goal is to enhance detection performance by using large format uncooled detector (640X480) with improved sensitivity and higher frame rates (up to 120HZ).

Impacts assessment of dynamic speed harmonization with queue warning : task 3, impacts assessment report.

DOT National Transportation Integrated Search

2015-06-01

This report assesses the impacts of a prototype of Dynamic Speed Harmonization (SPD-HARM) with Queue Warning (Q-WARN), which are two component applications of the Intelligent Network Flow Optimization (INFLO) bundle. The assessment is based on an ext...

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, C. J.; Carey, L. D.; Schultz, E. V.; Stano, G. T.; Blakeslee, R.; Goodman, S. J.

2014-01-01

The purpose of the total lightning jump algorithm (LJA) is to provide forecasters with an additional tool to identify potentially hazardous thunderstorms, yielding increased confidence in decisions within the operational warning environment. The LJA was first developed to objectively indentify rapid increases in total lightning (also termed "lightning jumps") that occur prior to the observance of severe and hazardous weather (Williams et al. 1999, Schultz et al. 2009, Gatlin and Goodman 2010, Schultz et al. 2011). However, a physical and framework leading up to and through the time of a lightning jump is still lacking within the literature. Many studies infer that there is a large increase in the updraft prior to or during the jump, but are not specific on what properties of the updraft are indeed increasing (e.g., maximum updraft speed vs volume or both) likely because these properties were not specifically observed. Therefore, the purpose of this work is to physically associate lightning jump occurrence to polarimetric and multi-Doppler radar measured thunderstorm intensity metrics and severe weather occurrence, thus providing a conceptual model that can be used to adapt the LJA to current operations.

Analysis and design of the ultraviolet warning optical system based on interference imaging

NASA Astrophysics Data System (ADS)

Wang, Wen-cong; Hu, Hui-jun; Jin, Dong-dong; Chu, Xin-bo; Shi, Yu-feng; Song, Juan; Liu, Jin-sheng; Xiao, Ting; Shao, Si-pei

2017-10-01

Ultraviolet warning technology is one of the important methods for missile warning. It provides a very effective way to detect the target for missile approaching alarm. With the development of modern technology, especially the development of information technology at high speed, the ultraviolet early warning system plays an increasingly important role. Compared to infrared warning, the ultraviolet warning has high efficiency and low false alarm rate. In the modern warfare, how to detect the threats earlier, prevent and reduce the attack of precision-guided missile has become a new challenge of missile warning technology. Because the ultraviolet warning technology has high environmental adaptability, the low false alarm rate, small volume and other advantages, in the military field applications it has been developed rapidly. For the ultraviolet warning system, the optimal working waveband is 250 nm 280 nm (Solar Blind UV) due to the strong absorption of ozone layer. According to current application demands for solar blind ultraviolet detection and warning, this paper proposes ultraviolet warning optical system based on interference imaging, which covers solar blind ultraviolet (250nm-280nm) and dual field. This structure includes a primary optical system, an ultraviolet reflector array, an ultraviolet imaging system and an ultraviolet interference imaging system. It makes use of an ultraviolet beam-splitter to achieve the separation of two optical systems. According to the detector and the corresponding application needs of two visual field of the optical system, the calculation and optical system design were completed. After the design, the MTF of the two optical system is more than 0.8@39lp/mm.A single pixel energy concentration is greater than 80%.

Technical implementation plan for the ShakeAlert production system: an Earthquake Early Warning system for the West Coast of the United States

USGS Publications Warehouse

Given, Douglas D.; Cochran, Elizabeth S.; Heaton, Thomas; Hauksson, Egill; Allen, Richard; Hellweg, Peggy; Vidale, John; Bodin, Paul

2014-01-01

Earthquake Early Warning (EEW) systems can provide as much as tens of seconds of warning to people and automated systems before strong shaking arrives. The United States Geological Survey (USGS) and its partners are developing such an EEW system, called ShakeAlert, for the West Coast of the United States. This document describes the technical implementation of that system, which leverages existing stations and infrastructure of the Advanced National Seismic System (ANSS) regional networks to achieve this new capability. While significant progress has been made in developing the ShakeAlert early warning system, improved robustness of each component of the system and additional testing and certification are needed for the system to be reliable enough to issue public alerts. Major components of the system include dense networks of ground motion sensors, telecommunications from those sensors to central processing systems, algorithms for event detection and alert creation, and distribution systems to alert users. Capital investment costs for a West Coast EEW system are projected to be $38.3M, with additional annual maintenance and operations totaling $16.1M—in addition to current ANSS expenditures for earthquake monitoring. An EEW system is complementary to, but does not replace, other strategies to mitigate earthquake losses. The system has limitations: false and missed alerts are possible, and the area very near to an earthquake epicenter may receive little or no warning. However, such an EEW system would save lives, reduce injuries and damage, and improve community resilience by reducing longer-term economic losses for both public and private entities.

The social impacts of the heat-health watch/warning system in Phoenix, Arizona: assessing the perceived risk and response of the public.

PubMed

Kalkstein, Adam J; Sheridan, Scott C

2007-10-01

Heat is the leading weather-related killer in the United States. Although previous research suggests that social influences affect human responses to natural disaster warnings, no studies have examined the social impacts of heat or heat warnings on a population. Here, 201 surveys were distributed in Metropolitan Phoenix to determine the social impacts of the heat warning system, or more specifically, to gauge risk perception and warning response. Consistent with previous research, increased risk perception of heat results in increased response to a warning. Different social factors such as sex, race, age, and income all play an important role in determining whether or not people will respond to a warning. In particular, there is a strong sense of perceived risk to the heat among Hispanics which translates to increased response when heat warnings are issued. Based on these findings, suggestions are presented to help improve the Phoenix Heat Warning System.

Solar Energetic Particle Warnings from a Coronagraph

NASA Technical Reports Server (NTRS)

St Cyr, O. C.; Posner, A.; Burkepile, J. T.

2017-01-01

We report here the concept of using near-real time observations from a coronagraph to provide early warning of a fast coronal mass ejection (CME) and the possible onset of a solar energetic particle (SEP) event. The 1 January 2016, fast CME, and its associated SEP event are cited as an example. The CME was detected by the ground-based K-Cor coronagraph at Mauna Loa Solar Observatory and by the SOHO Large Angle and Spectrometric Coronagraph. The near-real-time availability of the high-cadence K-Cor observations in the low corona leads to an obvious question: Why has no one attempted to use a coronagraph as an early warning device for SEP events? The answer is that the low image cadence and the long latency of existing spaceborne coronagraphs make them valid for archival studies but typically unsuitable for near-real-time forecasting. The January 2016 event provided favorable CME viewing geometry and demonstrated that the primary component of a prototype ground-based system for SEP warnings is available several hours on most days. We discuss how a conceptual CME-based warning system relates to other techniques, including an estimate of the relative SEP warning times, and how such a system might be realized.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)

PubMed Central

Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

2016-01-01

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

PubMed

Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

2016-07-13

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

Safety impact of an integrated crash warning system based on field test data.

DOT National Transportation Integrated Search

2011-06-13

This paper provides the results of an analysis : conducted to assess the safety impact of an integrated : vehicle-based crash warning system based on : naturalistic driving data collected from a field : operational test. The system incorporates four ...

Framing Indoor Tanning Warning Messages to Reduce Skin Cancer Risks Among Young Women: Implications for Research and Policy.

PubMed

Mays, Darren; Tercyak, Kenneth P

2015-08-01

We investigated the impact of indoor tanning device warnings that communicate the risks associated with indoor tanning (i.e., loss framed) or the benefits of avoiding indoor tanning (i.e., gain framed). A convenience sample of non-Hispanic White women aged 18 to 30 years who tanned indoors at least once in the past year (n = 682) participated in a within-subjects experiment. Participants completed baseline measures and reported indoor tanning intentions and intentions to quit indoor tanning in response to 5 warning messages in random order. A text-only control warning was based on Food and Drug Administration-required warnings for indoor tanning devices. Experimental warnings included graphic content and were either gain or loss framed. In multivariable analyses, gain-framed warnings did not differ from the control warning on women's intentions to tan indoors, but they prompted stronger intentions to quit than the control message. Loss-framed warnings significantly reduced intentions to tan indoors and increased intentions to quit indoor tanning compared with control and gain-framed warnings. The public health impact of indoor tanning device warnings can be enhanced by incorporating graphic content and leveraging gain- and loss-framed messaging.

Beale Air Force Base, Perimeter Acquisition Vehicle Entry PhasedArray Warning ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Beale Air Force Base, Perimeter Acquisition Vehicle Entry Phased-Array Warning System, Satellite Communications Terminal, End of Spencer Paul Road, north of Warren Shingle Road (14th Street), Marysville, Yuba County, CA

Beale Air Force Base, Perimeter Acquisition Vehicle Entry PhasedArray Warning ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Beale Air Force Base, Perimeter Acquisition Vehicle Entry Phased-Array Warning System, Electric Substation, End of Spencer Paul Road, north of Warren Shingle Road (14th Street), Marysville, Yuba County, CA

Beale Air Force Base, Perimeter Acquisition Vehicle Entry PhasedArray Warning ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Beale Air Force Base, Perimeter Acquisition Vehicle Entry Phased-Array Warning System, Microwave Equipment Building, End of Spencer Paul Road, north of Warren Shingle Road (14th Street), Marysville, Yuba County, CA

The Goes-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved storm diagnostic capability with the Advanced Baseline Imager. The GLM will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. In this paper we will report on new Nowcasting and storm warning applications being developed and evaluated at various NOAA Testbeds.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Early identification systems for emerging foodborne hazards.

PubMed

Marvin, H J P; Kleter, G A; Prandini, A; Dekkers, S; Bolton, D J

2009-05-01

This paper provides a non-exhausting overview of early warning systems for emerging foodborne hazards that are operating in the various places in the world. Special attention is given to endpoint-focussed early warning systems (i.e. ECDC, ISIS and GPHIN) and hazard-focussed early warning systems (i.e. FVO, RASFF and OIE) and their merit to successfully identify a food safety problem in an early stage is discussed. Besides these early warning systems which are based on monitoring of either disease symptoms or hazards, also early warning systems and/or activities that intend to predict the occurrence of a food safety hazard in its very beginning of development or before that are described. Examples are trend analysis, horizon scanning, early warning systems for mycotoxins in maize and/or wheat and information exchange networks (e.g. OIE and GIEWS). Furthermore, recent initiatives that aim to develop predictive early warning systems based on the holistic principle are discussed. The assumption of the researchers applying this principle is that developments outside the food production chain that are either directly or indirectly related to the development of a particular food safety hazard may also provide valuable information to predict the development of this hazard.

[Ecological security early-warning in Zhoushan Islands based on variable weight model].

PubMed

Zhou, Bin; Zhong, Lin-sheng; Chen, Tian; Zhou, Rui

2015-06-01

Ecological security early warning, as an important content of ecological security research, is of indicating significance in maintaining regional ecological security. Based on driving force, pressure, state, impact and response (D-P-S-I-R) framework model, this paper took Zhoushan Islands in Zhejiang Province as an example to construct the ecological security early warning index system, test degrees of ecological security early warning of Zhoushan Islands from 2000 to 2012 by using the method of variable weight model, and forecast ecological security state of 2013-2018 by Markov prediction method. The results showed that the variable weight model could meet the study needs of ecological security early warning of Zhoushan Islands. There was a fluctuant rising ecological security early warning index from 0.286 to 0.484 in Zhoushan Islands between year 2000 and 2012, in which the security grade turned from "serious alert" into " medium alert" and the indicator light turned from "orange" to "yellow". The degree of ecological security warning was "medium alert" with the light of "yellow" for Zhoushan Islands from 2013 to 2018. These findings could provide a reference for ecological security maintenance of Zhoushan Islands.

Crisis management during anaesthesia: the development of an anaesthetic crisis management manual

PubMed Central

Runciman, W; Kluger, M; Morris, R; Paix, A; Watterson, L; Webb, R

2005-01-01

Background: All anaesthetists have to handle life threatening crises with little or no warning. However, some cognitive strategies and work practices that are appropriate for speed and efficiency under normal circumstances may become maladaptive in a crisis. It was judged in a previous study that the use of a structured "core" algorithm (based on the mnemonic COVER ABCD–A SWIFT CHECK) would diagnose and correct the problem in 60% of cases and provide a functional diagnosis in virtually all of the remaining 40%. It was recommended that specific sub-algorithms be developed for managing the problems underlying the remaining 40% of crises and assembled in an easy-to-use manual. Sub-algorithms were therefore developed for these problems so that they could be checked for applicability and validity against the first 4000 anaesthesia incidents reported to the Australian Incident Monitoring Study (AIMS). Methods: The need for 24 specific sub-algorithms was identified. Teams of practising anaesthetists were assembled and sets of incidents relevant to each sub-algorithm were identified from the first 4000 reported to AIMS. Based largely on successful strategies identified in these reports, a set of 24 specific sub-algorithms was developed for trial against the 4000 AIMS reports and assembled into an easy-to-use manual. A process was developed for applying each component of the core algorithm COVER at one of four levels (scan-check-alert/ready-emergency) according to the degree of perceived urgency, and incorporated into the manual. The manual was disseminated at a World Congress and feedback was obtained. Results: Each of the 24 specific crisis management sub-algorithms was tested against the relevant incidents among the first 4000 reported to AIMS and compared with the actual management by the anaesthetist at the time. It was judged that, if the core algorithm had been correctly applied, the appropriate sub-algorithm would have been resolved better and/or faster in one in eight of all incidents, and would have been unlikely to have caused harm to any patient. The descriptions of the validation of each of the 24 sub-algorithms constitute the remaining 24 papers in this set. Feedback from five meetings each attended by 60–100 anaesthetists was then collated and is included. Conclusion: The 24 sub-algorithms developed form the basis for developing a rational evidence-based approach to crisis management during anaesthesia. The COVER component has been found to be satisfactory in real life resuscitation situations and the sub-algorithms have been used successfully for several years. It would now be desirable for carefully designed simulator based studies, using naive trainees at the start of their training, to systematically examine the merits and demerits of various aspects of the sub-algorithms. It would seem prudent that these sub-algorithms be regarded, for the moment, as decision aids to support and back up clinicians' natural responses to a crisis when all is not progressing as expected. PMID:15933282

Crisis management during anaesthesia: the development of an anaesthetic crisis management manual.

PubMed

Runciman, W B; Kluger, M T; Morris, R W; Paix, A D; Watterson, L M; Webb, R K

2005-06-01

All anaesthetists have to handle life threatening crises with little or no warning. However, some cognitive strategies and work practices that are appropriate for speed and efficiency under normal circumstances may become maladaptive in a crisis. It was judged in a previous study that the use of a structured "core" algorithm (based on the mnemonic COVER ABCD-A SWIFT CHECK) would diagnose and correct the problem in 60% of cases and provide a functional diagnosis in virtually all of the remaining 40%. It was recommended that specific sub-algorithms be developed for managing the problems underlying the remaining 40% of crises and assembled in an easy-to-use manual. Sub-algorithms were therefore developed for these problems so that they could be checked for applicability and validity against the first 4000 anaesthesia incidents reported to the Australian Incident Monitoring Study (AIMS). The need for 24 specific sub-algorithms was identified. Teams of practising anaesthetists were assembled and sets of incidents relevant to each sub-algorithm were identified from the first 4000 reported to AIMS. Based largely on successful strategies identified in these reports, a set of 24 specific sub-algorithms was developed for trial against the 4000 AIMS reports and assembled into an easy-to-use manual. A process was developed for applying each component of the core algorithm COVER at one of four levels (scan-check-alert/ready-emergency) according to the degree of perceived urgency, and incorporated into the manual. The manual was disseminated at a World Congress and feedback was obtained. Each of the 24 specific crisis management sub-algorithms was tested against the relevant incidents among the first 4000 reported to AIMS and compared with the actual management by the anaesthetist at the time. It was judged that, if the core algorithm had been correctly applied, the appropriate sub-algorithm would have been resolved better and/or faster in one in eight of all incidents, and would have been unlikely to have caused harm to any patient. The descriptions of the validation of each of the 24 sub-algorithms constitute the remaining 24 papers in this set. Feedback from five meetings each attended by 60-100 anaesthetists was then collated and is included. The 24 sub-algorithms developed form the basis for developing a rational evidence-based approach to crisis management during anaesthesia. The COVER component has been found to be satisfactory in real life resuscitation situations and the sub-algorithms have been used successfully for several years. It would now be desirable for carefully designed simulator based studies, using naive trainees at the start of their training, to systematically examine the merits and demerits of various aspects of the sub-algorithms. It would seem prudent that these sub-algorithms be regarded, for the moment, as decision aids to support and back up clinicians' natural responses to a crisis when all is not progressing as expected.

New generation of meteorology cameras

NASA Astrophysics Data System (ADS)

Janout, Petr; Blažek, Martin; Páta, Petr

2017-12-01

A new generation of the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) camera includes new features such as monitoring of rain and storm clouds during the day observation. Development of the new generation of weather monitoring cameras responds to the demand for monitoring of sudden weather changes. However, new WILLIAM cameras are ready to process acquired image data immediately, release warning against sudden torrential rains, and send it to user's cell phone and email. Actual weather conditions are determined from image data, and results of image processing are complemented by data from sensors of temperature, humidity, and atmospheric pressure. In this paper, we present the architecture, image data processing algorithms of mentioned monitoring camera and spatially-variant model of imaging system aberrations based on Zernike polynomials.

Effect of audio in-vehicle red light-running warning message on driving behavior based on a driving simulator experiment.

PubMed

Yan, Xuedong; Liu, Yang; Xu, Yongcun

2015-01-01

Drivers' incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message. In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message "The red light is impending. Please decelerate!" The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection. The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers' red-entry times without a warning were longer than those with a warning. This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.

Beale Air Force Base, Perimeter Acquisition Vehicle Entry PhasedArray Warning ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Beale Air Force Base, Perimeter Acquisition Vehicle Entry Phased-Array Warning System, Civil Engineering Storage Building, End of Spencer Paul Road, north of Warren Shingle Road (14th Street), Marysville, Yuba County, CA

THE DECENNIAL OF AIR FORCE SPACE COMMAND’S ONLY GROUND BASED MISSILE WARNING CLASSIC ASSOCIATE UNIT: BENEFITS, DRAWBACKS, AND CHALLENGES

DTIC Science & Technology

2016-02-16

for future threats and challenges. In the Ground Based Missile Warning and Space Surveillance mission set, this means developing...Warning and Space Surveillance for North America .43 For 45 years, Clear AFS was solely an Active Duty remote assignment. That was up until 2006 when the ...National Guard and Homeland Defense activities. § 901 provides a definition for the term “homeland defense activity” and it

Hydrologic ensembles based on convection-permitting precipitation nowcasts for flash flood warnings

NASA Astrophysics Data System (ADS)

Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Ramos, Maria-Helena

2017-04-01

In order to better anticipate flash flood events and provide timely warnings to communities at risk, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium ungauged basins. Based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014), the current version of the system runs a simplified hourly distributed hydrologic model with operational radar-gauge QPE grids from Météo-France at a 1-km2 resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. To further extend the effective warning lead time while accounting for hydrometeorological uncertainties, the flash flood warning system is being enhanced to include Météo-France's AROME-NWC high-resolution precipitation nowcasts as time-lagged ensembles and multiple sets of hydrological regionalized parameters. The operational deterministic precipitation forecasts, from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015), were provided at a 2.5-km resolution for a 6-hr forecast horizon for 9 significant rain events from September 2014 to June 2016. The time-lagged approach is a practical choice of accounting for the atmospheric forecast uncertainty when no extensive forecast archive is available for statistical modelling. The evaluation on 781 French basins showed significant improvements in terms of flash flood event detection and effective warning lead-time, compared to warnings from the current AIGA setup (without any future precipitation). We also discuss how to effectively communicate verification information to help determine decision-relevant warning thresholds for flood magnitude and probability. Javelle, P., Demargne, J., Defrance, D., Arnaud, P., 2014. Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, doi: 10.1080/02626667.2014.923970 Auger, L., Dupont, O., Hagelin, S., Brousseau, P., Brovelli, P., 2015. AROME-NWC: a new nowcasting tool based on an operational mesoscale forecasting system. Quarterly Journal of the Royal Meteorological Society, 141: 1603-1611, doi:10.1002/qj.2463

Text-based Analytics for Biosurveillance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charles, Lauren E.; Smith, William P.; Rounds, Jeremiah

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related tomore » biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).« less

Forecasting seizures in dogs with naturally occurring epilepsy.

PubMed

Howbert, J Jeffry; Patterson, Edward E; Stead, S Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W Douglas; Litt, Brian; Worrell, Gregory A

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

PubMed Central

Stead, S. Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H.; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W. Douglas; Litt, Brian; Worrell, Gregory A.

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring. PMID:24416133

Dengue Contingency Planning: From Research to Policy and Practice

PubMed Central

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J.; Sánchez Tejeda, Gustavo; Lloyd, Linda S.; Hakim, Lokman; Bowman, Leigh R.; Horstick, Olaf; Coelho, Giovanini

2016-01-01

Background Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Methodology/Principal findings Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Conclusions/Significance Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan. PMID:27653786

Systems and Sensors for Debris-flow Monitoring and Warning

PubMed Central

Arattano, Massimo; Marchi, Lorenzo

2008-01-01

Debris flows are a type of mass movement that occurs in mountain torrents. They consist of a high concentration of solid material in water that flows as a wave with a steep front. Debris flows can be considered a phenomenon intermediate between landslides and water floods. They are amongst the most hazardous natural processes in mountainous regions and may occur under different climatic conditions. Their destructiveness is due to different factors: their capability of transporting and depositing huge amounts of solid materials, which may also reach large sizes (boulders of several cubic meters are commonly transported by debris flows), their steep fronts, which may reach several meters of height and also their high velocities. The implementation of both structural and non-structural control measures is often required when debris flows endanger routes, urban areas and other infrastructures. Sensor networks for debris-flow monitoring and warning play an important role amongst non-structural measures intended to reduce debris-flow risk. In particular, debris flow warning systems can be subdivided into two main classes: advance warning and event warning systems. These two classes employ different types of sensors. Advance warning systems are based on monitoring causative hydrometeorological processes (typically rainfall) and aim to issue a warning before a possible debris flow is triggered. Event warning systems are based on detecting debris flows when these processes are in progress. They have a much smaller lead time than advance warning ones but are also less prone to false alarms. Advance warning for debris flows employs sensors and techniques typical of meteorology and hydrology, including measuring rainfall by means of rain gauges and weather radar and monitoring water discharge in headwater streams. Event warning systems use different types of sensors, encompassing ultrasonic or radar gauges, ground vibration sensors, videocameras, avalanche pendulums, photocells, trip wires etc. Event warning systems for debris flows have a strong linkage with debris-flow monitoring that is carried out for research purposes: the same sensors are often used for both monitoring and warning, although warning systems have higher requirements of robustness than monitoring systems. The paper presents a description of the sensors employed for debris-flow monitoring and event warning systems, with attention given to advantages and drawbacks of different types of sensors. PMID:27879828

Cigarette packaging and health warnings: the impact of plain packaging and message framing on young smokers

PubMed Central

Mays, Darren; Niaura, Raymond S.; Evans, W. Douglas; Hammond, David; Luta, George; Tercyak, Kenneth P.

2014-01-01

Objective This study examined the impact of pictorial cigarette warning labels, warning label message framing, and plain cigarette packaging on young adult smokers’ motivation to quit. Methods Smokers ages 18–30 (n=740) from a consumer research panel were randomized to one of four experimental conditions where they viewed online images of 4 cigarette packs with warnings about lung disease, cancer, stroke/heart disease, and death, respectively. Packs differed across conditions by warning message framing (gain versus loss) and packaging (branded versus plain). Measures captured demographics, smoking behavior, covariates, and motivation to quit in response to cigarette packs. Results Pictorial warnings about lung disease and cancer generated the strongest motivation to quit across conditions. Adjusting for pre-test motivation and covariates, a message framing by packaging interaction revealed gain-framed warnings on plain packs generated greater motivation to quit for lung disease, cancer, and mortality warnings (p < 0.05), compared with loss-framed warnings on plain packs. Conclusions Warnings combining pictorial depictions of smoking-related health risks with text-based messages about how quitting reduces risks may achieve better outcomes among young adults, especially in countries considering or implementing plain packaging regulations. PMID:24420310

Is More Better? - Night Vision Enhancement System's Pedestrian Warning Modes and Older Drivers.

PubMed

Brown, Timothy; He, Yefei; Roe, Cheryl; Schnell, Thomas

2010-01-01

Pedestrian fatalities as a result of vehicle collisions are much more likely to happen at night than during day time. Poor visibility due to darkness is believed to be one of the causes for the higher vehicle collision rate at night. Existing studies have shown that night vision enhancement systems (NVES) may improve recognition distance, but may increase drivers' workload. The use of automatic warnings (AW) may help minimize workload, improve performance, and increase safety. In this study, we used a driving simulator to examine performance differences of a NVES with six different configurations of warning cues, including: visual, auditory, tactile, auditory and visual, tactile and visual, and no warning. Older drivers between the ages of 65 and 74 participated in the study. An analysis based on the distance to pedestrian threat at the onset of braking response revealed that tactile and auditory warnings performed the best, while visual warnings performed the worst. When tactile or auditory warnings were presented in combination with visual warning, their effectiveness decreased. This result demonstrated that, contrary to general sense regarding warning systems, multi-modal warnings involving visual cues degraded the effectiveness of NVES for older drivers.

Is More Better? — Night Vision Enhancement System’s Pedestrian Warning Modes and Older Drivers

PubMed Central

Brown, Timothy; He, Yefei; Roe, Cheryl; Schnell, Thomas

2010-01-01

Pedestrian fatalities as a result of vehicle collisions are much more likely to happen at night than during day time. Poor visibility due to darkness is believed to be one of the causes for the higher vehicle collision rate at night. Existing studies have shown that night vision enhancement systems (NVES) may improve recognition distance, but may increase drivers’ workload. The use of automatic warnings (AW) may help minimize workload, improve performance, and increase safety. In this study, we used a driving simulator to examine performance differences of a NVES with six different configurations of warning cues, including: visual, auditory, tactile, auditory and visual, tactile and visual, and no warning. Older drivers between the ages of 65 and 74 participated in the study. An analysis based on the distance to pedestrian threat at the onset of braking response revealed that tactile and auditory warnings performed the best, while visual warnings performed the worst. When tactile or auditory warnings were presented in combination with visual warning, their effectiveness decreased. This result demonstrated that, contrary to general sense regarding warning systems, multi-modal warnings involving visual cues degraded the effectiveness of NVES for older drivers. PMID:21050616

Landslide and Flood Warning System Prototypes based on Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Hloupis, George; Stavrakas, Ilias; Triantis, Dimos

2010-05-01

Wireless sensor networks (WSNs) are one of the emerging areas that received great attention during the last few years. This is mainly due to the fact that WSNs have provided scientists with the capability of developing real-time monitoring systems equipped with sensors based on Micro-Electro-Mechanical Systems (MEMS). WSNs have great potential for many applications in environmental monitoring since the sensor nodes that comprised from can host several MEMS sensors (such as temperature, humidity, inertial, pressure, strain-gauge) and transducers (such as position, velocity, acceleration, vibration). The resulting devices are small and inexpensive but with limited memory and computing resources. Each sensor node contains a sensing module which along with an RF transceiver. The communication is broadcast-based since the network topology can change rapidly due to node failures [1]. Sensor nodes can transmit their measurements to central servers through gateway nodes without any processing or they make preliminary calculations locally in order to produce results that will be sent to central servers [2]. Based on the above characteristics, two prototypes using WSNs are presented in this paper: A Landslide detection system and a Flood warning system. Both systems sent their data to central processing server where the core of processing routines exists. Transmission is made using Zigbee and IEEE 802.11b protocol but is capable to use VSAT communication also. Landslide detection system uses structured network topology. Each measuring node comprises of a columnar module that is half buried to the area under investigation. Each sensing module contains a geophone, an inclinometer and a set of strain gauges. Data transmitted to central processing server where possible landslide evolution is monitored. Flood detection system uses unstructured network topology since the failure rate of sensor nodes is expected higher. Each sensing module contains a custom water level sensor (based on plastic optical fiber). Data transmitted directly to server where the early warning algorithms monitor the water level variations in real time. Both sensor nodes use power harvesting techniques in order to extend their battery life as much as possible. [1] Yick J.; Mukherjee, B.; Ghosal, D. Wireless sensor network survey. Comput. Netw. 2008, 52, 2292-2330. [2] Garcia, M.; Bri, D.; Boronat, F.; Lloret, J. A new neighbor selection strategy for group-based wireless sensor networks, In The Fourth International Conference on Networking and Services (ICNS 2008), Gosier, Guadalupe, March 16-21, 2008.

Early warning model based on correlated networks in global crude oil markets

NASA Astrophysics Data System (ADS)

Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

2018-01-01

Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Impact of Tobacco-Related Health Warning Labels across Socioeconomic, Race and Ethnic Groups: Results from a Randomized Web-Based Experiment

PubMed Central

Cantrell, Jennifer; Vallone, Donna M.; Thrasher, James F.; Nagler, Rebekah H.; Feirman, Shari P.; Muenz, Larry R.; He, David Y.; Viswanath, Kasisomayajula

2013-01-01

Background The U.S. Family Smoking Prevention and Tobacco Control Act of 2009 requires updating of the existing text-only health warning labels on tobacco packaging with nine new warning statements accompanied by pictorial images. Survey and experimental research in the U.S. and other countries supports the effectiveness of pictorial health warning labels compared with text-only warnings for informing smokers about the risks of smoking and encouraging cessation. Yet very little research has examined differences in reactions to warning labels by race/ethnicity, education or income despite evidence that population subgroups may differ in their ability to process health information. The purpose of the present study was to evaluate the potential impact of pictorial warning labels compared with text-only labels among U.S. adult smokers from diverse racial/ethnic and socioeconomic subgroups. Methods/Findings Participants were adult smokers recruited from two online research panels (n = 3,371) into a web-based experimental study to view either the new pictorial warnings or text-only warnings. Participants viewed the labels and reported their reactions. Adjusted regression models demonstrated significantly stronger reactions for the pictorial condition for each outcome salience (b = 0.62, p<.001); perceived impact (b = 0.44, p<.001); credibility (OR = 1.41, 95% CI = 1.22−1.62), and intention to quit (OR = 1.30, 95% CI = 1.10−1.53). No significant results were found for interactions between condition and race/ethnicity, education, or income. The only exception concerned the intention to quit outcome, where the condition-by-education interaction was nearly significant (p = 0.057). Conclusions Findings suggest that the greater impact of the pictorial warning label compared to the text-only warning is consistent across diverse racial/ethnic and socioeconomic populations. Given their great reach, pictorial health warning labels may be one of the few tobacco control policies that have the potential to reduce communication inequalities across groups. Policies that establish strong pictorial warning labels on tobacco packaging may be instrumental in reducing the toll of the tobacco epidemic, particularly within vulnerable communities. PMID:23341895

Framing Indoor Tanning Warning Messages to Reduce Skin Cancer Risks Among Young Women: Implications for Research and Policy

PubMed Central

Tercyak, Kenneth P.

2015-01-01

Objectives. We investigated the impact of indoor tanning device warnings that communicate the risks associated with indoor tanning (i.e., loss framed) or the benefits of avoiding indoor tanning (i.e., gain framed). Methods. A convenience sample of non-Hispanic White women aged 18 to 30 years who tanned indoors at least once in the past year (n = 682) participated in a within-subjects experiment. Participants completed baseline measures and reported indoor tanning intentions and intentions to quit indoor tanning in response to 5 warning messages in random order. A text-only control warning was based on Food and Drug Administration–required warnings for indoor tanning devices. Experimental warnings included graphic content and were either gain or loss framed. Results. In multivariable analyses, gain-framed warnings did not differ from the control warning on women’s intentions to tan indoors, but they prompted stronger intentions to quit than the control message. Loss-framed warnings significantly reduced intentions to tan indoors and increased intentions to quit indoor tanning compared with control and gain-framed warnings. Conclusions. The public health impact of indoor tanning device warnings can be enhanced by incorporating graphic content and leveraging gain- and loss-framed messaging. PMID:26066932

A fundamental conflict of care: Nurses' accounts of balancing patients' sleep with taking vital sign observations at night.

PubMed

Hope, Joanna; Recio-Saucedo, Alejandra; Fogg, Carole; Griffiths, Peter; Smith, Gary B; Westwood, Greta; Schmidt, Paul E

2017-12-21

To explore why adherence to vital sign observations scheduled by an early warning score protocol reduces at night. Regular vital sign observations can reduce avoidable deterioration in hospital. early warning score protocols set the frequency of these observations by the severity of a patient's condition. Vital sign observations are taken less frequently at night, even with an early warning score in place, but no literature has explored why. A qualitative interpretative design informed this study. Seventeen semi-structured interviews with nursing staff working on wards with varying levels of adherence to scheduled vital sign observations. A thematic analysis approach was used. At night, nursing teams found it difficult to balance the competing care goals of supporting sleep with taking vital sign observations. The night-time frequency of these observations was determined by clinical judgement, ward-level expectations of observation timing and the risk of disturbing other patients. Patients with COPD or dementia could be under-monitored, while patients nearing the end of life could be over-monitored. In this study, we found an early warning score algorithm focused on deterioration prevention did not account for long-term management or palliative care trajectories. Nurses were therefore less inclined to wake such patients to take vital sign observations at night. However, the perception of widespread exceptions and lack of evidence regarding optimum frequency risks delegitimising the early warning score approach. This may pose a risk to patient safety, particularly patients with dementia or chronic conditions. Nurses should document exceptions and discuss these with the wider team. Hospitals should monitor why vital sign observations are missed at night, identify which groups are under-monitored and provide guidance on prioritising competing expectations. early warning score protocols should take account of different care trajectories. © 2017 The Authors. Journal of Clinical Nursing Published by John Wiley & Sons Ltd.

A cooperative positioning algorithm for DSRC enabled vehicular networks

NASA Astrophysics Data System (ADS)

Efatmaneshnik, M.; Kealy, A.; Alam, N.; Dempster, A. G.

2011-12-01

Many of the safety related applications that can be facilitated by Dedicated Short Range Communications (DSRC), such as vehicle proximity warnings, automated braking (e.g. at level crossings), speed advisories, pedestrian alerts etc., rely on a robust vehicle positioning capability such as that provided by a Global Navigation Satellite System (GNSS). Vehicles in remote areas, entering tunnels, high rise areas or any high multipath/ weak signal environment will challenge the integrity of GNSS position solutions, and ultimately the safety application it underpins. To address this challenge, this paper presents an innovative application of Cooperative Positioning techniques within vehicular networks. CP refers to any method of integrating measurements from different positioning systems and sensors in order to improve the overall quality (accuracy and reliability) of the final position solution. This paper investigates the potential of the DSRC infrastructure itself to provide an inter-vehicular ranging signal that can be used as a measurement within the CP algorithm. In this paper, time-based techniques of ranging are introduced and bandwidth requirements are investigated and presented. The robustness of the CP algorithm to inter-vehicle connection failure as well as GNSS dropouts is also demonstrated using simulation studies. Finally, the performance of the Constrained Kalman Filter used to integrate GNSS measurements with DSRC derived range estimates within a typical VANET is described and evaluated.

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms.

PubMed

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Fu, Xiaolin

2018-05-08

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Lack of pregnancy warnings on over-the-counter dermatologic products containing potentially harmful hydroquinone.

PubMed

Bio, L L; Cies, J J

2017-07-01

To determine the presence of pregnancy warnings on over-the-counter (OTC) dermatologic products with hydroquinone, a potentially harmful ingredient. Data were obtained from the Food and Drug Administration National Drug Code Directory and Label Repository to identify OTC dermatologic products containing hydroquinone. Products were stratified based on pregnancy or general warning presence (WP) or absence (WA). Product characteristics were compared between groups: hydroquinone concentration, presence of external packaging, indication and warning statements. Of the 112 products studied, 21 had a pregnancy warning and 3 included a general warning against use: WP (n=24) and WA (n=88) group. External packaging was more prevalent in the WP group compared to WA (62.5% vs 29.5%, P=0.004). Majority of OTC dermatologic products containing hydroquinone did not have a pregnancy warning. This highlights the need for improved labeling and informs providers caring for pregnant women of OTC labeling limitations.

Community-based early warning systems for flood risk mitigation in Nepal

NASA Astrophysics Data System (ADS)

Smith, Paul J.; Brown, Sarah; Dugar, Sumit

2017-03-01

This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2-3 to 7-8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Estimation for aerial detection effectiveness with cooperation efficiency factors of early-warning aircraft in early-warning detection SoS under BSC framework

NASA Astrophysics Data System (ADS)

Zhu, Feng; Hu, Xiaofeng; He, Xiaoyuan; Guo, Rui; Li, Kaiming; Yang, Lu

2017-11-01

In the military field, the performance evaluation of early-warning aircraft deployment or construction is always an important problem needing to be explored. As an effective approach of enterprise management and performance evaluation, Balanced Score Card (BSC) attracts more and more attentions and is studied more and more widely all over the world. It can also bring feasible ideas and technical approaches for studying the issue of the performance evaluation of the deployment or construction of early-warning aircraft which is the important component in early-warning detection system of systems (SoS). Therefore, the deep explored researches are carried out based on the previously research works. On the basis of the characteristics of space exploration and aerial detection effectiveness of early-warning detection SoS and the cardinal principle of BSC are analyzed simply, and the performance evaluation framework of the deployment or construction of early-warning aircraft is given, under this framework, aimed at the evaluation issue of aerial detection effectiveness of early-warning detection SoS with the cooperation efficiency factors of the early-warning aircraft and other land based radars, the evaluation indexes are further designed and the relative evaluation model is further established, especially the evaluation radar chart being also drawn to obtain the evaluation results from a direct sight angle. Finally, some practical computer simulations are launched to prove the validity and feasibility of the research thinking and technologic approaches which are proposed in the paper.

Design of vehicle intelligent anti-collision warning system

NASA Astrophysics Data System (ADS)

Xu, Yangyang; Wang, Ying

2018-05-01

This paper mainly designs a low cost, high-accuracy, micro-miniaturization, and digital display and acousto-optic alarm features of the vehicle intelligent anti-collision warning system that based on MCU AT89C51. The vehicle intelligent anti-collision warning system includes forward anti-collision warning system, auto parking systems and reversing anti-collision radar system. It mainly develops on the basis of ultrasonic distance measurement, its performance is reliable, thus the driving safety is greatly improved and the parking security and efficiency enhance enormously.

The GOES-R Geostationary Lightning Mapper (GLM)

NASA Astrophysics Data System (ADS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas; Bailey, Jeffrey; Buechler, Dennis; Carey, Larry; Schultz, Chris; Bateman, Monte; McCaul, Eugene; Stano, Geoffrey

2013-05-01

The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8 km with a product refresh rate of less than 20 s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

Detection of G-Induced Loss of Consciousness (G-LOC) prognosis through EMG monitoring on gastrocnemius muscle in flight.

PubMed

Booyong Choi; Yongkyun Lee; Taehwan Cho; Hyojin Koo; Dongsoo Kim

2015-08-01

G-Induced Loss of Consciousness (G-LOC) is mainly caused by the sudden acceleration in the direction of +Gz axis from the fighter pilots, and is considered as an emergent situation of which fighter pilots are constantly aware. In order to resist against G-LOC, fighter pilots are subject to run Anti-G straining maneuver (AGSM), which includes L-1 respiration maneuvering and muscular contraction of the whole body. The purpose of this study is to create a G-LOC warning alarm prior to G-LOC by monitoring the Electromyogram (EMG) of the gastrocnemius muscle on the calf, which goes under constant muscular contraction during the AGSM process. EMG data was retrieved from pilots and pilot trainees of the Korean Air Force, during when subjects were under high G-trainings on a human centrifugal simulator. Out of the EMG features, integrated absolute value (IAV), reflecting muscle contraction, and waveform length (WL), reflecting muscle contraction and fatigue, have shown a rapid decay during the alarm phase, 3 seconds before G-LOC, compared to that of a normal phase withstanding G-force. Such results showed consistency amongst pilots and pilot trainees who were under G-LOC. Based on these findings, this study developed an algorithm which can detect G-LOC prognosis during flight, and at the same time, generate warning signals. The probability of G-LOC occurrence is detected through monitoring the decay trend and degree of the IVA and WL value of when the pilot initiates AGSM during sudden acceleration above 6G. Conclusively, this G-LOC prognosis detecting and warning system is a customized, real-time countermeasure which enhanced the accuracy of detecting G-LOC.

Spatio-temporal coupling of EEG signals in epilepsy

NASA Astrophysics Data System (ADS)

Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

2011-05-01

Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

Applying the zero-inflated Poisson model with random effects to detect abnormal rises in school absenteeism indicating infectious diseases outbreak.

PubMed

Song, X X; Zhao, Q; Tao, T; Zhou, C M; Diwan, V K; Xu, B

2018-05-30

Records of absenteeism from primary schools are valuable data for infectious diseases surveillance. However, the analysis of the absenteeism is complicated by the data features of clustering at zero, non-independence and overdispersion. This study aimed to generate an appropriate model to handle the absenteeism data collected in a European Commission granted project for infectious disease surveillance in rural China and to evaluate the validity and timeliness of the resulting model for early warnings of infectious disease outbreak. Four steps were taken: (1) building a 'well-fitting' model by the zero-inflated Poisson model with random effects (ZIP-RE) using the absenteeism data from the first implementation year; (2) applying the resulting model to predict the 'expected' number of absenteeism events in the second implementation year; (3) computing the differences between the observations and the expected values (O-E values) to generate an alternative series of data; (4) evaluating the early warning validity and timeliness of the observational data and model-based O-E values via the EARS-3C algorithms with regard to the detection of real cluster events. The results indicate that ZIP-RE and its corresponding O-E values could improve the detection of aberrations, reduce the false-positive signals and are applicable to the zero-inflated data.

Improved Conflict Detection for Reducing Operational Errors in Air Traffic Control

NASA Technical Reports Server (NTRS)

Paielli, Russell A.; Erzberger, Hainz

2003-01-01

An operational error is an incident in which an air traffic controller allows the separation between two aircraft to fall below the minimum separation standard. The rates of such errors in the US have increased significantly over the past few years. This paper proposes new detection methods that can help correct this trend by improving on the performance of Conflict Alert, the existing software in the Host Computer System that is intended to detect and warn controllers of imminent conflicts. In addition to the usual trajectory based on the flight plan, a "dead-reckoning" trajectory (current velocity projection) is also generated for each aircraft and checked for conflicts. Filters for reducing common types of false alerts were implemented. The new detection methods were tested in three different ways. First, a simple flightpath command language was developed t o generate precisely controlled encounters for the purpose of testing the detection software. Second, written reports and tracking data were obtained for actual operational errors that occurred in the field, and these were "replayed" to test the new detection algorithms. Finally, the detection methods were used to shadow live traffic, and performance was analysed, particularly with regard to the false-alert rate. The results indicate that the new detection methods can provide timely warnings of imminent conflicts more consistently than Conflict Alert.

Precession missile feature extraction using sparse component analysis of radar measurements

NASA Astrophysics Data System (ADS)

Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des

2012-12-01

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

Infrared dim target detection based on visual attention

NASA Astrophysics Data System (ADS)

Wang, Xin; Lv, Guofang; Xu, Lizhong

2012-11-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented. After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map. Then the salient regions where the potential targets exist in are extracted by searching through the saliency map with a control mechanism of winner-take-all (WTA) competition and inhibition-of-return (IOR). At last, these regions are identified by the characteristics of the dim IR targets, so the true targets are detected, and the spurious objects are rejected. The experiments are performed for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

PubMed

Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

2015-11-01

There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 °C when used to predict the temperature at the seat and skin interface 15 min ahead, but required 45 min data prior to give this accuracy. Although the 45 min front loading of data appears large (in proportion to the 15 min prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 min required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid exacerbating the skin disorders of patients who suffer from low body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms.

PubMed

Chan, Wai Sum; Recknagel, Friedrich; Cao, Hongqing; Park, Ho-Dong

2007-05-01

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.

A real-time tracking system of infrared dim and small target based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Rong, Sheng-hui; Zhou, Hui-xin; Qin, Han-lin; Wang, Bing-jian; Qian, Kun

2014-11-01

A core technology in the infrared warning system is the detection tracking of dim and small targets with complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our system can achieve higher detection rate and lower false alarm rate.

Dust Storm Feature Identification and Tracking from 4D Simulation Data

NASA Astrophysics Data System (ADS)

Yu, M.; Yang, C. P.

2016-12-01

Dust storms cause significant damage to health, property and the environment worldwide every year. To help mitigate the damage, dust forecasting models simulate and predict upcoming dust events, providing valuable information to scientists, decision makers, and the public. Normally, the model simulations are conducted in four-dimensions (i.e., latitude, longitude, elevation and time) and represent three-dimensional (3D), spatial heterogeneous features of the storm and its evolution over space and time. This research investigates and proposes an automatic multi-threshold, region-growing based identification algorithm to identify critical dust storm features, and track the evolution process of dust storm events through space and time. In addition, a spatiotemporal data model is proposed, which can support the characterization and representation of dust storm events and their dynamic patterns. Quantitative and qualitative evaluations for the algorithm are conducted to test the sensitivity, and capability of identify and track dust storm events. This study has the potential to assist a better early warning system for decision-makers and the public, thus making hazard mitigation plans more effective.

Developing an automatic classification system of vegetation anomalies for early warning with the ASAP (Anomaly hot Spots of Agricultural Production) system

NASA Astrophysics Data System (ADS)

Meroni, M.; Rembold, F.; Urbano, F.; Lemoine, G.

2016-12-01

Anomaly maps and time profiles of remote sensing derived indicators relevant to monitor crop and vegetation stress can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for SDG 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide monthly warning of production deficits in water-limited agriculture worldwide. The first step is fully automated and aims at classifying each administrative unit (1st sub-national level) into a number of possible warning levels, ranging from "none" to "watch" and up to "extended alarm". The second step involves the verification of the automatic warnings and integration into a short national level analysis by agricultural analysts. In this paper we describe the methodological development of the automatic vegetation anomaly classification system. Warnings are triggered only during the crop growing season, defined by a remote sensing based phenology. The classification takes into consideration the fraction of the agricultural and rangelands area for each administrative unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index (SPI), computed at 1 and 3-month scale) and one biophysical indicator (the cumulative NDVI from the start of the growing season). The severity of the warning thus depends on the timing, the nature and the number of indicators for which an anomaly is detected. The prototype system is using global NDVI images of the METOP sensor, while a second version is being developed based on 1km Modis NDVI with temporal smoothing and near real time filtering. Also a specific water balance model is under development to include agriculture water stress information in addition to the SPI. The monthly warning classification and crop condition assessment will be made available on a website and will strengthen the JRC support to information products based on consensus assessment such as the GEOGLAM Crop Monitor for Early Warning.

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

USGS Publications Warehouse

Bose, Maren; Graves, Robert; Gill, David; Callaghan, Scott; Maechling, Phillip J.

2014-01-01

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (<0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of >415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s.

Early warning system for Douglas-fir tussock moth outbreaks in the Western United States.

Treesearch

Gary E. Daterman; John M. Wenz; Katharine A. Sheehan

2004-01-01

The Early Warning System is a pheromone-based trapping system used to detect outbreaks of Douglas-fir tussock moth (DFTM, Orgyia pseudotsugata) in the western United States. Millions of acres are susceptible to DFTM defoliation, but Early Warning System monitoring focuses attention only on the relatively limited areas where outbreaks may be...

Exposure to a Mnemonic Interferes with Recall of Suicide Warning Signs in a Community-Based Suicide Prevention Program

ERIC Educational Resources Information Center

Bryan, Craig J.; Steiner-Pappalardo, Nicole; Rudd, M. David

2009-01-01

The incremental impact of adding a mnemonic to remember suicide warning signs to the Air Force Suicide Prevention Program (AFSPP) community awareness briefing was investigated with a sample of young, junior-enlisted airmen. Participants in the standard briefing significantly increased their ability to list suicide warning signs and improved…

Wavelet-based automatic determination of the P- and S-wave arrivals

NASA Astrophysics Data System (ADS)

Bogiatzis, P.; Ishii, M.

2013-12-01

The detection of P- and S-wave arrivals is important for a variety of seismological applications including earthquake detection and characterization, and seismic tomography problems such as imaging of hydrocarbon reservoirs. For many years, dedicated human-analysts manually selected the arrival times of P and S waves. However, with the rapid expansion of seismic instrumentation, automatic techniques that can process a large number of seismic traces are becoming essential in tomographic applications, and for earthquake early-warning systems. In this work, we present a pair of algorithms for efficient picking of P and S onset times. The algorithms are based on the continuous wavelet transform of the seismic waveform that allows examination of a signal in both time and frequency domains. Unlike Fourier transform, the basis functions are localized in time and frequency, therefore, wavelet decomposition is suitable for analysis of non-stationary signals. For detecting the P-wave arrival, the wavelet coefficients are calculated using the vertical component of the seismogram, and the onset time of the wave is identified. In the case of the S-wave arrival, we take advantage of the polarization of the shear waves, and cross-examine the wavelet coefficients from the two horizontal components. In addition to the onset times, the automatic picking program provides estimates of uncertainty, which are important for subsequent applications. The algorithms are tested with synthetic data that are generated to include sudden changes in amplitude, frequency, and phase. The performance of the wavelet approach is further evaluated using real data by comparing the automatic picks with manual picks. Our results suggest that the proposed algorithms provide robust measurements that are comparable to manual picks for both P- and S-wave arrivals.

Evaluation of the National Weather Service Extreme Cold Warning Experiment in North Dakota

PubMed Central

Chiu, Cindy H.; Vagi, Sara J.; Wolkin, Amy F.; Martin, John Paul; Noe, Rebecca S.

2016-01-01

Dangerously cold weather threatens life and property. During periods of extreme cold due to wind chill, the National Weather Service (NWS) issues wind chill warnings to prompt the public to take action to mitigate risks. Wind chill warnings are based on ambient temperatures and wind speeds. Since 2010, NWS has piloted a new extreme cold warning issued for cold temperatures in wind and nonwind conditions. The North Dakota Department of Health, NWS, and the Centers for Disease Control and Prevention collaborated in conducting household surveys in Burleigh County, North Dakota, to evaluate this new warning. The objectives of the evaluation were to assess whether residents heard the new warning and to determine if protective behaviors were prompted by the warning. This was a cross-sectional survey design using the Community Assessment for Public Health Emergency Response (CASPER) methodology to select a statistically representative sample of households from Burleigh County. From 10 to 11 April 2012, 188 door-to-door household interviews were completed. The CASPER methodology uses probability sampling with weighted analysis to estimate the number and percentage of households with a specific response within Burleigh County. The majority of households reported having heard both the extreme cold and wind chill warnings, and both warnings prompted protective behaviors. These results suggest this community heard the new warning and took protective actions after hearing the warning. PMID:27239260

Geoethical considerations in early warning of flooding and landslides: Case study from Norway

NASA Astrophysics Data System (ADS)

Devoli, Graziella; Kleivane Krøgli, Ingeborg; Dahl, Mads Peter; Colleuille, Hervé; Nykjær Boje, Søren; Sund, Monica

2015-04-01

The Norwegian Water Resources and Energy Directorate (NVE) runs the national early warning systems (EWS) for flooding and shallow landslides in Norway. The two EWSs have been operational since the late 1980s and 2013 respectively, and are based on weather forecasts, various hydro-meteorological prognosis and expert evaluation. Daily warning levels and related information to the public is prepared and presented through custom build internet platforms. In natural hazards sciences, the risk of a specific threat is defined as the product of hazard and consequence. In this context an EWS is intended to work as a mitigation measure in lowering the consequence and thus the risk of the threat. One of several factors determining the quality of such an EWS, is how warnings are communicated to the public. In contrary to what is common practice in some other countries, experts working with EWS in Norway cannot be held personally responsible for consequences of warnings being issued or not. However, the communication of warnings for flooding and landslides at NVE still implies many considerations of geoethical kind. Which are the consequences today for the forecasters when erroneous warning messages are sent because based on a poorly documented analysis? What is for example the most responsible way to describe uncertainties in warnings issued? What is the optimal compromise between avoiding false alarms and not sending out a specific warning? Is it responsible to rely on a "gut feeling"? Some authorities complain in receiving warning messages too often. Is it responsible to begin notifying these, only in cases of "high hazard level" and no longer in cases of "moderate hazard level"? Is it acceptable to issue general warnings for large geographical areas without being able to pinpoint the treat on local scale? What responsibility lies within the EWS in recommending evacuation or other practical measures to local authorities? By presenting how early warnings of flooding and landslides are communicated in Norway and discussing the questions above, we intend to add to the discussion on what is the ethical responsibility for scientists performing forecasting and communication of natural hazards.

Pictorial cigarette pack warnings: a meta-analysis of experimental studies

PubMed Central

Noar, Seth M; Hall, Marissa G; Francis, Diane B; Ribisl, Kurt M; Pepper, Jessica K; Brewer, Noel T

2016-01-01

Objective To inform international research and policy, we conducted a meta-analysis of the experimental literature on pictorial cigarette pack warnings. Data sources We systematically searched 7 computerised databases in April 2013 using several search terms. We also searched reference lists of relevant articles. Study selection We included studies that used an experimental protocol to test cigarette pack warnings and reported data on both pictorial and text-only conditions. 37 studies with data on 48 independent samples (N=33 613) met criteria. Data extraction and synthesis Two independent coders coded all study characteristics. Effect sizes were computed from data extracted from study reports and were combined using random effects meta-analytic procedures. Results Pictorial warnings were more effective than text-only warnings for 12 of 17 effectiveness outcomes (all p<0.05). Relative to text-only warnings, pictorial warnings (1) attracted and held attention better; (2) garnered stronger cognitive and emotional reactions; (3) elicited more negative pack attitudes and negative smoking attitudes and (4) more effectively increased intentions to not start smoking and to quit smoking. Participants also perceived pictorial warnings as being more effective than text-only warnings across all 8 perceived effectiveness outcomes. Conclusions The evidence from this international body of literature supports pictorial cigarette pack warnings as more effective than text-only warnings. Gaps in the literature include a lack of assessment of smoking behaviour and a dearth of theory-based research on how warnings exert their effects. PMID:25948713

Exposure to graphic warning labels on cigarette packages: Effects on implicit and explicit attitudes towards smoking among young adults.

PubMed

Macy, Jonathan T; Chassin, Laurie; Presson, Clark C; Yeung, Ellen

2016-01-01

To test the effect of exposure to the US Food and Drug Administration's proposed graphic images with text warning statements for cigarette packages on implicit and explicit attitudes towards smoking. A two-session web-based study was conducted with 2192 young adults 18-25-years-old. During session one, demographics, smoking behaviour, and baseline implicit and explicit attitudes were assessed. Session two, completed on average 18 days later, contained random assignment to viewing one of three sets of cigarette packages, graphic images with text warnings, text warnings only, or current US Surgeon General's text warnings. Participants then completed post-exposure measures of implicit and explicit attitudes. ANCOVAs tested the effect of condition on the outcomes, controlling for baseline attitudes. Smokers who viewed packages with graphic images plus text warnings demonstrated more negative implicit attitudes compared to smokers in the other conditions (p = .004). For the entire sample, explicit attitudes were more negative for those who viewed graphic images plus text warnings compared to those who viewed current US Surgeon General's text warnings (p = .014), but there was no difference compared to those who viewed text-only warnings. Graphic health warnings on cigarette packages can influence young adult smokers' implicit attitudes towards smoking.

Physical and Dynamical Linkages Between Lightning Jumps and Storm Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Blakeslee, Richard J.; Goodman, Steven J.

2014-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. Currently, the lightning jump algorithm is being tested in two separate but important efforts. Schultz et al. (2014; this conference) is exploring the transition of the algorithm from its research based formulation to a fully objective algorithm that includes storm tracking, Geostationary Lightning Mapper (GLM) Proxy data and the lightning jump algorithm. Chronis et al. (2014) provides context for the transition to current operational forecasting using lightning mapping array based products. However, what remains is an end-to-end physical and dynamical basis for coupling total lightning flash rates to severe storm manifestation, so the forecaster has a reason beyond simple correlation to utilize the lightning jump algorithm within their severe storm conceptual models. Therefore, the physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relationship to updraft strength, updraft volume, precipitation-sized ice mass, etc.; however, their relationship specifically to lightning jumps is fragmented within the literature. Thus the goal of this study is to use multiple Doppler and polarimetric radar techniques to resolve the physical and dynamical storm characteristics specifically around the time of the lightning jump. This information will help forecasters anticipate lightning jump occurrence, or even be of use to determine future characteristics of a given storm (e.g., development of a mesocyclone, downdraft, or hail signature on radar), providing additional lead time/confidence in the severe storm warning paradigm.

Physical and Dynamical Linkages between Lightning Jumps and Storm Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Blakeslee, Richard J.; Goodman, Steven J.

2014-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. Currently, the lightning jump algorithm is being tested in two separate but important efforts. Schultz et al. (2014; this conference) is exploring the transition of the algorithm from its research based formulation to a fully objective algorithm that includes storm tracking, Geostationary Lightning Mapper (GLM) Proxy data and the lightning jump algorithm. Chronis et al. (2014; this conference) provides context for the transition to current operational forecasting using lightning mapping array based products. However, what remains is an end-to-end physical and dynamical basis for coupling total lightning flash rates to severe storm manifestation, so the forecaster has a reason beyond simple correlation to utilize the lightning jump algorithm within their severe storm conceptual models. Therefore, the physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relationship to updraft strength, updraft volume, precipitation-sized ice mass, etc.; however, their relationship specifically to lightning jumps is fragmented within the literature. Thus the goal of this study is to use multiple Doppler and polarimetric radar techniques to resolve the physical and dynamical storm characteristics specifically around the time of the lightning jump. This information will help forecasters anticipate lightning jump occurrence, or even be of use to determine future characteristics of a given storm (e.g., development of a mesocyclone, downdraft, or hail signature on radar), providing additional lead time/confidence in the severe storm warning paradigm.

The GOES-R Series Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas M.

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model.

PubMed

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-03-02

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

PubMed Central

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-01-01

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. PMID:28257122

Impact of the 2014 Food and Drug Administration Warnings Against Power Morcellation.

PubMed

Lum, Deirdre A; Sokol, Eric R; Berek, Jonathan S; Schulkin, Jay; Chen, Ling; McElwain, Cora-Ann; Wright, Jason D

2016-01-01

To determine whether members of the AAGL Advancing Minimally Invasive Gynecologic Surgery Worldwide (AAGL) and members of the American College of Obstetricians and Gynecologists Collaborative Ambulatory Research Network (ACOG CARN) have changed their clinical practice based on the 2014 Food and Drug Administration (FDA) warnings against power morcellation. A survey study. Participants were invited to complete this online survey (Canadian Task Force classification II-2). AAGL and ACOG CARN members. An online anonymous survey with 24 questions regarding demographics and changes to clinical practice during minimally invasive myomectomies and hysterectomies based on the 2014 FDA warnings against power morcellation. A total of 615 AAGL members and 54 ACOG CARN members responded (response rates of 8.2% and 60%, respectively). Before the FDA warnings, 85.8% and 86.9%, respectively, were using power morcellation during myomectomies and hysterectomies. After the FDA warnings, 71.1% and 75.8% of respondents reported stopping the use of power morcellation during myomectomies and hysterectomies. The most common reasons cited for discontinuing the use of power morcellation or using it less often were hospital mandate (45.6%), the concern for legal consequences (16.1%), and the April 2014 FDA warning (13.9%). Nearly half of the respondents (45.6%) reported an increase in their rate of laparotomy. Most (80.3%) believed that the 2014 FDA warnings have not led to an improvement in patient outcomes and have led to harming patients (55.1%). AAGL and ACOG CARN respondents reported decreased use of power morcellation during minimally invasive gynecologic surgery after the 2014 FDA warnings, the most common reason cited being hospital mandate. Rates of laparotomy have increased. Most members surveyed believe that the FDA warnings have not improved patient outcomes. Copyright © 2016 AAGL. Published by Elsevier Inc. All rights reserved.

[Analysis and experimental verification of sensitivity and SNR of laser warning receiver].

PubMed

Zhang, Ji-Long; Wang, Ming; Tian, Er-Ming; Li, Xiao; Wang, Zhi-Bin; Zhang, Yue

2009-01-01

In order to countermeasure increasingly serious threat from hostile laser in modern war, it is urgent to do research on laser warning technology and system, and the sensitivity and signal to noise ratio (SNR) are two important performance parameters in laser warning system. In the present paper, based on the signal statistical detection theory, a method for calculation of the sensitivity and SNR in coherent detection laser warning receiver (LWR) has been proposed. Firstly, the probabilities of the laser signal and receiver noise were analyzed. Secondly, based on the threshold detection theory and Neyman-Pearson criteria, the signal current equation was established by introducing detection probability factor and false alarm rate factor, then, the mathematical expressions of sensitivity and SNR were deduced. Finally, by using method, the sensitivity and SNR of the sinusoidal grating laser warning receiver developed by our group were analyzed, and the theoretic calculation and experimental results indicate that the SNR analysis method is feasible, and can be used in performance analysis of LWR.

CISN ShakeAlert: Using early warnings for earthquakes in California

NASA Astrophysics Data System (ADS)

Vinci, M.; Hellweg, M.; Jones, L. M.; Khainovski, O.; Schwartz, K.; Lehrer, D.; Allen, R. M.; Neuhauser, D. S.

2009-12-01

Educated users who have developed response plans and procedures are just as important for an earthquake early warning (EEW) system as are the algorithms and computers that process the data and produce the warnings. In Japan, for example, the implementation of the EEW system which now provides advanced alerts of ground shaking included intense outreach efforts to both institutional and individual recipients. Alerts are now used in automatic control systems that stop trains, place sensitive equipment in safe mode and isolate hazards while the public takes cover. In California, the California Integrated Seismic Network (CISN) is now developing and implementing components of a prototype system for EEW, ShakeAlert. As this processing system is developed, we invite a suite of perspective users from critical industries and institutions throughout California to partner with us in developing useful ShakeAlert products and procedures. At the same time, we will support their efforts to determine and implement appropriate responses to an early warning of earthquake shaking. As a first step, in a collaboration with BART, we have developed a basic system allowing BART’s operation center to receive realtime ground shaking information from more than 150 seismic stations operating in the San Francisco Bay Area. BART engineers are implementing a display system for this information. Later phases will include the development of improved response procedures utilizing this information. We plan to continue this collaboration to include more sophisticated information from the prototype CISN ShakeAlert system.

Predictors of compliance with tornado warnings issued in Joplin, Missouri, in 2011.

PubMed

Paul, Bimal Kanti; Stimers, Mitchel; Caldas, Marcellus

2015-01-01

Joplin, a city in the southwest corner of Missouri, United States, suffered an EF-5 tornado in the late afternoon of 22 May 2011. This event, which claimed the lives of 162 people, represents the deadliest single tornado to strike the US since modern record-keeping began in 1950. This study examines the factors associated with responses to tornado warnings. Based on a post-tornado survey of survivors in Joplin, it reveals that tornado warnings were adequate and timely. Multivariate logistic regression identified four statistically significant determinants of compliance with tornado warnings: number of warning sources, whether respondents were at home when the tornado struck, past tornado experience, and gender. The findings suggest several recommendations, the implementation of which will further improve responses to tornado warnings. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014.

Combining Real-Time Seismic and GPS Data for Earthquake Early Warning (Invited)

NASA Astrophysics Data System (ADS)

Boese, M.; Heaton, T. H.; Hudnut, K. W.

2013-12-01

Scientists at Caltech, UC Berkeley, the Univ. of SoCal, the Univ. of Washington, the US Geological Survey, and ETH Zurich have developed an earthquake early warning (EEW) demonstration system for California and the Pacific Northwest. To quickly determine the earthquake magnitude and location, 'ShakeAlert' currently processes and interprets real-time data-streams from ~400 seismic broadband and strong-motion stations within the California Integrated Seismic Network (CISN). Based on these parameters, the 'UserDisplay' software predicts and displays the arrival and intensity of shaking at a given user site. Real-time ShakeAlert feeds are currently shared with around 160 individuals, companies, and emergency response organizations to educate potential users about EEW and to identify needs and applications of EEW in a future operational warning system. Recently, scientists at the contributing institutions have started to develop algorithms for ShakeAlert that make use of high-rate real-time GPS data to improve the magnitude estimates for large earthquakes (M>6.5) and to determine slip distributions. Knowing the fault slip in (near) real-time is crucial for users relying on or operating distributed systems, such as for power, water or transportation, especially if these networks run close to or across large faults. As shown in an earlier study, slip information is also useful to predict (in a probabilistic sense) how far a fault rupture will propagate, thus enabling more robust probabilistic ground-motion predictions at distant locations. Finally, fault slip information is needed for tsunami warning, such as in the Cascadia subduction-zone. To handle extended fault-ruptures of large earthquakes in real-time, Caltech and USGS Pasadena are currently developing and testing a two-step procedure that combines seismic and geodetic data; in the first step, high-frequency strong-motion amplitudes are used to rapidly classify near-and far-source stations. Then, the location and extent of the 2D fault rupture is determined from comparison with pre-calculated generic and fault-specific templates ('FinDer' algorithm, Finite Fault Rupture Detector). In the second step, long-period dynamic displacement amplitudes from the GPS sites are back-projected onto this rupture line/plane to estimate the slip amplitudes ('GPSlip' algorithm). The corresponding back-projection relations were empirically derived from a suite of 3D waveform simulations. We are currently testing our approach in southern California (both real-time and offline), although not yet included in the current distribution of ShakeAlert. RTK/PPP(AR) solutions from the RTNet software at USGS Pasadena currently provide 1 Hz real-time position times series at ~100 GPS sensor locations. Output is in openly available in JSON format. We and UNAVCO have tested onsite (in-receiver) PPP(AR) processing using Trimble NetR9 receivers with RTX & GLONASS options enabled, of which Caltech has recently purchased 41 new units. These special GPS receivers will provide 5 Hz position and velocity streams. We will deliver the GPS RTX output (in GSOF format) into the EEW system (in Earthworm tracebuf2 format). The new receivers are to be installed at 'zipper array' stations of the SCSN in upcoming months. In addition, we have developed a framework for end-to-end offline testing with archived and simulated waveform data.

The Financial Benefit of Early Flood Warnings in Europe

NASA Astrophysics Data System (ADS)

Pappenberger, Florian; Cloke, Hannah L.; Wetterhall, Fredrik; Parker, Dennis J.; Richardson, David; Thielen, Jutta

2015-04-01

Effective disaster risk management relies on science based solutions to close the gap between prevention and preparedness measures. The outcome of consultations on the UNIDSR post-2015 framework for disaster risk reduction highlight the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management in order to save people's lives and property and reduce the overall impact of severe events. In particular, continental and global scale flood forecasting systems provide vital information to various decision makers with which early warnings of floods can be made. Here the potential monetary benefits of early flood warnings using the example of the European Flood Awareness System (EFAS) are calculated based on pan-European Flood damage data and calculations of potential flood damage reductions. The benefits are of the order of 400 Euro for every 1 Euro invested. Because of the uncertainties which accompany the calculation, a large sensitivity analysis is performed in order to develop an envelope of possible financial benefits. Current EFAS system skill is compared against perfect forecasts to demonstrate the importance of further improving the skill of the forecasts. Improving the response to warnings is also essential in reaping the benefits of flood early warnings.

School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood.

PubMed

Henry, Kimberly L; Knight, Kelly E; Thornberry, Terence P

2012-02-01

Over the past 5 years, a great deal of attention has been paid to the development of early warning systems for dropout prevention. These warning systems use a set of indicators based on official school records to identify youth at risk for dropout and then appropriately target intervention. The current study builds on this work by assessing the extent to which a school disengagement warning index predicts not only dropout but also other problem behaviors during middle adolescence, late adolescence, and early adulthood. Data from the Rochester Youth Development Study (N = 911, 73% male, 68% African American, and 17% Latino) were used to examine the effects of a school disengagement warning index based on official 8th and 9th grade school records on subsequent dropout, as well as serious delinquency, official offending, and problem substance use during middle adolescence, late adolescence, and early adulthood. Results indicate that the school disengagement warning index is robustly related to dropout as well as serious problem behaviors across the three developmental stages, even after controlling for important potential confounders. High school dropout mediates the effect of the warning index on serious problem behaviors in early adulthood.

School Disengagement as a Predictor of Dropout, Delinquency, and Problem Substance Use during Adolescence and Early Adulthood

PubMed Central

Henry, Kimberly L.; Knight, Kelly E.; Thornberry, Terence P.

2015-01-01

Over the past five years, a great deal of attention has been paid to the development of early warning systems for dropout prevention. These warning systems use a set of indicators based on official school records to identify youth at risk for dropout and then appropriately target intervention. The current study builds on this work by assessing the extent to which a school disengagement warning index predicts not only dropout but also other problem behaviors during middle adolescence, late adolescence, and early adulthood. Data from the Rochester Youth Development Study (n=911, 73% male, 68% African American, and 17% Latino) were used to examine the effects of a school disengagement warning index based on official 8th and 9th grade school records on subsequent dropout, as well as serious delinquency, official offending, and problem substance use during middle adolescence, late adolescence, and early adulthood. Results indicate that the school disengagement warning index is robustly related to dropout as well as serious problem behaviors across the three developmental stages, even after controlling for important potential confounders. High school dropout mediates the effect of the warning index on serious problem behaviors in early adulthood. PMID:21523389

Study on visual detection method for wind turbine blade failure

NASA Astrophysics Data System (ADS)

Chen, Jianping; Shen, Zhenteng

2018-02-01

Start your abstract here…At present, the non-destructive testing methods of the wind turbine blades has fiber bragg grating, sound emission and vibration detection, but there are all kinds of defects, and the engineering application is difficult. In this regard, three-point slope deviation method, which is a kind of visual inspection method, is proposed for monitoring the running status of wind turbine blade based on the image processing technology. A better blade image can be got through calibration, image splicing, pretreatment and threshold segmentation algorithm. Design of the early warning system to monitor wind turbine blade running condition, recognition rate, stability and impact factors of the method were statistically analysed. The experimental results shown showed that it has highly accurate and good monitoring effect.

GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-07-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic algorithms and allows one to obtain thresholds for the prediction of slope failures using dates of landslide activations and rainfall series. It can be applied to either single landslides or a set of similar slope movements in a homogeneous environment. Calibration of the model provides families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from the kernels, the corresponding mobility functions (i.e., the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to the hydro-geological complexity of the site. Generally, shorter base times are expected for shallow slope instabilities compared to larger-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing measured or forecasted rainfall series. Examples of application of GASAKe to a medium-size slope movement (the Uncino landslide at San Fili, in Calabria, southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of occurrence of the slope movements. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e., neither missing nor false alarms) has been achieved using five activations. As for temporal validation, the experiments performed by considering further dates of activation have also proved satisfactory. In view of early-warning applications for civil protection, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model against different types of slope instabilities characterized by several historical activations. Nevertheless, further refinements are still needed for application to landslide risk mitigation within early-warning and decision-support systems.

Runway Safety Monitor Algorithm for Runway Incursion Detection and Alerting

NASA Technical Reports Server (NTRS)

Green, David F., Jr.; Jones, Denise R. (Technical Monitor)

2002-01-01

The Runway Safety Monitor (RSM) is an algorithm for runway incursion detection and alerting that was developed in support of NASA's Runway Incursion Prevention System (RIPS) research conducted under the NASA Aviation Safety Program's Synthetic Vision System element. The RSM algorithm provides pilots with enhanced situational awareness and warnings of runway incursions in sufficient time to take evasive action and avoid accidents during landings, takeoffs, or taxiing on the runway. The RSM currently runs as a component of the NASA Integrated Display System, an experimental avionics software system for terminal area and surface operations. However, the RSM algorithm can be implemented as a separate program to run on any aircraft with traffic data link capability. The report documents the RSM software and describes in detail how RSM performs runway incursion detection and alerting functions for NASA RIPS. The report also describes the RIPS flight tests conducted at the Dallas-Ft Worth International Airport (DFW) during September and October of 2000, and the RSM performance results and lessons learned from those flight tests.

Near the conflagration: the wide duty to warn.

PubMed

Helminski, F

1993-07-01

The "duty to warn" has become fixed in US law since the 1976 case of Tarasoff v Regents of the University of California. In that case, the California Supreme Court decided that psychotherapists whose patients make a specific, serious threat of violence against a specific, clearly identifiable potential victim have a duty to warn the intended victim, directly or indirectly, of the threat. Tarasoff inspired several successful and unsuccessful lawsuits. A recent Vermont case has extended the duty to warn in that state to a threat of damage to property when persons may be physically harmed. The duty to warn is explicitly based on considerations of social utility and, as such, is attractive for courts to expand because an apparently minimal effort by therapists will often prevent substantial harm to victims. Some states have codified the duty to warn in a statute, but other states have refused to adopt the Tarasoff reasoning. In the absence of clear legal decisions to the contrary, psychotherapists may well anticipate that the duty to warn operates in their states.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Status of Public Earthquake Early Warning in the U.S

NASA Astrophysics Data System (ADS)

Given, D. D.

2013-12-01

Earthquake Early Warning (EEW) is a proven use of seismological science that can give people and businesses outside the epicentral area of a large earthquake up to a minute to take protective actions before the most destructive shaking hits them. Since 2006 several organizations have been collaborating to create such a system in the United States. These groups include the US Geological Survey, Caltech, UC Berkeley, the University of Washington, the Southern California Earthquake Center, the Swiss Federal Institute of Technology, Zürich, the California Office of Emergency Services, and the California Geological Survey. A demonstration version of the system, called ShakeAlert, began sending test notifications to selected users in California in January 2012. In August 2012 San Francisco's Bay Area Rapid Transit district began slowing and stopping trains in response to strong ground shaking. The next step in the project is to progress to a production prototype for the west coast. The system is built on top of the considerable technical and organizational earthquake monitoring infrastructure of the Advanced National Seismic System (ANSS). While a fully functional, robust, public EEW system will require significant new investment and development in several major areas, modest progress is being made with current resources. First, high-quality sensors must be installed with sufficient density, particularly near source faults. Where possible, we are upgrading and augmenting the existing ANSS networks on the west coast. Second, data telemetry from those sensors must be engineered for speed and reliability. Next, robust central processing infrastructure is being designed and built. Also, computer algorithms to detect and characterize the evolving earthquake must be further developed and tested. Last year the Gordon and Betty Moore Foundation funded USGS, Caltech, UCB and UW to accelerate R&D efforts. Every available means of distributing alerts must be used to insure the system's effectiveness. We have developed an internet-based UserDisplay application and a smartphone app based on Google Cloud Messaging. In addition, USGS has applied for authorization to alert over FEMA's Integrated Pubic Alert and Warning System. We are also working with private companies to develop alert distribution channels and end user implementation capabilities. Finally, because policy makers, institutional users, and the public must be educated about the system, social scientists and communicators are determining how to communicate the alerts most effectively. Progress is also being made in several related areas. Real-time GPS position data is becoming available on a large scale and algorithms are being developed to use these data to rapidly characterize the fault rupture as it propagates. New, advanced seismological and geodetic algorithms for the Cascadia megathrust and San Andreas fault are being developed. We are exploring public-private partnerships to develop commercial EEW applications. And Federal, State and local agencies are working out their roles and responsibilities in building, operating and educating users about the system. There is much more to be done and funding the creation and operation of this new capability is a challenge in the current budget climate. However, our goal is to build an EEW system before the next big earthquake rather than in its aftermath.

Detection of ground motions using high-rate GPS time-series

NASA Astrophysics Data System (ADS)

Psimoulis, Panos A.; Houlié, Nicolas; Habboub, Mohammed; Michel, Clotaire; Rothacher, Markus

2018-05-01

Monitoring surface deformation in real-time help at planning and protecting infrastructures and populations, manage sensitive production (i.e. SEVESO-type) and mitigate long-term consequences of modifications implemented. We present RT-SHAKE, an algorithm developed to detect ground motions associated with landslides, sub-surface collapses, subsidences, earthquakes or rock falls. RT-SHAKE detects first transient changes in individual GPS time series before investigating for spatial correlation(s) of observations made at neighbouring GPS sites and eventually issue a motion warning. In order to assess our algorithm on fast (seconds to minute), large (from 1 cm to meters) and spatially consistent surface motions, we use the 1 Hz GEONET GNSS network data of the Tohoku-Oki MW9.0 2011 as a test scenario. We show the delay of detection of seismic wave arrival by GPS records is of ˜10 seconds with respect to an identical analysis based on strong-motion data and this time delay depends on the level of the time-variable noise. Nevertheless, based on the analysis of the GPS network noise level and ground motion stochastic model, we show that RT-SHAKE can narrow the range of earthquake magnitude, by setting a lower threshold of detected earthquakes to MW6.5-7, if associated with a real-time automatic earthquake location system.

Flash floods warning technique based on wireless communication networks data

NASA Astrophysics Data System (ADS)

David, Noam; Alpert, Pinhas; Messer, Hagit

2010-05-01

Flash floods can occur throughout or subsequent to rainfall events, particularly in cases where the precipitation is of high-intensity. Unfortunately, each year these floods cause severe property damage and heavy casualties. At present, there are no sufficient real time flash flood warning facilities found to cope with this phenomenon. Here we show the tremendous potential of flash floods advanced warning based on precipitation measurements of commercial microwave links. As was recently shown, wireless communication networks supply high resolution precipitation measurements at ground level while often being situated in flood prone areas, covering large parts of these hazardous regions. We present the flash flood warning potential of the wireless communication system for two different cases when floods occurred at the Judean desert and at the northern Negev in Israel. In both cases, an advanced warning regarding the hazard could have been announced based on this system. • This research was supported by THE ISRAEL SCIENCE FOUNDATION (grant No. 173/08). This work was also supported by a grant from the Yeshaya Horowitz Association, Jerusalem. Additional support was given by the PROCEMA-BMBF project and by the GLOWA-JR BMBF project.

Standardised (plain) cigarette packaging increases attention to both text-based and graphical health warnings: experimental evidence.

PubMed

Shankleman, M; Sykes, C; Mandeville, K L; Di Costa, S; Yarrow, K

2015-01-01

To investigate whether standardised cigarette packaging increases the time spent looking at health warnings, regardless of the format of those warnings. A factorial (two pack styles x three warning types) within-subject experiment, with participants randomised to different orders of conditions, completed at a university in London, UK. Mock-ups of cigarette packets were presented to participants with their branded portion in either standardised (plain) or manufacturer-designed (branded) format. Health warnings were present on all packets, representing all three types currently in use in the UK: black & white text, colour text, or colour images with accompanying text. Gaze position was recorded using a specialised eye tracker, providing the main outcome measure, which was the mean proportion of a five-second viewing period spent gazing at the warning-label region of the packet. An opportunity sample of 30 (six male, mean age = 23) young adults met the following inclusion criteria: 1) not currently a smoker; 2) <100 lifetime cigarettes smoked; 3) gaze position successfully tracked for > 50% viewing time. These participants spent a greater proportion of the available time gazing at the warning-label region when the branded section of the pack was standardised (following current Australian guidelines) rather than containing the manufacturer's preferred design (mean difference in proportions = 0.078, 95% confidence interval 0.049 to 0.106, p < 0.001). There was no evidence that this effect varied based on the type of warning label (black & white text vs. colour text vs. colour image & text; interaction p = 0.295). During incidental viewing of cigarette packets, young adult never-smokers are likely to spend more time looking at health warnings if manufacturers are compelled to use standardised packaging, regardless of the warning design. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé; Calvello, Michele

2017-06-01

The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and was designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Decision-making in the LEWS is based upon rainfall thresholds, hydrometeorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. Daily alerts are issued throughout the country considering variable size warning zones. Warnings are issued once per day for the following 3 days and can be updated according to weather forecasts and information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating number of landslides and warning levels issued in a warning zone. In the past, this method has been exclusively employed to analyse the performance of regional early warning models considering fixed warning zones. Herein, an original approach is proposed for the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable size areas. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013-2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classes of landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered.

The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: a systematic review.

PubMed

Wuytack, Francesca; Meskell, Pauline; Conway, Aislinn; McDaid, Fiona; Santesso, Nancy; Hickey, Fergal G; Gillespie, Paddy; Raymakers, Adam J N; Smith, Valerie; Devane, Declan

2017-12-06

Changes to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs. We searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables. We identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified. Early warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

Embedded Alcohol Messages in Television Series: The Interactive Effect of Warnings and Audience Connectedness on Viewers' Alcohol Beliefs*

PubMed Central

RUSSELL, DALE W.; RUSSELL, CRISTEL ANTONIA

2014-01-01

Objective This research investigates whether warning viewers about the presence of embedded messages in the content of a television episode affects viewers' drinking beliefs and whether audi ence connectedness moderates the warning's impact. Method Two hun dred fifty college students participated in a laboratory experiment approximating a real-life television viewing experience. They viewed an actual television series episode containing embedded alcohol messages, and their subsequent beliefs about alcohol consequences were measured. Experimental conditions differed based on a 2 (Connectedness Level: low vs high) × 2 (Timing of the Warning: before or after the episode) × 2 (Emphasis of Warning: advertising vs health message) design. Connectedness was measured, and the timing and emphasis of the warnings were manipulated. The design also included a control condition where there was no warning. Results The findings indicate that warning view ers about embedded messages in the content of a program can yield sig nificant differences in viewers' beliefs about alcohol. However, the warning's impact differs depending on the viewers' level of connectedness to the program. In particular, in comparison with the no-warning control condition, the advertising prewarning produced lower positive beliefs about alcohol and its consequences but only for the low-connected viewers. Highly connected viewers were not affected by a warning emphasizing advertising messages embedded in the program, but a warning emphasizing health produced significantly higher negative be liefs about drinking than in the control condition. Conclusions The presence of many positive portrayals of drinking and alcohol product placements in television series has led many to suggest ways to counter their influence. However, advocates of warnings should be conscious of their differential impact on high- and low-connected viewers. PMID:18432390

How reaction to cigarette packet health warnings influence quitting: Findings from the ITC Four Country survey

PubMed Central

Borland, Ron; Yong, Hua-Hie; Wilson, Nick; Fong, Geoffrey T.; Hammond, David; Michael Cummings, K.; Hosking, Warwick; McNeill, Ann

2015-01-01

Objectives To examine prospectively the impact of health warnings on quitting activity. Design Five waves (2002–2006) of a cohort survey where reactions to health warnings at one survey wave are used to predict cessation activity at the next wave, controlling for country (proxy for warning differences) and other factors. These analyses were replicated on four wave-to-wave transitions. Setting and participants Smokers from Australia, Canada, the UK and USA. Samples were Waves 1–2: n=6525; Waves 2–3: n=5257; Waves 3–4:n=4439; and Waves 4–5: n=3993. Measures Warning salience, cognitive responses (thoughts of harm and of quitting), forgoing of cigarettes and avoidance of warnings were examined as predictors of quit attempts, and of quitting success among those who tried (1 month sustained abstinence), replicated across four wave-to-wave transitions. Results All four responses to warnings were independently predictive of quitting activity in bivariate analyses. In multivariate analyses, forgoing cigarettes and cognitive responses to the warnings both prospectively predicted making quit attempts in all replications. However, avoiding warnings did not consistently add predictive value, and there was no consistent pattern for warning salience. There were no interactions by country. Some, but not all, of the effects were mediated by quitting intentions. There were no consistent effects on quit success. Conclusions This study adds to the evidence that forgoing cigarettes as a result of noticing warnings and quit-related cognitive reactions to warnings are consistent prospective predictors of making quit attempts. This work strengthens the evidence base for governments to go beyond the FCTC to mandate health warnings on tobacco products that stimulate the highest possible levels of these reactions. PMID:19215595

Pictorial cigarette pack warnings: a meta-analysis of experimental studies.

PubMed

Noar, Seth M; Hall, Marissa G; Francis, Diane B; Ribisl, Kurt M; Pepper, Jessica K; Brewer, Noel T

2016-05-01

To inform international research and policy, we conducted a meta-analysis of the experimental literature on pictorial cigarette pack warnings. We systematically searched 7 computerised databases in April 2013 using several search terms. We also searched reference lists of relevant articles. We included studies that used an experimental protocol to test cigarette pack warnings and reported data on both pictorial and text-only conditions. 37 studies with data on 48 independent samples (N=33,613) met criteria. Two independent coders coded all study characteristics. Effect sizes were computed from data extracted from study reports and were combined using random effects meta-analytic procedures. Pictorial warnings were more effective than text-only warnings for 12 of 17 effectiveness outcomes (all p<0.05). Relative to text-only warnings, pictorial warnings (1) attracted and held attention better; (2) garnered stronger cognitive and emotional reactions; (3) elicited more negative pack attitudes and negative smoking attitudes and (4) more effectively increased intentions to not start smoking and to quit smoking. Participants also perceived pictorial warnings as being more effective than text-only warnings across all 8 perceived effectiveness outcomes. The evidence from this international body of literature supports pictorial cigarette pack warnings as more effective than text-only warnings. Gaps in the literature include a lack of assessment of smoking behaviour and a dearth of theory-based research on how warnings exert their effects. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Do graphic health warning labels have an impact on adolescents' smoking-related beliefs and behaviours?

PubMed

White, Victoria; Webster, Bernice; Wakefield, Melanie

2008-09-01

To assess the impact of the introduction of graphic health warning labels on cigarette packets on adolescents at different smoking uptake stages. School-based surveys conducted in the year prior to (2005) and approximately 6 months after (2006) the introduction of the graphic health warnings. The 2006 survey was conducted after a TV advertising campaign promoting two new health warnings. Secondary schools in greater metropolitan Melbourne, Australia. Students in year levels 8-12: 2432 students in 2005, and 2050 in 2006, participated. Smoking uptake stage, intention to smoke, reported exposure to cigarette packs, knowledge of health effects of smoking, cognitive processing of warning labels and perceptions of cigarette pack image. At baseline, 72% of students had seen cigarette packs in the previous 6 months, while at follow-up 77% had seen packs and 88% of these had seen the new warning labels. Cognitive processing of warning labels increased, with students more frequently reading, attending to, thinking and talking about warning labels at follow-up. Experimental and established smokers thought about quitting and forgoing cigarettes more at follow-up. At follow-up intention to smoke was lower among those students who had talked about the warning labels and had forgone cigarettes. Graphic warning labels on cigarette packs are noticed by the majority of adolescents, increase adolescents' cognitive processing of these messages and have the potential to lower smoking intentions. Our findings suggest that the introduction of graphic warning labels may help to reduce smoking among adolescents.

Cigarette packaging and health warnings: the impact of plain packaging and message framing on young smokers.

PubMed

Mays, Darren; Niaura, Raymond S; Evans, W Douglas; Hammond, David; Luta, George; Tercyak, Kenneth P

2015-03-01

This study examined the impact of pictorial cigarette-warning labels, warning-label message framing and plain cigarette packaging, on young adult smokers' motivation to quit. Smokers aged 18-30 years (n=740) from a consumer research panel were randomised to one of four experimental conditions where they viewed online images of four cigarette packs with warnings about lung disease, cancer, stroke/heart disease and death, respectively. Packs differed across conditions by warning-message framing (gain vs loss) and packaging (branded vs plain). Measures captured demographics, smoking behaviour, covariates and motivation to quit in response to cigarette packs. Pictorial warnings about lung disease and cancer generated the strongest motivation to quit across conditions. Adjusting for pretest motivation and covariates, a message framing by packaging interaction revealed gain-framed warnings on plain packs generated greater motivation to quit for lung disease, cancer and mortality warnings (p<0.05), compared with loss-framed warnings on plain packs. Warnings combining pictorial depictions of smoking-related health risks with text-based messages about how quitting reduces risks, may achieve better outcomes among young adults, especially in countries considering or implementing plain packaging regulations. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Exposure to Graphic Warning Labels on Cigarette Packages: Effects on Implicit and Explicit Attitudes toward Smoking among Young Adults

PubMed Central

Macy, Jonathan T.; Chassin, Laurie; Presson, Clark C.; Yeung, Ellen

2015-01-01

Objective Test the effect of exposure to the U.S. Food and Drug Administration’s proposed graphic images with text warning statements for cigarette packages on implicit and explicit attitudes toward smoking. Design and methods A two-session web-based study was conducted with 2192 young adults 18–25 years old. During session one, demographics, smoking behavior, and baseline implicit and explicit attitudes were assessed. Session two, completed on average 18 days later, contained random assignment to viewing one of three sets of cigarette packages, graphic images with text warnings, text warnings only, or current U.S Surgeon General’s text warnings. Participants then completed post-exposure measures of implicit and explicit attitudes. ANCOVAs tested the effect of condition on the outcomes, controlling for baseline attitudes. Results Smokers who viewed packages with graphic images plus text warnings demonstrated more negative implicit attitudes compared to smokers in the other conditions (p=.004). For the entire sample, explicit attitudes were more negative for those who viewed graphic images plus text warnings compared to those who viewed current U.S. Surgeon General’s text warnings (p=.014), but there was no difference compared to those who viewed text-only warnings. Conclusion Graphic health warnings on cigarette packages can influence young adult smokers’ implicit attitudes toward smoking. PMID:26442992

Landslide risk mitigation by means of early warning systems

NASA Astrophysics Data System (ADS)

Calvello, Michele

2017-04-01

Among the many options available to mitigate landslide risk, early warning systems may be used where, in specific circumstances, the risk to life increases above tolerable levels. A coherent framework to classify and analyse landslide early warning systems (LEWS) is herein presented. Once the objectives of an early warning strategy are defined depending on the scale of analysis and the type of landslides to address, the process of designing and managing a LEWS should synergically employ technical and social skills. A classification scheme for the main components of LEWSs is proposed for weather-induced landslides. The scheme is based on a clear distinction among: i) the landslide model, i.e. a functional relationship between weather characteristics and landslide events considering the geotechnical, geomorphological and hydro-geological characterization of the area as well as an adequate monitoring strategy; ii) the warning model, i.e. the landslide model plus procedures to define the warning events and to issue the warnings; iii) the warning system, i.e. the warning model plus warning dissemination procedures, communication and education tools, strategies for community involvement and emergency plans. Each component of a LEWS is related to a number of actors involved with their deployment, operational activities and management. For instance, communication and education, community involvement and emergency plans are all significantly influenced by people's risk perception and by operational aspects system managers need to address in cooperation with scientists.

Response time effects of alerting tone and semantic context for synthesized voice cockpit warnings

NASA Technical Reports Server (NTRS)

Simpson, C. A.; Williams, D. H.

1980-01-01

Some handbooks and human factors design guides have recommended that a voice warning should be preceded by a tone to attract attention to the warning. As far as can be determined from a search of the literature, no experimental evidence supporting this exists. A fixed-base simulator flown by airline pilots was used to test the hypothesis that the total 'system-time' to respond to a synthesized voice cockpit warning would be longer when the message was preceded by a tone because the voice itself was expected to perform both the alerting and the information transfer functions. The simulation included realistic ATC radio voice communications, synthesized engine noise, cockpit conversation, and realistic flight routes. The effect of a tone before a voice warning was to lengthen response time; that is, responses were slower with an alerting tone. Lengthening the voice warning with another work, however, did not increase response time.

A Method to Increase Drivers' Trust in Collision Warning Systems Based on Reliability Information of Sensor

NASA Astrophysics Data System (ADS)

Tsutsumi, Shigeyoshi; Wada, Takahiro; Akita, Tokihiko; Doi, Shun'ichi

Driver's workload tends to be increased during driving under complicated traffic environments like a lane change. In such cases, rear collision warning is effective for reduction of cognitive workload. On the other hand, it is pointed out that false alarm or missing alarm caused by sensor errors leads to decrease of driver' s trust in the warning system and it can result in low efficiency of the system. Suppose that reliability information of the sensor is provided in real-time. In this paper, we propose a new warning method to increase driver' s trust in the system even with low sensor reliability utilizing the sensor reliability information. The effectiveness of the warning methods is shown by driving simulator experiments.

Using SMAP data to improve drought early warning over the US Great Plains

NASA Astrophysics Data System (ADS)

Fu, R.; Fernando, N.; Tang, W.

2015-12-01

A drought prone region such as the Great Plains of the United States (US GP) requires credible and actionable drought early warning. Such information cannot simply be extracted from available climate forecasts because of their large uncertainties at regional scales, and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA North American Multi-Model Ensemble experiment (NMME) are much more reliable for winter and spring than for the summer season for the US GP. To mitigate the weaknesses of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies, as the scientific basis for a statistical drought early warning system. This system uses percentile soil moisture anomalies in spring as a key input to provide a probabilistic summer drought early warning. The latter outperforms the dynamic prediction over the US Southern Plains and has been used by the Texas state water agency to support state drought preparedness. A main source of uncertainty for this drought early warning system is the soil moisture input obtained from the NOAA Climate Forecasting System (CFS). We are testing use of the beta version of NASA Soil Moisture Active Passive (SMAP) soil moisture data, along with the Soil Moisture and Ocean Salinity (SMOS), and the long-term Essential Climate Variable Soil Moisture (ECV-SM) soil moisture data, to reduce this uncertainty. Preliminary results based on ECV-SM suggests satellite based soil moisture data could improve early warning of rainfall anomalies over the western US GP with less dense vegetation. The skill degrades over the eastern US GP where denser vegetation is found. We evaluate our SMAP-based drought early warning for 2015 summer against observations.

An early warning system for groundwater pollution based on the assessment of groundwater pollution risks.

NASA Astrophysics Data System (ADS)

Zhang, Weihong.; Zhao, Yongsheng; Hong, Mei; Guo, Xiaodong

2009-04-01

Groundwater pollution usually is complex and concealed, remediation of which is difficult, high cost, time-consuming, and ineffective. An early warning system for groundwater pollution is needed that detects groundwater quality problems and gets the information necessary to make sound decisions before massive groundwater quality degradation occurs. Groundwater pollution early warning were performed by considering comprehensively the current groundwater quality, groundwater quality varying trend and groundwater pollution risk . The map of the basic quality of the groundwater was obtained by fuzzy comprehensive evaluation or BP neural network evaluation. Based on multi-annual groundwater monitoring datasets, Water quality state in sometime of the future was forecasted using time-sequenced analyzing methods. Water quality varying trend was analyzed by Spearman's rank correlative coefficient.The relative risk map of groundwater pollution was estimated through a procedure that identifies, cell by cell,the values of three factors, that is inherent vulnerability, load risk of pollution source and contamination hazard. DRASTIC method was used to assess inherent vulnerability of aquifer. Load risk of pollution source was analyzed based on the potential of contamination and pollution degree. Assessment index of load risk of pollution source which involves the variety of pollution source, quantity of contaminants, releasing potential of pollutants, and distance were determined. The load risks of all sources considered by GIS overlay technology. Early warning model of groundwater pollution combined with ComGIS technology organically, the regional groundwater pollution early-warning information system was developed, and applied it into Qiqiha'er groundwater early warning. It can be used to evaluate current water quality, to forecast water quality changing trend, and to analyze space-time influencing range of groundwater quality by natural process and human activities. Keywords: groundwater pollution, early warning, aquifer vulnerability, pollution load, pollution risk, ComGIS

Lack of association between stroke symptom knowledge and intent to call 911: a population-based survey.

PubMed

Fussman, Chris; Rafferty, Ann P; Lyon-Callo, Sarah; Morgenstern, Lewis B; Reeves, Mathew J

2010-07-01

Excessive prehospital delay between acute stroke onset and hospital arrival is an ongoing problem. Translating knowledge of stroke warning signs into appropriate action is critical to decrease prehospital delay. Our objectives were to estimate the proportion of Michigan adults who would react appropriately by calling 911 when presented with hypothetical stroke-related scenarios and to examine the association between knowledge of warning signs and calling 911. In 2004, questions regarding initial response to health-related scenarios were added to the Michigan Behavioral Risk Factor Survey, a population-based telephone survey of adults. We calculated the proportion of respondents who would call 911 in response to 3 stroke-related scenarios and examined the association between stroke warning sign knowledge and 911 activation. Among 4841 adults, 27.6% (95% CI, 26.2 to 29.0) had adequate knowledge of stroke warning signs (defined as reporting 3 correct warning signs), and 14.0% (95% CI, 12.9 to 15.1) reported they would call 911 for all 3 stroke-related scenarios. Knowledge of specific stroke warning signs was only modestly associated with calling 911 in response to medical scenarios that involved the same stroke symptom (OR, 1.17 to 1.39). Even among those with adequate knowledge of stroke warning signs, only 17.6% (95% CI, 15.5 to 20.0) would call 911 for all 3 stroke scenarios. In this population-based survey, stroke symptom knowledge was not associated with the intent to call 911 for stroke. This study emphasizes the critical role of motivation in addition to symptom knowledge to reducing delay time to hospital arrival for stroke.

Focus Upon Implementing the GGOS Decadal Vision for Geohazards Monitoring

NASA Astrophysics Data System (ADS)

LaBrecque, John; Stangl, Gunter

2017-04-01

The Global Geodetic Observing System of the IAG identified present and future roles for Geodesy in the development and well being of the global society. The GGOS is focused upon the development of infrastructure, information, analysis, and educational systems to advance the International Global Reference Frame, the International Celestial Reference System, the International Height Reference System, atmospheric dynamics, sea level change and geohazards monitoring. The geohazards initiative is guided by an eleven nation working group initially focused upon the development and integration of regional multi-GNSS networks and analysis systems for earthquake and tsunami early warning. The opportunities and challenges being addressed by the Geohazards working group include regional network design, algorithm development and implementation, communications, funding, and international agreements on data access. This presentation will discuss in further detail these opportunities and challenges for the GGOS focus upon earthquake and tsunami early warning.

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.

PubMed

Ramgopal, Sriram; Thome-Souza, Sigride; Jackson, Michele; Kadish, Navah Ester; Sánchez Fernández, Iván; Klehm, Jacquelyn; Bosl, William; Reinsberger, Claus; Schachter, Steven; Loddenkemper, Tobias

2014-08-01

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy. Copyright © 2014. Published by Elsevier Inc.

High Fidelity and Multiscale Algorithms for Collisional-radiative and Nonequilibrium Plasmas (Briefing Charts)

DTIC Science & Technology

2014-07-01

of models for variable conditions: – Use implicit models to eliminate constraint of sequence of fast time scales: c, ve, – Price to pay: lack...collisions: – Elastic – Bragiinski terms – Inelastic – warning! Rates depend on both T and relative velocity – Multi-fluid CR model from...merge/split for particle management, efficient sampling, inelastic collisions … – Level grouping schemes of electronic states, for dynamical coarse

Development of a Global Agricultural Hotspot Detection and Early Warning System

NASA Astrophysics Data System (ADS)

Lemoine, G.; Rembold, F.; Urbano, F.; Csak, G.

2015-12-01

The number of web based platforms for crop monitoring has grown rapidly over the last years and anomaly maps and time profiles of remote sensing derived indicators can be accessed online thanks to a number of web based portals. However, while these systems make available a large amount of crop monitoring data to the agriculture and food security analysts, there is no global platform which provides agricultural production hotspot warning in a highly automatic and timely manner. Therefore a web based system providing timely warning evidence as maps and short narratives is currently under development by the Joint Research Centre. The system (called "HotSpot Detection System of Agriculture Production Anomalies", HSDS) will focus on water limited agricultural systems worldwide. The automatic analysis of relevant meteorological and vegetation indicators at selected administrative units (Gaul 1 level) will trigger warning messages for the areas where anomalous conditions are observed. The level of warning (ranging from "watch" to "alert") will depend on the nature and number of indicators for which an anomaly is detected. Information regarding the extent of the agricultural areas concerned by the anomaly and the progress of the agricultural season will complement the warning label. In addition, we are testing supplementary detailed information from other sources for the areas triggering a warning. These regard the automatic web-based and food security-tailored analysis of media (using the JRC Media Monitor semantic search engine) and the automatic detection of active crop area using Sentinel 1, upcoming Sentinel-2 and Landsat 8 imagery processed in Google Earth Engine. The basic processing will be fully automated and updated every 10 days exploiting low resolution rainfall estimates and satellite vegetation indices. Maps, trend graphs and statistics accompanied by short narratives edited by a team of crop monitoring experts, will be made available on the website on a monthly basis.

Operational wind shear detection and warning - The 'CLAWS' experience at Denver and future objectives

NASA Technical Reports Server (NTRS)

Mccarthy, John; Wilson, James W.; Hjelmfelt, Mark R.

1986-01-01

An operational wind shear detection and warning experiment was conducted at Denver's Stapleton International Airport in summer 1984. Based on meteorological interpretation of scope displays from a Doppler weather radar, warnings were transmitted to the air traffic control tower via voice radio. Analyses of results indicated real skill in daily microburst forecasts and very short-term (less than 5-min) warnings. Wind shift advisories with 15-30 min forecasts, permitted more efficient runway reconfigurations. Potential fuel savings were estimated at $875,000/yr at Stapleton. The philosophy of future development toward an automated, operational system is discussed.

Study on the early warning mechanism for the security of blast furnace hearths

NASA Astrophysics Data System (ADS)

Zhao, Hong-bo; Huo, Shou-feng; Cheng, Shu-sen

2013-04-01

The campaign life of blast furnace (BF) hearths has become the limiting factor for safety and high efficiency production of modern BFs. However, the early warning mechanism of hearth security has not been clear. In this article, based on heat transfer calculations, heat flux and erosion monitoring, the features of heat flux and erosion were analyzed and compared among different types of hearths. The primary detecting elements, mathematical models, evaluating standards, and warning methods were discussed. A novel early warning mechanism with the three-level quantificational standards was proposed for BF hearth security.

Evaluation of Intersection Collision Warning Systems in Minnesota

DOT National Transportation Integrated Search

2017-10-01

The Minnesota Department of Transportation (MnDOT) is investing significant resources in intersection collision warning systems (ICWS) based on early indications of effectiveness. However, the effectiveness is not well documented, and negative change...

Implementing Obstetric Early Warning Systems.

PubMed

Friedman, Alexander M; Campbell, Mary L; Kline, Carolyn R; Wiesner, Suzanne; D'Alton, Mary E; Shields, Laurence E

2018-04-01

Severe maternal morbidity and mortality are often preventable and obstetric early warning systems that alert care providers of potential impending critical illness may improve maternal safety. While literature on outcomes and test characteristics of maternal early warning systems is evolving, there is limited guidance on implementation. Given current interest in early warning systems and their potential role in care, the 2017 Society for Maternal-Fetal Medicine (SMFM) Annual Meeting dedicated a session to exploring early warning implementation across a wide range of hospital settings. This manuscript reports on key points from this session. While implementation experiences varied based on factors specific to individual sites, common themes relevant to all hospitals presenting were identified. Successful implementation of early warnings systems requires administrative and leadership support, dedication of resources, improved coordination between nurses, providers, and ancillary staff, optimization of information technology, effective education, evaluation of and change in hospital culture and practices, and support in provider decision-making. Evolving data on outcomes on early warning systems suggest that maternal risk may be reduced. To effectively reduce maternal, risk early warning systems that capture deterioration from a broad range of conditions may be required in addition to bundles tailored to specific conditions such as hemorrhage, thromboembolism, and hypertension.

Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework

PubMed Central

Torous, John; Thompson, Wesley

2016-01-01

Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations. PMID:27604265

Alcohol Warnings and Moderate Drinking Patterns among Italian University Students: An Exploratory Study

PubMed Central

Annunziata, Azzurra; Vecchio, Riccardo; Mariani, Angela

2017-01-01

The introduction of health warnings on labels to correct externalities associated with alcohol consumption is heavily debated and has been explored from different perspectives. The current paper aims to analyse the interest and attitudes of Italian university students regarding health warnings on alcoholic beverages and to verify the existence of segments that differ in terms of attitudes towards such warnings. Our results show that young consumers consider health warnings quite important, although the degree of perceived utility differs in relation to the type of warning. Cluster analysis shows the existence of three groups of young consumers with different degrees of attention and perceived utility of warnings on alcoholic beverages, but also in relation to drinking behaviour and awareness of social and health risks related to alcohol consumption. In brief, Italian young adults with moderate consumption behaviour view label warnings positively, while this attitude is weaker among younger adults and those with riskier consumption behaviours. Our findings, albeit limited and based on stated and not revealed data, support the need for appropriate tools to improve the availability of information among young adults on the risks of excessive alcohol consumption and increased awareness of the importance of moderate drinking. PMID:28629138

Including trait-based early warning signals helps predict population collapse

PubMed Central

Clements, Christopher F.; Ozgul, Arpat

2016-01-01

Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait information alongside traditional abundance-based early warning signals in a single metric of risk, our generalizable approach provides a powerful new way to assess what populations may be on the verge of collapse. PMID:27009968

Finite-Fault and Other New Capabilities of CISN ShakeAlert

NASA Astrophysics Data System (ADS)

Boese, M.; Felizardo, C.; Heaton, T. H.; Hudnut, K. W.; Hauksson, E.

2013-12-01

Over the past 6 years, scientists at Caltech, UC Berkeley, the Univ. of Southern California, the Univ. of Washington, the US Geological Survey, and ETH Zurich (Switzerland) have developed the 'ShakeAlert' earthquake early warning demonstration system for California and the Pacific Northwest. We have now started to transform this system into a stable end-to-end production system that will be integrated into the daily routine operations of the CISN and PNSN networks. To quickly determine the earthquake magnitude and location, ShakeAlert currently processes and interprets real-time data-streams from several hundred seismic stations within the California Integrated Seismic Network (CISN) and the Pacific Northwest Seismic Network (PNSN). Based on these parameters, the 'UserDisplay' software predicts and displays the arrival and intensity of shaking at a given user site. Real-time ShakeAlert feeds are currently being shared with around 160 individuals, companies, and emergency response organizations to gather feedback about the system performance, to educate potential users about EEW, and to identify needs and applications of EEW in a future operational warning system. To improve the performance during large earthquakes (M>6.5), we have started to develop, implement, and test a number of new algorithms for the ShakeAlert system: the 'FinDer' (Finite Fault Rupture Detector) algorithm provides real-time estimates of locations and extents of finite-fault ruptures from high-frequency seismic data. The 'GPSlip' algorithm estimates the fault slip along these ruptures using high-rate real-time GPS data. And, third, a new type of ground-motion prediction models derived from over 415,000 rupture simulations along active faults in southern California improves MMI intensity predictions for large earthquakes with consideration of finite-fault, rupture directivity, and basin response effects. FinDer and GPSlip are currently being real-time and offline tested in a separate internal ShakeAlert installation at Caltech. Real-time position and displacement time series from around 100 GPS sensors are obtained in JSON format from RTK/PPP(AR) solutions using the RTNet software at USGS Pasadena. However, we have also started to investigate the usage of onsite (in-receiver) processing using NetR9 with RTX and tracebuf2 output format. A number of changes to the ShakeAlert processing, xml message format, and the usage of this information in the UserDisplay software were necessary to handle the new finite-fault and slip information from the FinDer and GPSlip algorithms. In addition, we have developed a framework for end-to-end off-line testing with archived and simulated waveform data using the Earthworm tankplayer. Detailed background information about the algorithms, processing, and results from these test runs will be presented.

Integration of launch/impact discrimination algorithm with the UTAMS platform

NASA Astrophysics Data System (ADS)

Desai, Sachi; Morcos, Amir; Tenney, Stephen; Mays, Brian

2008-04-01

An acoustic array, integrated with an algorithm to discriminate potential Launch (LA) or Impact (IM) events, was augmented by employing the Launch Impact Discrimination (LID) algorithm for mortar events. We develop an added situational awareness capability to determine whether the localized event is a mortar launch or mortar impact at safe standoff distances. The algorithm utilizes a discrete wavelet transform to exploit higher harmonic components of various sub bands of the acoustic signature. Additional features are extracted via the frequency domain exploiting harmonic components generated by the nature of event, i.e. supersonic shrapnel components at impact. The further extrapolations of these features are employed with a neural network to provide a high level of confidence for discrimination and classification. The ability to discriminate between these events is of great interest on the battlefield. Providing more information and developing a common picture of situational awareness. Algorithms exploit the acoustic sensor array to provide detection and identification of IM/LA events at extended ranges. The integration of this algorithm with the acoustic sensor array for mortar detection provides an early warning detection system giving greater battlefield information for field commanders. This paper will describe the integration of the algorithm with a candidate sensor and resulting field tests.

Exploring the Role of Social Memory of Floods for Designing Flood Early Warning Operations

NASA Astrophysics Data System (ADS)

Girons Lopez, Marc; Di Baldassarre, Giuliano; Grabs, Thomas; Halldin, Sven; Seibert, Jan

2016-04-01

Early warning systems are an important tool for natural disaster mitigation practices, especially for flooding events. Warnings rely on near-future forecasts to provide time to take preventive actions before a flood occurs, thus reducing potential losses. However, on top of the technical capacities, successful warnings require an efficient coordination and communication among a range of different actors and stakeholders. The complexity of integrating the technical and social spheres of warning systems has, however, resulted in system designs neglecting a number of important aspects such as social awareness of floods thus leading to suboptimal results. A better understanding of the interactions and feedbacks among the different elements of early warning systems is therefore needed to improve their efficiency and therefore social resilience. When designing an early warning system two important decisions need to be made regarding (i) the hazard magnitude at and from which a warning should be issued and (ii) the degree of confidence required for issuing a warning. The first decision is usually taken based on the social vulnerability and climatic variability while the second one is related to the performance (i.e. accuracy) of the forecasting tools. Consequently, by estimating the vulnerability and the accuracy of the forecasts, these two variables can be optimized to minimize the costs and losses. Important parameters with a strong influence on the efficiency of warning systems such as social awareness are however not considered in their design. In this study we present a theoretical exploration of the impact of social awareness on the design of early warning systems. For this purpose we use a definition of social memory of flood events as a proxy for flood risk awareness and test its effect on the optimization of the warning system design variables. Understanding the impact of social awareness on warning system design is important to make more robust warnings that can better adapt to different social settings and more efficiently reduce vulnerability.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-02-01

In this paper a new detection scheme for Convective Initation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting Convective Initation with geostationary satellite data and uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one High Resolution Visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims for identifying the typical development of quickly developing convective cells in an early stage. The different criteria include timetrends of the 10.8 IR channel and IR channel differences as well as their timetrends. To provide the trend fields an optical flow based method is used, the Pyramidal Matching algorithm which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM and is verified for seven days which comprise different weather situations in Central Europe. Contrasted with the original early stage detection scheme of Cb-TRAM skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for synoptic conditions with upper cold air masses triggering convection.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-08-01

In this paper a new detection scheme for convective initiation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting CI with geostationary satellite data. It uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared (IR) criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one high-resolution visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims to identify the typical development of quickly developing convective cells in an early stage. The different criteria include time trends of the 10.8 IR channel, and IR channel differences, as well as their time trends. To provide the trend fields an optical-flow-based method is used: the pyramidal matching algorithm, which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM, and is verified for seven days which comprise different weather situations in central Europe. Contrasted with the original early-stage detection scheme of Cb-TRAM, skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for the synoptic class "cold air" masses.

Study of Water Pollution Early Warning Framework Based on Internet of Things

NASA Astrophysics Data System (ADS)

Chengfang, H.; Xiao, X.; Dingtao, S.; Bo, C.; Xiongfei, W.

2016-06-01

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

Offline Performance of the Filter Bank EEW Algorithm in the 2014 M6.0 South Napa Earthquake

NASA Astrophysics Data System (ADS)

Meier, M. A.; Heaton, T. H.; Clinton, J. F.

2014-12-01

Medium size events like the M6.0 South Napa earthquake are very challenging for EEW: the damage such events produce can be severe, but it is generally confined to relatively small zones around the epicenter and the shaking duration is short. This leaves a very short window for timely EEW alerts. Algorithms that wait for several stations to trigger before sending out EEW alerts are typically not fast enough for these kind of events because their blind zone (the zone where strong ground motions start before the warnings arrive) typically covers all or most of the area that experiences strong ground motions. At the same time, single station algorithms are often too unreliable to provide useful alerts. The filter bank EEW algorithm is a new algorithm that is designed to provide maximally accurate and precise earthquake parameter estimates with minimum data input, with the goal of producing reliable EEW alerts when only a very small number of stations have been reached by the p-wave. It combines the strengths of single station and network based algorithms in that it starts parameter estimates as soon as 0.5 seconds of data are available from the first station, but then perpetually incorporates additional data from the same or from any number of other stations. The algorithm analyzes the time dependent frequency content of real time waveforms with a filter bank. It then uses an extensive training data set to find earthquake records from the past that have had similar frequency content at a given time since the p-wave onset. The source parameters of the most similar events are used to parameterize a likelihood function for the source parameters of the ongoing event, which can then be maximized to find the most likely parameter estimates. Our preliminary results show that the filter bank EEW algorithm correctly estimated the magnitude of the South Napa earthquake to be ~M6 with only 1 second worth of data at the nearest station to the epicenter. This estimate is then confirmed when updates based on more data from stations at farther distances become available. Because these early estimates saturate at ~M6.5, however, the magnitude estimate might have had to be considered a minimum bound.

NOAA/West Coast and Alaska Tsunami Warning Center Pacific Ocean response criteria

USGS Publications Warehouse

Whitmore, P.; Benz, H.; Bolton, M.; Crawford, G.; Dengler, L.; Fryer, G.; Goltz, J.; Hansen, R.; Kryzanowski, K.; Malone, S.; Oppenheimer, D.; Petty, E.; Rogers, G.; Wilson, Jim

2008-01-01

New West Coast/Alaska Tsunami Warning Center (WCATWC) response criteria for earthquakes occurring in the Pacific basin are presented. Initial warning decisions are based on earthquake location, magnitude, depth, and - dependent on magnitude - either distance from source or precomputed threat estimates generated from tsunami models. The new criteria will help limit the geographical extent of warnings and advisories to threatened regions, and complement the new operational tsunami product suite. Changes to the previous criteria include: adding hypocentral depth dependence, reducing geographical warning extent for the lower magnitude ranges, setting special criteria for areas not well-connected to the open ocean, basing warning extent on pre-computed threat levels versus tsunami travel time for very large events, including the new advisory product, using the advisory product for far-offshore events in the lower magnitude ranges, and specifying distances from the coast for on-shore events which may be tsunamigenic. This report sets a baseline for response criteria used by the WCATWC considering its processing and observational data capabilities as well as its organizational requirements. Criteria are set for tsunamis generated by earthquakes, which are by far the main cause of tsunami generation (either directly through sea floor displacement or indirectly by triggering of slumps). As further research and development provides better tsunami source definition, observational data streams, and improved analysis tools, the criteria will continue to adjust. Future lines of research and development capable of providing operational tsunami warning centers with better tools are discussed.

High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William; Mach, Douglas M.

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.

Efficient near-real-time monitoring of 3D surface displacements in complex landslide scenarios

NASA Astrophysics Data System (ADS)

Allasia, Paolo; Manconi, Andrea; Giordan, Daniele; Baldo, Marco; Lollino, Giorgio

2013-04-01

Ground deformation measurements play a key role in monitoring activities of landslides. A wide spectrum of instruments and methods is nowadays available, going from in-situ to remote sensing approaches. In emergency scenarios, monitoring is often based on automated instruments capable to achieve accurate measurements, possibly with a very high temporal resolution, in order to achieve the best information about the evolution of the landslide in near-real-time, aiming at early warning purposes. However, the available tools for a rapid and efficient exploitation, understanding and interpretation of the retrieved measurements is still a challenge. This issue is particularly relevant in contexts where monitoring is fundamental to support early warning systems aimed at ensuring safety to people and/or infrastructures. Furthermore, in many cases the results obtained might be of difficult reading and divulgation, especially when people of different backgrounds are involved (e.g. scientists, authorities, civil protection operators, decision makers, etc.). In this work, we extend the concept of automatic and near real time from the acquisition of measurements to the data processing and divulgation, in order to achieve an efficient monitoring of surface displacements in landslide scenarios. We developed an algorithm that allows to go automatically and in near-real-time from the acquisition of 3D displacements on a landslide area to the efficient divulgation of the monitoring results via WEB. This set of straightforward procedures is called ADVICE (ADVanced dIsplaCement monitoring system for Early warning), and has been already successfully applied in several emergency scenarios. The algorithm includes: (i) data acquisition and transfer protocols; (ii) data collection, filtering, and validation; (iii) data analysis and restitution through a set of dedicated software, such as ©3DA [1]; (iv) recognition of displacement/velocity threshold and early warning (v) short term prediction of the temporal evolution of the landslide, e.g. through the failure forecast method; (vi) publication of the results on a dedicated webpage. Here we show the results gained in the area of Montaguto (southern Italy, ca. 100 km northeast from Naples), where a large-scale earthflow reached the bottom of the valley and severely damaged the SP90 provincial road, as well as the national railroad [2]. We discuss how the use of ADVICE has speed-up and facilitated the understanding of the landslide evolution, the communication of the monitoring results to the partners, and consequently the decision-making process in a critical landslide scenario. [1] Manconi, A., P. Allasia, D. Giordan, M. Baldo, G. Lollino and A. Corazza, Near-real-time 3D surface deformation model obtained via RTS measurements. In Procedings of World Landslide Forum 2, October 3-9, 2011, Rome, Italy. [2] Giordan, D., P. Allasia, A. Manconi, M. Baldo, G. Lollino, M. Santangelo, M. Cardinali and F. Guzzetti, "Morphological evolution of a large earthflow: the Montaguto landslide southern Italy", Geomorphology, in press.

Exposure to a mnemonic interferes with recall of suicide warning signs in a community-based suicide prevention program.

PubMed

Bryan, Craig J; Steiner-Pappalardo, Nicole; Rudd, M David

2009-04-01

The incremental impact of adding a mnemonic to remember suicide warning signs to the Air Force Suicide Prevention Program (AFSPP) community awareness briefing was investigated with a sample of young, junior-enlisted airmen. Participants in the standard briefing significantly increased their ability to list suicide warning signs and improved consistency with an expert consensus list, whereas participants in the standard briefing plus mnemonic demonstrated no learning. Both groups demonstrated positive changes in beliefs about suicide. Results suggest that inclusion of the mnemonic in the AFSPP briefing interfered with participants' ability to learn suicide warning signs, and that increased confidence in the perceived ability to recognize suicide risk is not related to actual ability to accurately recall warning signs.

Data Delivery Latency Improvements And First Steps Towards The Distributed Computing Of The Caltech/USGS Southern California Seismic Network Earthquake Early Warning System

NASA Astrophysics Data System (ADS)

Stubailo, I.; Watkins, M.; Devora, A.; Bhadha, R. J.; Hauksson, E.; Thomas, V. I.

2016-12-01

The USGS/Caltech Southern California Seismic Network (SCSN) is a modern digital ground motion seismic network. It develops and maintains Earthquake Early Warning (EEW) data collection and delivery systems in southern California as well as real-time EEW algorithms. Recently, Behr et al., SRL, 2016 analyzed data from several regional seismic networks deployed around the globe. They showed that the SCSN was the network with the smallest data communication delays or latency. Since then, we have reduced further the telemetry delays for many of the 330 current sites. The latency has been reduced on average from 2-6 sec to 0.4 seconds by tuning the datalogger parameters and/or deploying software upgrades. Recognizing the latency data as one of the crucial parameters in EEW, we have started archiving the per-packet latencies in mseed format for all the participating sites in a similar way it is traditionally done for the seismic waveform data. The archived latency values enable us to understand and document long-term changes in performance of the telemetry links. We can also retroactively investigate how latent the waveform data were during a specific event or during a specific time period. In addition the near-real time latency values are useful for monitoring and displaying the real-time station latency, in particular to compare different telemetry technologies. A future step to reduce the latency is to deploy the algorithms on the dataloggers at the seismic stations and transmit either the final solutions or intermediate parameters to a central processing center. To implement this approach, we are developing a stand-alone version of the OnSite algorithm to run on the dataloggers in the field. This will increase the resiliency of the SCSN to potential telemetry restrictions in the immediate aftermath of a large earthquake, either by allowing local alarming by the single station, or permitting transmission of lightweight parametric information rather than continuous waveform data to the central processing facility. State-of-the-art development of Internet of Things (IoT) tools and platforms, which can be used to distribute and maintain software on a large number of remote devices are making this approach to earthquake early warning more feasible.

Clinically relevant hypoglycemia prediction metrics for event mitigation.

PubMed

Harvey, Rebecca A; Dassau, Eyal; Zisser, Howard C; Bevier, Wendy; Seborg, Dale E; Jovanovič, Lois; Doyle, Francis J

2012-08-01

The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment. Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia. The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead. The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Petersen, W.; Buechler, D. E.; Krehbiel, P. R.; Gatlin, P.; Zubrick, S.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)

Validation of the Intensive Care Unit Early Warning Dashboard: Quality Improvement Utilizing a Retrospective Case-Control Evaluation.

PubMed

Kavanaugh, Michael J; So, Joanne D; Park, Peter J; Davis, Konrad L

2017-02-01

Risk stratification with the Modified Early Warning System (MEWS) or electronic cardiac arrest trigger (eCART) has been utilized with ward patients to preemptively identify high-risk patients who might benefit from enhanced monitoring, including early intensive care unit (ICU) transfer. In-hospital mortality from cardiac arrest is ∼80%, making preventative interventions an important focus area. ICUs have lower patient to nurse ratios than wards, resulting in less emphasis on the development of ICU early warning systems. Our institution developed an early warning dashboard (EWD) identifying patients who may benefit from earlier interventions. Using the adverse outcomes of cardiac arrest, ICU mortality, and ICU readmissions, a retrospective case-control study was performed using three demographic items (age, diabetes, and morbid obesity) and 24 EWD measured items, including vital signs, laboratory values, ventilator information, and other clinical information, to validate the EWD. Ten statistically significant areas were identified for cardiac arrest and 13 for ICU death. Identified items included heart rate, dialysis, leukocytosis, and lactate. The ICU readmission outcome was compared to controls from both ICU patients and ward patients, and statistical significance was identified for respiratory rate >30. With several statistically significant data elements, the EWD parameters have been incorporated into advanced clinical decision algorithms to identify at-risk ICU patients. Earlier identification and treatment of organ failure in the ICU improve outcomes and the EWD can serve as a safety measure for both at-risk in-house patients and also extend critical care expertise through telemedicine to smaller hospitals.

Landquake dynamics inferred from seismic source inversion: Greenland and Sichuan events of 2017

NASA Astrophysics Data System (ADS)

Chao, W. A.

2017-12-01

In June 2017 two catastrophic landquake events occurred in Greenland and Sichuan. The Greenland event leads to tsunami hazard in the small town of Nuugaarsiaq. A landquake in Sichuan hit the town, which resulted in over 100 death. Both two events generated the strong seismic signals recorded by the real-time global seismic network. I adopt an inversion algorithm to derive the landquake force time history (LFH) using the long-period waveforms, and the landslide volume ( 76 million m3) can be rapidly estimated, facilitating the tsunami-wave modeling for early warning purpose. Based on an integrated approach involving tsunami forward simulation and seismic waveform inversion, this study has significant implications to issuing actionable warnings before hazardous tsunami waves strike populated areas. Two single-forces (SFs) mechanism (two block model) yields the best explanation for Sichuan event, which demonstrates that secondary event (seismic inferred volume: 8.2 million m3) may be mobilized by collapse-mass hitting from initial rock avalanches ( 5.8 million m3), likely causing a catastrophic disaster. The later source with a force magnitude of 0.9967×1011 N occurred 70 seconds after first mass-movement occurrence. In contrast, first event has the smaller force magnitude of 0.8116×1011 N. In conclusion, seismically inferred physical parameters will substantially contribute to improving our understanding of landquake source mechanisms and mitigating similar hazards in other parts of the world.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.

PubMed

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-31

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

NASA Astrophysics Data System (ADS)

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-01

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Putting Man in the Machine: Exploiting Expertise to Enhance Multiobjective Design of Water Supply Monitoring Network

NASA Astrophysics Data System (ADS)

Bode, F.; Nowak, W.; Reed, P. M.; Reuschen, S.

2016-12-01

Drinking-water well catchments need effective early-warning monitoring networks. Groundwater water supply wells in complex urban environments are in close proximity to a myriad of potential industrial pollutant sources that could irreversibly damage their source aquifers. These urban environments pose fiscal and physical challenges to designing monitoring networks. Ideal early-warning monitoring networks would satisfy three objectives: to detect (1) all potential contaminations within the catchment (2) as early as possible before they reach the pumping wells, (3) while minimizing costs. Obviously, the ideal case is nonexistent, so we search for tradeoffs using multiobjective optimization. The challenge of this optimization problem is the high number of potential monitoring-well positions (the search space) and the non-linearity of the underlying groundwater flow-and-transport problem. This study evaluates (1) different ways to effectively restrict the search space in an efficient way, with and without expert knowledge, (2) different methods to represent the search space during the optimization and (3) the influence of incremental increases in uncertainty in the system. Conductivity, regional flow direction and potential source locations are explored as key uncertainties. We show the need and the benefit of our methods by comparing optimized monitoring networks for different uncertainty levels with networks that seek to effectively exploit expert knowledge. The study's main contributions are the different approaches restricting and representing the search space. The restriction algorithms are based on a point-wise comparison of decision elements of the search space. The representation of the search space can be either binary or continuous. For both cases, the search space must be adjusted properly. Our results show the benefits and drawbacks of binary versus continuous search space representations and the high potential of automated search space restriction algorithms for high-dimensional, highly non-linear optimization problems.

Can consumers self-select for appropriate use of an over-the-counter statin? The Self Evaluation of Lovastatin to Enhance Cholesterol Treatment Study.

PubMed

Brass, Eric P; Vassil, Theodore; Replogle, Amy; Hwang, Peggy; Rusche, Steven; Shiffman, Saul; Levine, Jeffrey G

2008-05-15

Access to over-the-counter (OTC) statins has the potential to improve public health by reducing cardiovascular events. The Self Evaluation of Lovastatin to Enhance Cholesterol Treatment (SELECT) Study was designed to assess consumers' ability to self-select for treatment with lovastatin in an unsupervised setting. Subjects examined proposed OTC lovastatin cartons with labels that detailed an algorithm for self-selection based on age, lipid profile, and cardiovascular risk factors. Subjects viewed a carton with either a low-density lipoprotein cholesterol-based self-selection algorithm or one based on total cholesterol. Labels also contained warnings against use based on health conditions that might increase the risk of adverse events. Subjects were asked if the drug was appropriate for their use (self-assessment) and whether they would like to purchase the drug (purchase decision). A total of 1,326 consumers provided self-assessment decisions. After viewing the low-density lipoprotein cholesterol-based label, 82%, 36%, and 82% of those who self-assessed that the drug was appropriate for their use were correct with respect to the age, lipid, and risk-factor criteria, respectively. Corresponding numbers for the total cholesterol algorithm were 85%, 50% and 75%. Almost 90% of women aged <55 years who evaluated the drug indicated the drug was not right for them, and women in this age group made up only 9% of the total group of subjects who believed the drug was appropriate for their use. The label was also effective in discouraging use by women who were or may become pregnant, consumers with liver disease, and those with potential drug interactions. In conclusion, SELECT showed that consumers could use an OTC drug label in an unsupervised setting to appropriately self-select for self-management of their cholesterol with lovastatin.

Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three

NASA Astrophysics Data System (ADS)

Steinhardt, Charles L.; Jermyn, Adam S.

2018-02-01

Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.

Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

NASA Astrophysics Data System (ADS)

Akhoondzadeh, Mehdi

2015-07-01

Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

Early warning method of Glacial Lake Outburst Floods based on temperature and rainfall

NASA Astrophysics Data System (ADS)

Liu, Jingjing; Su, Pengcheng; Cheng, Zunlan

2017-04-01

Glacial lake outburst floods (GLOFs) are serious disasters in glacial areas. At present, glaciers are retreating while glacial lake area and the outburst risk increases due to the global warming. Therefore, the research of early warning method of GLOFs is important to prevent and reduce the disasters. This paper provides an early warning method using the temperature and rainfall as indices. The daily growth rate of positive antecedent accumulative temperature and the antecedent thirty days accumulative precipitation are calculated for 21 events of GLOF before 2010, based on data from the 21 meteorological stations nearby. The result shows that all the events are above the curve, TV = -0.0193RDC + 3.0018, which can be taken as the early warning threshold curve. This has been verified by the GLOF events in the Ranzeaco glacial lake on 2013-07-05.

Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak

PubMed Central

Jack, Mhairi; Futro, Agnieszka; Talbot, Darren; Zhu, Qiming; Barclay, David; Baxter, Emma M.

2018-01-01

Tail biting is a major welfare and economic problem for indoor pig producers worldwide. Low tail posture is an early warning sign which could reduce tail biting unpredictability. Taking a precision livestock farming approach, we used Time-of-flight 3D cameras, processing data with machine vision algorithms, to automate the measurement of pig tail posture. Validation of the 3D algorithm found an accuracy of 73.9% at detecting low vs. not low tails (Sensitivity 88.4%, Specificity 66.8%). Twenty-three groups of 29 pigs per group were reared with intact (not docked) tails under typical commercial conditions over 8 batches. 15 groups had tail biting outbreaks, following which enrichment was added to pens and biters and/or victims were removed and treated. 3D data from outbreak groups showed the proportion of low tail detections increased pre-outbreak and declined post-outbreak. Pre-outbreak, the increase in low tails occurred at an increasing rate over time, and the proportion of low tails was higher one week pre-outbreak (-1) than 2 weeks pre-outbreak (-2). Within each batch, an outbreak and a non-outbreak control group were identified. Outbreak groups had more 3D low tail detections in weeks -1, +1 and +2 than their matched controls. Comparing 3D tail posture and tail injury scoring data, a greater proportion of low tails was associated with more injured pigs. Low tails might indicate more than just tail biting as tail posture varied between groups and over time and the proportion of low tails increased when pigs were moved to a new pen. Our findings demonstrate the potential for a 3D machine vision system to automate tail posture detection and provide early warning of tail biting on farm. PMID:29617403

ElarmS Earthquake Early Warning System 2016 Performance and New Research

NASA Astrophysics Data System (ADS)

Chung, A. I.; Allen, R. M.; Hellweg, M.; Henson, I. H.; Neuhauser, D. S.

2016-12-01

The ElarmS earthquake early warning system has been detecting earthquakes throughout California since 2007. It is one of the algorithms that contributes to the West Coast ShakeAlert, a prototype earthquake early warning system being developed for the US West Coast. ElarmS is also running in the Pacific Northwest, and in Israel, Chile, Turkey, and Peru in test mode. We summarize the performance of the ElarmS system over the past year and review some of the more problematic events that the system has encountered. During the first half of 2016 (2016-01-01 through 2016-07-21), ElarmS successfully alerted on all events with ANSS catalog magnitudes M>3 in the Los Angeles area. The mean alert time for these 9 events was just 4.84 seconds. In the San Francisco Bay Area, ElarmS detected 26 events with ANSS catalog magnitudes M>3. The alert times for these events is 9.12 seconds. The alert times are longer in the Bay Area than in the Los Angeles area due to the sparser network of stations in the Bay Area. 7 Bay Area events were not detected by ElarmS. These events occurred in areas where there is less dense station coverage. In addition, ElarmS sent alerts for 13 of the 16 moderately-sized (ANSS catalog magnitudes M>4) events that occurred throughout the state of California. One of those missed events was a M4.5 that occurred far offshore in the northernmost part of the state. The other two missed events occurred inland in regions with sparse station coverage. Over the past year, we have worked towards the implementation of a new filterbank teleseismic filter algorithm, which we will discuss. Other than teleseismic events, a significant cause of false alerts and severely mislocated events is spurious triggers being associated with triggers from a real earthquake. Here, we address new approaches to filtering out problematic triggers.

Severe wind gust thresholds for Meteoalarm derived from uniform return periods in ECA&D

NASA Astrophysics Data System (ADS)

Stepek, A.; Wijnant, I. L.; van der Schrier, G.; van den Besselaar, E. J. M.; Klein Tank, A. M. G.

2012-06-01

In this study we present an alternative wind gust warning guideline for Meteoalarm, the severe weather warning website for Europe. There are unrealistically large differences in levels and issuing frequencies of all warning levels currently in use between neighbouring Meteoalarm countries. This study provides a guide for the Meteoalarm community to review their wind gust warning thresholds. A more uniform warning system is achieved by using one pan-European return period per warning level. The associated return values will be different throughout Europe because they depend on local climate conditions, but they will not change abruptly at country borders as is currently the case for the thresholds. As return values are a measure of the possible danger of an event and its impact on society, they form an ideal basis for a warning system. Validated wind gust measurements from the European Climate Assessment and Dataset (ECA&D, http://www.ecad.eu) were used to calculate return values of the annual maximum wind gust. The current thresholds are compared with return values for 3 different return periods: 10 times a year return periods for yellow warnings, 2 yr periods for orange and 5 yr periods for red warnings. So far 10 countries provide wind gust data to ECA&D. Due to the ECA&D completeness requirements and the fact that some countries provided too few stations to be representative for that country, medians of the return values of annual maximum wind gust could be calculated for 6 of the 10 countries. Alternative guideline thresholds are presented for Norway, Ireland, The Netherlands, Germany, the Czech Republic and Spain and the need to distinguish between coastal, inland and mountainous regions is demonstrated. The new thresholds based on uniform return periods differ significantly from the current ones, particularly for coastal and mountainous areas. We are aware of other, sometimes binding factors (e.g. laws) that prevent participating counties from implementing this climatology based warning system.

Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

NASA Astrophysics Data System (ADS)

Wang, Baocheng; Qu, Dandan; Tian, Qing; Pang, Liping

2018-05-01

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system (OFPS) is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform (MT) is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation (BP) neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Earthquake Early Warning Management based on Client-Server using Primary Wave data from Vibrating Sensor

NASA Astrophysics Data System (ADS)

Laumal, F. E.; Nope, K. B. N.; Peli, Y. S.

2018-01-01

Early warning is a warning mechanism before an actual incident occurs, can be implemented on natural events such as tsunamis or earthquakes. Earthquakes are classified in tectonic and volcanic types depend on the source and nature. The tremor in the form of energy propagates in all directions as Primary and Secondary waves. Primary wave as initial earthquake vibrations propagates longitudinally, while the secondary wave propagates like as a sinusoidal wave after Primary, destructive and as a real earthquake. To process the primary vibration data captured by the earthquake sensor, a network management required client computer to receives primary data from sensors, authenticate and forward to a server computer to set up an early warning system. With the water propagation concept, a method of early warning system has been determined in which some sensors are located on the same line, sending initial vibrations as primary data on the same scale and the server recommended to the alarm sound as an early warning.

Gender differences in teacher-student interactions in science classrooms

NASA Astrophysics Data System (ADS)

Jones, M. Gail; Wheatley, Jack

1990-12-01

Thirty physical science and 30 chemistry classes, which contained a total of 1332 students, were observed using the Brophy-Good Teacher-Child Dyadic Interaction System. Classroom interactions were examined for gender differences that may contribute to the underrepresentation of women in physics and engineering courses and subsequent careers. The Brophy-Good coding process allows for examination of patterns of interactions for individuals and groups of pupils. An analysis of variance of the data yielded a significant main effect for teacher praise, call outs, procedural questions, and behavioral warnings based on the sex of the student and a significant teacher-sex main effect for direct questions. Significant two-way interactions were found for the behavioral warning variable for teacher sex and subject by student sex. Female teachers warned male students significantly more than female students. Male teachers warned both genders with similar frequency. Male students also received significantly more behavioral warnings in physical science classes than female students. In chemistry classes, both male and female students received approximately the same number of behavioral warnings.

The Earthquake Early Warning System In Southern Italy: Performance Tests And Next Developments

NASA Astrophysics Data System (ADS)

Zollo, A.; Elia, L.; Martino, C.; Colombelli, S.; Emolo, A.; Festa, G.; Iannaccone, G.

2011-12-01

PRESTo (PRobabilistic and Evolutionary early warning SysTem) is the software platform for Earthquake Early Warning (EEW) in Southern Italy, that integrates recent algorithms for real-time earthquake location, magnitude estimation and damage assessment, into a highly configurable and easily portable package. The system is under active experimentation based on the Irpinia Seismic Network (ISNet). PRESTo processes the live streams of 3C acceleration data for P-wave arrival detection and, while an event is occurring, promptly performs event detection and provides location, magnitude estimations and peak ground shaking predictions at target sites. The earthquake location is obtained by an evolutionary, real-time probabilistic approach based on an equal differential time formulation. At each time step, it uses information from both triggered and not-yet-triggered stations. Magnitude estimation exploits an empirical relationship that correlates it to the filtered Peak Displacement (Pd), measured over the first 2-4 s of P-signal. Peak ground-motion parameters at any distance can be finally estimated by ground motion prediction equations. Alarm messages containing the updated estimates of these parameters can thus reach target sites before the destructive waves, enabling automatic safety procedures. Using the real-time data streaming from the ISNet network, PRESTo has produced a bulletin for about a hundred low-magnitude events occurred during last two years. Meanwhile, the performances of the EEW system were assessed off-line playing-back the records for moderate and large events from Italy, Spain and Japan and synthetic waveforms for large historical events in Italy. These tests have shown that, when a dense seismic network is deployed in the fault area, PRESTo produces reliable estimates of earthquake location and size within 5-6 s from the event origin time (To). Estimates are provided as probability density functions whose uncertainty typically decreases with time, obtaining a stable solution within 10 s from To. The regional approach was recently integrated with a threshold-based early warning method for the definition of alert levels and the estimation of the Potential Damaged Zone (PDZ) in which the highest intensity levels are expected. The dominant period Tau_c and the peak displacement (Pd) are simultaneously measured in a 3s window after the first P-arrival time. Pd and Tau_c are then compared with threshold values, previously established through an empirical regression analysis, that define a decisional table with four alert levels. According to the real-time measured values of Pd and tau_c, each station provides a local alert level that can be used to warn distant sites and to define the extent of the PDZ. The integrated system was validated off-line for the M6.3, 2009 Central Italy earthquake and ten large Japanese events, due to the low-magnitude events currently occurring in Irpinia. The results confirmed the feasibility and the robustness of such an approach, providing reliable predictions of the earthquake damaging effects, that is a relevant information for the efficient planning of the rescue operations in the immediate post-event emergency phase.

An improvement of the Earthworm Based Earthquake Alarm Reporting system in Taiwan

NASA Astrophysics Data System (ADS)

Chen, D. Y.; Hsiao, N. C.; Yih-Min, W.

2017-12-01

The Central Weather Bureau of Taiwan (CWB) has operated the Earthworm Based Earthquake Alarm Reporting (eBEAR) system for the purpose of earthquake early warning (EEW). The system has been used to report EEW messages to the general public since 2016 through text message from the mobile phones and the television programs. The system for inland earthquakes is able to provide accurate and fast warnings. The average epicenter error is about 5 km and the processing time is about 15 seconds. The epicenter error is defined as the distance between the epicenter estimated by the EEW system and the epicenter estimated by man. The processing time is defined as the time difference between the time earthquakes occurred and the time the system issued warning. The CWB seismic network consist about 200 seismic stations. In some area of Taiwan the distance between each seismic station is about 10 km. It means that when an earthquake occurred the seismic P wave is able to propagate through 6 stations, which is the minimum number of required stations in the EEW system, within 20 km. If the latency of data transmitting is about 1 sec, the P-wave velocity is about 6 km per sec and we take 3-sec length time window to estimate earthquake magnitude, then the processing should be around 8 sec. In fact, however, the average processing time is larger than this figure. Because some outliers of P-wave onset picks may exist in the beginning of the earthquake occurrence, the Geiger's method we used in the EEW system for earthquake location is not stable. It usually takes more time to wait for enough number of good picks. In this study we used grid search method to improve the estimations of earthquake location. The MAXEL algorithm (Sheen et al., 2015, 2016) was tested in the EEW system by simulating historical earthquakes occurred in Taiwan. The results show the processing time can be reduced and the location accuracy is acceptable for EEW purpose.

Detecting anomalies in astronomical signals using machine learning algorithms embedded in an FPGA

NASA Astrophysics Data System (ADS)

Saez, Alejandro F.; Herrera, Daniel E.

2016-07-01

Taking a large interferometer for radio astronomy, such as the ALMA1 telescope, where the amount of stations (50 in the case of ALMA's main array, which can extend to 64 antennas) produces an enormous amount of data in a short period of time - visibilities can be produced every 16msec or total power information every 1msec (this means up to 2016 baselines). With the aforementioned into account it is becoming more difficult to detect problems in the signal produced by each antenna in a timely manner (one antenna produces 4 x 2GHz spectral windows x 2 polarizations, which means a 16 GHz bandwidth signal which is later digitized using 3-bits samplers). This work will present an approach based on machine learning algorithms for detecting problems in the already digitized signal produced by the active antennas (the set of antennas which is being used in an observation). The aim of this work is to detect unsuitable, or totally corrupted, signals. In addition, this development also provides an almost real time warning which finally helps stop and investigate the problem in order to avoid collecting useless information.

Towards cross-lingual alerting for bursty epidemic events.

PubMed

Collier, Nigel

2011-10-06

Online news reports are increasingly becoming a source for event-based early warning systems that detect natural disasters. Harnessing the massive volume of information available from multilingual newswire presents as many challanges as opportunities due to the patterns of reporting complex spatio-temporal events. In this article we study the problem of utilising correlated event reports across languages. We track the evolution of 16 disease outbreaks using 5 temporal aberration detection algorithms on text-mined events classified according to disease and outbreak country. Using ProMED reports as a silver standard, comparative analysis of news data for 13 languages over a 129 day trial period showed improved sensitivity, F1 and timeliness across most models using cross-lingual events. We report a detailed case study analysis for Cholera in Angola 2010 which highlights the challenges faced in correlating news events with the silver standard. The results show that automated health surveillance using multilingual text mining has the potential to turn low value news into high value alerts if informed choices are used to govern the selection of models and data sources. An implementation of the C2 alerting algorithm using multilingual news is available at the BioCaster portal http://born.nii.ac.jp/?page=globalroundup.

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits.

PubMed

Gámez Serna, Citlalli; Ruichek, Yassine

2017-06-14

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle's speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, 'curve analysis extraction' and 'speed limits database creation' are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.

Impact of the FDA warning of potential ceftriaxone and calcium interactions on drug use policy in clinical practice.

PubMed

Esterly, John S; Steadman, Emily; Scheetz, Marc H

2011-06-01

In September 2007, the FDA issued an alert recommending that ceftriaxone and calcium-containing solutions should not be administered to any patient within 48 h of each other. Due to the widespread use of ceftriaxone, significant concern was expressed by the greater healthcare community about the warning, which the FDA eventually retracted in April of 2009. We sought to quantify the impact of the warning on healthcare institutions. A survey was administered to the membership of the Society of Infectious Diseases Pharmacists to quantify perceived changes in ceftriaxone use among healthcare institutions across the United States. A survey of Infectious Diseases experts was conducted. Participants were queried for hospital policies/drug use statistics during two times: immediately after the FDA warning and approximately 13 months post warning (preceding the FDA retraction). Related changes in formulary, drug-use policy, and the number of employee hours that were devoted to addressing the FDA warning were assessed. Ninety-four surveys representing 94 hospital systems were included in the analysis. Approximately half (n = 49, 52%) of respondent institutions enacted at least one drug-use policy change based on the warning; one institution removed ceftriaxone from a clinical protocol. Institutions' final interpretations of the warning differed slightly from initial understanding of the warning, and there was an overall minor decrease in the perceived use of ceftriaxone. The majority of those surveyed (n = 70, 74%) estimated that their respective institutions devoted between 1 and 49 employee hours to address the warning. Hospitals with ID pharmacists had minimal changes to ceftriaxone use after the 2007 FDA warning. Specialized pharmacists may be uniquely situated to help hospitals interpret global recommendations locally.

Early Warning System for reducing disaster risk: the technological platform DEWETRA for the Republic of Serbia

NASA Astrophysics Data System (ADS)

Massabo, Marco; Molini, Luca; Kostic, Bojan; Campanella, Paolo; Stevanovic, Slavimir

2015-04-01

Disaster risk reduction has long been recognized for its role in mitigating the negative environmental, social and economic impacts of natural hazards. Flood Early Warning System is a disaster risk reduction measure based on the capacities of institutions to observe and predict extreme hydro-meteorological events and to disseminate timely and meaningful warning information; it is furthermore based on the capacities of individuals, communities and organizations to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss. An operational definition of an Early Warning System has been suggested by ISDR - UN Office for DRR [15 January 2009]: "EWS is the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss.". ISDR continues by commenting that a people-centered early warning system necessarily comprises four key elements: 1-knowledge of the risks; 2-monitoring, analysis and forecasting of the hazards; 3-communication or dissemination of alerts and warnings; and 4- local capabilities to respond to the warnings received." The technological platform DEWETRA supports the strengthening of the first three key elements of EWS suggested by ISDR definition, hence to improve the capacities to build real-time risk scenarios and to inform and warn the population in advance The technological platform DEWETRA has been implemented for the Republic of Serbia. DEWETRA is a real time-integrate system that supports decision makers for risk forecasting and monitoring and for distributing warnings to end-user and to the general public. The system is based on the rapid availability of different data that helps to establish up-to-date and reliable risk scenarios. The integration of all relevant data for risk management significantly increases the value of available information and the level of knowledge of forecasters and disaster managers. Different data, forecast and monitoring products, which are generated by different national and international institution and organizations, can be visualized and processed in real-time within the platform. DEWETRA is a web application ensuring the capillary distribution of information among institutions. The system is used as an infrastructure for exchanging and sharing data, procedures, models and expertise among the Sector of Emergency Management (SEM), the Republic Hydro-Meteorological Service of Serbia (RHMSS) and the Serbian Public Water Companies (PWCs): Serbia Waters, Vojvodina Waters and Belgrade Waters.

Evacuation transportation management : task five : operational concept.

DOT National Transportation Integrated Search

2009-06-26

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Evacuation transportation management. Task five, Operational concept

DOT National Transportation Integrated Search

2006-01-01

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Early warning reporting categories analysis of recall and complaints data.

DOT National Transportation Integrated Search

2001-12-31

This analysis was performed to assist the National Highway Traffic Safety Administration (NHTSA) in identifying components and systems to be included in early warning reporting (EWR) categories that would be based upon historical safety-related recal...

A probabilistic approach of the Flash Flood Early Warning System (FF-EWS) in Catalonia based on radar ensemble generation

NASA Astrophysics Data System (ADS)

Velasco, David; Sempere-Torres, Daniel; Corral, Carles; Llort, Xavier; Velasco, Enrique

2010-05-01

Early Warning Systems (EWS) are commonly identified as the most efficient tools in order to improve the preparedness and risk management against heavy rains and Flash Floods (FF) with the objective of reducing economical losses and human casualties. In particular, flash floods affecting torrential Mediterranean catchments are a key element to be incorporated within operational EWSs. The characteristic high spatial and temporal variability of the storms requires high-resolution data and methods to monitor/forecast the evolution of rainfall and its hydrological impact in small and medium torrential basins. A first version of an operational FF-EWS has been implemented in Catalonia (NE Spain) under the name of EHIMI system (Integrated Tool for Hydrometeorological Forecasting) with the support of the Catalan Water Agency (ACA) and the Meteorological Service of Catalonia (SMC). Flash flood warnings are issued based on radar-rainfall estimates. Rainfall estimation is performed on radar observations with high spatial and temporal resolution (1km2 and 10 minutes) in order to adapt the warning scale to the 1-km grid of the EWS. The method is based on comparing observed accumulated rainfall against rainfall thresholds provided by the regional Intensity-Duration-Frequency (IDF) curves. The so-called "aggregated rainfall warning" at every river cell is obtained as the spatially averaged rainfall over its associated upstream draining area. Regarding the time aggregation of rainfall, the critical duration is thought to be an accumulation period similar to the concentration time of each cachtment. The warning is issued once the forecasted rainfall accumulation exceeds the rainfall thresholds mentioned above, which are associated to certain probability of occurrence. Finally, the hazard warning is provided and shown to the decision-maker in terms of exceeded return periods at every river cell covering the whole area of Catalonia. The objective of the present work includes the probabilistic component to the FF-EWS. As a first step, we have incorporated the uncertainty in rainfall estimates and forecasts based on an ensemble of equiprobable rainfall scenarios. The presented study has focused on a number of rainfall events and the performance of the FF-EWS evaluated in terms of its ability to produce probabilistic hazard warnings for decision-making support.

Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations.

PubMed

Wu, Yina; Abdel-Aty, Mohamed; Ding, Yaoxian; Jia, Bin; Shi, Qi; Yan, Xuedong

2018-07-01

The Type II dilemma zone describes the road segment to a signalized intersection where drivers have difficulties to decide either stop or go at the onset of yellow signal. Such phenomenon can result in an increased crash risk at signalized intersections. Different types of warning systems have been proposed to help drivers make decisions. Although the warning systems help to improve drivers' behavior, they also have several disadvantages such as increasing rear-end crashes or red-light running (RLR) violations. In this study, a new warning system called pavement marking with auxiliary countermeasure (PMAIC) is proposed to reduce the dilemma zone and enhance the traffic safety at signalized intersections. The proposed warning system integrates the pavement marking and flashing yellow system which can provide drivers with better suggestions about stop/go decisions based on their arriving time and speed. In order to evaluate the performance of the proposed warning system, this paper presents a cellular automata (CA) simulation study. The CA simulations are conducted for four different scenarios in total, including the typical intersection without warning system, the intersection with flashing green countermeasure, the intersection with pavement marking, and the intersection with the PMAIC warning system. Before the specific CA simulation analysis, a logistic regression model is calibrated based on field video data to predict drivers' general stop/go decisions. Also, the rules of vehicle movements in the CA models under the influence by different warning systems are proposed. The proxy indicators of rear-end crash and potential RLR violations were estimated and used to evaluate safety levels for the different scenarios. The simulation results showed that the PMAIC countermeasure consistently offered best performance to reduce rear-end crash and RLR violation. Meanwhile, the results indicate that the flashing-green countermeasure could not effectively reduce either rear-end crash risk or RLR violations. Also, it is found that the pavement-marking countermeasure has positive effects on reducing the rear-end risk while it may increase the probability of RLR violation. Lastly, the implementation of the proposed warning system is discussed with the consideration of connected-vehicle technology. It is expected that the dilemma zone issues can be efficiently addressed if the proposed countermeasure can be employed within connected vehicle technology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Earthquake Early Warning and Public Policy: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Goltz, J. D.; Bourque, L.; Tierney, K.; Riopelle, D.; Shoaf, K.; Seligson, H.; Flores, P.

2003-12-01

Development of an earthquake early warning capability and pilot project were objectives of TriNet, a 5-year (1997-2001) FEMA-funded project to develop a state-of-the-art digital seismic network in southern California. In parallel with research to assemble a protocol for rapid analysis of earthquake data and transmission of a signal by TriNet scientists and engineers, the public policy, communication and educational issues inherent in implementation of an earthquake early warning system were addressed by TriNet's outreach component. These studies included: 1) a survey that identified potential users of an earthquake early warning system and how an earthquake early warning might be used in responding to an event, 2) a review of warning systems and communication issues associated with other natural hazards and how lessons learned might be applied to an alerting system for earthquakes, 3) an analysis of organization, management and public policy issues that must be addressed if a broad-based warning system is to be developed and 4) a plan to provide earthquake early warnings to a small number of organizations in southern California as an experimental prototype. These studies provided needed insights into the social and cultural environment in which this new technology will be introduced, an environment with opportunities to enhance our response capabilities but also an environment with significant barriers to overcome to achieve a system that can be sustained and supported. In this presentation we will address the main public policy issues that were subjects of analysis in these studies. They include a discussion of the possible division of functions among organizations likely to be the principle partners in the management of an earthquake early warning system. Drawing on lessons learned from warning systems for other hazards, we will review the potential impacts of false alarms and missed events on warning system credibility, the acceptability of fully automated warning systems and equity issues associated with possible differential access to warnings. Finally, we will review the status of legal authorities and liabilities faced by organizations that assume various warning system roles and possible approaches to setting up a pilot project to introduce early warning. Our presentation will suggest that introducing an early warning system requires multi-disciplinary and multi-agency cooperation and thoughtful discussion among organizations likely to be providers and participants in an early warning system. Recalling our experience with earthquake prediction, we will look at early warning as a promising but unproven technology and recommend moving forward with caution and patience.

eqMAXEL: A new automatic earthquake location algorithm implementation for Earthworm

NASA Astrophysics Data System (ADS)

Lisowski, S.; Friberg, P. A.; Sheen, D. H.

2017-12-01

A common problem with automated earthquake location systems for a local to regional scale seismic network is false triggering and false locations inside the network caused by larger regional to teleseismic distance earthquakes. This false location issue also presents a problem for earthquake early warning systems where societal impacts of false alarms can be very expensive. Towards solving this issue, Sheen et al. (2016) implemented a robust maximum-likelihood earthquake location algorithm known as MAXEL. It was shown with both synthetics and real-data for a small number of arrivals, that large regional events were easily identifiable through metrics in the MAXEL algorithm. In the summer of 2017, we collaboratively implemented the MAXEL algorithm into a fully functional Earthworm module and tested it in regions of the USA where false detections and alarming are observed. We show robust improvement in the ability of the Earthworm system to filter out regional and teleseismic events that would have falsely located inside the network using the traditional Earthworm hypoinverse solution. We also explore using different grid sizes in the implementation of the MAXEL algorithm, which was originally designed with South Korea as the target network size.

Simulating the Impact of Sugar-Sweetened Beverage Warning Labels in Three Cities.

PubMed

Lee, Bruce Y; Ferguson, Marie C; Hertenstein, Daniel L; Adam, Atif; Zenkov, Eli; Wang, Peggy I; Wong, Michelle S; Gittelsohn, Joel; Mui, Yeeli; Brown, Shawn T

2018-02-01

A number of locations have been considering sugar-sweetened beverage point-of-purchase warning label policies to help address rising adolescent overweight and obesity prevalence. To explore the impact of such policies, in 2016 detailed agent-based models of Baltimore, Philadelphia, and San Francisco were developed, representing their populations, school locations, and food sources, using data from various sources collected between 2005 and 2014. The model simulated, over a 7-year period, the mean change in BMI and obesity prevalence in each of the cities from sugar-sweetened beverage warning label policies. Data analysis conducted between 2016 and 2017 found that implementing sugar-sweetened beverage warning labels at all sugar-sweetened beverage retailers lowered obesity prevalence among adolescents in all three cities. Point-of-purchase labels with 8% efficacy (i.e., labels reducing probability of sugar-sweetened beverage consumption by 8%) resulted in the following percentage changes in obesity prevalence: Baltimore: -1.69% (95% CI= -2.75%, -0.97%, p<0.001); San Francisco: -4.08% (95% CI= -5.96%, -2.2%, p<0.001); Philadelphia: -2.17% (95% CI= -3.07%, -1.42%, p<0.001). Agent-based simulations showed how warning labels may decrease overweight and obesity prevalence in a variety of circumstances with label efficacy and literacy rate identified as potential drivers. Implementing a warning label policy may lead to a reduction in obesity prevalence. Focusing on warning label design and store compliance, especially at supermarkets, may further increase the health impact. Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

The effect of graphic cigarette warning labels on smoking behavior: evidence from the Canadian experience.

PubMed

Azagba, Sunday; Sharaf, Mesbah F

2013-03-01

There is a substantial literature that graphic tobacco warnings are effective; however, there is limited evidence based on actual smoking behavior. The objective of this paper is to assess the effect of graphic cigarette warning labels on smoking prevalence and quit attempts. A nationally representative sample of individuals aged 15 years and older from the Canadian National Population Health Survey 1998-2008 is used. The sample consists of 4,853 individuals for the smoking prevalence regression and 1,549 smokers for quit attempts. The generalized estimating equation (GEE) model was used to examine the population-averaged (marginal) effects of tobacco graphic warnings on smoking prevalence and quit attempts. To assess the effect of graphic tobacco health warnings on smoking behavior, we used a scaled variable that takes the value of 0 for the first 6 months in 2001, then increases gradually to 1 from December 2001. We found that graphic warnings had a statistically significant effect on smoking prevalence and quit attempts. In particular, the warnings decreased the odds of being a smoker (odds ratio [OR] = 0.875; 95% CI = 0.821-0.932) and increased the odds of making a quit attempt (OR = 1.330, CI = 1.187-1.490). Similar results were obtained when we allowed for more time for the warnings to appear in retail outlets. This study adds to the growing body of evidence on the effectiveness of graphic warnings. Our findings suggest that warnings had a significant effect on smoking prevalence and quit attempts in Canada.

Automatic identification of alpine mass movements based on seismic and infrasound signals

NASA Astrophysics Data System (ADS)

Schimmel, Andreas; Hübl, Johannes

2017-04-01

The automatic detection and identification of alpine mass movements like debris flows, debris floods or landslides gets increasing importance for mitigation measures in the densely populated and intensively used alpine regions. Since this mass movement processes emits characteristically seismic and acoustic waves in the low frequency range this events can be detected and identified based on this signals. So already several approaches for detection and warning systems based on seismic or infrasound signals has been developed. But a combination of both methods, which can increase detection probability and reduce false alarms is currently used very rarely and can serve as a promising method for developing an automatic detection and identification system. So this work presents an approach for a detection and identification system based on a combination of seismic and infrasound sensors, which can detect sediment related mass movements from a remote location unaffected by the process. The system is based on one infrasound sensor and one geophone which are placed co-located and a microcontroller where a specially designed detection algorithm is executed which can detect mass movements in real time directly at the sensor site. Further this work tries to get out more information from the seismic and infrasound spectrum produced by different sediment related mass movements to identify the process type and estimate the magnitude of the event. The system is currently installed and tested on five test sites in Austria, two in Italy and one in Switzerland as well as one in Germany. This high number of test sites is used to get a large database of very different events which will be the basis for a new identification method for alpine mass movements. These tests shows promising results and so this system provides an easy to install and inexpensive approach for a detection and warning system.

Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

NASA Astrophysics Data System (ADS)

Sellami, Yamine; Imine, Hocine; Boubezoul, Abderrahmane; Cadiou, Jean-Charles

2018-03-01

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach.

A novel wearable smart button system for fall detection

NASA Astrophysics Data System (ADS)

Zhuang, Wei; Sun, Xiang; Zhi, Yueyan; Han, Yue; Mao, Hande

2017-05-01

Fall has been the second most cause of accidental injury to death in the world. It has been a serious threat to the physical and mental health of the elders. Therefore, developing wearable node system with fall detecting ability has become increasingly pressing at present. A novel smart button for long-term fall detection is proposed in this paper, which is able to accurately monitor the falling behavior, and sending warning message online as well. The smart button is based on the tri-axis acceleration sensor which is used to collect the body motion signals. By using the statistical metrics of acceleration characteristics, a new SVM classification algorithm with high positive accuracy and stability is proposed so as to classify the falls and activities of daily living, and the results can be real-time displayed on Android based mobile phone. The experiments show that our wearable node system can continuously monitor the falling behavior with positive rate 94.8%.

Developmental differences in false-event rejection: Effects of memorability-based warnings.

PubMed

Ghetti, Simona; Castelli, Paola

2006-08-01

The present study investigated the development of the memorability-based strategy, a metacognitive process through which individuals reject the occurrence of false events if they do not remember the events and they expect them to be highly memorable. Previous research found that only older children spontaneously use this strategy. In the present study, we examined whether providing children with relevant information about expected event-memorability and inferences derived from it induced strategy use. Children aged 5, 7, and 9 (n = 144) were asked about true and false (high- and low-memorability) autobiographical events. Participants were either interviewed according to the standard "lost-in-the-mall" procedure, or were additionally provided with warnings. Warnings were either congruent or incongruent with assessments and decision processes involved in the strategy use. Results showed that receiving memorability-congruent warnings increased false-event rejection rates in 7- and 9-year-olds, but not in 5-year-olds. However, only older children were more likely to reject high-memorability compared with low-memorability false events. Developmental trajectories and factors affecting reliance on the memorability-based strategy are discussed.

Research on Disaster Early Warning and Disaster Relief Integrated Service System Based on Block Data Theory

NASA Astrophysics Data System (ADS)

Yang, J.; Zhang, H.; Wang, C.; Tang, D.

2018-04-01

With the continuous development of social economy, the interaction between mankind and nature has become increasingly evident. Disastrous global catastrophes have occurred from time to time, causing huge losses to people's lives and property. All governments recognize the importance of the establishment of disaster early warning and release mechanisms, and it is also an urgent issue to improve the comprehensive service level of emergency response and disaster relief. However, disaster early warning and emergency relief information is usually generated by different departments, and the diverse data sources, difficult integration, and limited release speed have always been difficult issues to be solved. Block data is the aggregation of various distributed (point data) and segmentation (data) big data on a specific platform and make them happen continuous polymerization effect, block data theory is a good solution to cross-sectoral, cross-platform Disaster information data sharing and integration problems. This paper attempts to discuss the integrated service mechanism of disaster information aggregation and disaster relief based on block data theory and introduces a location-based integrated service system for disaster early warning and disaster relief.

The GOES-R Geostationary Lightning Mapper (GLM)

NASA Astrophysics Data System (ADS)

Goodman, S. J.; Blakeslee, R. J.; Koshak, W. J.; Mach, D. M.; Bailey, J. C.; Buechler, D. E.; Carey, L. D.; Schultz, C. J.; Bateman, M. G.; McCaul, E., Jr.; Stano, G. T.

2012-12-01

The Geostationary Operational Environmental Satellite (GOES-R) series provides the continuity for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning activity (in-cloud and cloud-to-ground lightning flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low earth orbit, and from ground-based lightning networks and intensive pre-launch field campaigns. GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extends their combined climatology over the western hemisphere into the coming decades. Science and application development along with pre-operational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings. Results from recent field campaigns and forecaster evaluations on the utility of the total lightning products will be presented.

Vehicle infrastructure integration (VII) based road-condition warning system for highway collision prevention.

DOT National Transportation Integrated Search

2009-05-01

As a major ITS initiative, the Vehicle Infrastructure Integration (VII) program is to revolutionize : transportation by creating an enabling communication infrastructure that will open up a wide range of : safety applications. The road-condition warn...

Highway-rail intersection GPS-based in-vehicle warning systems - literature review and recommendations.

DOT National Transportation Integrated Search

2012-12-31

In 2008, there were 2,395 incidents at highway-rail intersections (level crossings) in the United States, resulting in 939 injuries and 287 fatalities. Crossing elimination, grade separation, and the implementation of traditional warning devices are ...

Evacuation transportation management : task four: interview and survey results.

DOT National Transportation Integrated Search

2006-06-26

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Evacuation transportation management. Task four, Interview and survey results

DOT National Transportation Integrated Search

2006-01-01

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Graphic gambling warnings: how they affect emotions, cognitive responses and attitude change.

PubMed

Muñoz, Yaromir; Chebat, Jean-Charles; Borges, Adilson

2013-09-01

The present study focuses on the effects of graphic warnings related to excessive gambling. It is based upon a theoretical model derived from both the Protection Motivation Theory (PMT) and the Elaboration Likelihood Model (ELM). We focus on video lottery terminal (VLT), one of the most hazardous format in the gaming industry. Our cohort consisted of 103 actual gamblers who reported previous gambling activity on VLT's on a regular basis. We assess the effectiveness of graphic warnings vs. text-only warnings and the effectiveness of two major arguments (i.e., family vs. financial disruption). A 2 × 2 factorial design was used to test the direct and combined effects of two variables (i.e., warning content and presence vs. absence of a graphic). It was found that the presence of a graphic enhances both cognitive appraisal and fear, and has positive effects on the Depth of Information Processing. In addition, graphic content combined with family disruptions is more effective for changing attitudes and complying with the warning than other combinations of the manipulated variables. It is proposed that ELM and PMT complement each other to explain the effects of warnings. Theoretical and practical implications are discussed.

The Efficacy of Cigarette Warning Labels on Health Beliefs in the United States and Mexico

PubMed Central

MUTTI, SEEMA; HAMMOND, DAVID; REID, JESSICA L.; THRASHER, JAMES F.

2013-01-01

Concern over health risks is the most common motivation for quitting smoking. Health warnings on tobacco packages are among the most prominent interventions to convey the health risks of smoking. Face-to-face surveys were conducted in Mexico (n=1,072), and a web-based survey was conducted in the US (n=1,449) to examine the efficacy of health warning labels on health beliefs. Respondents were randomly assigned to view two sets of health warnings (each with one text-only warning and 5–6 pictorial warnings) for two different health effects. Respondents were asked whether they believed smoking caused 12 different health effects. Overall, the findings indicate high levels of health knowledge in both countries for some health effects, although significant knowledge gaps remained; for example: less than half of respondents agreed that smoking causes impotence and less than one third agreed that smoking causes gangrene. Mexican respondents endorsed a greater number of correct beliefs about the health impact of smoking than the US sample. In both countries, viewing related health warning labels increased beliefs about the health risks of smoking, particularly for less well-known health effects, such as gangrene, impotence, and stroke. PMID:23905611

A hazard-independent approach for the standardised multi-channel dissemination of warning messages

NASA Astrophysics Data System (ADS)

Esbri Palomares, M. A.; Hammitzsch, M.; Lendholt, M.

2012-04-01

The tsunami disaster affecting the Indian Ocean region on Christmas 2004 demonstrated very clearly the shortcomings in tsunami detection, public warning processes as well as intergovernmental warning message exchange in the Indian Ocean region. In that regard, early warning systems require that the dissemination of early warning messages has to be executed in way that ensures that the message delivery is timely; the message content is understandable, usable and accurate. To that end, diverse and multiple dissemination channels must be used to increase the chance of the messages reaching all affected persons in a hazard scenario. In addition to this, usage of internationally accepted standards for the warning dissemination such as the Common Alerting Protocol (CAP) and Emergency Data Exchange Language (EDXL) Distribution Element specified by the Organization for the Advancement of Structured Information Standards (OASIS) increase the interoperability among different warning systems enabling thus the concept of system-of-systems proposed by GEOSS. The project Distant Early Warning System (DEWS), co-funded by the European Commission under the 6th Framework Programme, aims at strengthening the early warning capacities by building an innovative generation of interoperable tsunami early warning systems based on the above mentioned concepts following a Service-oriented Architecture (SOA) approach. The project focuses on the downstream part of the hazard information processing where customized, user-tailored warning messages and alerts flow from the warning centre to the responsible authorities and/or the public with their different needs and responsibilities. The information logistics services within DEWS generate tailored EDXL-DE/CAP warning messages for each user that must receive the message according to their preferences, e.g., settings for language, interested areas, dissemination channels, etc.. However, the significant difference in the implementation and capabilities of different dissemination channels such as SMS, email and television, have bearing on the information processing required for delivery and consumption of a DEWS EDXL-DE/CAP message over each dissemination channel. These messages may include additional information in the form of maps, graphs, documents, sensor observations, etc. Therefore, the generated messages are pre-processed by channel adaptors in the information dissemination services converting it into a format that is suitable for end-to-end delivery over the dissemination channels without any semantic distortion. The approach followed by DEWS for disseminating warnings not only relies on traditional communication ways used by the already established early warnings such as the delivery of faxes and phone calls but takes into consideration the use of other broadly used communication channels such as SMS, email, narrowcast and broadcast television, instant messaging, Voice over IP, and radio. It also takes advantage of social media channels like RSS feeds, Facebook, Twitter, etc., enabling a multiplier effect, like in the case of radio and television, and thus allowing to create mash-ups by aggregating other sources of information to the original message. Finally, status information is also important in order to assess and understand whether the process of disseminating the warning to the message consumers has been successfully completed or the process failed at some point of the dissemination chain. To that end, CAP-based messages generated within the information dissemination services provide the semantics for those fields that are of interest within the context of reporting the warning dissemination status in DEWS.

Application of Seismic Array Processing to Tsunami Early Warning

NASA Astrophysics Data System (ADS)

An, C.; Meng, L.

2015-12-01

Tsunami wave predictions of the current tsunami warning systems rely on accurate earthquake source inversions of wave height data. They are of limited effectiveness for the near-field areas since the tsunami waves arrive before data are collected. Recent seismic and tsunami disasters have revealed the need for early warning to protect near-source coastal populations. In this work we developed the basis for a tsunami warning system based on rapid earthquake source characterisation through regional seismic array back-projections. We explored rapid earthquake source imaging using onshore dense seismic arrays located at regional distances on the order of 1000 km, which provides faster source images than conventional teleseismic back-projections. We implement this method in a simulated real-time environment, and analysed the 2011 Tohoku earthquake rupture with two clusters of Hi-net stations in Kyushu and Northern Hokkaido, and the 2014 Iquique event with the Earthscope USArray Transportable Array. The results yield reasonable estimates of rupture area, which is approximated by an ellipse and leads to the construction of simple slip models based on empirical scaling of the rupture area, seismic moment and average slip. The slip model is then used as the input of the tsunami simulation package COMCOT to predict the tsunami waves. In the example of the Tohoku event, the earthquake source model can be acquired within 6 minutes from the start of rupture and the simulation of tsunami waves takes less than 2 min, which could facilitate a timely tsunami warning. The predicted arrival time and wave amplitude reasonably fit observations. Based on this method, we propose to develop an automatic warning mechanism that provides rapid near-field warning for areas of high tsunami risk. The initial focus will be Japan, Pacific Northwest and Alaska, where dense seismic networks with the capability of real-time data telemetry and open data accessibility, such as the Japanese HiNet (>800 instruments) and the Earthscope USArray Transportable Array (~400 instruments), are established.

Experience from three years of local capacity development for tsunami early warning in Indonesia: challenges, lessons and the way ahead

NASA Astrophysics Data System (ADS)

Spahn, H.; Hoppe, M.; Vidiarina, H. D.; Usdianto, B.

2010-07-01

Five years after the 2004 tsunami, a lot has been achieved to make communities in Indonesia better prepared for tsunamis. This achievement is primarily linked to the development of the Indonesian Tsunami Early Warning System (InaTEWS). However, many challenges remain. This paper describes the experience with local capacity development for tsunami early warning (TEW) in Indonesia, based on the activities of a pilot project. TEW in Indonesia is still new to disaster management institutions and the public, as is the paradigm of Disaster Risk Reduction (DRR). The technology components of InaTEWS will soon be fully operational. The major challenge for the system is the establishment of clear institutional arrangements and capacities at national and local levels that support the development of public and institutional response capability at the local level. Due to a lack of information and national guidance, most local actors have a limited understanding of InaTEWS and DRR, and often show little political will and priority to engage in TEW. The often-limited capacity of local governments is contrasted by strong engagement of civil society organisations that opt for early warning based on natural warning signs rather than technology-based early warning. Bringing together the various actors, developing capacities in a multi-stakeholder cooperation for an effective warning system are key challenges for the end-to-end approach of InaTEWS. The development of local response capability needs to receive the same commitment as the development of the system's technology components. Public understanding of and trust in the system comes with knowledge and awareness on the part of the end users of the system and convincing performance on the part of the public service provider. Both sides need to be strengthened. This requires the integration of TEW into DRR, clear institutional arrangements, national guidance and intensive support for capacity development at local levels as well as dialogue between the various actors.

Landslide susceptibility and early warning model for shallow landslide in Taiwan

NASA Astrophysics Data System (ADS)

Huang, Chun-Ming; Wei, Lun-Wei; Chi, Chun-Chi; Chang, Kan-Tsun; Lee, Chyi-Tyi

2017-04-01

This study aims to development a regional susceptibility model and warning threshold as well as the establishment of early warning system in order to prevent and reduce the losses caused by rainfall-induced shallow landslides in Taiwan. For the purpose of practical application, Taiwan is divided into nearly 185,000 slope units. The susceptibility and warning threshold of each slope unit were analyzed as basic information for disaster prevention. The geological characteristics, mechanism and the occurrence time of landslides were recorded for more than 900 cases through field investigation and interview of residents in order to discuss the relationship between landslides and rainfall. Logistic regression analysis was performed to evaluate the landslide susceptibility and an I3-R24 rainfall threshold model was proposed for the early warning of landslides. The validations of recent landslide cases show that the model was suitable for the warning of regional shallow landslide and most of the cases can be warned 3 to 6 hours in advanced. We also propose a slope unit area weighted method to establish local rainfall threshold on landslide for vulnerable villages in order to improve the practical application. Validations of the local rainfall threshold also show a good agreement to the occurrence time reported by newspapers. Finally, a web based "Rainfall-induced Landslide Early Warning System" is built and connected to real-time radar rainfall data so that landslide real-time warning can be achieved. Keywords: landslide, susceptibility analysis, rainfall threshold

Development of Complexity Science and Technology Tools for NextGen Airspace Research and Applications

NASA Technical Reports Server (NTRS)

Holmes, Bruce J.; Sawhill, Bruce K.; Herriot, James; Seehart, Ken; Zellweger, Dres; Shay, Rick

2012-01-01

The objective of this research by NextGen AeroSciences, LLC is twofold: 1) to deliver an initial "toolbox" of algorithms, agent-based structures, and method descriptions for introducing trajectory agency as a methodology for simulating and analyzing airspace states, including bulk properties of large numbers of heterogeneous 4D aircraft trajectories in a test airspace -- while maintaining or increasing system safety; and 2) to use these tools in a test airspace to identify possible phase transition structure to predict when an airspace will approach the limits of its capacity. These 4D trajectories continuously replan their paths in the presence of noise and uncertainty while optimizing performance measures and performing conflict detection and resolution. In this approach, trajectories are represented as extended objects endowed with pseudopotential, maintaining time and fuel-efficient paths by bending just enough to accommodate separation while remaining inside of performance envelopes. This trajectory-centric approach differs from previous aircraft-centric distributed approaches to deconfliction. The results of this project are the following: 1) we delivered a toolbox of algorithms, agent-based structures and method descriptions as pseudocode; and 2) we corroborated the existence of phase transition structure in simulation with the addition of "early warning" detected prior to "full" airspace. This research suggests that airspace "fullness" can be anticipated and remedied before the airspace becomes unsafe.

Pedestrian detection based on redundant wavelet transform

NASA Astrophysics Data System (ADS)

Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

2016-10-01

Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

DISTANT EARLY WARNING SYSTEM for Tsunamis - A wide-area and multi-hazard approach

NASA Astrophysics Data System (ADS)

Hammitzsch, Martin; Lendholt, Matthias; Wächter, Joachim

2010-05-01

The DEWS (Distant Early Warning System) [1] project, funded under the 6th Framework Programme of the European Union, has the objective to create a new generation of interoperable early warning systems based on an open sensor platform. This platform integrates OGC [2] SWE [3] compliant sensor systems for the rapid detection of hazardous events, like earthquakes, sea level anomalies, ocean floor occurrences, and ground displacements in the case of tsunami early warning. Based on the upstream information flow DEWS focuses on the improvement of downstream capacities of warning centres especially by improving information logistics for effective and targeted warning message aggregation for a multilingual environment. Multiple telecommunication channels will be used for the dissemination of warning messages. Wherever possible, existing standards have been integrated. The Command and Control User Interface (CCUI), a rich client application based on Eclipse RCP (Rich Client Platform) [4] and the open source GIS uDig [5], integrates various OGC services. Using WMS (Web Map Service) [6] and WFS (Web Feature Service) [7] spatial data are utilized to depict the situation picture and to integrate a simulation system via WPS (Web Processing Service) [8] to identify affected areas. Warning messages are compiled and transmitted in the OASIS [9] CAP (Common Alerting Protocol) [10] standard together with addressing information defined via EDXL-DE (Emergency Data Exchange Language - Distribution Element) [11]. Internal interfaces are realized with SOAP [12] web services. Based on results of GITEWS [13] - in particular the GITEWS Tsunami Service Bus [14] - the DEWS approach provides an implementation for tsunami early warning systems but other geological paradigms are going to follow, e.g. volcanic eruptions or landslides. Therefore in future also multi-hazard functionality is conceivable. The specific software architecture of DEWS makes it possible to dock varying sensors to the system and to extend the CCUI with hazard specific functionality. The presentation covers the DEWS project, the system architecture and the CCUI in conjunction with details of information logistics. The DEWS Wide Area Centre connecting national centres to allow the international communication and warning exchange is presented also. REFERENCES: [1] DEWS, www.dews-online.org [2] OGC, www.opengeospatial.org [3] SWE, www.opengeospatial.org/projects/groups/sensorweb [4] Eclipse RCP, www.eclipse.org/home/categories/rcp.php [5] uDig, udig.refractions.net [6] WMS, www.opengeospatial.org/standards/wms [7] WFS, www.opengeospatial.org/standards/wfs [8] WPS, www.opengeospatial.org/standards/wps [9] OASIS, www.oasis-open.org [10] CAP, www.oasis-open.org/specs/#capv1.1 [11] EDXL-DE, www.oasis-open.org/specs/#edxlde-v1.0 [12] SOAP, www.w3.org/TR/soap [13] GITEWS (German Indonesian Tsunami Early Warning System) is a project of the German Federal Government to aid the recon¬struction of the tsunami-prone Indian Ocean region, www.gitews.org [14] The Tsunami Service Bus is the GITEWS sensor system integration platform offering standardised services for the detection and monitoring of tsunamis

Prediction of heart abnormality using MLP network

NASA Astrophysics Data System (ADS)

Hashim, Fakroul Ridzuan; Januar, Yulni; Mat, Muhammad Hadzren; Rizman, Zairi Ismael; Awang, Mat Kamil

2018-02-01

Heart abnormality does not choose gender, age and races when it strikes. With no warning signs or symptoms, it can result to a sudden death of the patient. Generally, heart's irregular electrical activity is defined as heart abnormality. Via implementation of Multilayer Perceptron (MLP) network, this paper tries to develop a program that allows the detection of heart abnormality activity. Utilizing several training algorithms with Purelin activation function, an amount of heartbeat signals received through the electrocardiogram (ECG) will be employed to condition the MLP network.

The Global Emergency Observation and Warning System

NASA Technical Reports Server (NTRS)

Bukley, Angelia P.; Mulqueen, John A.

1994-01-01

Based on an extensive characterization of natural hazards, and an evaluation of their impacts on humanity, a set of functional technical requirements for a global warning and relief system was developed. Since no technological breakthroughs are required to implement a global system capable of performing the functions required to provide sufficient information for prevention, preparedness, warning, and relief from natural disaster effects, a system is proposed which would combine the elements of remote sensing, data processing, information distribution, and communications support on a global scale for disaster mitigation.

Lane marking/striping to improve image processing lane departure warning systems.

DOT National Transportation Integrated Search

2007-05-01

Vision-based Lane Departure Warning Systems (LDWS) depend on pavement marking tracking to : determine that vehicles perform unintended drifts out of the travel lanes. Thus, it is expected that : the performances of these LDWS be influenced by the vis...

Driver acceptance of collision warning applications based on heavy-truck V2V technology

DOT National Transportation Integrated Search

2016-10-01

Battelle conducted a series of driver acceptance clinics (DACs) with heavy-truck drivers to gauge their acceptance of collision-warning applications using vehicle-to-vehicle (V2V) communication technology. This report describes the results from Volpe...

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure.

PubMed

Chen, Yulong; Irfan, Muhammad; Uchimura, Taro; Zhang, Ke

2018-03-27

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate.

Body size shifts and early warning signals precede the historic collapse of whale stocks.

PubMed

Clements, Christopher F; Blanchard, Julia L; Nash, Kirsty L; Hindell, Mark A; Ozgul, Arpat

2017-06-22

Predicting population declines is a key challenge in the face of global environmental change. Abundance-based early warning signals have been shown to precede population collapses; however, such signals are sensitive to the low reliability of abundance estimates. Here, using historical data on whales harvested during the 20th century, we demonstrate that early warning signals can be present not only in the abundance data, but also in the more reliable body size data of wild populations. We show that during the period of commercial whaling, the mean body size of caught whales declined dramatically (by up to 4 m over a 70-year period), leading to early warning signals being detectable up to 40 years before the global collapse of whale stocks. Combining abundance and body size data can reduce the length of the time series required to predict collapse, and decrease the chances of false positive early warning signals.

Tsunami Amplitude Estimation from Real-Time GNSS.

NASA Astrophysics Data System (ADS)

Jeffries, C.; MacInnes, B. T.; Melbourne, T. I.

2017-12-01

Tsunami early warning systems currently comprise modeling of observations from the global seismic network, deep-ocean DART buoys, and a global distribution of tide gauges. While these tools work well for tsunamis traveling teleseismic distances, saturation of seismic magnitude estimation in the near field can result in significant underestimation of tsunami excitation for local warning. Moreover, DART buoy and tide gauge observations cannot be used to rectify the underestimation in the available time, typically 10-20 minutes, before local runup occurs. Real-time GNSS measurements of coseismic offsets may be used to estimate finite faulting within 1-2 minutes and, in turn, tsunami excitation for local warning purposes. We describe here a tsunami amplitude estimation algorithm; implemented for the Cascadia subduction zone, that uses continuous GNSS position streams to estimate finite faulting. The system is based on a time-domain convolution of fault slip that uses a pre-computed catalog of hydrodynamic Green's functions generated with the GeoClaw shallow-water wave simulation software and maps seismic slip along each section of the fault to points located off the Cascadia coast in 20m of water depth and relies on the principle of the linearity in tsunami wave propagation. The system draws continuous slip estimates from a message broker, convolves the slip with appropriate Green's functions which are then superimposed to produce wave amplitude at each coastal location. The maximum amplitude and its arrival time are then passed into a database for subsequent monitoring and display. We plan on testing this system using a suite of synthetic earthquakes calculated for Cascadia whose ground motions are simulated at 500 existing Cascadia GPS sites, as well as real earthquakes for which we have continuous GNSS time series and surveyed runup heights, including Maule, Chile 2010 and Tohoku, Japan 2011. This system has been implemented in the CWU Geodesy Lab for the Cascadia subduction zone but will be expanded to the circum-Pacific as real-time processing of international GNSS data streams become available.

Antidepressant drugs and the risk of suicide in children and adolescents.

PubMed

Isacsson, Göran; Rich, Charles L

2014-04-01

Government agencies have issued warnings about the use of antidepressant medications in children, adolescents, and young adults since 2003. The statements warn that such medications may cause de novo 'suicidality' in some people. This review explores the data on the treatment of depression that led to these warnings and subsequent data that are relevant to the warnings. It also addresses the effectiveness of antidepressant treatment in general and the relationship of suicide rates to antidepressant treatment. It concludes that the decisions for the 'black box' warnings were based on biased data and invalid assumptions. Furthermore, the decisions were unsupported by the observational data regarding suicide in young people that existed in 2003. The following recommendations would seem to follow from these observations. First, drug authorities should re-evaluate the basis for their imposed warnings on antidepressant medicines, and analyze the actual public health consequences the warnings have had. In the absence of substantial evidence supporting the warnings, they should be removed. Second, physicians and other providers with prescription privileges should continue to be educated regarding the importance of aggressively treating depression in young people, using antidepressants when indicated. Third, physicians and other professionals who treat depressed young people must always be aware of the risk of suicide (albeit quite low) and observe them closely for any signs of increased risk of suicide. This is necessary regardless of the type of treatment being provided.

Coupling a regional warning system to a semantic engine on online news for enhancing landslide prediction

NASA Astrophysics Data System (ADS)

Battistini, Alessandro; Rosi, Ascanio; Segoni, Samuele; Catani, Filippo; Casagli, Nicola

2017-04-01

Landslide inventories are basic data for large scale landslide modelling, e.g. they are needed to calibrate and validate rainfall thresholds, physically based models and early warning systems. The setting up of landslide inventories with traditional methods (e.g. remote sensing, field surveys and manual retrieval of data from technical reports and local newspapers) is time consuming. The objective of this work is to automatically set up a landslide inventory using a state-of-the art semantic engine based on data mining on online news (Battistini et al., 2013) and to evaluate if the automatically generated inventory can be used to validate a regional scale landslide warning system based on rainfall-thresholds. The semantic engine scanned internet news in real time in a 50 months test period. At the end of the process, an inventory of approximately 900 landslides was set up for the Tuscany region (23,000 km2, Italy). The inventory was compared with the outputs of the regional landslide early warning system based on rainfall thresholds, and a good correspondence was found: e.g. 84% of the events reported in the news is correctly identified by the model. In addition, the cases of not correspondence were forwarded to the rainfall threshold developers, which used these inputs to update some of the thresholds. On the basis of the results obtained, we conclude that automatic validation of landslide models using geolocalized landslide events feedback is possible. The source of data for validation can be obtained directly from the internet channel using an appropriate semantic engine. We also automated the validation procedure, which is based on a comparison between forecasts and reported events. We verified that our approach can be automatically used for a near real time validation of the warning system and for a semi-automatic update of the rainfall thresholds, which could lead to an improvement of the forecasting effectiveness of the warning system. In the near future, the proposed procedure could operate in continuous time and could allow for a periodic update of landslide hazard models and landslide early warning systems with minimum human intervention. References: Battistini, A., Segoni, S., Manzo, G., Catani, F., Casagli, N. (2013). Web data mining for automatic inventory of geohazards at national scale. Applied Geography, 43, 147-158.

Satellite Data Aid Monitoring of Nation's Forests

NASA Technical Reports Server (NTRS)

2014-01-01

The USDA Forest Service’s Asheville, North Carolina-based Eastern Forest Environmental Threat Assessment Center and Prineville, Oregon-based Western Wildlands Environmental Threat Assessment Center partnered with Stennis Space Center and other agencies to create an early warning system to identify, characterize, and track disturbances from potential forest threats. The result was ForWarn, which is now being used by federal and state forest and natural resource managers.

Performance analysis of landslide early warning systems at regional scale: the EDuMaP method

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Calvello, Michele

2016-04-01

Landslide early warning systems (LEWSs) reduce landslide risk by disseminating timely and meaningful warnings when the level of risk is judged intolerably high. Two categories of LEWSs, can be defined on the basis of their scale of analysis: "local" systems and "regional" systems. LEWSs at regional scale (ReLEWSs) are used to assess the probability of occurrence of landslides over appropriately-defined homogeneous warning zones of relevant extension, typically through the prediction and monitoring of meteorological variables, in order to give generalized warnings to the public. Despite many studies on ReLEWSs, no standard requirements exist for assessing their performance. Empirical evaluations are often carried out by simply analysing the time frames during which significant high-consequence landslides occurred in the test area. Alternatively, the performance evaluation is based on 2x2 contingency tables computed for the joint frequency distribution of landslides and alerts, both considered as dichotomous variables. In all these cases, model performance is assessed neglecting some important aspects which are peculiar to ReLEWSs, among which: the possible occurrence of multiple landslides in the warning zone; the duration of the warnings in relation to the time of occurrence of the landslides; the level of the warning issued in relation to the landslide spatial density in the warning zone; the relative importance system managers attribute to different types of errors. An original approach, called EDuMaP method, is proposed to assess the performance of landslide early warning models operating at regional scale. The method is composed by three main phases: Events analysis, Duration Matrix, Performance analysis. The events analysis phase focuses on the definition of landslide (LEs) and warning events (WEs), which are derived from available landslides and warnings databases according to their spatial and temporal characteristics by means of ten input parameters. The evaluation of time associated with the occurrence of landslide events (LE) in relation to the occurrence of warning events (WE) in their respective classes is a fundamental step to determine the duration matrix elements. On the other hand the classification of LEs and WEs establishes the structure of the duration matrix. Indeed, the number of rows and columns of the matrix is equal to the number of classes defined for the warning and landslide events, respectively. Thus the matrix is not expressed as a 2x2 contingency and LEs and WEs are not expressed as dichotomous variables. The final phase of the method is the evaluation of the duration matrix based on a set of performance criteria assigning a performance meaning to the element of the matrix. To this aim different criteria can be defined, for instance employing an alert classification scheme derived from 2x2 contingency tables or assigning a colour code to the elements of the matrix in relation to their grade of correctness. Finally, performance indicators can be derived from the performance criteria to quantify successes and errors of the early warning models. EDuMaP has been already applied to different real case studies, highlighting the adaptability of the method to analyse the performance of structurally different ReLEWSs.

Tsunami Detection by High Frequency Radar Beyond the Continental Shelf: II. Extension of Time Correlation Algorithm and Validation on Realistic Case Studies

NASA Astrophysics Data System (ADS)

Grilli, Stéphan T.; Guérin, Charles-Antoine; Shelby, Michael; Grilli, Annette R.; Moran, Patrick; Grosdidier, Samuel; Insua, Tania L.

2017-08-01

In past work, tsunami detection algorithms (TDAs) have been proposed, and successfully applied to offline tsunami detection, based on analyzing tsunami currents inverted from high-frequency (HF) radar Doppler spectra. With this method, however, the detection of small and short-lived tsunami currents in the most distant radar ranges is challenging due to conflicting requirements on the Doppler spectra integration time and resolution. To circumvent this issue, in Part I of this work, we proposed an alternative TDA, referred to as time correlation (TC) TDA, that does not require inverting currents, but instead detects changes in patterns of correlations of radar signal time series measured in pairs of cells located along the main directions of tsunami propagation (predicted by geometric optics theory); such correlations can be maximized when one signal is time-shifted by the pre-computed long wave propagation time. We initially validated the TC-TDA based on numerical simulations of idealized tsunamis in a simplified geometry. Here, we further develop, extend, and apply the TC algorithm to more realistic tsunami case studies. These are performed in the area West of Vancouver Island, BC, where Ocean Networks Canada recently deployed a HF radar (in Tofino, BC), to detect tsunamis from far- and near-field sources, up to a 110 km range. Two case studies are considered, both simulated using long wave models (1) a far-field seismic, and (2) a near-field landslide, tsunami. Pending the availability of radar data, a radar signal simulator is parameterized for the Tofino HF radar characteristics, in particular its signal-to-noise ratio with range, and combined with the simulated tsunami currents to produce realistic time series of backscattered radar signal from a dense grid of cells. Numerical experiments show that the arrival of a tsunami causes a clear change in radar signal correlation patterns, even at the most distant ranges beyond the continental shelf, thus making an early tsunami detection possible with the TC-TDA. Based on these results, we discuss how the new algorithm could be combined with standard methods proposed earlier, based on a Doppler analysis, to develop a new tsunami detection system based on HF radar data, that could increase warning time. This will be the object of future work, which will be based on actual, rather than simulated, radar data.

Volvo and Infiniti drivers' experiences with select crash avoidance technologies.

PubMed

Braitman, Keli A; McCartt, Anne T; Zuby, David S; Singer, Jeremiah

2010-06-01

Vehicle-based crash avoidance systems can potentially reduce crashes, but success depends on driver acceptance and understanding. This study gauged driver use, experience, and acceptance among early adopters of select technologies. Telephone interviews were conducted in early 2009 with 380 owners of Volvo vehicles equipped with forward collision warning with autobrake, lane departure warning, side-view assist, and/or active bi-xenon headlights and 485 owners of Infiniti vehicles with lane departure warning/prevention. Most owners kept systems turned on most of the time, especially forward collision warning with autobrake and side-view assist. The exception was lane departure prevention; many owners were unaware they had it, and the system must be activated each time the vehicle is started. Most owners reported being safer with the technologies and would want them again on their next vehicles. Perceived false or unnecessary warnings were fairly common, particularly with side-view assist. Some systems were annoying, especially lane departure warning. Many owners reported safer driving behaviors such as greater use of turn signals (lane departure warning), increased following distance (forward collision warning), and checking side mirrors more frequently (side-view assist), but some reported driving faster at night (active headlights). Despite some unnecessary or annoying warnings, most Volvo and Infiniti owners use crash avoidance systems most of the time. Among early adopters, the first requirement of effective warning systems (that owners use the technology) seems largely met. Systems requiring activation by drivers for each trip are used less often. Owner experience with the latest technologies from other automobile manufacturers should be studied, as well as for vehicles on which technologies are standard (versus optional) equipment. The effectiveness of technologies in preventing and mitigating crashes and injuries, and user acceptance of interfaces, should be examined as more vehicles with advanced technologies penetrate the fleet.

Influence of warning information changes on emergency response

NASA Astrophysics Data System (ADS)

Heisterkamp, Tobias; Ulbrich, Uwe; Glade, Thomas; Tetzlaff, Gerd

2014-05-01

Mitigation and risk reduction of natural hazards is significantly related to the possibility of predicting the actual event. Some hazards can already be forecasted several days in advance. For these hazards, early warning systems have been developed, installed and improved over the years. The formation of winter storms for example can be recognized up to one week before they pass through Central Europe. This relative long early warning time has the advantage that forecasters can concretise the warnings over time. Therefore, warnings can even be adapted to alternating conditions within the process, the observation or changes in its modelling. Emergency managers are one group of warning recipients in the civil protection sector. They have to prepare or initiate prevention or response measures at a specific point of time, depending on the required lead time of the referring actions. At this point of time already, the forecast and its equivalent warning, has to be assumed as a stage of reality, hence the decision-makers have to come to a conclusion. These decisions are based on spatial and temporal knowledge of the forecasted event and the consequential situation of risk. With incoming warning updates, the detailed status of information is permanently being alternated. Consequently, decisions can be influenced by the development of the warning situation and the inherent tendency before a certain point of time. They can also be adapted to updates later on, according to the changing 'decision reality'. The influence of these dynamic hazard situations on operational planning and response by emergency managers is investigated in case studies on winter storms for Berlin, Germany. Therefore, the issued warnings by the weather service and data of operation of Berlin Fire Brigades are analysed and compared. This presentation shows and discusses first results.

Rapid Tsunami Inundation Forecast from Near-field or Far-field Earthquakes using Pre-computed Tsunami Database: Pelabuhan Ratu, Indonesia

NASA Astrophysics Data System (ADS)

Gusman, A. R.; Setiyono, U.; Satake, K.; Fujii, Y.

2017-12-01

We built pre-computed tsunami inundation database in Pelabuhan Ratu, one of tsunami-prone areas on the southern coast of Java, Indonesia. The tsunami database can be employed for a rapid estimation of tsunami inundation during an event. The pre-computed tsunami waveforms and inundations are from a total of 340 scenarios ranging from 7.5 to 9.2 in moment magnitude scale (Mw), including simple fault models of 208 thrust faults and 44 tsunami earthquakes on the plate interface, as well as 44 normal faults and 44 reverse faults in the outer-rise region. Using our tsunami inundation forecasting algorithm (NearTIF), we could rapidly estimate the tsunami inundation in Pelabuhan Ratu for three different hypothetical earthquakes. The first hypothetical earthquake is a megathrust earthquake type (Mw 9.0) offshore Sumatra which is about 600 km from Pelabuhan Ratu to represent a worst-case event in the far-field. The second hypothetical earthquake (Mw 8.5) is based on a slip deficit rate estimation from geodetic measurements and represents a most likely large event near Pelabuhan Ratu. The third hypothetical earthquake is a tsunami earthquake type (Mw 8.1) which often occur south off Java. We compared the tsunami inundation maps produced by the NearTIF algorithm with results of direct forward inundation modeling for the hypothetical earthquakes. The tsunami inundation maps produced from both methods are similar for the three cases. However, the tsunami inundation map from the inundation database can be obtained in much shorter time (1 min) than the one from a forward inundation modeling (40 min). These indicate that the NearTIF algorithm based on pre-computed inundation database is reliable and useful for tsunami warning purposes. This study also demonstrates that the NearTIF algorithm can work well even though the earthquake source is located outside the area of fault model database because it uses a time shifting procedure for the best-fit scenario searching.

Scalable Conjunction Processing using Spatiotemporally Indexed Ephemeris Data

NASA Astrophysics Data System (ADS)

Budianto-Ho, I.; Johnson, S.; Sivilli, R.; Alberty, C.; Scarberry, R.

2014-09-01

The collision warnings produced by the Joint Space Operations Center (JSpOC) are of critical importance in protecting U.S. and allied spacecraft against destructive collisions and protecting the lives of astronauts during space flight. As the Space Surveillance Network (SSN) improves its sensor capabilities for tracking small and dim space objects, the number of tracked objects increases from thousands to hundreds of thousands of objects, while the number of potential conjunctions increases with the square of the number of tracked objects. Classical filtering techniques such as apogee and perigee filters have proven insufficient. Novel and orders of magnitude faster conjunction analysis algorithms are required to find conjunctions in a timely manner. Stellar Science has developed innovative filtering techniques for satellite conjunction processing using spatiotemporally indexed ephemeris data that efficiently and accurately reduces the number of objects requiring high-fidelity and computationally-intensive conjunction analysis. Two such algorithms, one based on the k-d Tree pioneered in robotics applications and the other based on Spatial Hash Tables used in computer gaming and animation, use, at worst, an initial O(N log N) preprocessing pass (where N is the number of tracked objects) to build large O(N) spatial data structures that substantially reduce the required number of O(N^2) computations, substituting linear memory usage for quadratic processing time. The filters have been implemented as Open Services Gateway initiative (OSGi) plug-ins for the Continuous Anomalous Orbital Situation Discriminator (CAOS-D) conjunction analysis architecture. We have demonstrated the effectiveness, efficiency, and scalability of the techniques using a catalog of 100,000 objects, an analysis window of one day, on a 64-core computer with 1TB shared memory. Each algorithm can process the full catalog in 6 minutes or less, almost a twenty-fold performance improvement over the baseline implementation running on the same machine. We will present an overview of the algorithms and results that demonstrate the scalability of our concepts.

Recognition Stage for a Speed Supervisor Based on Road Sign Detection

PubMed Central

Carrasco, Juan-Pablo; de la Escalera, Arturo; Armingol, José María

2012-01-01

Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network.

Systematic Review of Measures Used in Pictorial Cigarette Pack Warning Experiments.

PubMed

Francis, Diane B; Hall, Marissa G; Noar, Seth M; Ribisl, Kurt M; Brewer, Noel T

2017-10-01

We sought to describe characteristics and psychometric properties of measures used in pictorial cigarette pack warning experiments and provide recommendations for future studies. Our systematic review identified 68 pictorial cigarette pack warning experiments conducted between 2000 and 2016 in 22 countries. Two independent coders coded all studies on study features, including sample characteristics, theoretical framework, and constructs assessed. We also coded measurement characteristics, including construct, number of items, source, reliability, and validity. We identified 278 measures representing 61 constructs. The most commonly assessed construct categories were warning reactions (62% of studies) and perceived effectiveness (60%). The most commonly used outcomes were affective reactions (35%), perceived likelihood of harm (22%), intention to quit smoking (22%), perceptions that warnings motivate people to quit smoking (18%), and credibility (16%). Only 4 studies assessed smoking behavior. More than half (54%) of all measures were single items. For multi-item measures, studies reported reliability data 68% of the time (mean α = 0.88, range α = 0.68-0.98). Studies reported sources of measures only 33% of the time and rarely reported validity data. Of 68 studies, 37 (54%) mentioned a theory as informing the study. Our review found great variability in constructs and measures used to evaluate the impact of cigarette pack pictorial warnings. Many measures were single items with unknown psychometric properties. Recommendations for future studies include a greater emphasis on theoretical models that inform measurement, use of reliable and validated (preferably multi-item) measures, and better reporting of measure sources. Robust and consistent measurement is important for building a strong, cumulative evidence base to support pictorial cigarette pack warning policies. This systematic review of experimental studies of pictorial cigarette warnings demonstrates the need for standardized, theory-based measures. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Implementing drought early warning systems: policy lessons and future needs

NASA Astrophysics Data System (ADS)

Iglesias, Ana; Werner, Micha; Maia, Rodrigo; Garrote, Luis; Nyabeze, Washington

2014-05-01

Drought forecasting and Warning provides the potential of reducing impacts to society due to drought events. The implementation of effective drought forecasting and warning, however, requires not only science to support reliable forecasting, but also adequate policy and societal response. Here we propose a protocol to develop drought forecasting and early warning based in the international cooperation of African and European institutions in the DEWFORA project (EC, 7th Framework Programme). The protocol includes four major phases that address the scientific knowledge and the social capacity to use the knowledge: (a) What is the science available? Evaluating how signs of impending drought can be detected and predicted, defining risk levels, and analysing of the signs of drought in an integrated vulnerability approach. (b) What are the societal capacities? In this the institutional framework that enables policy development is evaluated. The protocol gathers information on vulnerability and pending hazard in advance so that early warnings can be declared at sufficient lead time and drought mitigation planning can be implemented at an early stage. (c) How can science be translated into policy? Linking science indicators into the actions/interventions that society needs to implement, and evaluating how policy is implemented. Key limitations to planning for drought are the social capacities to implement early warning systems. Vulnerability assessment contributes to identify these limitations and therefore provides crucial information to policy development. Based on the assessment of vulnerability we suggest thresholds for management actions to respond to drought forecasts and link predictive indicators to relevant potential mitigation strategies. Vulnerability assessment is crucial to identify relief, coping and management responses that contribute to a more resilient society. (d) How can society benefit from the forecast? Evaluating how information is provided to potentially affected groups, and how mitigation strategies can be taken in response. This paper presents an outline of the protocol that was developed in the DEWFORA project, outlining the complementary roles of science, policy and societal uptake in effective drought forecasting and warning. A consensus on the need to emphasise the social component of early warning was reached when testing the DEWFORA early warning system protocol among experts from 18 countries.

Towards Operational Meteotsunami Early Warning System: the Adriatic Project MESSI

NASA Astrophysics Data System (ADS)

Vilibic, I.; Sepic, J.; Denamiel, C. L.; Mihanovic, H.; Muslim, S.; Tudor, M.; Ivankovic, D.; Jelavic, D.; Kovacevic, V.; Masce, T.; Dadic, V.; Gacic, M.; Horvath, K.; Monserrat, S.; Rabinovich, A.; Telisman-Prtenjak, M.

2017-12-01

A number of destructive meteotsunamis - atmospherically-driven long ocean waves in a tsunami frequency band - occurred during the last decade through the world oceans. Owing to significant damage caused by these meteotsunamis, several scientific groups (occasionally in collaboration with public offices) have started developing meteotsunami warning systems. Creation of one such system has been initialized in the late 2015 within the MESSI (Meteotsunamis, destructive long ocean waves in the tsunami frequency band: from observations and simulations towards a warning system) project. Main goal of this project is to build a prototype of a meteotsunami warning system for the eastern Adriatic coast. The system will be based on real-time measurements, operational atmosphere and ocean modeling and real time decision-making process. Envisioned MESSI meteotsunami warning system consists of three modules: (1) synoptic warning module, which will use established correlation between forecasted synoptic fields and high-frequency sea level oscillations to provide qualitative meteotsunami forecasts for up to a week in advance, (2) probabilistic premodeling prediction module, which will use operational WRF-ROMS-ADCIRC modeling system and compare the forecast with an atlas of presimulations to get the probabilistic meteotsunami forecast for up to three days in advance, and (3) real-time module, which is based on real time tracking of properties of air pressure disturbance (amplitude, speed, direction, period, ...) and their real-time comparison with the atlas of meteotsunami simulations. System will be tested on recent meteotsunami events which were recorded in the MESSI area shortly after the operational meteotsunami network installation. Albeit complex, such a multilevel warning system has a potential to be adapted to most meteotsunami hot spots, simply by tuning the system parameters to the available atmospheric and ocean data.

REWSET: A prototype seismic and tsunami early warning system in Rhodes island, Greece

NASA Astrophysics Data System (ADS)

Papadopoulos, Gerasimos; Argyris, Ilias; Aggelou, Savvas; Karastathis, Vasilis

2014-05-01

Tsunami warning in near-field conditions is a critical issue in the Mediterranean Sea since the most important tsunami sources are situated within tsunami wave travel times starting from about five minutes. The project NEARTOWARN (2012-2013) supported by the EU-DG ECHO contributed substantially to the development of new tools for the near-field tsunami early warning in the Mediterranean. One of the main achievements is the development of a local warning system in the test-site of Rhodes island (Rhodes Early Warning System for Earthquakes and Tsunamis - REWSET). The system is composed by three main subsystems: (1) a network of eight seismic early warning devices installed in four different localities of the island, one in the civil protection, another in the Fire Brigade and another two in municipality buildings; (2) two radar-type (ultrasonic) tide-gauges installed in the eastern coastal zine of the island which was selected since research on the historical earthquake and tsunami activity has indicated that the most important, near-field tsunami sources are situated offshore to the east of Rhodes; (3) a crisis Geographic Management System (GMS), which is a web-based and GIS-based application incorporating a variety of thematic maps and other information types. The seismic early warning devices activate by strong (magnitude around 6 or more) earthquakes occurring at distances up to about 100 km from Rhodes, thus providing immediate mobilization of the civil protection. The tide-gauges transmit sea level data, while during the crisis the GMS supports decisions to be made by civil protection. In the near future it is planned the REWSET system to be integrated with national and international systems. REWSET is a prototype which certainly could be developed in other coastal areas of the Mediterranean and beyond.

Fast Moment Magnitude Determination from P-wave Trains for Bucharest Rapid Early Warning System (BREWS)

NASA Astrophysics Data System (ADS)

Lizurek, Grzegorz; Marmureanu, Alexandru; Wiszniowski, Jan

2017-03-01

Bucharest, with a population of approximately 2 million people, has suffered damage from earthquakes in the Vrancea seismic zone, which is located about 170 km from Bucharest, at a depth of 80-200 km. Consequently, an earthquake early warning system (Bucharest Rapid earthquake Early Warning System or BREWS) was constructed to provide some warning about impending shaking from large earthquakes in the Vrancea zone. In order to provide quick estimates of magnitude, seismic moment was first determined from P-waves and then a moment magnitude was determined from the moment. However, this magnitude may not be consistent with previous estimates of magnitude from the Romanian Seismic Network. This paper introduces the algorithm using P-wave spectral levels and compares them with catalog estimates. The testing procedure used waveforms from about 90 events with catalog magnitudes from 3.5 to 5.4. Corrections to the P-wave determined magnitudes according to dominant intermediate depth events mechanism were tested for November 22, 2014, M5.6 and October 17, M6 events. The corrections worked well, but unveiled overestimation of the average magnitude result of about 0.2 magnitude unit in the case of shallow depth event ( H < 60 km). The P-wave spectral approach allows for the relatively fast estimates of magnitude for use in BREWS. The average correction taking into account the most common focal mechanism for radiation pattern coefficient may lead to overestimation of the magnitude for shallow events of about 0.2 magnitude unit. However, in case of events of intermediate depth of M6 the resulting M w is underestimated at about 0.1-0.2. We conclude that our P-wave spectral approach is sufficiently robust for the needs of BREWS for both shallow and intermediate depth events.

Detecting Tsunami Source Energy and Scales from GNSS & Laboratory Experiments

NASA Astrophysics Data System (ADS)

Song, Y. T.; Yim, S. C.; Mohtat, A.

2016-12-01

Historically, tsunami warnings based on the earthquake magnitude have not been very accurate. According to the 2006 U.S. Government Accountability Office report, an unacceptable 75% false alarm rate has prevailed in the Pacific Ocean (GAO-06-519). One of the main reasons for those inaccurate warnings is that an earthquake's magnitude is not the scale or power of the resulting tsunami. For the last 10 years, we have been developing both theories and algorithms to detect tsunami source energy and scales, instead of earthquake magnitudes per se, directly from real-time Global Navigation Satellite System (GNSS) stations along coastlines for early warnings [Song 2007; Song et al., 2008; Song et al., 2012; Xu and Song 2013; Titov et al, 2016]. Here we will report recent progress on two fronts: 1) Examples of using GNSS in detecting the tsunami energy scales for the 2004 Sumatra M9.1 earthquake, the 2005 Nias M8.7 earthquake, the 2010 M8.8 Chilean earthquake, the 2011 M9.0 Tohoku-Oki earthquake, and the 2015 M8.3 Illapel earthquake. 2) New results from recent state-of-the-art wave-maker experiments and comparisons with GNSS data will also be presented. Related reference: Titov, V., Y. T. Song, L. Tang, E. N. Bernard, Y. Bar-Sever, and Y. Wei (2016), Consistent estimates of tsunami energy show promise for improved early warning, Pur Appl. Geophs., DOI: 10.1007/s00024-016-1312-1. Xu, Z. and Y. T. Song (2013), Combining the all-source Green's functions and the GPS-derived source for fast tsunami prediction - illustrated by the March 2011 Japan tsunami, J. Atmos. Oceanic Tech., jtechD1200201. Song, Y. T., I. Fukumori, C. K. Shum, and Y. Yi (2012), Merging tsunamis of the 2011 Tohoku-Oki earthquake detected over the open ocean, Geophys. Res. Lett., doi:10.1029/2011GL050767. Song, Y. T., L.-L. Fu, V. Zlotnicki, C. Ji, V. Hjorleifsdottir, C.K. Shum, and Y. Yi, 2008: The role of horizontal impulses of the faulting continental slope in generating the 26 December 2004 Tsunami (2007), Ocean Modelling, doi:10.1016/j.ocemod.2007.10.007. Song, Y. T. (2007) Detecting tsunami genesis and scales directly from coastal GPS stations, Geophys. Res. Lett., 34, L19602, doi:10.1029/2007GL031681.

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Peters-Lidard, Christa; Adler, Robert; Kumar, Sujay; Harrison, Ken

2011-01-01

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

A vantage from space can detect earlier drought onset: an approach using relative humidity.

PubMed

Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao

2015-02-25

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems.

A Vantage from Space Can Detect Earlier Drought Onset: An Approach Using Relative Humidity

PubMed Central

Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao

2015-01-01

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems. PMID:25711500

Flash flood warnings for ungauged basins based on high-resolution precipitation forecasts

NASA Astrophysics Data System (ADS)

Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Janet, Bruno

2016-04-01

Early detection of flash floods, which are typically triggered by severe rainfall events, is still challenging due to large meteorological and hydrologic uncertainties at the spatial and temporal scales of interest. Also the rapid rising of waters necessarily limits the lead time of warnings to alert communities and activate effective emergency procedures. To better anticipate such events and mitigate their impacts, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium (up to 1000 km²) ungauged basins based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The current deterministic AIGA system has been run in real-time in the South of France since 2005 and has been tested in the RHYTMME project (rhytmme.irstea.fr/). It ingests the operational radar-gauge QPE grids from Météo-France to run a simplified hourly distributed hydrologic model at a 1-km² resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. The calibration and regionalization of the hydrologic model has been recently enhanced for implementing the national flash flood warning system for the entire French territory by 2016. To further extend the effective warning lead time, the flash flood warning system is being enhanced to ingest Météo-France's AROME-NWC high-resolution precipitation nowcasts. The AROME-NWC system combines the most recent available observations with forecasts from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015). AROME-NWC pre-operational deterministic precipitation forecasts, produced every hour at a 2.5-km resolution for a 6-hr forecast horizon, were provided for 3 significant rain events in September and November 2014 and ingested as time-lagged ensembles. The time-lagged approach is a practical choice of accounting for the atmospheric forecast uncertainty when no extensive forecast archive is available for statistical modelling. The evaluation on 185 basins in the South of France showed significant improvements in terms of flash flood event detection and effective warning lead-time, compared to warnings from the current AIGA setup (without any future precipitation). Various verification metrics (e.g., Relative Mean Error, Brier Skill Score) show the skill of ensemble precipitation and flow forecasts compared to single-valued persistency benchmarks. Planned enhancements include integrating additional probabilistic NWP products (e.g., AROME precipitation ensembles on longer forecast horizon), accounting for and reducing hydrologic uncertainties from the model parameters and initial conditions via data assimilation, and developing a comprehensive observational and post-event damage database to determine decision-relevant warning thresholds for flood magnitude and probability. Javelle, P., Demargne, J., Defrance, D., Arnaud, P., 2014. Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, doi: 10.1080/02626667.2014.923970 Auger, L., Dupont, O., Hagelin, S., Brousseau, P., Brovelli, P., 2015. AROME-NWC: a new nowcasting tool based on an operational mesoscale forecasting system. Quarterly Journal of the Royal Meteorological Society, 141: 1603-1611, doi: 10.1002/qj.2463

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk.

PubMed

Ceccato, P; Connor, S J; Jeanne, I; Thomson, M C

2005-03-01

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

Certification of windshear performance with RTCA class D radomes

NASA Technical Reports Server (NTRS)

Mathews, Bruce D.; Miller, Fran; Rittenhouse, Kirk; Barnett, Lee; Rowe, William

1994-01-01

Superposition testing of detection range performance forms a digital signal for input into a simulation of signal and data processing equipment and algorithms to be employed in a sensor system for advanced warning of hazardous windshear. For suitable pulse-Doppler radar, recording of the digital data at the input to the digital signal processor furnishes a realistic operational scenario and environmentally responsive clutter signal including all sidelobe clutter, ground moving target indications (GMTI), and large signal spurious due to mainbeam clutter and/or RFI respective of the urban airport clutter and aircraft scenarios (approach and landing antenna pointing). For linear radar system processes, a signal at the same point in the process from a hazard phenomena may be calculated from models of the scattering phenomena, for example, as represented in fine 3 dimensional reflectivity and velocity grid structures. Superposition testing furnishes a competing signal environment for detection and warning time performance confirmation of phenomena uncontrollable in a natural environment.

Caution and Warning Alarm Design and Evaluation for NASA CEV Auditory Displays: SHFE Information Presentation Directed Research Project (DRPP) report 12.07

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Godfroy, Martine; Sandor, Aniko; Holden, Kritina

2008-01-01

The design of caution-warning signals for NASA s Crew Exploration Vehicle (CEV) and other future spacecraft will be based on both best practices based on current research and evaluation of current alarms. A design approach is presented based upon cross-disciplinary examination of psychoacoustic research, human factors experience, aerospace practices, and acoustical engineering requirements. A listening test with thirteen participants was performed involving ranking and grading of current and newly developed caution-warning stimuli under three conditions: (1) alarm levels adjusted for compliance with ISO 7731, "Danger signals for work places - Auditory Danger Signals", (2) alarm levels adjusted to an overall 15 dBA s/n ratio and (3) simulated codec low-pass filtering. Questionnaire data yielded useful insights regarding cognitive associations with the sounds.

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

Wavelet based analysis of multi-electrode EEG-signals in epilepsy

NASA Astrophysics Data System (ADS)

Hein, Daniel A.; Tetzlaff, Ronald

2005-06-01

For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model

NASA Astrophysics Data System (ADS)

Sun, Fengru

2018-01-01

This paper chooses the agricultural listed companies as the research object, compares the financial situation of the enterprise and the theory of financial early warning, combines the financial status of the agricultural listed companies, selects the relevant cash flow indicators, discusses the application of the Logistic financial early warning model in the agricultural listed companies, Agricultural enterprises get better development. Research on financial early warning of agricultural listed companies will help the agricultural listed companies to predict the financial crisis. Financial early warning model is simple to establish, operational and strong, the use of financial early warning model, to help enterprises in the financial crisis before taking rapid and effective measures, which can avoid losses. Help enterprises to discover signs of deterioration of the financial situation in time to maintain the sustainable development of agricultural enterprises. In addition, through the financial early warning model, investors can correctly identify the financial situation of agricultural enterprises, and can evaluate the financial situation of agricultural enterprises and to help investors to invest in scientific and rational, beneficial to investors to analyze the safety of investment. But also help the relevant regulatory agencies to effectively monitor the market and promote the healthy and stable development of the market.

Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling.

PubMed

Recknagel, Friedrich; Orr, Philip T; Bartkow, Michael; Swanepoel, Annelie; Cao, Hongqing

2017-11-01

An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains. Copyright © 2017 Elsevier B.V. All rights reserved.

Volcano early warning system based on MSG-SEVIRI multispectral data

NASA Astrophysics Data System (ADS)

Ganci, Gaetana; Vicari, Annamaria; Del Negro, Ciro

2010-05-01

Spaceborne remote sensing of high-temperature volcanic features offers an excellent opportunity to monitor the onset and development of new eruptive activity. Particularly, images with lower spatial but higher temporal resolution from meteorological satellites have been proved to be a sound instrument for continuous monitoring of volcanic activity, even though the relevant volcanic characteristics are much smaller than the nominal pixel size. The launch of Spinning Enhanced Visible and Infrared Imager (SEVIRI), on August 2002, onboard the geosynchronous platforms MSG1 and MSG2, has opened a new perspective for near real-time volcano monitoring by providing images at 15 minutes interval. Indeed, in spite of the low spatial resolution (3 km2 at nadir), the high frequency of observations afforded by the MSG SEVIRI was recently applied both for forest fire detection and for the monitoring of effusive volcanoes in Europe and Africa. Our Laboratory of Technologies (TecnoLab) at INGV-CT has been developing methods and know-how for the automated acquisition and management of MSG SEVIRI data. To provide a basis for real-time response during eruptive events, we designed and developed the automated system called HOTSAT. Our algorithm takes advantages from both spectral and spatial comparisons. Firstly, we use an adaptive thresholding procedure based on the computation of the spatial standard deviation derived from the immediately neighboring of each pixel to detect "potential" hot pixels. Secondly, it is required to further assess as true or false hotspot detections base on other thresholds test derived from the SEVIRI middle infrared (MIR, 3.9 μm) brightness temperatures taking into account its statistic behavior. Following these procedures, all the computations are based on dynamic thresholds reducing the number of false alarm due to atmospheric conditions. Our algorithm allows also the derivation of radiative power at all "hot" pixels. This is carried out using the MIR radiance method introduced by Wooster et al. [2003] for forest fires. It's based on an approximation of the Plank's Law as a power law. No assumption is made on the thermal structure of the pixel. The radiant flux, i.e. the fire radiative power, is proportional to the calibrated radiance associated to the hot part of the pixel computed as the difference between the observed hotspot pixel radiance in the SEVIRI MIR channel and the background radiance that would have been observed at the same location in the absence of thermal anomalies. The HOTSAT early warning system based on SEVIRI multispectral data is now suitable to be employed in an operational system of volcano monitoring. To validate and test the system some real cases on Mt Etna are presented.

Environment Agency England flood warning systems

NASA Astrophysics Data System (ADS)

Strong, Chris; Walters, Mark; Haynes, Elizabeth; Dobson, Peter

2015-04-01

Context In England around 5 million homes are at risk of flooding. We invest significantly in flood prevention and management schemes but we can never prevent all flooding. Early alerting systems are fundamental to helping us reduce the impacts of flooding. The Environment Agency has had the responsibility for flood warning since 1996. In 2006 we invested in a new dissemination system that would send direct messages to pre-identified recipients via a range of channels. Since then we have continuously improved the system and service we offer. In 2010 we introduced an 'opt-out' service where we pre-registered landline numbers in flood risk areas, significantly increasing the customer base. The service has performed exceptionally well under intense flood conditions. Over a period of 3 days in December 2013, when England was experiencing an east coast storm surge, the system sent nearly 350,000 telephone messages, 85,000 emails and 70,000 text messages, with a peak call rate of around 37,000 per hour and 100% availability. The Floodline Warnings Direct (FWD) System FWD provides warnings in advance of flooding so that people at risk and responders can take action to minimise the impact of the flood. Warnings are sent via telephone, fax, text message, pager or e-mail to over 1.1 million properties located within flood risk areas in England. Triggers for issuing alerts and warnings include attained and forecast river levels and rainfall in some rapidly responding locations. There are three levels of warning: Flood Alert, Flood Warning and Severe Flood Warning, and a stand down message. The warnings can be updated to include relevant information to help inform those at risk. Working with our current provider Fujitsu, the system is under a programme of continuous improvement including expanding the 'opt-out' service to mobile phone numbers registered to at risk addresses, allowing mobile registration to the system for people 'on the move' and providing access to registration via third parties. The 'Future Flood Warning System' Our research shows that people want more choice on how they access and receive warnings. Many want a service tailored to their own risk, rather than that of their community. They also want more information about the forecast and the situation to that they can make decisions personal to their circumstances. Our future flood warning system will build upon the success of our existing service and will aim to: • provide our customers with a more flexible and personalised self-service approach which caters for the diverse range of user needs • alert people wherever they are, not just in properties • be flexible enough to respond to user feedback to make improvements and utilise new technology as it becomes available • provide real-time visualisation of system performance, to assist our flood response • capture greater levels of information from the recipients of our warnings • be efficient for operators of the system and utilise automation where relevant • take a risk based approach to resilience to provide the highest level of reliability when needed at a reduced cost

Performance Analysis of a Citywide Real-time Landslide Early Warning System in Korea

NASA Astrophysics Data System (ADS)

Park, Joon-Young; Lee, Seung-Rae; Kang, Sinhang; Lee, Deuk-hwan; Nedumpallile Vasu, Nikhil

2017-04-01

Rainfall-induced landslide has been one of the major disasters in Korea since the beginning of 21st century when the global climate change started to give rise to the growth of the magnitude and frequency of extreme precipitation events. In order to mitigate the increasing damage to properties and loss of lives and to provide an effective tool for public officials to manage the landslide disasters, a real-time landslide early warning system with an advanced concept has been developed by taking into account for Busan, the second largest metropolitan city in Korea, as an operational test-bed. The system provides with warning information based on a five-level alert scheme (Normal, Attention, Watch, Alert, and Emergency) using the forecasted/observed rainfall data or the data obtained from ground monitoring (volumetric water content and matric suction). The alert levels are determined by applying seven different thresholds in a step-wise manner following a decision tree. In the pursuit of improved reliability of an early warning level assigned to a specific area, the system makes assessments repetitively using the thresholds of different theoretical backgrounds including statistical(empirical), physically-based, and mathematical analyses as well as direct measurement-based approaches. By mapping the distribution of the five early warning levels determined independently for each of tens of millions grids covering the entire mountainous area of Busan, the regional-scale system can also provide with the early warning information for a specific local area. The fact that the highest warning level is determined by using a concept of a numerically-modelled potential debris-flow risk is another distinctive feature of the system. This study tested the system performance by applying it for four previous rainy seasons in order to validate the operational applicability. During the rainy seasons of 2009, 2011, and 2014, the number of landslides recorded throughout Busan's territory reached 156, 64, and 37, respectively. In 2016, only three landslides were recorded even though the city experienced a couple of heavy rainfall events during the rainy season. The system performance test results show good agreement with the observation results for the past rainfall events. It seems that the system can also provide with reliable warning information for the future rainfall events.

Auditory displays as occasion setters.

PubMed

Mckeown, Denis; Isherwood, Sarah; Conway, Gareth

2010-02-01

The aim of this study was to evaluate whether representational sounds that capture the richness of experience of a collision enhance performance in braking to avoid a collision relative to other forms of warnings in a driving simulator. There is increasing interest in auditory warnings that are informative about their referents. But as well as providing information about some intended object, warnings may be designed to set the occasion for a rich body of information about the outcomes of behavior in a particular context. These richly informative warnings may offer performance advantages, as they may be rapidly processed by users. An auditory occasion setter for a collision (a recording of screeching brakes indicating imminent collision) was compared with two other auditory warnings (an abstract and an "environmental" sound), a speech message, a visual display, and no warning in a fixed-base driving simulator as interfaces to a collision avoidance system. The main measure was braking response times at each of two headways (1.5 s and 3 s) to a lead vehicle. The occasion setter demonstrated statistically significantly faster braking responses at each headway in 8 out of 10 comparisons (with braking responses equally fast to the abstract warning at 1.5 s and the environmental warning at 3 s). Auditory displays that set the occasion for an outcome in a particular setting and for particular behaviors may offer small but critical performance enhancements in time-critical applications. The occasion setter could be applied in settings where speed of response by users is of the essence.

Comparing Alcohol Marketing and Alcohol Warning Message Policies Across Canada.

PubMed

Wettlaufer, Ashley; Cukier, Samantha N; Giesbrecht, Norman

2017-08-24

In order to reduce harms from alcohol, evidence-based policies are to be introduced and sustained. To facilitate the dissemination of policies that reduce alcohol-related harms by documenting, comparing, and sharing information on effective alcohol polices related to restrictions on alcohol marketing and alcohol warning messaging in 10 Canadian provinces. Team members developed measurable indicators to assess policies on (a) restrictions on alcohol marketing, and (b) alcohol warning messaging. Indicators were peer-reviewed by three alcohol policy experts, refined, and data were collected, submitted for validation by provincial experts, and scored independently by two team members. The national average score was 52% for restrictions on marketing policies and 18% for alcohol warning message policies. Most provinces had marketing regulations that went beyond the federal guidelines with penalties for violating marketing regulations. The provincial liquor boards' web pages focused on product promotion, and there were few restrictions on sponsorship activities. No province has implemented alcohol warning labels, and Ontario was the sole province to have legislated warning signs at all points-of-sale. Most provinces provided a variety of warning signs to be displayed voluntarily at points-of-sale; however, the quality of messages varied. Conclusions/Importance: There is extensive alcohol marketing with comparatively few messages focused on the potential harms associated with alcohol. It is recommended that governments collaborate with multiple stakeholders to maximize the preventive impact of restrictions on alcohol marketing and advertising, and a broader implementation of alcohol warning messages.

The quest for wisdom: lessons from 17 tsunamis, 2004-2014.

PubMed

Okal, Emile A

2015-10-28

Since the catastrophic Sumatra-Andaman tsunami took place in 2004, 16 other tsunamis have resulted in significant damage and 14 in casualties. We review the fundamental changes that have affected our command of tsunami issues as scientists, engineers and decision-makers, in the quest for improved wisdom in this respect. While several scientific paradigms have had to be altered or abandoned, new algorithms, e.g. the W seismic phase and real-time processing of fast-arriving seismic P waves, give us more powerful tools to estimate in real time the tsunamigenic character of an earthquake. We assign to each event a 'wisdom index' based on the warning issued (or not) during the event, and on the response of the population. While this approach is admittedly subjective, it clearly shows several robust trends: (i) we have made significant progress in our command of far-field warning, with only three casualties in the past 10 years; (ii) self-evacuation by educated populations in the near field is a key element of successful tsunami mitigation; (iii) there remains a significant cacophony between the scientific community and decision-makers in industry and government as documented during the 2010 Maule and 2011 Tohoku events; and (iv) the so-called 'tsunami earthquakes' generating larger tsunamis than expected from the size of their seismic source persist as a fundamental challenge, despite scientific progress towards characterizing these events in real time. © 2015 The Author(s).

Integrated vehicle-based safety systems (IVBSS) : human factors and driver-vehicle interface (DVI) summary report

DOT National Transportation Integrated Search

2008-02-01

The IVBSS program is a four-year, two-phase project to design and evaluate an integrated crash warning system for forward collision, lateral drift, lane-change merge, and curve speed warnings for both light vehicles and heavy trucks. This report, cov...

The role of integrating natural and social science concepts for risk governance and the design of people-centred early warning systems. Case study from the German-Indonesian Tsunami Early Warning System Project (GITEWS)

NASA Astrophysics Data System (ADS)

Gebert, Niklas; Post, Joachim

2010-05-01

The development of early warning systems are one of the key domains of adaptation to global environmental change and contribute very much to the development of societal reaction and adaptive capacities to deal with extreme events. Especially, Indonesia is highly exposed to tsunami. In average every three years small and medium size tsunamis occur in the region causing damage and death. In the aftermath of the Indian Ocean Tsunami 2004, the German and Indonesian government agreed on a joint cooperation to develop a People Centered End-to-End Early Warning System (GITEWS). The analysis of risk and vulnerability, as an important step in risk (and early warning) governance, is a precondition for the design of effective early warning structures by delivering the knowledge base for developing institutionalized quick response mechanisms of organizations involved in the issuing of a tsunami warning, and of populations exposed to react to warnings and to manage evacuation before the first tsunami wave hits. Thus, a special challenge for developing countries is the governance of complex cross-sectoral and cross-scale institutional, social and spatial processes and requirements for the conceptualization, implementation and optimization of a people centered tsunami early warning system. In support of this, the risk and vulnerability assessment of the case study aims at identifying those factors that constitute the causal structure of the (dis)functionality between the technological warning and the social response system causing loss of life during an emergency situation: Which social groups are likely to be less able to receive and respond to an early warning alert? And, are people able to evacuate in due time? Here, only an interdisciplinary research approach is capable to analyze the socio-spatial and environmental conditions of vulnerability and risk and to produce valuable results for decision makers and civil society to manage tsunami risk in the early warning context. This requires the integration of natural / spatial and social science concepts, methods and data: E.g. a scenario based approach for tsunami inundation modeling was developed to provide decision makers with options to decide up to what level they aim to protect their people and territory, on the contrary household surveys were conducted for the spatial analysis of the evacuation preparedness of the population as a function of place specific hazard, risk, warning and evacuation perception; remote sensing was applied for the spatial analysis (land-use) of the socio-physical conditions of a city and region for evacuation; and existing social / population statistics were combined with land-use data for the precise spatial mapping of the population exposed to tsunami risks. Only by utilizing such a comprehensive assessment approach valuable information for risk governance can be generated. The results are mapped using GIS and designed according to the specific needs of different end-users, such as public authorities involved in the design of warning dissemination strategies, land-use planners (shelter planning, road network configuration) and NGOs mandated to provide education for the general public on tsunami risk and evacuation behavior. The case study of the city of Padang (one of the pilot areas of GITEWS), Indonesia clearly show, that only by intersecting social (vulnerability) and natural hazards research a comprehensive picture on tsunami risk can be provided with which risk governance in the early warning context can be conducted in a comprehensive, systemic and sustainable manner.

Robust range estimation with a monocular camera for vision-based forward collision warning system.

PubMed

Park, Ki-Yeong; Hwang, Sun-Young

2014-01-01

We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments.

Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System

PubMed Central

2014-01-01

We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments. PMID:24558344

[Is combining metronidazole and alcohol really hazardous?].

PubMed

Fjeld, Hilde; Raknes, Guttorm

2014-09-16

It is common practice to warn against intake of alcohol (ethanol) when taking metronidazole because of the risk of an effect similar to disulfiram (Antabuse). In this article we investigate whether such a warning has any real basis. KNOWLEDGE BASE: The article is based on a review of relevant literature retrieved through a search in PubMed. A search was also made in the WHO's database on adverse effects. No in-vitro studies, animal models, reports of adverse effects or clinical studies provide any convincing evidence of a disulfiram-like interaction between ethanol and metronidazole. The warning against simultaneous use of alcohol and metronidazole appear to be based on laboratory experiments and individual case histories in which the reported reactions are equally likely to have been caused by ethanol alone or by adverse effects of metronidazole. Recent research does not confirm a clinically relevant interaction between ethanol and metronidazole.

a Process-Based Drought Early Warning Indicator for Supporting State Drought Mitigation Decision

NASA Astrophysics Data System (ADS)

Fu, R.; Fernando, D. N.; Pu, B.

2014-12-01

Drought prone states such as Texas requires creditable and actionable drought early warning ranging from seasonal to multi-decadal scales. Such information cannot be simply extracted from the available climate prediction and projections because of their large uncertainties at regional scales and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA national multi-models ensemble experiment (NMME) and the IPCC AR5 (CMIP5) models, are much more reliable for winter and spring than for the summer season for the US Southern Plains. They also show little connection between the droughts in winter/spring and those in summer, in contrast to the observed dry memory from spring to summer over that region. To mitigate the weakness of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies. Based on these key processes and related fields, we have developed a multivariate principle component statistical model to provide a probabilistic summer drought early warning indicator, using the observed or predicted climate conditions in winter and spring on seasonal scale and climate projection for the mid-21stcentury. The summer drought early warning indicator is constructed in a similar way to the NOAA probabilistic predictions that are familiar to water resource managers. The indicator skill is assessed using the standard NOAA climate prediction assessment tools, i.e., the two alternative forced choice (2AFC) and the Receiver Operating Characteristic (ROC). Comparison with long-term observations suggest that this summer drought early warning indicator is able to capture nearly all the strong summer droughts and outperform the dynamic prediction in this regard over the US Southern Plains. This early warning indicator has been used by the state water agency in May 2014 in briefing the state drought preparedness council and will be provided to stake holders through the website of the Texas state water planning agency. We will also present the results of our ongoing work on using NASA satellite based soil moisture and vegetation stress measurements to further improve the reliability of the summer drought early warning indicator.

Meteotsunamis, destructive tsunami-like waves: from observations and simulations towards a warning system (MESSI)

NASA Astrophysics Data System (ADS)

Sepic, Jadranka; Vilibic, Ivica

2016-04-01

Atmospherically-generated tsunami-like waves, also known as meteotsunamis, pose a severe threat for exposed coastlines. Although not as destructive as ordinary tsunamis, several meters high meteotsunami waves can bring destruction, cause loss of human lives and raise panic. For that reason, MESSI, an integrative meteotsunami research & warning project, has been developed and will be presented herein. The project has a threefold base: (1) research of atmosphere-ocean interaction with focus on (i) source processes in the atmosphere, (ii) energy transfer to the ocean and (iii) along-propagation growth of meteotsunami waves; (2) estimation of meteotsunami occurrence rates in past, present and future climate, and mapping of meteotsunami hazard; (3) construction of a meteotsunami warning system prototype, with the latter being the main objective of the project. Due to a great frequency of meteotsunamis and its complex bathymetry which varies from the shallow shelf in the north towards deep pits in the south, with a number of funnel-shaped bays and harbours substantially amplifying incoming tsunami-like waves, the Adriatic, northernmost of the Mediterranean seas, has been chosen as an ideal area for realization of the MESSI project and implementation of the warning system. This warning system will however be designed to allow for a wider applicability and easy-to-accomplish transfer to other endangered locations. The architecture of the warning system will integrate several components: (1) real-time measurements of key oceanographic and atmospheric parameters, (2) coupled atmospheric-ocean models run in real time (warning) mode, and (3) semi-automatic procedures and protocols for warning of civil protection, local authorities and public. The effectiveness of the warning system will be tested over the historic events.

Do larger graphic health warnings on standardised cigarette packs increase adolescents’ cognitive processing of consumer health information and beliefs about smoking-related harms?

PubMed Central

White, Victoria; Williams, Tahlia; Faulkner, Agatha; Wakefield, Melanie

2015-01-01

Objective To examine the impact of plain packaging of cigarettes with enhanced graphic health warnings on Australian adolescents’ cognitive processing of warnings and awareness of different health consequences of smoking. Methods Cross-sectional school-based surveys conducted in 2011 (prior to introduction of standardised packaging, n=6338) and 2013 (7–12 months afterwards, n=5915). Students indicated frequency of attending to, reading, thinking or talking about warnings. Students viewed a list of diseases or health effects and were asked to indicate whether each was caused by smoking. Two—‘kidney and bladder cancer’ and ‘damages gums and teeth’—were new while the remainder had been promoted through previous health warnings and/or television campaigns. The 60% of students seeing a cigarette pack in previous 6 months in 2011 and 65% in 2013 form the sample for analysis. Changes in responses over time are examined. Results Awareness that smoking causes bladder cancer increased between 2011 and 2013 (p=0.002). There was high agreement with statements reflecting health effects featured in previous warnings or advertisements with little change over time. Exceptions to this were increases in the proportion agreeing that smoking was a leading cause of death (p<0.001) and causes blindness (p<0.001). The frequency of students reading, attending to, thinking or talking about the health warnings on cigarette packs did not change. Conclusions Acknowledgement of negative health effects of smoking among Australian adolescents remains high. Apart from increased awareness of bladder cancer, new requirements for packaging and health warnings did not increase adolescents’ cognitive processing of warning information. PMID:28407612

Design of flood early warning system with wifi network based on smartphone

NASA Astrophysics Data System (ADS)

Supani, Ahyar; Andriani, Yuli; Taqwa, Ahmad

2017-11-01

Today, the development using internet of things enables activities surrounding us to be monitored, controlled, predicted and calculated remotely through connections to the internet network such as monitoring activities of long-distance flood warning with information technology. Applying an information technology in the field of flood early warning has been developed in the world, either connected to internet network or not. The internet network that has been done in this paper is the design of WiFi network to access data of rainfall, water level and flood status at any time with a smartphone coming from flood early warning system. The results obtained when test of data accessing with smartphone are in form of rainfall and water level graphs against time and flood status indicators consisting of 3 flood states: Standby 2, Standby 1 and Flood. It is concluded that data are from flood early warning system has been able to accessed and displayed on smartphone via WiFi network in any time and real time.

Using fear appeals in warning labels to promote responsible gambling among VLT players: the key role of depth of information processing.

PubMed

Munoz, Yaromir; Chebat, Jean-Charles; Suissa, Jacob Amnon

2010-12-01

Video lottery terminals (VLT) are a highly lucrative gambling format, but at the same time they are among the most hazardous. Previous research has shown that threatening warnings may be an appropriate approach for promoting protective behavior. The present study explores the potential benefits of threatening warnings in the fight against compulsive gambling. A 4 × 2 factorial design experiment was used to test our model based on both Elaboration Likelihood Model and Protection Motivation Theory. 258 VLT adult players (58% males, 42% females) with various degrees of problem gambling were exposed to three threat levels (plus a control condition) from two different sources (i.e., either a medical source or a source related to the provider of VLT's). Our results show that both higher threat warnings and the medical source of warnings enhance Depth of Information Processing. It was also found that Depth of Information Processing affects positively attitude change and compliance intentions. The theoretical and managerial implications are discussed.

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure

PubMed Central

Chen, Yulong; Irfan, Muhammad; Uchimura, Taro; Zhang, Ke

2018-01-01

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate. PMID:29584699

Design and internal validation of an obstetric early warning score: secondary analysis of the Intensive Care National Audit and Research Centre Case Mix Programme database.

PubMed

Carle, C; Alexander, P; Columb, M; Johal, J

2013-04-01

We designed and internally validated an aggregate weighted early warning scoring system specific to the obstetric population that has the potential for use in the ward environment. Direct obstetric admissions from the Intensive Care National Audit and Research Centre's Case Mix Programme Database were randomly allocated to model development (n = 2240) or validation (n = 2200) sets. Physiological variables collected during the first 24 h of critical care admission were analysed. Logistic regression analysis for mortality in the model development set was initially used to create a statistically based early warning score. The statistical score was then modified to create a clinically acceptable early warning score. Important features of this clinical obstetric early warning score are that the variables are weighted according to their statistical importance, a surrogate for the FI O2 /Pa O2 relationship is included, conscious level is assessed using a simplified alert/not alert variable, and the score, trigger thresholds and response are consistent with the new non-obstetric National Early Warning Score system. The statistical and clinical early warning scores were internally validated using the validation set. The area under the receiver operating characteristic curve was 0.995 (95% CI 0.992-0.998) for the statistical score and 0.957 (95% CI 0.923-0.991) for the clinical score. Pre-existing empirically designed early warning scores were also validated in the same way for comparison. The area under the receiver operating characteristic curve was 0.955 (95% CI 0.922-0.988) for Swanton et al.'s Modified Early Obstetric Warning System, 0.937 (95% CI 0.884-0.991) for the obstetric early warning score suggested in the 2003-2005 Report on Confidential Enquiries into Maternal Deaths in the UK, and 0.973 (95% CI 0.957-0.989) for the non-obstetric National Early Warning Score. This highlights that the new clinical obstetric early warning score has an excellent ability to discriminate survivors from non-survivors in this critical care data set. Further work is needed to validate our new clinical early warning score externally in the obstetric ward environment. Anaesthesia © 2013 The Association of Anaesthetists of Great Britain and Ireland.

Development of web-based services for an ensemble flood forecasting and risk assessment system

NASA Astrophysics Data System (ADS)

Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

2010-05-01

Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

Public understanding of cyclone warning in India: Can wind be predicted?

PubMed

Dash, Biswanath

2015-11-01

In spite of meteorological warning, many human lives are lost every year to cyclone mainly because vulnerable populations were not evacuated on time to a safe shelter as per recommendation. It raises several questions, most prominently what explains people's behaviour in the face of such danger from a cyclonic storm? How do people view meteorological advisories issued for cyclone and what role they play in defining the threat? What shapes public response during such situation? This article based on an ethnographic study carried out in coastal state of Odisha, India, argues that local public recognising inherent limitations of meteorological warning, fall back on their own system of observation and forecasting. Not only are the contents of cyclone warning understood, its limitations are accommodated and explained. © The Author(s) 2014.

Implementation and Challenges of the Tsunami Warning System in the Western Mediterranean

NASA Astrophysics Data System (ADS)

Schindelé, F.; Gailler, A.; Hébert, H.; Loevenbruck, A.; Gutierrez, E.; Monnier, A.; Roudil, P.; Reymond, D.; Rivera, L.

2015-03-01

The French Tsunami Warning Center (CENALT) has been in operation since 2012. It is contributing to the North-eastern and Mediterranean (NEAM) tsunami warning and mitigation system coordinated by the United Nations Educational, Scientific, and Cultural Organization, and benefits from data exchange with several foreign institutes. This center is supported by the French Government and provides French civil-protection authorities and member states of the NEAM region with relevant messages for assessing potential tsunami risk when an earthquake has occurred in the Western Mediterranean sea or the Northeastern Atlantic Ocean. To achieve its objectives, CENALT has developed a series of innovative techniques based on recent research results in seismology for early tsunami warning, monitoring of sea level variations and detection capability, and effective numerical computation of ongoing tsunamis.

Impact of social preparedness on flood early warning systems

NASA Astrophysics Data System (ADS)

Girons Lopez, M.; Di Baldassarre, G.; Seibert, J.

2017-01-01

Flood early warning systems play a major role in the disaster risk reduction paradigm as cost-effective methods to mitigate flood disaster damage. The connections and feedbacks between the hydrological and social spheres of early warning systems are increasingly being considered as key aspects for successful flood mitigation. The behavior of the public and first responders during flood situations, determined by their preparedness, is heavily influenced by many behavioral traits such as perceived benefits, risk awareness, or even denial. In this study, we use the recency of flood experiences as a proxy for social preparedness to assess its impact on the efficiency of flood early warning systems through a simple stylized model and implemented this model using a simple mathematical description. The main findings, which are based on synthetic data, point to the importance of social preparedness for flood loss mitigation, especially in circumstances where the technical forecasting and warning capabilities are limited. Furthermore, we found that efforts to promote and preserve social preparedness may help to reduce disaster-induced losses by almost one half. The findings provide important insights into the role of social preparedness that may help guide decision-making in the field of flood early warning systems.

Experiences from coordinated national-level landslide and flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

2015-04-01

While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

Detecting and tracking dust outbreaks by using high temporal resolution satellite data

NASA Astrophysics Data System (ADS)

Sannazzaro, Filomena; Marchese, Francesco; Filizzola, Carolina; Tramutoli, Valerio; Pergola, Nicola; Mazzeo, Giuseppe; Paciello, Rossana

2013-04-01

A dust storm is a meteorological phenomenon generated by the action of wind, mainly in arid and semi-arid regions of the planet, particularly at subtropical latitudes. Dust outbreaks, of which frequency increases from year to year concurrently with climate change and reduction of moisture in the soil, may strongly impact on human activity as well as on environment and climate. Efficient early warning systems are then required to monitor them and to mitigate their effects. Satellite remote sensing thanks to a global coverage, to a high frequency of observation and low costs of data represents an important tool for studying and monitoring dust outbreaks. Several techniques have been then proposed to detect and monitor these phenomena from space, analyzing signal in different bands of the electromagnetic spectrum. In particular, methods based on the reverse absorption behaviour of silicate particles in comparison with ice crystals and water droplets, at 11 and 12 micron wavelengths, have been largely employed for detecting dust, although some important issues both in terms of reliability and sensitivity still remain. In this work, an optimized configuration of an innovative algorithm for dust detection, based on the largely accepted Robust Satellite Techniques (RST) multi-temporal approach, is then presented. This optimized algorithm configuration is tested here on Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, analyzing some important dust events affecting Mediterranean basin in recent years. Results of this study, assessed on the basis of independent satellite-based aerosol products, generated by using the Total Ozone Mapping Spectrometer (TOMS), the Ozone Monitoring Instrument (OMI), and the Moderate Resolution Imaging Spectroradiometer (MODIS) data, show that when the spectral resolution of SEVIRI is properly exploited dust and meteorological clouds may be better discriminated. These results encourage further experimentations of the proposed algorithm in view of a possible future implementation in operational monitoring systems.

Modeling Driver Behavior near Intersections in Hidden Markov Model

PubMed Central

Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei

2016-01-01

Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838

How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits †

PubMed Central

Gámez Serna, Citlalli; Ruichek, Yassine

2017-01-01

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger. PMID:28613251

Requirements Flowdown for Prognostics and Health Management

NASA Technical Reports Server (NTRS)

Goebel, Kai; Saxena, Abhinav; Roychoudhury, Indranil; Celaya, Jose R.; Saha, Bhaskar; Saha, Sankalita

2012-01-01

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

Soldier detection using unattended acoustic and seismic sensors

NASA Astrophysics Data System (ADS)

Naz, P.; Hengy, S.; Hamery, P.

2012-06-01

During recent military conflicts, as well as for security interventions, the urban zone has taken a preponderant place. Studies have been initiated in national and in international programs to stimulate the technical innovations for these specific scenarios. For example joint field experiments have been organized by the NATO group SET-142 to evaluate the capability for the detection and localization of snipers, mortars or artillery guns using acoustic devices. Another important operational need corresponds to the protection of military sites or buildings. In this context, unattended acoustic and seismic sensors are envisaged to contribute to the survey of specific points by the detection of approaching enemy soldiers. This paper describes some measurements done in an anechoic chamber and in free field to characterize typical sounds generated by the soldier activities (walking, crawling, weapon handling, radio communication, clothing noises...). Footstep, speech and some specific impulsive sounds are detectable at various distances from the source. Such detection algorithms may be easily merged with the existing weapon firing detection algorithms to provide a more generic "battlefield acoustic" early warning system. Results obtained in various conditions (grassy terrain, gravel path, road, forest) will be presented. A method to extrapolate the distances of detection has been developed, based on an acoustic propagation model and applied to the laboratory measurements.

Method for detecting and avoiding flight hazards

NASA Astrophysics Data System (ADS)

von Viebahn, Harro; Schiefele, Jens

1997-06-01

Today's aircraft equipment comprise several independent warning and hazard avoidance systems like GPWS, TCAS or weather radar. It is the pilot's task to monitor all these systems and take the appropriate action in case of an emerging hazardous situation. The developed method for detecting and avoiding flight hazards combines all potential external threats for an aircraft into a single system. It is based on an aircraft surrounding airspace model consisting of discrete volume elements. For each element of the volume the threat probability is derived or computed from sensor output, databases, or information provided via datalink. The position of the own aircraft is predicted by utilizing a probability distribution. This approach ensures that all potential positions of the aircraft within the near future are considered while weighting the most likely flight path. A conflict detection algorithm initiates an alarm in case the threat probability exceeds a threshold. An escape manoeuvre is generated taking into account all potential hazards in the vicinity, not only the one which caused the alarm. The pilot gets a visual information about the type, the locating, and severeness o the threat. The algorithm was implemented and tested in a flight simulator environment. The current version comprises traffic, terrain and obstacle hazards avoidance functions. Its general formulation allows an easy integration of e.g. weather information or airspace restrictions.

Real-time 3D change detection of IEDs

NASA Astrophysics Data System (ADS)

Wathen, Mitch; Link, Norah; Iles, Peter; Jinkerson, John; Mrstik, Paul; Kusevic, Kresimir; Kovats, David

2012-06-01

Road-side bombs are a real and continuing threat to soldiers in theater. CAE USA recently developed a prototype Volume based Intelligence Surveillance Reconnaissance (VISR) sensor platform for IED detection. This vehicle-mounted, prototype sensor system uses a high data rate LiDAR (1.33 million range measurements per second) to generate a 3D mapping of roadways. The mapped data is used as a reference to generate real-time change detection on future trips on the same roadways. The prototype VISR system is briefly described. The focus of this paper is the methodology used to process the 3D LiDAR data, in real-time, to detect small changes on and near the roadway ahead of a vehicle traveling at moderate speeds with sufficient warning to stop the vehicle at a safe distance from the threat. The system relies on accurate navigation equipment to geo-reference the reference run and the change-detection run. Since it was recognized early in the project that detection of small changes could not be achieved with accurate navigation solutions alone, a scene alignment algorithm was developed to register the reference run with the change detection run prior to applying the change detection algorithm. Good success was achieved in simultaneous real time processing of scene alignment plus change detection.

Research on Early Warning of Chinese Food Safety Based on Social Physics

NASA Astrophysics Data System (ADS)

Ma, Yonghuan; Niu, Wenyuan; Li, Qianqian

Based on social physics, this paper designs the index system of food safety, builds early warning model of food safety, calculates the degree of food safety, and assesses the state of early warning of 2007 in China. The result shows the degree of food safety is near 0.7 in securer state, belonging to slight emergency. It is much lower in eastern areas of developed regions, belonging to insecure state in the mass. That the food safety is ensured in major grain producing areas, Inner Mongolia, Ningxia and Xinjiang is the prerequisite of realizing the food safety of China. The result also shows four significant indices, grain production capacity, grain circulation order, grain demand and grain supply, which are important indicatio to control food safety.

Enabling analytical and Modeling Tools for Enhanced Disease Surveillance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dawn K. Manley

2003-04-01

Early detection, identification, and warning are essential to minimize casualties from a biological attack. For covert attacks, sick people are likely to provide the first indication of an attack. An enhanced medical surveillance system that synthesizes distributed health indicator information and rapidly analyzes the information can dramatically increase the number of lives saved. Current surveillance methods to detect both biological attacks and natural outbreaks are hindered by factors such as distributed ownership of information, incompatible data storage and analysis programs, and patient privacy concerns. Moreover, because data are not widely shared, few data mining algorithms have been tested on andmore » applied to diverse health indicator data. This project addressed both integration of multiple data sources and development and integration of analytical tools for rapid detection of disease outbreaks. As a first prototype, we developed an application to query and display distributed patient records. This application incorporated need-to-know access control and incorporated data from standard commercial databases. We developed and tested two different algorithms for outbreak recognition. The first is a pattern recognition technique that searches for space-time data clusters that may signal a disease outbreak. The second is a genetic algorithm to design and train neural networks (GANN) that we applied toward disease forecasting. We tested these algorithms against influenza, respiratory illness, and Dengue Fever data. Through this LDRD in combination with other internal funding, we delivered a distributed simulation capability to synthesize disparate information and models for earlier recognition and improved decision-making in the event of a biological attack. The architecture incorporates user feedback and control so that a user's decision inputs can impact the scenario outcome as well as integrated security and role-based access-control for communicating between distributed data and analytical tools. This work included construction of interfaces to various commercial database products and to one of the data analysis algorithms developed through this LDRD.« less

The role of health consciousness in predicting attention to health warning messages.

PubMed

Kaskutas, L A; Greenfield, T K

1997-01-01

Guided by information processing theory and the health belief model, this paper considers the relationship between health consciousness among the general population and attention to environmental health warnings about alcohol consumption. Mechanisms of exposure to three dominant types of impersonal alcohol-related health messages in the environment are explored. Cross-sectional survey using telephone interview data. A representative nationwide sample of adults was interviewed in 1993 (n = 1026), with a response rate of 63%. Key variables include exposure to warning labels on alcoholic beverages, to point-of-sale posters, and to advertisements in the media, as well as respondents' alcohol consumption, health problems (indicative of salience of health warnings), and level of health consciousness assessed by items tapping concern with nutrition and seeking information on health topics. In the total sample, over a third had seen a warning label or poster and almost all had seen an advertisement about the risks associated with alcohol consumption in 1993. Survey respondents scored very high on five individual items that make up the health consciousness scale introduced here, with 69% endorsing all items. The scale demonstrated good internal reliability (alpha = .70) and was significantly correlated (p < .01) with not enjoying getting drunk and with usually reading product warning labels, suggesting construct validity. Yet the hypothesized strong relationships between health consciousness and attention to health warnings about drinking were not observed; nor was salience of messages a predictor of recall. Importantly, high proportions of underage drinkers and young adults at elevated risk for drinking problems are reached by container warning label messages. Mechanisms of exposure recall vary based on message source, with "container label recall" associated with heavier drinking, younger age, and purchasing patterns; "poster recall" associated with purchasing and health consciousness; and "advertisement recall" associated with heavy consumption and younger age. These results are contrary to predictions from skeptics of broad-based informational interventions, who argue that only the already-health conscious are attentive to health warnings about the risks of alcohol consumption. These data suggest that the label is reaching intended target audiences, especially younger people, males, and heavier alcohol consumers. Future research in predicting attention to impersonal health warnings in the environment should continue to improve the assessment of constructs such as salience and health consciousness, and should further test the applicability of available theoretical models. Subsequent research should also consider additional measures to tap mechanisms of exposure to impersonal health messages to enable a better understanding of the population that is not being reached by such public health interventions.

Validation of Satellite-based Rainfall Estimates for Severe Storms (Hurricanes & Tornados)

NASA Astrophysics Data System (ADS)

Nourozi, N.; Mahani, S.; Khanbilvardi, R.

2005-12-01

Severe storms such as hurricanes and tornadoes cause devastating damages, almost every year, over a large section of the United States. More accurate forecasting intensity and track of a heavy storm can help to reduce if not to prevent its damages to lives, infrastructure, and economy. Estimating accurate high resolution quantitative precipitation (QPE) from a hurricane, required to improve the forecasting and warning capabilities, is still a challenging problem because of physical characteristics of the hurricane even when it is still over the ocean. Satellite imagery seems to be a valuable source of information for estimating and forecasting heavy precipitation and also flash floods, particularly for over the oceans where the traditional ground-based gauge and radar sources cannot provide any information. To improve the capability of a rainfall retrieval algorithm for estimating QPE of severe storms, its product is evaluated in this study. High (hourly 4km x 4km) resolutions satellite infrared-based rainfall products, from the NESDIS Hydro-Estimator (HE) and also PERSIANN (Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Networks) algorithms, have been tested against NEXRAD stage-IV and rain gauge observations in this project. Three strong hurricanes: Charley (category 4), Jeanne (category 3), and Ivan (category 3) that caused devastating damages over Florida in the summer 2004, have been considered to be investigated. Preliminary results demonstrate that HE tends to underestimate rain rates when NEXRAD shows heavy storm (rain rates greater than 25 mm/hr) and to overestimate when NEXRAD gives low rainfall amounts, but PERSIANN tends to underestimate rain rates, in general.

Literature Search for Federal Highway Administration (ITS-JPO) : assessment of state of the practice and state of the art in evacuation transportation management

DOT National Transportation Integrated Search

2006-02-06

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

77 FR 36389 - Airworthiness Directives; Bell Helicopter Textron Canada, Limited, Helicopters

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-19

...-temperature warning light, and based on those findings, installing an NVIS filter. This AD was prompted by the... over-temperature warning light by installing a filter to prevent degradation of the pilot's vision... require installing an NVIS filter, part number (P/N) ASU-TOTGAG-1. The proposed requirements were intended...

Early Warning Signs. A Solution-Finding Report

ERIC Educational Resources Information Center

Sullivan, Robert, Comp.

2017-01-01

This Solution-Finding Report provides information, requested by Tara Zuber with the Great Lakes Comprehensive Center (GLCC) at American Institutes for Research (AIR), for resources with evidence-based practices that look at the social and emotional causes that impact the lack of student learning and engagement, for GLCC's Early Warning Signs work.…

Toward the Real-Time Tsunami Parameters Prediction

NASA Astrophysics Data System (ADS)

Lavrentyev, Mikhail; Romanenko, Alexey; Marchuk, Andrey

2013-04-01

Today, a wide well-developed system of deep ocean tsunami detectors operates over the Pacific. Direct measurements of tsunami-wave time series are available. However, tsunami-warning systems fail to predict basic parameters of tsunami waves on time. Dozens examples could be provided. In our view, the lack of computational power is the main reason of these failures. At the same time, modern computer technologies such as, GPU (graphic processing unit) and FPGA (field programmable gates array), can dramatically improve data processing performance, which may enhance timely tsunami-warning prediction. Thus, it is possible to address the challenge of real-time tsunami forecasting for selected geo regions. We propose to use three new techniques in the existing tsunami warning systems to achieve real-time calculation of tsunami wave parameters. First of all, measurement system (DART buoys location, e.g.) should be optimized (both in terms of wave arriving time and amplitude parameter). The corresponding software application exists today and is ready for use [1]. We consider the example of the coastal line of Japan. Numerical tests show that optimal installation of only 4 DART buoys (accounting the existing sea bed cable) will reduce the tsunami wave detection time to only 10 min after an underwater earthquake. Secondly, as was shown by this paper authors, the use of GPU/FPGA technologies accelerates the execution of the MOST (method of splitting tsunami) code by 100 times [2]. Therefore, tsunami wave propagation over the ocean area 2000*2000 km (wave propagation simulation: time step 10 sec, recording each 4th spatial point and 4th time step) could be calculated at: 3 sec with 4' mesh 50 sec with 1' mesh 5 min with 0.5' mesh The algorithm to switch from coarse mesh to the fine grain one is also available. Finally, we propose the new algorithm for tsunami source parameters determination by real-time processing the time series, obtained at DART. It is possible to approximate the measured time series by a linear combination of synthetic marigrams. Coefficients of such linear combination are calculated with the help of orthogonal decomposition. The algorithm is very fast and demonstrates good accuracy. Summing up, using the example of the coastal line of Japan, wave height evaluation will be available in 12-14 minutes after the earthquake even before the wave approaches the nearest shore point (usually, it takes places in about 20 minutes). The determination of the optimal sensors' location using genetic algorithm / A.S.Astrakova, D.V.Bannikov, S.G.Cherny, M.M.Lavrentiev // 3rd Nordic EMW Summer School, Turku, Finland, June, 2009: proceedings - Finland: TUSC General Publications, 2009. - N 53. - P.5-22. M.Lavrentiev Jr., A.Romanenko, "Modern Hardware Solutions to Speed Up Tsunami Simulation Codes", Geophysical research abstracts, Vol. 12, EGU2010-3835, 2010

An automatic tsunami warning system: TREMORS application in Europe

NASA Astrophysics Data System (ADS)

Reymond, D.; Robert, S.; Thomas, Y.; Schindelé, F.

1996-03-01

An integrated system named TREMORS (Tsunami Risk Evaluation through seismic Moment of a Real-time System) has been installed in EVORA station, in Portugal which has been affected by historical tsunamis. The system is based on a three component long period seismic station linked to a compatible IBM_PC with a specific software. The goals of this system are the followings: detect earthquake, locate them, compute their seismic moment, give a seismic warning. The warnings are based on the seismic moment estimation and all the processing are made automatically. The finality of this study is to check the quality of estimation of the main parameters of interest in a goal of tsunami warning: the location which depends of azimuth and distance, and at last the seismic moment, M 0, which controls the earthquake size. The sine qua non condition for obtaining an automatic location is that the 3 main seismic phases P, S, R must be visible. This study gives satisfying results (automatic analysis): ± 5° errors in azimuth and epicentral distance, and a standard deviation of less than a factor 2 for the seismic moment M 0.

The Lake Victoria Intense Storm Early Warning System (VIEWS)

NASA Astrophysics Data System (ADS)

Thiery, Wim; Gudmundsson, Lukas; Bedka, Kristopher; Semazzi, Fredrick; Lhermitte, Stef; Willems, Patrick; van Lipzig, Nicole; Seneviratne, Sonia I.

2017-04-01

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on NWP, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the input dataset. We then optimise the configuration and show that also false alarms contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Early warnings of hazardous thunderstorms over Lake Victoria

NASA Astrophysics Data System (ADS)

Thiery, Wim; Gudmundsson, Lukas; Bedka, Kristopher; Semazzi, Fredrick H. M.; Lhermitte, Stef; Willems, Patrick; van Lipzig, Nicole P. M.; Seneviratne, Sonia I.

2017-07-01

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on numerical weather prediction, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the type of input dataset. We then optimise the configuration and show that false alarms also contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Technology to Reduce Hypoglycemia

PubMed Central

Yeoh, Ester; Choudhary, Pratik

2015-01-01

Hypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article reviews technological strategies, from simple to more advanced technologies, which may help prevent or mitigate exposure to hypoglycemia. More efficient insulin delivery systems, bolus advisor calculators, data downloads providing information on glucose trends, continuous glucose monitoring with alarms warning of hypoglycemia, predictive algorithms, and finally closed loop insulin delivery systems are reviewed. The building blocks to correct use and interpretation of this range of available technology require patient education and appropriate patient selection. PMID:25883167

Development of a national Flash flood warning system in France using the AIGA method: first results and main issues

NASA Astrophysics Data System (ADS)

Javelle, Pierre; Organde, Didier; Demargne, Julie; de Saint-Aubin, Céline; Garandeau, Léa; Janet, Bruno; Saint-Martin, Clotilde; Fouchier, Catherine

2016-04-01

Developing a national flash flood (FF) warning system is an ambitious and difficult task. On one hand it rises huge expectations from exposed populations and authorities since induced damages are considerable (ie 20 casualties in the recent October 2015 flood at the French Riviera). But on the other hand, many practical and scientific issues have to be addressed and limitations should be clearly stated. The FF warning system to be implemented by 2016 in France by the SCHAPI (French national service in charge of flood forecasting) will be based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The AIGA method has been experimented in real time in the south of France in the RHYTMME project (http://rhytmme.irstea.fr). It consists in comparing discharges generated by a simple conceptual hourly hydrologic model run at a 1-km² resolution to reference flood quantiles of different return periods, at any point along the river network. The hydrologic model ingests operational rainfall radar-gauge products from Météo-France. Model calibration was based on ~700 hydrometric stations over the 2002-2015 period and then hourly discharges were computed at ~76 000 catchment outlets, with areas ranging from 10 to 3 500 km², over the last 19 years. This product makes it possible to calculate reference flood quantiles at each outlet. The on-going evaluation of the FF warnings is currently made at two levels: in a 'classical' way, using discharges available at the hydrometric stations, but also in a more 'exploratory' way, by comparing past flood reports and warnings issued by the system over the 76 000 catchment outlets. The interest of the last method is that it better fit the system objectives since it is designed to monitor small ungauged catchments. Javelle, P., Demargne, J., Defrance, D, .Pansu, J, .Arnaud, P. (2014). Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 59(7), 1390-1402. doi: 10.1080/02626667.2014.923970

Local Tsunami Warnings using GNSS and Seismic Data.

NASA Astrophysics Data System (ADS)

Hirshorn, B. F.

2017-12-01

Tsunami warning Centers (TWC's) must issue warnings based on imperfect and limited data. Uncertainties increase in the near field, where a tsunami reaches the closest coastal populations to the causative earthquake in a half hour or less. In the absence of a warning, the usual advice is "When the ground shakes so severely that it's difficult to stand, move uphill and away from the coast." But, what if the shaking is not severe? If, for example, the earthquake ruptures slowly (producing very little perceived shaking) this advice will fail. Unfortunately these "Tsunami" earthquakes are not rare: tsunamis from slow earthquakes off of Nicaragua in 1992, and Java in 1994 and 2006, killed 179, 250 and 637 people, respectively, even though very few nearby coastal residents felt any strong ground shaking. TWC's must therefore warn the closest coastal populations to the causative earthquake, where over 80% of the Tsunami based casualties typically occur, as soon possible after earthquake rupture begins. The NWS Tsunami Warning Centers (TWCs) currently issue local Tsunami Warnings for the US West Coast, Hawaii, and the Puerto Rico - Virgin Island region within 2-4 minutes after origin time. However, our initial short period Magnitude estimates saturate over about Mw 6.5, and Mwp underestimates Mw for events larger than about Mw 7.5 when using data in the 0 to 3 degree epicentral distance range, severely underestimating the danger of a potential Tsunami in the near field. Coastal GNSS networks complement seismic monitoring networks, and enable unsaturated estimates of Mw within 2-3 minutes of earthquake origin time. NASA/JPL, SIO, USGS, CWU, UCB and UW, with funding and guidance from NASA, and leveraging the USGS funded ShakeAlert development, have been working with the National Weather Service TWC's to incorporate real-time GNSS and seismogeodetic data into their operations. These data will soon provide unsaturated estimates of moment magnitude, Centroid Moment Tensor solutions, coseismic crustal deformation, and fault slip models within a few minutes after earthquake initiation. The sea floor deformation associated with the earthquake slip can then be used as an initial condition for an automatically generated tsunami propagation and coastal inundation model for coastal warnings.

Australian Smokers' and Nonsmokers' Exposure to Antismoking Warnings in Day-to-Day Life: A Pilot Study.

PubMed

Schüz, Natalie; Ferguson, Stuart G

2015-07-01

Smokers and nonsmokers can encounter a variety of antismoking messages in their everyday life. Antismoking warnings often involve fear appeals to which particularly smokers may react in a defensive manner by avoiding or derogating the messages, or downplaying their personal risk. However, previous studies testing the effects of antismoking warnings have either been retrospective or lab-based, thus introducing potential recall biases and yielding limited ecological validity. We used ecological momentary assessment (EMA) to give an overview on the number, type, and locations where individuals encounter such messages and to examine their immediate reactions. In an EMA study, 33 smokers and 37 never-smokers logged every encounter with antismoking warnings during 2.5 weeks (1,237 participant days of monitoring). After randomly selected encounters, several markers of defensiveness were assessed. On average, nonsmokers reported noticing significantly fewer warnings than smokers (M = 0.49/day vs. M = 2.14/day). Both groups saw the majority of warnings on cigarette packages. Smokers reported a significantly higher level of message derogation and a significantly lower level of message acceptance than nonsmokers. There were no differences in feelings of vulnerability between smokers and nonsmokers upon encountering the warnings. The overall number of encounters with antismoking warnings in people's everyday life is relatively low, particularly among smokers. Smokers are likely to avoid messages and respond defensively, thus limiting their potential effectiveness. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The impact of structural packaging design on young adult smokers' perceptions of tobacco products.

PubMed

Borland, Ron; Savvas, Steven; Sharkie, Fiona; Moore, Karen

2013-03-01

To examine the extent that novel cigarette pack shapes and openings have on smokers' perceptions of those packs and the cigarettes contained within. Using a web-based survey, 160 young adult ever-smokers (18-29 years) were shown computer images of plain packaged cigarette packs in five different shapes. This was followed by packs illustrating five different methods of opening. Brand (prestige or budget) and size of the health warnings (30% or 70% warning size) were between-subject conditions. Respondents ranked packs on attractiveness, perceived quality of the cigarettes contained within and extent that the pack distracted from health warnings. Ratings of attractiveness and perceived quality were significantly associated in both substudies, but tendency to distract from warnings was more independent. Significant differences were found between the pack shapes on attractiveness, perceived quality and distraction from warnings. Standard, 2×10 and 4×5 packs were ranked less attractive than Bevelled and Rounded packs. 2×10 and 4×5 packs were also perceived as lower quality than Bevelled and Rounded packs. The Standard pack was less distracting to health warnings than all other shapes except the 2×10 pack. Pack openings were perceived as different on quality of cigarettes contained and extent of distraction to warnings. The Standard Flip-top was rated significantly lower in distracting from warnings than all other openings. Pack shape and pack opening affect ever-smokers' perceptions of the packs and the cigarettes they contain. This means that they have the potential to create appeal and differentiate products and thus should be regulated.

Early warning of orographically induced floods and landslides in Western Norway

NASA Astrophysics Data System (ADS)

Leine, Ann-Live; Wang, Thea; Boje, Søren

2017-04-01

In Western Norway, landslides and debris flows are commonly initiated by short-term orographic rainfall or intensity peaks during a prolonged rainfall event. In recent years, the flood warning service in Norway has evolved from being solely a flood forecasting service to also integrating landslides into its early warning systems. As both floods and landslides are closely related to the same hydrometeorological processes, particularly in small catchments, there is a natural synergy between monitoring flood and landslide risk. The Norwegian Flood and Landslide Hazard Forecasting and Warning Service issues regional landslide hazard warnings based on hydrological models, threshold values, observations and weather forecasts. Intense rainfall events and/or orographic precipitation that, under certain topographic conditions, significantly increase the risk of debris avalanches and debris floods are lately receiving more research focus from the Norwegian warning service. Orographic precipitation is a common feature in W-Norway, when moist and relatively mild air arrives from the Atlantic. Steep mountain slopes covered by glacial till makes the region prone to landslides, as well as flooding. The operational early warning system in Norway requires constant improvement, especially with the enhanced number of intense rainfall events that occur in a warming climate. Here, we examine different cases of intense rainfall events which have lead to landslides and debris flows, as well as increased runoff in fast responding small catchments. The main objective is to increase the understanding of the hydrometeorological conditions related to these events, in order to make priorities for the future development of the warning service.

Acquisition and use of Orlando, Florida and Continental Airbus radar flight test data

NASA Technical Reports Server (NTRS)

Eide, Michael C.; Mathews, Bruce

1992-01-01

Westinghouse is developing a lookdown pulse Doppler radar for production as the sensor and processor of a forward looking hazardous windshear detection and avoidance system. A data collection prototype of that product was ready for flight testing in Orlando to encounter low level windshear in corroboration with the FAA-Terminal Doppler Weather Radar (TDWR). Airborne real-time processing and display of the hazard factor were demonstrated with TDWR facilitated intercepts and penetrations of over 80 microbursts in a three day period, including microbursts with hazard factors in excess of .16 (with 500 ft. PIREP altitude loss) and the hazard factor display at 6 n.mi. of a visually transparent ('dry') microburst with TDWR corroborated outflow reflectivities of +5 dBz. Range gated Doppler spectrum data was recorded for subsequent development and refinement of hazard factor detection and urban clutter rejection algorithms. Following Orlando, the data collection radar was supplemental type certified for in revenue service on a Continental Airlines Airbus in an automatic and non-interferring basis with its ARINC 708 radar to allow Westinghouse to confirm its understanding of commercial aircraft installation, interface realities, and urban airport clutter. A number of software upgrades, all of which were verified at the Receiver-Transmitter-Processor (RTP) hardware bench with Orlando microburst data to produce desired advanced warning hazard factor detection, included some preliminary loads with automatic (sliding window average hazard factor) detection and annunciation recording. The current (14-APR-92) configured software is free from false and/or nuisance alerts (CAUTIONS, WARNINGS, etc.) for all take-off and landing approaches, under 2500 ft. altitude to weight-on-wheels, into all encountered airports, including Newark (NJ), LAX, Denver, Houston, Cleveland, etc. Using the Orlando data collected on hazardous microbursts, Westinghouse has developed a lookdown pulse Doppler radar product with signal and data processing algorithms which detect realistic microburst hazards and has demonstrated those algorithms produce no false alerts (or nuisance alerts) in urban airport ground moving vehicle (GMTI) and/or clutter environments.

Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch readiness in December 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models will be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) forecast offices in Southern and Eastern Region. This effort is designed to help improve our understanding of the application of these data in operational settings.

The Geostationary Lighting Mapper (GLM) for GOES-R: A New Operational Capability to Improve Storm Forecasts and Warnings

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R.; Koshak, William J.; Petersen, W. A.; Carey, L.; Mah, D.

2010-01-01

The next generation Geostationary Operational Environmental Satellite (GOES-R) series is a follow on to the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral (3x), spatial (4x), and temporal (5x) resolution for the Advanced Baseline Imager (ABI). The GLM, an optical transient detector and imager operating in the near-IR at 777.4 nm will map all (in-cloud and cloud-to-ground) lighting flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate Day-1 user readiness for this new capability.

Design and evaluation of security multimedia warnings for children's smartphones

NASA Astrophysics Data System (ADS)

Menzel, Wiebke; Tuchscheerer, Sven; Fruth, Jana; Kraetzer, Christian; Dittmann, Jana

2012-02-01

This article describes primarily the development and empiric validation of a design for security warning messages on smartphones for primary school children (7-10 years old). Our design approach for security warnings for children uses a specific character and is based on recommendations of a paediatrician expert. The design criteria are adapted to children's skills, e.g. their visual, acoustic, and haptic perception and their literacy. The developed security warnings are prototypically implemented in an iOS application (on the iPhone 3G/4G) where children are warned by a simulated anti-malware background service, while they are busy with another task. For the evaluation we select methods for empiric validation of the design approach from the field of usability testing ("think aloud" test, questionnaires, log-files, etc.). Our security warnings prototype is evaluated in an empiric user study with 13 primary school children, aged between 8 and 9 years and of different gender (5 girls, 8 boys). The evaluation analysis shows, that nearly all children liked the design of our security warnings. Surprisingly, on several security warning messages most of the children react in the right way after reading the warning, although the meaning couldn't be interpreted in the right way. Another interesting result is, that several children relate specific information, e.g. update, to a specific character. Furthermore, it could be seen that most of the primary school test candidates have little awareness of security threats on smartphones. It is a very strong argument to develop e.g. tutorials or websites in order to raise awareness and teach children how to recognize security threats and how to react to them. Our design approach of security warnings for children's smartphones can be a basis for warning on other systems or applications like tutorials, which are used by children. In a second investigation, we focus on webpages, designed for children since smartphones and webpages (the services behind) are more and more interconnected. From this point of view those services should continue the securityapproaches for children's smartphones. The webservices were evaluated among different criteria, e.g. data protection. The results of a first investigation are reported in this paper.

Failure warning of hydrous sandstone based on electroencephalogram technique

NASA Astrophysics Data System (ADS)

Tao, Kai; Zheng, Wei

2018-06-01

Sandstone is a type of rock mass that widely exists in nature. Moisture is an important factor that leads to sandstone structural failure. The major failure assessment methods of hydrous sandstone at present cannot satisfy real-time and portability requirements, especially lacks of warning function. In this study, acoustic emission (AE) and computed tomography (CT) techniques are combined for real-time failure assessment of hydrous sandstone. Eight visual colors for warning are screened according to different failure states, and an electroencephalogram (EEG) experiment is conducted to demonstrate their diverse excitations of the human brain's concentration.

ForWarn Forest Disturbance Change Detection System Provides a Weekly Snapshot of US Forest Conditions to Aid Forest Managers

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Spruce, J.; Kumar, J.; Hoffman, F. M.

2012-12-01

The Eastern Forest Environmental Threat Assessment Center and Western Wildland Environmental Assessment Center of the USDA Forest Service have collaborated with NASA Stennis Space Center to develop ForWarn, a forest monitoring tool that uses MODIS satellite imagery to produce weekly snapshots of vegetation conditions across the lower 48 United States. Forest and natural resource managers can use ForWarn to rapidly detect, identify, and respond to unexpected changes in the nation's forests caused by insects, diseases, wildfires, severe weather, or other natural or human-caused events. ForWarn detects most types of forest disturbances, including insects, disease, wildfires, frost and ice damage, tornadoes, hurricanes, blowdowns, harvest, urbanization, and landslides. It also detects drought, flood, and temperature effects, and shows early and delayed seasonal vegetation development. Operating continuously since January 2010, results show ForWarn to be a robust and highly capable tool for detecting changes in forest conditions. To help forest and natural resource managers rapidly detect, identify, and respond to unexpected changes in the nation's forests, ForWarn produces sets of national maps showing potential forest disturbances at 231m resolution every 8 days, and posts the results to the web for examination. ForWarn compares current greenness with the "normal," historically seen greenness that would be expected for healthy vegetation for a specific location and time of the year, and then identifies areas appearing less green than expected to provide a strategic national overview of potential forest disturbances that can be used to direct ground and aircraft efforts. In addition to forests, ForWarn also tracks potential disturbances in rangeland vegetation and agriculural crops. ForWarn is the first national-scale system of its kind based on remote sensing developed specifically for forest disturbances. The ForWarn system had an official unveiling and rollout in March 2012, initiated by a joint NASA and USDA press release, and followed by a series of training webinars. Almost 60 early-adopter state and federal forest managers attended at least one of the ForWarn rollout webinars. The ForWarn home page has had 2,632 unique visitors since rollout in March 2012, with 39% returning visits. ForWarn was used to map tornado scars from the historic April 27, 2011 tornado outbreak, and detected timber damage within more than a dozen tornado tracks across northern Mississippi, Alabama, and Georgia. ForWarn is the result of an ongoing, substantive cooperation among four different government agencies: USDA, NASA, USGS, and DOE. Disturbance maps are available on the web through the ForWarn Change Assessment Viewer at http://forwarn.forestthreats.org/fcav.

Civil Protection Practitioners' Response to Introducing Nowcasting in Weather Warnings

NASA Astrophysics Data System (ADS)

Ulbrich, Thorsten

2014-05-01

The HErZ project WEXICOM (Improving the process of weather warnings and extreme weather information in the chain from the meteorological forecasts to their communication for the Berlin conurbation) assesses the communication and use of weather warnings. In cooperation with DWD we conducted two online surveys with German relief forces before and after a nowcasting application was introduced into the weather warning platform FEWIS. The aim is to investigate how relief workers make use of the additional information. DWD supports German civil protection by providing the warning platform FeWIS (Fire brigade Weather Information System) for registered relief workers. The platform provides information on meteorological hazards needed to take precautions and to support rescue actions. In June 2013 DWD added nowcasted estimates of storm attributes including warning cones based on a 1x1 km grid. The tool named "GewitterMonitor" is based on NowcastMIX and uses short-term weather models and observations to derive warnings with high precision on intensity, location and timing of thunder storm cells for the following two hours. A first survey provided prior to the addition of nowcasted information investigates how users benefit from FeWIS and how they perceive its functionality and reliability. Following the introduction users gain experience applying the nowcasting tool in the thunderstorm season 2013. In Winter 2013/2014 we conducted another online survey. The post-survey comprises questions on the use of the GewitterMonitor and on how the tool supports relief forces in responding to meteorological risks. The post survey also repeats questions on the perception of functionality and function of FeWIS and poses questions derived from the previous survey. This second survey collects practitioners feedback on GewitterMonitor and allows to detect changes in how users perceive the performance of FeWIS after the addition by relating responses to the prior survey.

An Early Warning System for Identification and Monitoring of Disturbances to Forest Ecosystems

NASA Astrophysics Data System (ADS)

Marshall, A. A.; Hoffman, F. M.; Kumar, J.; Hargrove, W. W.; Spruce, J.; Mills, R. T.

2011-12-01

Forest ecosystems are susceptible to damage due to threat events like wildfires, insect and disease attacks, extreme weather events, land use change, and long-term climate change. Early identification of such events is desired to devise and implement a protective response. The mission of the USDA Forest Service is to sustain the health, diversity, and productivity of the nation's forests. However, limited resources for aerial surveys and ground-based inspections are insufficient for monitoring the large areas covered by the U.S. forests. The USDA Forest Service, Oak Ridge National Laboratory, and NASA Stennis Space Center are developing an early warning system for the continuous tracking and long-term monitoring of disturbances and responses in forest ecosystems using high resolution satellite remote sensing data. Geospatiotemporal data mining techniques were developed and applied to normalized difference vegetation index (NDVI) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD 13 data at 250 m resolution on eight day intervals. Representative phenologically similar regions, or phenoregions, were developed for the conterminous United States (CONUS) by applying a k-means clustering algorithm to the NDVI data spanning the full eight years of the MODIS record. Annual changes in the phenoregions were quantitatively analyzed to identify the significant changes in phenological behavior. This methodology was successfully applied for identification of various forest disturbance events, including wildfire, tree mortality due to Mountain Pine Beetle, and other insect infestation and diseases, as well as extreme events like storms and hurricanes in the United States. Where possible, the results were validated and quantitatively compared with aerial and ground-based survey data available from different agencies. This system was able to identify most of the disturbances reported by aerial and ground-based surveys, and it also identified affected areas that were not covered by any of the surveys. Analysis results and validation data will be presented.

A Comparison of Multisensor Precipitation Estimation Methods in Complex Terrain for Flash Flood Warning and Mitigation

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, C. V.; Willie, D.; Reynolds, D.; Campbell, C.; Zhang, Y.; Sukovich, E.

2012-12-01

Investigating the uncertainties and improving the accuracy of quantitative precipitation estimation (QPE) is a critical mission of the National Oceanic and Atmospheric Administration (NOAA). QPE is extremely challenging in regions of complex terrain like the western U.S. because of the sparse coverage of ground-based radar, complex orographic precipitation processes, and the effects of beam blockages (e.g., Westrick et al. 1999). In addition, the rain gauge density in complex terrain is often inadequate to capture spatial variability in the precipitation patterns. The NOAA Hydrometeorology Testbed (HMT) conducts research on precipitation and weather conditions that can lead to flooding, and fosters transition of scientific advances and new tools into forecasting operations (see hmt.noaa.gov). The HMT program consists of a series of demonstration projects in different geographical regions to enhance understanding of region specific processes related to precipitation, including QPE. There are a number of QPE systems that are widely used across NOAA for precipitation estimation (e.g., Cifelli et al. 2011; Chandrasekar et al. 2012). Two of these systems have been installed at the NOAA Earth System Research Laboratory: Multisensor Precipitation Estimator (MPE) and National Mosaic and Multi-sensor QPE (NMQ) developed by NWS and NSSL, respectively. Both provide gridded QPE products that include radar-only, gauge-only and gauge-radar-merged, etc; however, these systems often provide large differences in QPE (in terms of amounts and spatial patterns) due to differences in Z-R selection, vertical profile of reflectivity correction, and gauge interpolation procedures. Determining the appropriate QPE product and quantification of QPE uncertainty is critical for operational applications, including water management decisions and flood warnings. For example, hourly QPE is used to correct radar based rain rates used by the Flash Flood Monitoring and Prediction (FFMP) package in the NWS forecast offices for issuance of flash flood warnings. This study will evaluate the performance of MPE and NMQ QPE products using independent gauges, object identification techniques for spatial verification and impact on surface runoff using a distributed hydrologic model. The effort will consist of baseline evaluations of these QPE systems to determine which combination of algorithm features is appropriate as well as investigate new methods for combining the gage and radar data. The Russian River Basin in California is used to demonstrate the comparison methodology with data collected from several rainfall events in March 2012.

A Walk through TRIDEC's intermediate Tsunami Early Warning System

NASA Astrophysics Data System (ADS)

Hammitzsch, M.; Reißland, S.; Lendholt, M.

2012-04-01

The management of natural crises is an important application field of the technology developed in the project Collaborative, Complex, and Critical Decision-Support in Evolving Crises (TRIDEC), co-funded by the European Commission in its Seventh Framework Programme. TRIDEC is based on the development of the German Indonesian Tsunami Early Warning System (GITEWS) and the Distant Early Warning System (DEWS) providing a service platform for both sensor integration and warning dissemination. In TRIDEC new developments in Information and Communication Technology (ICT) are used to extend the existing platform realising a component-based technology framework for building distributed tsunami warning systems for deployment, e.g. in the North-eastern Atlantic, the Mediterranean and Connected Seas (NEAM) region. The TRIDEC system will be implemented in three phases, each with a demonstrator. Successively, the demonstrators are addressing challenges, such as the design and implementation of a robust and scalable service infrastructure supporting the integration and utilisation of existing resources with accelerated generation of large volumes of data. These include sensor systems, geo-information repositories, simulation tools and data fusion tools. In addition to conventional sensors also unconventional sensors and sensor networks play an important role in TRIDEC. The system version presented is based on service-oriented architecture (SOA) concepts and on relevant standards of the Open Geospatial Consortium (OGC), the World Wide Web Consortium (W3C) and the Organization for the Advancement of Structured Information Standards (OASIS). In this way the system continuously gathers, processes and displays events and data coming from open sensor platforms to enable operators to quickly decide whether an early warning is necessary and to send personalized warning messages to the authorities and the population at large through a wide range of communication channels. The system integrates OGC Sensor Web Enablement (SWE) compliant sensor systems for the rapid detection of hazardous events, like earthquakes, sea level anomalies, ocean floor occurrences, and ground displacements. Using OGC Web Map Service (WMS) and Web Feature Service (WFS) spatial data are utilized to depict the situation picture. The integration of a simulation system to identify affected areas is considered using the OGC Web Processing Service (WPS). Warning messages are compiled and transmitted in the OASIS Common Alerting Protocol (CAP) together with addressing information defined via the OASIS Emergency Data Exchange Language - Distribution Element (EDXL-DE). The first system demonstrator has been designed and implemented to support plausible scenarios demonstrating the treatment of simulated tsunami threats with an essential subset of a National Tsunami Warning Centre (NTWC). The feasibility and the potentials of the implemented approach are demonstrated covering standard operations as well as tsunami detection and alerting functions. The demonstrator presented addresses information management and decision-support processes in a hypothetical natural crisis situation caused by a tsunami in the Eastern Mediterranean. Developments of the system are based to the largest extent on free and open source software (FOSS) components and industry standards. Emphasis has been and will be made on leveraging open source technologies that support mature system architecture models wherever appropriate. All open source software produced is foreseen to be published on a publicly available software repository thus allowing others to reuse results achieved and enabling further development and collaboration with a wide community including scientists, developers, users and stakeholders. This live demonstration is linked with the talk "TRIDEC Natural Crisis Management Demonstrator for Tsunamis" (EGU2012-7275) given in the session "Architecture of Future Tsunami Warning Systems" (NH5.7/ESSI1.7).

People-centred landslide early warning systems in the context of risk management

NASA Astrophysics Data System (ADS)

Haß, S.; Asch, K.; Fernandez-Steeger, T.; Arnhardt, C.

2009-04-01

In the current hazard research people-centred warning becomes more and more important, because different types of organizations and groups have to be involved in the warning process. This fact has to be taken into account when developing early warning systems. The effectiveness of early warning depends not only on technical capabilities but also on the preparedness of decision makers and their immediate response on how to act in case of emergency. Hence early warning systems have to be regarded in the context of an integrated and holistic risk management. Disaster Risk Reduction (DRR) measures include people-centred, timely and understandable warning. Further responsible authorities have to be identified in advance and standards for risk communication have to be established. Up to now, hazard and risk assessment for geohazards focuses on the development of inventory, susceptibility, hazard and risk maps. But often, especially in Europe, there are no institutional structures for managing geohazards and in addition there is a lack of an authority that is legally obliged to alarm on landslides at national or regional level. One of the main characteristics within the warning process for natural hazards e.g. in Germany is the split of responsibility between scientific authorities (wissenschaftliche Fachbehörde) and enforcement authorities (Vollzugsbehörde). The scientific authority provides the experts who define the methods and measures for monitoring and evaluate the hazard level. The main focus is the acquisition and evaluation of data and subsequently the distribution of information. The enforcement authority issues official warnings about dangerous natural phenomena. Hence the information chain in the context of early warning ranges over two different institutions, the forecast service and the warning service. But there doesn't exist a framework for warning processes in terms of landslides as yet. The concept for managing natural disasters is often reduced to hazard assessment and emergency response. Great importance is attached to the scientific understanding of hazards and protective structures, while analysis of socio-economic impacts and risk assessment are not considered enough. The reduction of vulnerability has to be taken into greater account. Also the information needs of different stakeholders have to be identified at an early stage and should be integrated in the development of early warning systems. The content of the warning message must be simple, understandable and should cover instructions on how to react. Further the timeliness of the messages has to be guarented. In this context the aim of the landslide monitoring and early warning system SLEWS (Sensor Based Landslide Early Warning System) is to integrate the above mentioned aspects of a holistic disaster and risk management. The technology of spatial data infrastructures and web services provides the use of multiple communication channels within an early warning system. Thus people-centred early warning messages and information about slope stability can be sent in nearly real-time. It has to be underlined that the technological information process is just one element of an effective warning system. Moreover the warning system has also to be considered as a social system and has to make allowance to socio-economic and gender aspects : «[...] Develop early warning systems that are people centered, in particular systems whose warnings are timely and understandable to those at risk, which take into account the demographic, gender, cultural and livelihood characteristics of the target audiences, including guidance on how to act upon warnings, and that support effective operations by disaster managers and other decision makers » (Hyogo Framework, 2005) References : UNITED NATIONS INTERNATIONAL STRATEGY FOR DISASTER REDUCTION SECRETARIAT (UNISDR) (2006): Developing early warning systems: a checklist, Third international conference on early warning (EWC III): from concept to action: 27-29 March 2006, Bonn, Germany. Geneva, Switzerland: International Strategy for Disaster Reduction. WORLD CONFERENCE ON DISASTER REDUCTION (2005) : Report of the World Conference on Disaster Reduction: Kobe, Hyogo, Japan, 18-22 January 2005. Geneva, Switzerland, Secretariat, World Conference on Disaster Reduction. INTER-AGENCY SECRETARIAT OF THE ISDR & GLOBAL PLATFORM FOR DISASTER RISK REDUCTION (2007): Disaster risk reduction: 2007 global review. Geneva, UN, ISDR.

A passive RFID-based location system for personnel and asset monitoring.

PubMed

Hsiao, Rong-Shue; Kao, Chun-Hao; Chen, Tian-Xiang; Chen, Jui-Lun

2018-01-01

Typical radio frequency identification (RFID) access control system can be ineffective if an unauthorized person tailgates an authorized person through an access area. To propose a system by using indoor locating and tracking techniques address this problem, which is to prevent unauthorized Alzheimer's and dementia patients from getting lost including by tailgating. To achieve accurate target location, passive RFID deployment strategy is studied and a fingerprinting based passive RFID localization algorithm is proposed. The proposed system was evaluated in a building environment to simulate the performance of access control. RFID reader was installed on ceiling near the access area and tags were stitched on both shoulders of the experiment subject's garments. The probability of the error distance within 0.3 m achieved 97% in the warning area; the location precision achieved 97% within 0.4 m in the monitoring area. The result showed that if an unauthorized person enters the restricted area, the system can initiate an alert signal accurately. Therefore, the proposed system is very suitable to be used in nursing home or hospital to prevent unauthorized personnel and assets entering/exiting a confined location.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response.

PubMed

Thapen, Nicholas; Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.

Task two : literature search for Federal Highway Administration (ITS-JPO) : assessment of state of the practice and state of the art in evacuation transportation management

DOT National Transportation Integrated Search

2006-01-01

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Development and Use of Early Warning Systems. SLDS Spotlight

ERIC Educational Resources Information Center

Curtin, Jenny; Hurwitch, Bill; Olson, Tom

2012-01-01

An early warning system is a data-based tool that helps predict which students are on the right path towards eventual graduation or other grade-appropriate goals. Through such systems, stakeholders at the school and district levels can view data from a wide range of perspectives and gain a deeper understanding of student data. This "Statewide…

Task three : technical memorandum for Federal Highway Administration (ITS-JPO) on case studies : assessment of state of the practice and state of the art in evacuation transportation management

DOT National Transportation Integrated Search

2006-01-01

Much of what is known about evacuations is based on preparations for incidents, such as hurricanes, for which there is advance warning. With advance warning, evacuations can be planned and managed using procedures and systems that have been developed...

Researchers warn of neglect to basic science

NASA Astrophysics Data System (ADS)

Banks, Michael

2010-03-01

Russia is losing its standing as a scientific powerhouse and its science is in a state of decline, according to a new report by the information-services provider Thomson Reuters. Entitled "The New Geography of Science: Research and Collaboration in Russia", the report warns that the country's research base "has a problem, and it shows little sign of a solution".

Prediction and warning system of SEP events and solar flares for risk estimation in space launch operations

NASA Astrophysics Data System (ADS)

García-Rigo, Alberto; Núñez, Marlon; Qahwaji, Rami; Ashamari, Omar; Jiggens, Piers; Pérez, Gustau; Hernández-Pajares, Manuel; Hilgers, Alain

2016-07-01

A web-based prototype system for predicting solar energetic particle (SEP) events and solar flares for use by space launch operators is presented. The system has been developed as a result of the European Space Agency (ESA) project SEPsFLAREs (Solar Events Prediction system For space LAunch Risk Estimation). The system consists of several modules covering the prediction of solar flares and early SEP Warnings (labeled Warning tool), the prediction of SEP event occurrence and onset, and the prediction of SEP event peak and duration. In addition, the system acquires data for solar flare nowcasting from Global Navigation Satellite Systems (GNSS)-based techniques (GNSS Solar Flare Detector, GSFLAD and the Sunlit Ionosphere Sudden Total Electron Content Enhancement Detector, SISTED) as additional independent products that may also prove useful for space launch operators.

Tsunami Early Warning via a Physics-Based Simulation Pipeline

NASA Astrophysics Data System (ADS)

Wilson, J. M.; Rundle, J. B.; Donnellan, A.; Ward, S. N.; Komjathy, A.

2017-12-01

Through independent efforts, physics-based simulations of earthquakes, tsunamis, and atmospheric signatures of these phenomenon have been developed. With the goal of producing tsunami forecasts and early warning tools for at-risk regions, we join these three spheres to create a simulation pipeline. The Virtual Quake simulator can produce thousands of years of synthetic seismicity on large, complex fault geometries, as well as the expected surface displacement in tsunamigenic regions. These displacements are used as initial conditions for tsunami simulators, such as Tsunami Squares, to produce catalogs of potential tsunami scenarios with probabilities. Finally, these tsunami scenarios can act as input for simulations of associated ionospheric total electron content, signals which can be detected by GNSS satellites for purposes of early warning in the event of a real tsunami. We present the most recent developments in this project.

MyShake: A smartphone seismic network for earthquake early warning and beyond

PubMed Central

Kong, Qingkai; Allen, Richard M.; Schreier, Louis; Kwon, Young-Woo

2016-01-01

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquake early warning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics. PMID:26933682

MyShake: A smartphone seismic network for earthquake early warning and beyond.

PubMed

Kong, Qingkai; Allen, Richard M; Schreier, Louis; Kwon, Young-Woo

2016-02-01

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquake early warning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics.

Lane detection using Randomized Hough Transform

NASA Astrophysics Data System (ADS)

Mongkonyong, Peerawat; Nuthong, Chaiwat; Siddhichai, Supakorn; Yamakita, Masaki

2018-01-01

According to the report of the Royal Thai Police between 2006 and 2015, lane changing without consciousness is one of the most accident causes. To solve this problem, many methods are considered. Lane Departure Warning System (LDWS) is considered to be one of the potential solutions. LDWS is a mechanism designed to warn the driver when the vehicle begins to move out of its current lane. LDWS contains many parts including lane boundary detection, driver warning and lane marker tracking. This article focuses on the lane boundary detection part. The proposed lane boundary detection detects the lines of the image from the input video and selects the lane marker of the road surface from those lines. Standard Hough Transform (SHT) and Randomized Hough Transform (RHT) are considered in this article. They are used to extract lines of an image. SHT extracts the lines from all of the edge pixels. RHT extracts only the lines randomly picked by the point pairs from edge pixels. RHT algorithm reduces the time and memory usage when compared with SHT. The increase of the threshold value in RHT will increase the voted limit of the line that has a high possibility to be the lane marker, but it also consumes the time and memory. By comparison between SHT and RHT with the different threshold values, 500 frames of input video from the front car camera will be processed. The accuracy and the computational time of RHT are similar to those of SHT in the result of the comparison.

Potential for Expanding the Near Real Time ForWarn Regional Forest Monitoring System to Include Alaska

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Gasser, Gerald; Hargrove, William; Smoot, James; Kuper, Philip D.

2014-01-01

The on-line near real time (NRT) ForWarn system is currently deployed to monitor regional forest disturbances within the conterminous United States (CONUS), using daily MODIS Aqua and Terra NDVI data to derive monitoring products. The Healthy Forest Restoration Act of 2003 mandated such a system. Work on ForWarn began in 2006 with development and validation of retrospective MODIS NDVI-based forest monitoring products. Subsequently, NRT forest disturbance monitoring products were demonstrated, leading to the actual system deployment in 2010. ForWarn provides new CONUS forest disturbance monitoring products every 8 days, using USGS eMODIS data for current NDVI. ForWarn currently does not cover Alaska, which includes extensive forest lands at risk to multiple biotic and abiotic threats. This poster discusses a case study using Alaska eMODIS Terra data to derive ForWarn like forest change products during the 2010 growing season. The eMODIS system provides current MODIS Terra NDVI products for Alaska. Resulting forest change products were assessed with ground, aerial, and Landsat reference data. When cloud and snow free, these preliminary products appeared to capture regional forest disturbances from insect defoliation and fires; however, more work is needed to mitigate cloud and snow contamination, including integration of eMODIS Aqua data.

Detection of warning surfaces in pedestrian environments: the importance for blind people of kerbs, depth, and structure of tactile surfaces.

PubMed

Ståhl, Agneta; Newman, Emma; Dahlin-Ivanoff, Synneve; Almén, Mai; Iwarsson, Sussane

2010-01-01

The overall purpose was to study whether and how persons with blindness detect warning surfaces with a long white cane in a real pedestrian environment after following a natural guidance surface to the warning surfaces. Of particular interest was the importance of kerb, depth, and structure of the warning surfaces. A concurrently mixed methods approach, with a combination of observation using a structured form together with 'think aloud' and a structured interview, was used. It was done with well-defined samples and study sites in an inter-disciplinary research context. The results show that the most important design characteristic for detection of the warning surfaces with a white cane is the structure of the surface, while the depth of the surface and availability of a kerb do not have any impact on the detection. A precondition was that there is a distinct natural guidance surface leading up to the warning surface. The probability among pedestrians with blindness to detect a tactile surface is not higher if the design solution has a kerb. This study also confirms the complexity of being a blind pedestrian in the traffic environment. The results can be used for evidence-based physical planning. The study also has implications for development of more efficient vision rehabilitation.

HRAS: a webserver for early warning of human health risk brought by aflatoxin.

PubMed

Hu, Ruifeng; Zeng, Xu; Gao, Weiwei; Wang, Qian; Liu, Zhihua

2013-02-01

Most people are aware that outdoor air pollution can damage their health, but many do not know that indoor air pollution can also exhibit significant negative health effects. Fungi parasitizing in air conditioning and ventilation systems can be one of indoor air pollution sources. Aflatoxin produced by Aspergillus flavus (A. flavus) became a central focus of indoor air pollution, especially in farmer markets. Therefore we developed an early warning system, Health Risk Assessment System, to estimate the growth rate of A. flavus, predict the amount of aflatoxin and provide early warning information. Firstly, the growth of A. flavus and the production of aflatoxin under different conditions were widely obtained through a comprehensive literature review. Secondly, three mathematical models were established to predict the A. flavus colony growth rate, lag phase duration and aflatoxin content, as functions of temperature and water activity based on present studies. Finally, all the results were evaluated by the user-supplied data using PHP programming language. We utilized the web page to show the results and display warning information. The JpGraph library was used to create a dynamic line chart, refreshing the warning information dynamically in real-time. The HARS provides accurate information for early warning purposes to let us take timely steps to protect ourselves.

How effective are the 6 European Society of Immunodeficiency warning signs for primary immunodeficiency disease?

PubMed

Arslan, Sevket; Ucar, Ramazan; Caliskaner, Ahmet Zafer; Reisli, Ismail; Guner, Sukru Nail; Sayar, Esra Hazar; Baloglu, Ismail

2016-02-01

The European Society of Immunodeficiency (ESID) developed 6 warning signs to promote the awareness of adult primary immunodeficiency disease (PID). To screen adult patients for the presence of PID using these 6 warning signs to determine the effectiveness of this protocol. Questions related to the ESID warning signs for adult PID were added to the standard outpatient clinic file system and asked of 3,510 patients who were admitted to our clinic for any reason. Patients with signs and/or suspicion of PID based on their medical history underwent immunologic investigation. In total, 24 patients were diagnosed as having a PID. The most common reason that patients with PID were admitted was frequent infection (n=18 [75%]), and the most common PID subgroup was common variable immunodeficiency (n=12 [50%]). Twenty patients with PID had at least one positive finding according to the ESID warning signs. Two patients with gastrointestinal concerns and 2 with dermatologic symptoms were also diagnosed as having a PID, although they did not have any of the ESID warning signs. The ESID warning signs do not specify the need for symptoms to diagnose a PIDs and do not include a comprehensive list of all signs and symptoms of PIDs. As a result, more than infection-centric questions are needed to identify adult patients with immunodeficiencies. Copyright © 2016 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

The GNSS-based component for the new Indonesian tsunami early warning centre provided by GITEWS

NASA Astrophysics Data System (ADS)

Falck, C.; Ramatschi, M.; Bartsch, M.; Merx, A.; Hoeberechts, J.; Rothacher, M.

2009-04-01

Introduction Nowadays GNSS technologies are used for a large variety of precise positioning applications. The accuracy can reach the mm level depending on the data analysis methods. GNSS technologies thus offer a high potential to support tsunami early warning systems, e.g., by detection of ground motions due to earthquakes and of tsunami waves on the ocean by GNSS instruments on a buoy. Although GNSS-based precise positioning is a standard method, it is not yet common to apply this technique under tight time constraints and, hence, in the absence of precise satellite orbits and clocks. The new developed GNSS-based component utilises on- and offshore measured GNSS data and is the first system of its kind that was integrated into an operational early warning system. (Indonesian Tsunami Early Warning Centre INATEWS, inaugurated at BMKG, Jakarta on November, 11th 2008) Motivation After the Tsunami event of 26th December 2004 the German government initiated the GITEWS project (German Indonesian Tsunami Early Warning System) to develop a tsunami early warning system for Indonesia. The GFZ Potsdam (German Research Centre for Geosciences) as the consortial leader of GITEWS also covers several work packages, most of them related to sensor systems. The geodetic branch (Department 1) of the GFZ was assigned to develop a GNSS-based component. Brief system description The system covers all aspects from sensor stations with new developed hard- and software designs, manufacturing and installation of stations, real-time data transfer issues, a new developed automatic near real-time data processing and a graphical user interface for early warning centre operators including training on the system. GNSS sensors are installed on buoys, at tide gauges and as real-time reference stations (RTR stations), either stand-alone or co-located with seismic sensors. The GNSS data are transmitted to the warning centre where they are processed in a near real-time data processing chain. For sensors on land the processing system delivers deviations from their normal, mean coordinates. The deviations or so called displacements are indicators for land mass movements which can occur, e.g., due to strong earthquakes. The ground motion information is a valuable source for a fast understanding of an earthquake's mechanism with possible relevance for a potentially following tsunami. By this means the GNSS system supports the decision finding process whether most probably a tsunami has been generated or not. For buoy based GNSS data the processing (differential, with GNSS reference station on land) delivers coordinates as well. Only the vertical component is of interest as it corresponds to the instant sea level height. Deviations to the mean sea level height are an indicator for a possibly passing tsunami wave. The graphical user interface (GUI) of the GNSS system supports both, a quick view for all staff members at the warning centre (24h/7d shifts) and deeper analysis by GNSS experts. The GNSS GUI system is implemented as a web-based application and allows all views to be displayed on different screens at the same time, even at remote locations. This is part of the concept, as it can support the dialogue between warning centre staff on duty or on standby and sensor station maintenance staff. Acknowledgements The GITEWS project (German Indonesian Tsunami Early Warning System) is carried out by a large group of scientists and engineers from (GFZ) German Research Centre for Geosciences and its partners from the German Aerospace Centre (DLR), the Alfred Wegener Institute for Polar and Marine Research (AWI), the GKSS Research Centre, the Konsortium Deutsche Meeresforschung (KDM), the Leibniz Institute for Marine Sciences (IFM-GEOMAR), the United Nations University (UNU), the Federal Institute for Geosciences and Natural Resources (BGR), the German Agency for Technical Cooperation (GTZ) and other international partners. Most relevant partners in Indonesia with respect to the GNSS component of GITEWS are the National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL), the National Metereology and Geophysics Agency (BMKG) and the National Agency for the Assessment and Application of Technology (BPPT). Funding is provided by the German Federal Ministry for Education and Research (BMBF), Grant 03TSU01.

Impacts of Fog Characteristics, Forward Illumination, and Warning Beacon Intensity Distribution on Roadway Hazard Visibility

PubMed Central

2016-01-01

Warning beacons are critical for the safety of transportation, construction, and utility workers. These devices need to produce sufficient luminous intensity to be visible without creating glare to drivers. Published standards for the photometric performance of warning beacons do not address their performance in conditions of reduced visibility such as fog. Under such conditions light emitted in directions other than toward approaching drivers can create scattered light that makes workers and other hazards less visible. Simulations of visibility of hazards under varying conditions of fog density, forward vehicle lighting, warning beacon luminous intensity, and intensity distribution were performed to assess their impacts on visual performance by drivers. Each of these factors can influence the ability of drivers to detect and identify workers and hazards along the roadway in work zones. Based on the results, it would be reasonable to specify maximum limits on the luminous intensity of warning beacons in directions that are unlikely to be seen by drivers along the roadway, limits which are not included in published performance specifications. PMID:27314058