A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2000-01-01

Helicopter Health Usage Monitoring Systems (HUMS) must provide reliable, real-time performance monitoring of helicopter operating parameters to prevent damage of flight critical components. Helicopter transmission diagnostics are an important part of a helicopter HUMS. In order to improve the reliability of transmission diagnostics, many researchers propose combining two technologies, vibration and oil monitoring, using data fusion and intelligent systems. Some benefits of combining multiple sensors to make decisions include improved detection capabilities and increased probability the event is detected. However, if the sensors are inaccurate, or the features extracted from the sensors are poor predictors of transmission health, integration of these sensors will decrease the accuracy of damage prediction. For this reason, one must verify the individual integrity of vibration and oil analysis methods prior to integrating the two technologies. This research focuses on comparing the capability of two vibration algorithms, FM4 and NA4, and a commercially available on-line oil debris monitor to detect pitting damage on spur gears in the NASA Glenn Research Center Spur Gear Fatigue Test Rig. Results from this research indicate that the rate of change of debris mass measured by the oil debris monitor is comparable to the vibration algorithms in detecting gear pitting damage.

Robust feature extraction for rapid classification of damage in composites

NASA Astrophysics Data System (ADS)

Coelho, Clyde K.; Reynolds, Whitney; Chattopadhyay, Aditi

2009-03-01

The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

Real time damage detection using recursive principal components and time varying auto-regressive modeling

NASA Astrophysics Data System (ADS)

Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.

2018-02-01

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving experiments performed on a cantilever beam subjected to earthquake excitation; a two-storey benchscale model with a TMD and, data from recorded responses of UCLA factor building demonstrate the efficacy of the proposed methodology as an ideal candidate for real time, reference free structural health monitoring.

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

NASA Astrophysics Data System (ADS)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

Thermography Inspection for Early Detection of Composite Damage in Structures During Fatigue Loading

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Burke, Eric R.; Parker, F. Raymond; Seebo, Jeffrey P.; Wright, Christopher W.; Bly, James B.

2012-01-01

Advanced composite structures are commonly tested under controlled loading. Understanding the initiation and progression of composite damage under load is critical for validating design concepts and structural analysis tools. Thermal nondestructive evaluation (NDE) is used to detect and characterize damage in composite structures during fatigue loading. A difference image processing algorithm is demonstrated to enhance damage detection and characterization by removing thermal variations not associated with defects. In addition, a one-dimensional multilayered thermal model is used to characterize damage. Lastly, the thermography results are compared to other inspections such as non-immersion ultrasonic inspections and computed tomography X-ray.

A Tensor-Based Structural Damage Identification and Severity Assessment

PubMed Central

Anaissi, Ali; Makki Alamdari, Mehrisadat; Rakotoarivelo, Thierry; Khoa, Nguyen Lu Dang

2018-01-01

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors. PMID:29301314

Nonlinear damage identification of breathing cracks in Truss system

NASA Astrophysics Data System (ADS)

Zhao, Jie; DeSmidt, Hans

2014-03-01

The breathing cracks in truss system are detected by Frequency Response Function (FRF) based damage identification method. This method utilizes damage-induced changes of frequency response functions to estimate the severity and location of structural damage. This approach enables the possibility of arbitrary interrogation frequency and multiple inputs/outputs which greatly enrich the dataset for damage identification. The dynamical model of truss system is built using the finite element method and the crack model is based on fracture mechanics. Since the crack is driven by tensional and compressive forces of truss member, only one damage parameter is needed to represent the stiffness reduction of each truss member. Assuming that the crack constantly breathes with the exciting frequency, the linear damage detection algorithm is developed in frequency/time domain using Least Square and Newton Raphson methods. Then, the dynamic response of the truss system with breathing cracks is simulated in the time domain and meanwhile the crack breathing status for each member is determined by the feedback from real-time displacements of member's nodes. Harmonic Fourier Coefficients (HFCs) of dynamical response are computed by processing the data through convolution and moving average filters. Finally, the results show the effectiveness of linear damage detection algorithm in identifying the nonlinear breathing cracks using different combinations of HFCs and sensors.

Smart concrete slabs with embedded tubular PZT transducers for damage detection

NASA Astrophysics Data System (ADS)

Gao, Weihang; Huo, Linsheng; Li, Hongnan; Song, Gangbing

2018-02-01

The objective of this study is to develop a new concept and methodology of smart concrete slab (SCS) with embedded tubular lead zirconate titanate transducer array for image based damage detection. Stress waves, as the detecting signals, are generated by the embedded tubular piezoceramic transducers in the SCS. Tubular piezoceramic transducers are used due to their capacity of generating radially uniform stress waves in a two-dimensional concrete slab (such as bridge decks and walls), increasing the monitoring range. A circular type delay-and-sum (DAS) imaging algorithm is developed to image the active acoustic sources based on the direct response received by each sensor. After the scattering signals from the damage are obtained by subtracting the baseline response of the concrete structures from those of the defective ones, the elliptical type DAS imaging algorithm is employed to process the scattering signals and reconstruct the image of the damage. Finally, two experiments, including active acoustic source monitoring and damage imaging for concrete structures, are carried out to illustrate and demonstrate the effectiveness of the proposed method.

Development of a fire detection algorithm for the COMS (Communication Ocean and Meteorological Satellite)

NASA Astrophysics Data System (ADS)

Kim, Goo; Kim, Dae Sun; Lee, Yang-Won

2013-10-01

The forest fires do much damage to our life in ecological and economic aspects. South Korea is probably more liable to suffer from the forest fire because mountain area occupies more than half of land in South Korea. They have recently launched the COMS(Communication Ocean and Meteorological Satellite) which is a geostationary satellite. In this paper, we developed forest fire detection algorithm using COMS data. Generally, forest fire detection algorithm uses characteristics of 4 and 11 micrometer brightness temperature. Our algorithm additionally uses LST(Land Surface Temperature). We confirmed the result of our fire detection algorithm using statistical data of Korea Forest Service and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer) images. We used the data in South Korea On April 1 and 2, 2011 because there are small and big forest fires at that time. The detection rate was 80% in terms of the frequency of the forest fires and was 99% in terms of the damaged area. Considering the number of COMS's channels and its low resolution, this result is a remarkable outcome. To provide users with the result of our algorithm, we developed a smartphone application for users JSP(Java Server Page). This application can work regardless of the smartphone's operating system. This study can be unsuitable for other areas and days because we used just two days data. To improve the accuracy of our algorithm, we need analysis using long-term data as future work.

Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models

NASA Astrophysics Data System (ADS)

Banerjee, Sourav; Liu, Lie; Liu, S. T.; Yuan, Fuh-Gwo; Beard, Shawn

2011-04-01

Materials State Awareness (MSA) goes beyond traditional NDE and SHM in its challenge to characterize the current state of material damage before the onset of macro-damage such as cracks. A highly reliable, minimally invasive system for MSA of Aerospace Structures, Naval structures as well as next generation space systems is critically needed. Development of such a system will require a reliable SHM system that can detect the onset of damage well before the flaw grows to a critical size. Therefore, it is important to develop an integrated SHM system that not only detects macroscale damages in the structures but also provides an early indication of flaw precursors and microdamages. The early warning for flaw precursors and their evolution provided by an SHM system can then be used to define remedial strategies before the structural damage leads to failure, and significantly improve the safety and reliability of the structures. Thus, in this article a preliminary concept of developing the Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to accurately and reliably detect the precursors to damages that occur to the structure are discussed. Experiments conducted in a laboratory environment shows potential of the proposed technique.

Negative Selection Algorithm for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Providing structural modules with self-integrity monitoring

NASA Astrophysics Data System (ADS)

Walton, W. B.; Ibanez, P.; Yessaie, G.

1988-08-01

With the advent of complex space structures (i.e., U.S. Space Station), the need for methods for remotely detecting structural damage will become greater. Some of these structures will have hundreds of individual structural elements (i.e., strut members). Should some of them become damaged, it could be virtually impossible to detect it using visual or similar inspection techniques. The damage of only a few individual members may or may not be a serious problem. However, should a significant number of the members be damaged, a significant problem could be created. The implementation of an appropriate remote damage detection scheme would greatly reduce the likelihood of a serious problem related to structural damage ever occurring. This report presents the results of the research conducted on remote structural damage detection approaches and the related mathematical algorithms. The research was conducted for the Small Business Innovation and Research (SBIR) Phase 2 National Aeronautics and Space Administration (NASA) Contract NAS7-961.

Providing structural modules with self-integrity monitoring

NASA Technical Reports Server (NTRS)

Walton, W. B.; Ibanez, P.; Yessaie, G.

1988-01-01

With the advent of complex space structures (i.e., U.S. Space Station), the need for methods for remotely detecting structural damage will become greater. Some of these structures will have hundreds of individual structural elements (i.e., strut members). Should some of them become damaged, it could be virtually impossible to detect it using visual or similar inspection techniques. The damage of only a few individual members may or may not be a serious problem. However, should a significant number of the members be damaged, a significant problem could be created. The implementation of an appropriate remote damage detection scheme would greatly reduce the likelihood of a serious problem related to structural damage ever occurring. This report presents the results of the research conducted on remote structural damage detection approaches and the related mathematical algorithms. The research was conducted for the Small Business Innovation and Research (SBIR) Phase 2 National Aeronautics and Space Administration (NASA) Contract NAS7-961.

Structural Health Monitoring challenges on the 10-MW offshore wind turbine model

NASA Astrophysics Data System (ADS)

Di Lorenzo, E.; Kosova, G.; Musella, U.; Manzato, S.; Peeters, B.; Marulo, F.; Desmet, W.

2015-07-01

The real-time structural damage detection on large slender structures has one of its main application on offshore Horizontal Axis Wind Turbines (HAWT). The renewable energy market is continuously pushing the wind turbine sizes and performances. This is the reason why nowadays offshore wind turbines concepts are going toward a 10 MW reference wind turbine model. The aim of the work is to perform operational analyses on the 10-MW reference wind turbine finite element model using an aeroelastic code in order to obtain long-time-low- cost simulations. The aeroelastic code allows simulating the damages in several ways: by reducing the edgewise/flapwise blades stiffness, by adding lumped masses or considering a progressive mass addiction (i.e. ice on the blades). The damage detection is then performed by means of Operational Modal Analysis (OMA) techniques. Virtual accelerometers are placed in order to simulate real measurements and to estimate the modal parameters. The feasibility of a robust damage detection on the model has been performed on the HAWT model in parked conditions. The situation is much more complicated in case of operating wind turbines because the time periodicity of the structure need to be taken into account. Several algorithms have been implemented and tested in the simulation environment. They are needed in order to carry on a damage detection simulation campaign and develop a feasible real-time damage detection method. In addition to these algorithms, harmonic removal tools are needed in order to dispose of the harmonics due to the rotation.

Defect detection around rebars in concrete using focused ultrasound and reverse time migration.

PubMed

Beniwal, Surendra; Ganguli, Abhijit

2015-09-01

Experimental and numerical investigations have been performed to assess the feasibility of damage detection around rebars in concrete using focused ultrasound and a Reverse Time Migration (RTM) based subsurface imaging algorithm. Since concrete is heterogeneous, an unfocused ultrasonic field will be randomly scattered by the aggregates, thereby masking information about damage(s). A focused ultrasonic field, on the other hand, increases the possibility of detection of an anomaly due to enhanced amplitude of the incident field in the focal region. Further, the RTM based reconstruction using scattered focused field data is capable of creating clear images of the inspected region of interest. Since scattering of a focused field by a damaged rebar differs qualitatively from that of an undamaged rebar, distinct images of damaged and undamaged situations are obtained in the RTM generated images. This is demonstrated with both numerical and experimental investigations. The total scattered field, acquired on the surface of the concrete medium, is used as input for the RTM algorithm to generate the subsurface image that helps to identify the damage. The proposed technique, therefore, has some advantage since knowledge about the undamaged scenario for the concrete medium is not necessary to assess its integrity. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-Dimensional Damage Detection for Surfaces and Structures

NASA Technical Reports Server (NTRS)

Williams, Martha; Lewis, Mark; Roberson, Luke; Medelius, Pedro; Gibson, Tracy; Parks, Steen; Snyder, Sarah

2013-01-01

Current designs for inflatable or semi-rigidized structures for habitats and space applications use a multiple-layer construction, alternating thin layers with thicker, stronger layers, which produces a layered composite structure that is much better at resisting damage. Even though such composite structures or layered systems are robust, they can still be susceptible to penetration damage. The ability to detect damage to surfaces of inflatable or semi-rigid habitat structures is of great interest to NASA. Damage caused by impacts of foreign objects such as micrometeorites can rupture the shell of these structures, causing loss of critical hardware and/or the life of the crew. While not all impacts will have a catastrophic result, it will be very important to identify and locate areas of the exterior shell that have been damaged by impacts so that repairs (or other provisions) can be made to reduce the probability of shell wall rupture. This disclosure describes a system that will provide real-time data regarding the health of the inflatable shell or rigidized structures, and information related to the location and depth of impact damage. The innovation described here is a method of determining the size, location, and direction of damage in a multilayered structure. In the multi-dimensional damage detection system, layers of two-dimensional thin film detection layers are used to form a layered composite, with non-detection layers separating the detection layers. The non-detection layers may be either thicker or thinner than the detection layers. The thin-film damage detection layers are thin films of materials with a conductive grid or striped pattern. The conductive pattern may be applied by several methods, including printing, plating, sputtering, photolithography, and etching, and can include as many detection layers that are necessary for the structure construction or to afford the detection detail level required. The damage is detected using a detector or sensory system, which may include a time domain reflectometer, resistivity monitoring hardware, or other resistance-based systems. To begin, a layered composite consisting of thin-film damage detection layers separated by non-damage detection layers is fabricated. The damage detection layers are attached to a detector that provides details regarding the physical health of each detection layer individually. If damage occurs to any of the detection layers, a change in the electrical properties of the detection layers damaged occurs, and a response is generated. Real-time analysis of these responses will provide details regarding the depth, location, and size estimation of the damage. Multiple damages can be detected, and the extent (depth) of the damage can be used to generate prognostic information related to the expected lifetime of the layered composite system. The detection system can be fabricated very easily using off-the-shelf equipment, and the detection algorithms can be written and updated (as needed) to provide the level of detail needed based on the system being monitored. Connecting to the thin film detection layers is very easy as well. The truly unique feature of the system is its flexibility; the system can be designed to gather as much (or as little) information as the end user feels necessary. Individual detection layers can be turned on or off as necessary, and algorithms can be used to optimize performance. The system can be used to generate both diagnostic and prognostic information related to the health of layer composite structures, which will be essential if such systems are utilized for space exploration. The technology is also applicable to other in-situ health monitoring systems for structure integrity.

Early detection of glaucoma using fully automated disparity analysis of the optic nerve head (ONH) from stereo fundus images

NASA Astrophysics Data System (ADS)

Sharma, Archie; Corona, Enrique; Mitra, Sunanda; Nutter, Brian S.

2006-03-01

Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. Traditional early detection techniques involve observing changes in the ONH through an ophthalmoscope. Stereo fundus photography is also routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the Heidelberg Retina Tomograph (HRT) has not been found to be sufficiently sensitive for early detection. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. A 3-D visualization of the disc and cup is achieved assuming camera geometry. High correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study involving 159 stereo fundus image pairs has already been demonstrated. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this paper, we present a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.

Threshold Assessment of Gear Diagnostic Tools on Flight and Test Rig Data

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Mosher, Marianne; Huff, Edward M.

2003-01-01

A method for defining thresholds for vibration-based algorithms that provides the minimum number of false alarms while maintaining sensitivity to gear damage was developed. This analysis focused on two vibration based gear damage detection algorithms, FM4 and MSA. This method was developed using vibration data collected during surface fatigue tests performed in a spur gearbox rig. The thresholds were defined based on damage progression during tests with damage. The thresholds false alarm rates were then evaluated on spur gear tests without damage. Next, the same thresholds were applied to flight data from an OH-58 helicopter transmission. Results showed that thresholds defined in test rigs can be used to define thresholds in flight to correctly classify the transmission operation as normal.

Flat Surface Damage Detection System (FSDDS)

NASA Technical Reports Server (NTRS)

Williams, Martha; Lewis, Mark; Gibson, Tracy; Lane, John; Medelius, Pedro; Snyder, Sarah; Ciarlariello, Dan; Parks, Steve; Carrejo, Danny; Rojdev, Kristina

2013-01-01

The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.

Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings

NASA Astrophysics Data System (ADS)

Wodecki, Jacek; Michalak, Anna; Zimroz, Radoslaw

2018-03-01

Harsh industrial conditions present in underground mining cause a lot of difficulties for local damage detection in heavy-duty machinery. For vibration signals one of the most intuitive approaches of obtaining signal with expected properties, such as clearly visible informative features, is prefiltration with appropriately prepared filter. Design of such filter is very broad field of research on its own. In this paper authors propose a novel approach to dedicated optimal filter design using progressive genetic algorithm. Presented method is fully data-driven and requires no prior knowledge of the signal. It has been tested against a set of real and simulated data. Effectiveness of operation has been proven for both healthy and damaged case. Termination criterion for evolution process was developed, and diagnostic decision making feature has been proposed for final result determinance.

Non-damaging laser therapy of the macula: Titration algorithm and tissue response

NASA Astrophysics Data System (ADS)

Palanker, Daniel; Lavinsky, Daniel; Dalal, Roopa; Huie, Philip

2014-02-01

Retinal photocoagulation typically results in permanent scarring and scotomata, which limit its applicability to the macula, preclude treatments in the fovea, and restrict the retreatments. Non-damaging approaches to laser therapy have been tested in the past, but the lack of reliable titration and slow treatment paradigms limited their clinical use. We developed and tested a titration algorithm for sub-visible and non-damaging treatments of the retina with pulses sufficiently short to be used with pattern laser scanning. The algorithm based on Arrhenius model of tissue damage optimizes the power and duration for every energy level, relative to the threshold of lesion visibility established during titration (and defined as 100%). Experiments with pigmented rabbits established that lesions in the 50-75% energy range were invisible ophthalmoscopically, but detectable with Fluorescein Angiography and OCT, while at 30% energy there was only very minor damage to the RPE, which recovered within a few days. Patients with Diabetic Macular Edema (DME) and Central Serous Retinopathy (CSR) have been treated over the edematous areas at 30% energy, using 200μm spots with 0.25 diameter spacing. No signs of laser damage have been detected with any imaging modality. In CSR patients, subretinal fluid resolved within 45 days. In DME patients the edema decreased by approximately 150μm over 60 days. After 3-4 months some patients presented with recurrence of edema, and they responded well to retreatment with the same parameters, without any clinically visible damage. This pilot data indicates a possibility of effective and repeatable macular laser therapy below the tissue damage threshold.

Online damage detection using recursive principal component analysis and recursive condition indicators

NASA Astrophysics Data System (ADS)

Krishnan, M.; Bhowmik, B.; Tiwari, A. K.; Hazra, B.

2017-08-01

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using recursive principal component analysis (RPCA) in conjunction with online damage indicators is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal modes in online using the rank-one perturbation method, and subsequently utilized to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/nonlinear-states that indicate damage. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. An online condition indicator (CI) based on the L2 norm of the error between actual response and the response projected using recursive eigenvector matrix updates over successive iterations is proposed. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data. The proposed CI, named recursive residual error, is also adopted for simultaneous spatio-temporal damage detection. Numerical simulations performed on five-degree of freedom nonlinear system under white noise and El Centro excitations, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. Successful results obtained from practical case studies involving experiments performed on a cantilever beam subjected to earthquake excitation, for full sensors and underdetermined cases; and data from recorded responses of the UCLA Factor building (full data and its subset) demonstrate the efficacy of the proposed methodology as an ideal candidate for real-time, reference free structural health monitoring.

Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters

NASA Astrophysics Data System (ADS)

Zheng, H.; Mita, A.

2007-10-01

This paper presents a two-stage damage diagnosis strategy for damage detection and localization. Auto-regressive moving-average (ARMA) models are fitted to time series of vibration signals recorded by sensors. In the first stage, a novel damage indicator, which is defined as the distance between ARMA models, is applied to damage detection. This stage can determine the existence of damage in the structure. Such an algorithm uses output only and does not require operator intervention. Therefore it can be embedded in the sensor board of a monitoring network. In the second stage, a pre-whitening filter is used to minimize the cross-correlation of multiple excitations. With this technique, the damage indicator can further identify the damage location and severity when the damage has been detected in the first stage. The proposed methodology is tested using simulation and experimental data. The analysis results clearly illustrate the feasibility of the proposed two-stage damage diagnosis methodology.

Acoustic emission localization based on FBG sensing network and SVR algorithm

NASA Astrophysics Data System (ADS)

Sai, Yaozhang; Zhao, Xiuxia; Hou, Dianli; Jiang, Mingshun

2017-03-01

In practical application, carbon fiber reinforced plastics (CFRP) structures are easy to appear all sorts of invisible damages. So the damages should be timely located and detected for the safety of CFPR structures. In this paper, an acoustic emission (AE) localization system based on fiber Bragg grating (FBG) sensing network and support vector regression (SVR) is proposed for damage localization. AE signals, which are caused by damage, are acquired by high speed FBG interrogation. According to the Shannon wavelet transform, time differences between AE signals are extracted for localization algorithm based on SVR. According to the SVR model, the coordinate of AE source can be accurately predicted without wave velocity. The FBG system and localization algorithm are verified on a 500 mm×500 mm×2 mm CFRP plate. The experimental results show that the average error of localization system is 2.8 mm and the training time is 0.07 s.

Satellite change detection of forest damage near the Chernobyl accident

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

McClellan, G.E.; Anno, G.H.

1992-01-01

A substantial amount of forest within a few kilometers of the Chernobyl nuclear reactor station was badly contaminated with radionuclides by the April 26, 1986, explosion and ensuing fire at reactor No. 4. Radiation doses to conifers in some areas were sufficient to cause discoloration of needles within a few weeks. Other areas, receiving smaller doses, showed foliage changes beginning 6 months to a year later. Multispectral imagery available from Landsat sensors is especially suited for monitoring such changes in vegetation. A series of Landsat Thematic Mapper images was developed that span the 2 yr following the accident. Quantitative dosemore » estimation for the exposed conifers requires an objective change detection algorithm and knowledge of the dose-time response of conifers to ionizing radiation. Pacific-Sierra Research Corporation's Hyperscout{trademark} algorithm is based on an advanced, sensitive technique for change detection particularly suited for multispectral images. The Hyperscout algorithm has been used to assess radiation damage to the forested areas around the Chernobyl nuclear power plant.« less

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Using Impact Modulation to Identify Loose Bolts on a Satellite

DTIC Science & Technology

2011-10-21

for public release; distribution is unlimited the literature to be an effective damage detection method for cracks, delamination, and fatigue in...to identify loose bolts and fatigue damage using optimized sensor locations using a Support Vector Machines algorithm to classify the dam- age. Finally...48] did preliminary work which showed that VM is effective in detecting fatigue cracks in engineering components despite changes in actuator location

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

NASA Astrophysics Data System (ADS)

Neri, P.

2017-05-01

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

Single-Input and Multiple-Output Surface Acoustic Wave Sensing for Damage Quantification in Piezoelectric Sensors.

PubMed

Pamwani, Lavish; Habib, Anowarul; Melandsø, Frank; Ahluwalia, Balpreet Singh; Shelke, Amit

2018-06-22

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage.

Vibration Based Sun Gear Damage Detection

NASA Technical Reports Server (NTRS)

Hood, Adrian; LaBerge, Kelsen; Lewicki, David; Pines, Darryll

2013-01-01

Seeded fault experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two vibration based methods are discussed that isolate the dynamics of the sun gear from that of the planet gears, bearings, input spiral bevel stage, and other components in and around the gearbox. Three damaged sun gears: two spalled and one cracked, serve as the focus of this current work. A non-sequential vibration separation algorithm was developed and the resulting signals analyzed. The second method uses only the time synchronously averaged data but takes advantage of the signal/source mapping required for vibration separation. Both algorithms were successful in identifying the spall damage. Sun gear damage was confirmed by the presence of sun mesh groups. The sun tooth crack condition was inconclusive.

Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

NASA Astrophysics Data System (ADS)

LeAnder, Robert; Chowdary, Myneni Sushma; Mokkapati, Swapnasri; Umbaugh, Scott E.

2008-03-01

Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM and SNR. The performances of these algorithms show that they have an appreciable amount of potential in helping ophthalmologists detect the severity of eye-related diseases and prevent vision loss.

An extended diffraction tomography method for quantifying structural damage using numerical Green's functions.

PubMed

Chan, Eugene; Rose, L R Francis; Wang, Chun H

2015-05-01

Existing damage imaging algorithms for detecting and quantifying structural defects, particularly those based on diffraction tomography, assume far-field conditions for the scattered field data. This paper presents a major extension of diffraction tomography that can overcome this limitation and utilises a near-field multi-static data matrix as the input data. This new algorithm, which employs numerical solutions of the dynamic Green's functions, makes it possible to quantitatively image laminar damage even in complex structures for which the dynamic Green's functions are not available analytically. To validate this new method, the numerical Green's functions and the multi-static data matrix for laminar damage in flat and stiffened isotropic plates are first determined using finite element models. Next, these results are time-gated to remove boundary reflections, followed by discrete Fourier transform to obtain the amplitude and phase information for both the baseline (damage-free) and the scattered wave fields. Using these computationally generated results and experimental verification, it is shown that the new imaging algorithm is capable of accurately determining the damage geometry, size and severity for a variety of damage sizes and shapes, including multi-site damage. Some aspects of minimal sensors requirement pertinent to image quality and practical implementation are also briefly discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Development of a Near-Real Time Hail Damage Swath Identification Algorithm for Vegetation

NASA Technical Reports Server (NTRS)

Bell, Jordan R.; Molthan, Andrew L.; Schultz, Lori A.; McGrath, Kevin M.; Burks, Jason E.

2015-01-01

The Midwest is home to one of the world's largest agricultural growing regions. Between the time period of late May through early September, and with irrigation and seasonal rainfall these crops are able to reach their full maturity. Using moderate to high resolution remote sensors, the monitoring of the vegetation can be achieved using the red and near-infrared wavelengths. These wavelengths allow for the calculation of vegetation indices, such as Normalized Difference Vegetation Index (NDVI). The vegetation growth and greenness, in this region, grows and evolves uniformly as the growing season progresses. However one of the biggest threats to Midwest vegetation during the time period is thunderstorms that bring large hail and damaging winds. Hail and wind damage to crops can be very expensive to crop growers and, damage can be spread over long swaths associated with the tracks of the damaging storms. Damage to the vegetation can be apparent in remotely sensed imagery and is visible from space after storms slightly damage the crops, allowing for changes to occur slowly over time as the crops wilt or more readily apparent if the storms strip material from the crops or destroy them completely. Previous work on identifying these hail damage swaths used manual interpretation by the way of moderate and higher resolution satellite imagery. With the development of an automated and near-real time hail swath damage identification algorithm, detection can be improved, and more damage indicators be created in a faster and more efficient way. The automated detection of hail damage swaths will examine short-term, large changes in the vegetation by differencing near-real time eight day NDVI composites and comparing them to post storm imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP. In addition land surface temperatures from these instruments will be examined as for hail damage swath identification. Initial validation of the automated algorithm is based upon Storm Prediction Center storm reports but also the National Severe Storm Laboratory (NSSL) Maximum Estimated Size Hail (MESH) product. Opportunities for future work are also shown, with focus on expansion of this algorithm with pixel-based image classification techniques for tracking surface changes as a result of severe weather.

Data fusion of multi-scale representations for structural damage detection

NASA Astrophysics Data System (ADS)

Guo, Tian; Xu, Zili

2018-01-01

Despite extensive researches into structural health monitoring (SHM) in the past decades, there are few methods that can detect multiple slight damage in noisy environments. Here, we introduce a new hybrid method that utilizes multi-scale space theory and data fusion approach for multiple damage detection in beams and plates. A cascade filtering approach provides multi-scale space for noisy mode shapes and filters the fluctuations caused by measurement noise. In multi-scale space, a series of amplification and data fusion algorithms are utilized to search the damage features across all possible scales. We verify the effectiveness of the method by numerical simulation using damaged beams and plates with various types of boundary conditions. Monte Carlo simulations are conducted to illustrate the effectiveness and noise immunity of the proposed method. The applicability is further validated via laboratory cases studies focusing on different damage scenarios. Both results demonstrate that the proposed method has a superior noise tolerant ability, as well as damage sensitivity, without knowing material properties or boundary conditions.

Guided wave propagation and spectral element method for debonding damage assessment in RC structures

NASA Astrophysics Data System (ADS)

Wang, Ying; Zhu, Xinqun; Hao, Hong; Ou, Jinping

2009-07-01

A concrete-steel interface spectral element is developed to study the guided wave propagation along the steel rebar in the concrete. Scalar damage parameters characterizing changes in the interface (debonding damage) are incorporated into the formulation of the spectral finite element that is used for damage detection of reinforced concrete structures. Experimental tests are carried out on a reinforced concrete beam with embedded piezoelectric elements to verify the performance of the proposed model and algorithm. Parametric studies are performed to evaluate the effect of different damage scenarios on wave propagation in the reinforced concrete structures. Numerical simulations and experimental results show that the method is effective to model wave propagation along the steel rebar in concrete and promising to detect damage in the concrete-steel interface.

Selective Excitation of Lamb-Waves for Damage Detection in Composites

NASA Astrophysics Data System (ADS)

Petculescu, G.; Krishnaswamy, S.; Achenbach, J. D.

2006-03-01

Sensors based on periodic arrays of coherent piezoelectric sources (comb design) are used to selectively excite and detect Lamb waves in aluminum and AS4/3601 unidirectional carbon-epoxy plates. 110 μm PVDF film poled in the thickness direction is used as piezoelectric material. An algorithm to eliminate the effect of coupling in amplitude measurements, using individual Lamb modes excited/detected by the same transducer pair, is described. A multiple-impact test showing a decrease in amplitude and group velocity as damage progresses is used as an example.

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

PubMed Central

Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2014-01-01

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

Guided Wave Delamination Detection and Quantification With Wavefield Data Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Campbell Leckey, Cara A.; Seebo, Jeffrey P.; Yu, Lingyu

2014-01-01

Unexpected damage can occur in aerospace composites due to impact events or material stress during off-nominal loading events. In particular, laminated composites are susceptible to delamination damage due to weak transverse tensile and inter-laminar shear strengths. Developments of reliable and quantitative techniques to detect delamination damage in laminated composites are imperative for safe and functional optimally-designed next-generation composite structures. In this paper, we investigate guided wave interactions with delamination damage and develop quantification algorithms by using wavefield data analysis. The trapped guided waves in the delamination region are observed from the wavefield data and further quantitatively interpreted by using different wavenumber analysis methods. The frequency-wavenumber representation of the wavefield shows that new wavenumbers are present and correlate to trapped waves in the damage region. These new wavenumbers are used to detect and quantify the delamination damage through the wavenumber analysis, which can show how the wavenumber changes as a function of wave propagation distance. The location and spatial duration of the new wavenumbers can be identified, providing a useful means not only for detecting the presence of delamination damage but also allowing for estimation of the delamination size. Our method has been applied to detect and quantify real delamination damage with complex geometry (grown using a quasi-static indentation technique). The detection and quantification results show the location, size, and shape of the delamination damage.

Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering.

PubMed

Sansone, Mario; Zeni, Olga; Esposito, Giovanni

2012-05-01

Comet assay is one of the most popular tests for the detection of DNA damage at single cell level. In this study, an algorithm for comet assay analysis has been proposed, aiming to minimize user interaction and providing reproducible measurements. The algorithm comprises two-steps: (a) comet identification via Gaussian pre-filtering and morphological operators; (b) comet segmentation via fuzzy clustering. The algorithm has been evaluated using comet images from human leukocytes treated with a commonly used DNA damaging agent. A comparison of the proposed approach with a commercial system has been performed. Results show that fuzzy segmentation can increase overall sensitivity, giving benefits in bio-monitoring studies where weak genotoxic effects are expected.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Rotor damage detection by using piezoelectric impedance

NASA Astrophysics Data System (ADS)

Qin, Y.; Tao, Y.; Mao, Y. F.

2016-04-01

Rotor is a core component of rotary machinery. Once the rotor has the damage, it may lead to a major accident. Thus the quantitative rotor damage detection method based on piezoelectric impedance is studied in this paper. With the governing equation of piezoelectric transducer (PZT) in a cylindrical coordinate, the displacement along the radius direction is derived. The charge of PZT is calculated by the electric displacement. Then, by the use of the obtained displacement and charge, an analytic piezoelectric impedance model of the rotor is built. Given the circular boundary condition of a rotor, annular elements are used as the analyzed objects and spectral element method is used to set up the damage detection model. The Electro-Mechanical (E/M) coupled impedance expression of an undamaged rotor is deduced with the application of a low-cost impedance test circuit. A Taylor expansion method is used to obtain the approximate E/M coupled impedance expression for the damaged rotor. After obtaining the difference between the undamaged and damaged rotor impedance, a rotor damage detection method is proposed. This method can directly calculate the change of bending stiffness of the structural elements, it follows that the rotor damage can be effectively detected. Finally, a preset damage configuration is used for the numerical simulation. The result shows that the quantitative damage detection algorithm based on spectral element method and piezoelectric impedance proposed in this paper can identify the location and the severity of the damaged rotor accurately.

Delamination detection by Multi-Level Wavelet Processing of Continuous Scanning Laser Doppler Vibrometry data

NASA Astrophysics Data System (ADS)

Chiariotti, P.; Martarelli, M.; Revel, G. M.

2017-12-01

A novel non-destructive testing procedure for delamination detection based on the exploitation of the simultaneous time and spatial sampling provided by Continuous Scanning Laser Doppler Vibrometry (CSLDV) and the feature extraction capability of Multi-Level wavelet-based processing is presented in this paper. The processing procedure consists in a multi-step approach. Once the optimal mother-wavelet is selected as the one maximizing the Energy to Shannon Entropy Ratio criterion among the mother-wavelet space, a pruning operation aiming at identifying the best combination of nodes inside the full-binary tree given by Wavelet Packet Decomposition (WPD) is performed. The pruning algorithm exploits, in double step way, a measure of the randomness of the point pattern distribution on the damage map space with an analysis of the energy concentration of the wavelet coefficients on those nodes provided by the first pruning operation. A combination of the point pattern distributions provided by each node of the ensemble node set from the pruning algorithm allows for setting a Damage Reliability Index associated to the final damage map. The effectiveness of the whole approach is proven on both simulated and real test cases. A sensitivity analysis related to the influence of noise on the CSLDV signal provided to the algorithm is also discussed, showing that the processing developed is robust enough to measurement noise. The method is promising: damages are well identified on different materials and for different damage-structure varieties.

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

NASA Astrophysics Data System (ADS)

Liu, Zenghua; Sun, Kunming; Song, Guorong; He, Cunfu; Wu, Bin

2016-03-01

In this work, a detection method for the damage in plate-like structure with a compact rectangular phased piezoelectric transducer array of 16 piezoelectric elements was presented. This compact array can not only detect and locate a single defect (through hole) in plate, but also identify multi-defects (through holes and surface defect simulated by an iron pillar glued to the plate). The experiments proved that the compact rectangular phased transducer array could detect the full range of plate structures and implement multiple-defect detection simultaneously. The processing algorithm proposed in this paper contains two parts: signal filtering and damage imaging. The former part was used to remove noise from signals. Continuous wavelet transform was applicable to signal filtering. Continuous wavelet transform can provide a plot of wavelet coefficients and the signal with narrow frequency band can be easily extracted from the plot. The latter part of processing algorithm was to implement damage detection and localization. In order to accurately locate defects and improve the imaging quality, two images were obtained from amplitude and phase information. One image was obtained with the Total Focusing Method (TFM) and another phase image was obtained with the Sign Coherence Factor (SCF). Furthermore, an image compounding technique for compact rectangular phased piezoelectric transducer array was proposed in this paper. With the proposed technique, the compounded image can be obtained by combining TFM image with SCF image, thus greatly improving the resolution and contrast of image.

Detection of multiple damages employing best achievable eigenvectors under Bayesian inference

NASA Astrophysics Data System (ADS)

Prajapat, Kanta; Ray-Chaudhuri, Samit

2018-05-01

A novel approach is presented in this work to localize simultaneously multiple damaged elements in a structure along with the estimation of damage severity for each of the damaged elements. For detection of damaged elements, a best achievable eigenvector based formulation has been derived. To deal with noisy data, Bayesian inference is employed in the formulation wherein the likelihood of the Bayesian algorithm is formed on the basis of errors between the best achievable eigenvectors and the measured modes. In this approach, the most probable damage locations are evaluated under Bayesian inference by generating combinations of various possible damaged elements. Once damage locations are identified, damage severities are estimated using a Bayesian inference Markov chain Monte Carlo simulation. The efficiency of the proposed approach has been demonstrated by carrying out a numerical study involving a 12-story shear building. It has been found from this study that damage scenarios involving as low as 10% loss of stiffness in multiple elements are accurately determined (localized and severities quantified) even when 2% noise contaminated modal data are utilized. Further, this study introduces a term parameter impact (evaluated based on sensitivity of modal parameters towards structural parameters) to decide the suitability of selecting a particular mode, if some idea about the damaged elements are available. It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori. An experimental study involving a laboratory scale shear building and different stiffness modification scenarios shows that the proposed approach is efficient enough to localize the stories with stiffness modification.

Active sensors for health monitoring of aging aerospace structures

NASA Astrophysics Data System (ADS)

Giurgiutiu, Victor; Redmond, James M.; Roach, Dennis P.; Rackow, Kirk

2000-06-01

A project to develop non-intrusive active sensors that can be applied on existing aging aerospace structures for monitoring the onset and progress of structural damage (fatigue cracks and corrosion) is presented. The state of the art in active sensors structural health monitoring and damage detection is reviewed. Methods based on (a) elastic wave propagation and (b) electro-mechanical (E/M) impedance technique are cited and briefly discussed. The instrumentation of these specimens with piezoelectric active sensors is illustrated. The main detection strategies (E/M impedance for local area detection and wave propagation for wide area interrogation) are discussed. The signal processing and damage interpretation algorithms are tuned to the specific structural interrogation method used. In the high frequency E/M impedance approach, pattern recognition methods are used to compare impedance signatures taken at various time intervals and to identify damage presence and progression from the change in these signatures. In the wave propagation approach, the acousto- ultrasonic methods identifying additional reflection generated from the damage site and changes in transmission velocity and phase are used. Both approaches benefit from the use of artificial intelligence neural networks algorithms that can extract damage features based on a learning process. Design and fabrication of a set of structural specimens representative of aging aerospace structures is presented. Three built-up specimens, (pristine, with cracks, and with corrosion damage) are used. The specimen instrumentation with active sensors fabricated at the University of South Carolina is illustrated. Preliminary results obtained with the E/M impedance method on pristine and cracked specimens are presented.

Evolutionary Algorithms Approach to the Solution of Damage Detection Problems

NASA Astrophysics Data System (ADS)

Salazar Pinto, Pedro Yoajim; Begambre, Oscar

2010-09-01

In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.

A novel sensitivity-based method for damage detection of structures under unknown periodic excitations

NASA Astrophysics Data System (ADS)

Naseralavi, S. S.; Salajegheh, E.; Fadaee, M. J.; Salajegheh, J.

2014-06-01

This paper presents a technique for damage detection in structures under unknown periodic excitations using the transient displacement response. The method is capable of identifying the damage parameters without finding the input excitations. We first define the concept of displacement space as a linear space in which each point represents displacements of structure under an excitation and initial condition. Roughly speaking, the method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering this novel geometrical viewpoint, an equation called kernel parallelization equation (KPE) is derived for damage detection under unknown periodic excitations and a sensitivity-based algorithm for solving KPE is proposed accordingly. The method is evaluated via three case studies under periodic excitations, which confirm the efficiency of the proposed method.

Baseline-free damage detection in composite plates based on the reciprocity principle

NASA Astrophysics Data System (ADS)

Huang, Liping; Zeng, Liang; Lin, Jing

2018-01-01

Lamb wave based damage detection techniques have been widely used in composite structures. In particular, these techniques usually rely on reference signals, which are significantly influenced by the operational and environmental conditions. To solve this issue, this paper presents a baseline-free damage inspection method based on the reciprocity principle. If a localized nonlinear scatterer exists along the wave path, the reciprocity breaks down. Through estimating the loss of reciprocity, the delamination could be detected. A reciprocity index (RI), which compares the discrepancy between the signal received in transducer B when emitting from transducer A and the signal received in A when the same source is located in B, is established to quantitatively analyze the reciprocity. Experimental results show that the RI value of a damaged path is much higher than that of a healthy path. In addition, the effects of the parameters of excitation signal (i.e., central frequency and bandwidth) and the position of delamination on the RI value are discussed. Furthermore, a RI based probabilistic imaging algorithm is proposed for detecting delamination damage of composite plates without reference signals. Finally, the effectiveness of this baseline-free damage detection method is validated by an experimental example.

Damage detection based on acceleration data using artificial immune system

NASA Astrophysics Data System (ADS)

Chartier, Sandra; Mita, Akira

2009-03-01

Nowadays, Structural Health Monitoring (SHM) is essential in order to prevent damages occurrence in civil structures. This is a particularly important issue as the number of aged structures is increasing. Damage detection algorithms are often based on changes in the modal properties like natural frequencies, modal shapes and modal damping. In this paper, damage detection is completed by using Artificial Immune System (AIS) theory directly on acceleration data. Inspired from the biological immune system, AIS is composed of several models like negative selection which has a great potential for this study. The negative selection process relies on the fact that T-cells, after their maturation, are sensitive to non self cells and can not detect self cells. Acceleration data were provided by using the numerical model of a 3-story frame structure. Damages were introduced, at particular times, by reduction of story's stiffness. Based on these acceleration data, undamaged data (equivalent to self data) and damaged data (equivalent to non self data) can be obtained and represented in the Hamming shape-space with a binary representation. From the undamaged encoded data, detectors (equivalent to T-cells) are derived and are able to detect damaged encoded data really efficiently by using the rcontiguous bits matching rule. Indeed, more than 95% of detection can be reached when efficient combinations of parameters are used. According to the number of detected data, the localization of damages can even be determined by using the differences between story's relative accelerations. Thus, the difference which presents the highest detection rate, generally up to 89%, is directly linked to the location of damage.

Damage localization of marine risers using time series of vibration signals

NASA Astrophysics Data System (ADS)

Liu, Hao; Yang, Hezhen; Liu, Fushun

2014-10-01

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

Develop an piezoelectric sensing based on SHM system for nuclear dry storage system

NASA Astrophysics Data System (ADS)

Ma, Linlin; Lin, Bin; Sun, Xiaoyi; Howden, Stephen; Yu, Lingyu

2016-04-01

In US, there are over 1482 dry cask storage system (DCSS) in use storing 57,807 fuel assemblies. Monitoring is necessary to determine and predict the degradation state of the systems and structures. Therefore, nondestructive monitoring is in urgent need and must be integrated into the fuel cycle to quantify the "state of health" for the safe operation of nuclear power plants (NPP) and radioactive waste storage systems (RWSS). Innovative approaches are desired to evaluate the degradation and damage of used fuel containers under extended storage. Structural health monitoring (SHM) is an emerging technology that uses in-situ sensory system to perform rapid nondestructive detection of structural damage as well as long-term integrity monitoring. It has been extensively studied in aerospace engineering over the past two decades. This paper presents the development of a SHM and damage detection methodology based on piezoelectric sensors technologies for steel canisters in nuclear dry cask storage system. Durability and survivability of piezoelectric sensors under temperature influence are first investigated in this work by evaluating sensor capacitance and electromechanical admittance. Toward damage detection, the PES are configured in pitch catch setup to transmit and receive guided waves in plate-like structures. When the inspected structure has damage such as a surface defect, the incident guided waves will be reflected or scattered resulting in changes in the wave measurements. Sparse array algorithm is developed and implemented using multiple sensors to image the structure. The sparse array algorithm is also evaluated at elevated temperature.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Damage detection in rotating machinery by means of entropy-based parameters

NASA Astrophysics Data System (ADS)

Tocarciuc, Alexandru; Bereteu, Liviu; ǎgǎnescu, Gheorghe Eugen, Dr

2014-11-01

The paper is proposing two new entropy-based parameters, namely Renyi Entropy Index (REI) and Sharma-Mittal Entropy Index (SMEI), for detecting the presence of failures (or damages) in rotating machinery, namely: belt structural damage, belt wheels misalignment, failure of the fixing bolt of the machine to its baseplate and eccentricities (i.e.: due to detaching a small piece of material or bad mounting of the rotating components of the machine). The algorithms to obtain the proposed entropy-based parameters are described and test data is used in order to assess their sensitivity. A vibration test bench is used for measuring the levels of vibration while artificially inducing damage. The deviation of the two entropy-based parameters is compared in two states of the vibration test bench: not damaged and damaged. At the end of the study, their sensitivity is compared to Shannon Entropic Index.

Automatic detection of DNA double strand breaks after irradiation using an γH2AX assay.

PubMed

Hohmann, Tim; Kessler, Jacqueline; Grabiec, Urszula; Bache, Matthias; Vordermark, Dyrk; Dehghani, Faramarz

2018-05-01

Radiation therapy belongs to the most common approaches for cancer therapy leading amongst others to DNA damage like double strand breaks (DSB). DSB can be used as a marker for the effect of radiation on cells. For visualization and assessing the extent of DNA damage the γH2AX foci assay is frequently used. The analysis of the γH2AX foci assay remains complicated as the number of γH2AX foci has to be counted. The quantification is mostly done manually, being time consuming and leading to person-dependent variations. Therefore, we present a method to automatically analyze the number of foci inside nuclei, facilitating and quickening the analysis of DSBs with high reliability in fluorescent images. First nuclei were detected in fluorescent images. Afterwards, the nuclei were analyzed independently from each other with a local thresholding algorithm. This approach allowed accounting for different levels of noise and detection of the foci inside the respective nucleus, using Hough transformation searching for circles. The presented algorithm was able to correctly classify most foci in cases of "high" and "average" image quality (sensitivity>0.8) with a low rate of false positive detections (positive predictive value (PPV)>0.98). In cases of "low" image quality the approach had a decreased sensitivity (0.7-0.9), depending on the manual control counter. The PPV remained high (PPV>0.91). Compared to other automatic approaches the presented algorithm had a higher sensitivity and PPV. The used automatic foci detection algorithm was capable of detecting foci with high sensitivity and PPV. Thus it can be used for automatic analysis of images of varying quality.

Scalable Algorithms for Global Scale Remote Sensing Applications

NASA Astrophysics Data System (ADS)

Vatsavai, R. R.; Bhaduri, B. L.; Singh, N.

2015-12-01

Recent decade has witnessed major changes on the Earth, for example, deforestation, varying cropping and human settlement patterns, and crippling damages due to disasters. Accurate damage assessment caused by major natural and anthropogenic disasters is becoming critical due to increases in human and economic loss. This increase in loss of life and severe damages can be attributed to the growing population, as well as human migration to the disaster prone regions of the world. Rapid assessment of these changes and dissemination of accurate information is critical for creating an effective emergency response. Change detection using high-resolution satellite images is a primary tool in assessing damages, monitoring biomass and critical infrastructures, and identifying new settlements. Existing change detection methods suffer from registration errors and often based on pixel (location) wise comparison of spectral observations from single sensor. In this paper we present a novel probabilistic change detection framework based on patch comparison and a GPU implementation that supports near real-time rapid damage exploration capability.

Damage identification via asymmetric active magnetic bearing acceleration feedback control

NASA Astrophysics Data System (ADS)

Zhao, Jie; DeSmidt, Hans; Yao, Wei

2015-04-01

A Floquet-based damage detection methodology for cracked rotor systems is developed and demonstrated on a shaft-disk system. This approach utilizes measured changes in the system natural frequencies to estimate the severity and location of shaft structural cracks during operation. The damage detection algorithms are developed with the initial guess solved by least square method and iterative damage parameter vector by updating the eigenvector updating. Active Magnetic Bearing is introduced to break the symmetric structure of rotor system and the tuning range of proper stiffness/virtual mass gains is studied. The system model is built based on energy method and the equations of motion are derived by applying assumed modes method and Lagrange Principle. In addition, the crack model is based on the Strain Energy Release Rate (SERR) concept in fracture mechanics. Finally, the method is synthesized via harmonic balance and numerical examples for a shaft/disk system demonstrate the effectiveness in detecting both location and severity of the structural damage.

Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Vincent, Kathleen L.; Vargas, Gracie; Motamedi, Massoud

2012-11-01

We have explored the use of optical coherence tomography (OCT) as a noninvasive tool for assessing the toxicity of topical microbicides, products used to prevent HIV, by monitoring the integrity of the vaginal epithelium. A novel feature-based segmentation algorithm using a nearest-neighbor classifier was developed to monitor changes in the morphology of vaginal epithelium. The two-step automated algorithm yielded OCT images with a clearly defined epithelial layer, enabling differentiation of normal and damaged tissue. The algorithm was robust in that it was able to discriminate the epithelial layer from underlying stroma as well as residual microbicide product on the surface. This segmentation technique for OCT images has the potential to be readily adaptable to the clinical setting for noninvasively defining the boundaries of the epithelium, enabling quantifiable assessment of microbicide-induced damage in vaginal tissue.

Development and validation of an automated operational modal analysis algorithm for vibration-based monitoring and tensile load estimation

NASA Astrophysics Data System (ADS)

Rainieri, Carlo; Fabbrocino, Giovanni

2015-08-01

In the last few decades large research efforts have been devoted to the development of methods for automated detection of damage and degradation phenomena at an early stage. Modal-based damage detection techniques are well-established methods, whose effectiveness for Level 1 (existence) and Level 2 (location) damage detection is demonstrated by several studies. The indirect estimation of tensile loads in cables and tie-rods is another attractive application of vibration measurements. It provides interesting opportunities for cheap and fast quality checks in the construction phase, as well as for safety evaluations and structural maintenance over the structure lifespan. However, the lack of automated modal identification and tracking procedures has been for long a relevant drawback to the extensive application of the above-mentioned techniques in the engineering practice. An increasing number of field applications of modal-based structural health and performance assessment are appearing after the development of several automated output-only modal identification procedures in the last few years. Nevertheless, additional efforts are still needed to enhance the robustness of automated modal identification algorithms, control the computational efforts and improve the reliability of modal parameter estimates (in particular, damping). This paper deals with an original algorithm for automated output-only modal parameter estimation. Particular emphasis is given to the extensive validation of the algorithm based on simulated and real datasets in view of continuous monitoring applications. The results point out that the algorithm is fairly robust and demonstrate its ability to provide accurate and precise estimates of the modal parameters, including damping ratios. As a result, it has been used to develop systems for vibration-based estimation of tensile loads in cables and tie-rods. Promising results have been achieved for non-destructive testing as well as continuous monitoring purposes. They are documented in the last sections of the paper.

Development of an Automatic Detection Program of Halo CMEs

NASA Astrophysics Data System (ADS)

Choi, K.; Park, M. Y.; Kim, J.

2017-12-01

The front-side halo CMEs are the major cause for large geomagnetic storms. Halo CMEs can result in damage to satellites, communication, electrical transmission lines and power systems. Thus automated techniques for detecting and analysing Halo CMEs from coronagraph data are of ever increasing importance for space weather monitoring and forecasting. In this study, we developed the algorithm that can automatically detect and do image processing the Halo CMEs in the images from the LASCO C3 coronagraph on board the SOHO spacecraft. With the detection algorithm, we derived the geometric and kinematical parameters of halo CMEs, such as source location, width, actual CME speed and arrival time at 21.5 solar radii.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Laser-based structural sensing and surface damage detection

NASA Astrophysics Data System (ADS)

Guldur, Burcu

Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense three-dimensional point clouds. This research investigates the use of high resolution three-dimensional terrestrial laser scanners with image capturing abilities as tools to capture geometric range data of complex scenes for structural engineering applications. Laser scanning technology is continuously improving, with commonly available scanners now capturing over 1,000,000 texture-mapped points per second with an accuracy of ~2 mm. However, automatically extracting meaningful information from point clouds remains a challenge, and the current state-of-the-art requires significant user interaction. The first objective of this research is to use widely accepted point cloud processing steps such as registration, feature extraction, segmentation, surface fitting and object detection to divide laser scanner data into meaningful object clusters and then apply several damage detection methods to these clusters. This required establishing a process for extracting important information from raw laser-scanned data sets such as the location, orientation and size of objects in a scanned region, and location of damaged regions on a structure. For this purpose, first a methodology for processing range data to identify objects in a scene is presented and then, once the objects from model library are correctly detected and fitted into the captured point cloud, these fitted objects are compared with the as-is point cloud of the investigated object to locate defects on the structure. The algorithms are demonstrated on synthetic scenes and validated on range data collected from test specimens and test-bed bridges. The second objective of this research is to combine useful information extracted from laser scanner data with color information, which provides information in the fourth dimension that enables detection of damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect using only laser scanner data; moreover, the color information also helps to track volumetric changes on structures such as spalling. Although using images with varying resolution to detect cracks is an extensively researched topic, damage detection using laser scanners with and without color images is a new research area that holds many opportunities for enhancing the current practice of visual inspections. The aim is to combine the best features of laser scans and images to create an automatic and effective surface damage detection method, which will reduce the need for skilled labor during visual inspections and allow automatic documentation of related information. This work enables developing surface damage detection strategies that integrate existing condition rating criteria for a wide range damage types that are collected under three main categories: small deformations already existing on the structure (cracks); damage types that induce larger deformations, but where the initial topology of the structure has not changed appreciably (e.g., bent members); and large deformations where localized changes in the topology of the structure have occurred (e.g., rupture, discontinuities and spalling). The effectiveness of the developed damage detection algorithms are validated by comparing the detection results with the measurements taken from test specimens and test-bed bridges.

Mapping Canopy Damage from Understory Fires in Amazon Forests Using Annual Time Series of Landsat and MODIS Data

NASA Technical Reports Server (NTRS)

Morton, Douglas C.; DeFries, Ruth S.; Nagol, Jyoteshwar; Souza, Carlos M., Jr.; Kasischke, Eric S.; Hurtt, George C.; Dubayah, Ralph

2011-01-01

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars less than 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (greater than 500 ha) burn scars that accounted for the majority of all fire-damaged forest in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 square kilometers) were an order of magnitude higher than during the 1997-1998 El Nino event (124 square kilometers and 39 square kilometers, respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.

Geometric identification and damage detection of structural elements by terrestrial laser scanner

NASA Astrophysics Data System (ADS)

Hou, Tsung-Chin; Liu, Yu-Wei; Su, Yu-Min

2016-04-01

In recent years, three-dimensional (3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the required precision as those have been provided by traditional structural monitoring technologies. Together with widely available fast computation for massive point cloud data processing, 3D laser scanning can serve as an efficient structural monitoring alternative for civil engineering communities. Currently most research efforts have focused on integrating/calculating the measured multi-station point cloud data, as well as modeling/establishing the 3D meshes of the scanned objects. Very little attention has been spent on extracting the information related to health conditions and mechanical states of structures. In this study, an automated numerical approach that integrates various existing algorithms for geometric identification and damage detection of structural elements were established. Specifically, adaptive meshes were employed for classifying the point cloud data of the structural elements, and detecting the associated damages from the calculated eigenvalues in each area of the structural element. Furthermore, kd-tree was used to enhance the searching efficiency of plane fitting which were later used for identifying the boundaries of structural elements. The results of geometric identification were compared with M3C2 algorithm provided by CloudCompare, as well as validated by LVDT measurements of full-scale reinforced concrete beams tested in laboratory. It shows that 3D laser scanning, through the established processing approaches of the point cloud data, can offer a rapid, nondestructive, remote, and accurate solution for geometric identification and damage detection of structural elements.

Method for detecting damage in carbon-fibre reinforced plastic-steel structures based on eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Li, Xuan; Liu, Zhiping; Jiang, Xiaoli; Lodewijks, Gabrol

2018-01-01

Eddy current pulsed thermography (ECPT) is well established for non-destructive testing of electrical conductive materials, featuring the advantages of contactless, intuitive detecting and efficient heating. The concept of divergence characterization of the damage rate of carbon fibre-reinforced plastic (CFRP)-steel structures can be extended to ECPT thermal pattern characterization. It was found in this study that the use of ECPT technology on CFRP-steel structures generated a sizeable amount of valuable information for comprehensive material diagnostics. The relationship between divergence and transient thermal patterns can be identified and analysed by deploying mathematical models to analyse the information about fibre texture-like orientations, gaps and undulations in these multi-layered materials. The developed algorithm enabled the removal of information about fibre texture and the extraction of damage features. The model of the CFRP-glue-steel structures with damage was established using COMSOL Multiphysics® software, and quantitative non-destructive damage evaluation from the ECPT image areas was derived. The results of this proposed method illustrate that damaged areas are highly affected by available information about fibre texture. This proposed work can be applied for detection of impact induced damage and quantitative evaluation of CFRP structures.

Wind turbine blade shear web disbond detection using rotor blade operational sensing and data analysis.

PubMed

Myrent, Noah; Adams, Douglas E; Griffith, D Todd

2015-02-28

A wind turbine blade's structural dynamic response is simulated and analysed with the goal of characterizing the presence and severity of a shear web disbond. Computer models of a 5 MW offshore utility-scale wind turbine were created to develop effective algorithms for detecting such damage. Through data analysis and with the use of blade measurements, a shear web disbond was quantified according to its length. An aerodynamic sensitivity study was conducted to ensure robustness of the detection algorithms. In all analyses, the blade's flap-wise acceleration and root-pitching moment were the clearest indicators of the presence and severity of a shear web disbond. A combination of blade and non-blade measurements was formulated into a final algorithm for the detection and quantification of the disbond. The probability of detection was 100% for the optimized wind speed ranges in laminar, 30% horizontal shear and 60% horizontal shear conditions. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Method development of damage detection in asymmetric buildings

NASA Astrophysics Data System (ADS)

Wang, Yi; Thambiratnam, David P.; Chan, Tommy H. T.; Nguyen, Andy

2018-01-01

Aesthetics and functionality requirements have caused most buildings to be asymmetric in recent times. Such buildings exhibit complex vibration characteristics under dynamic loads as there is coupling between the lateral and torsional components of vibration, and are referred to as torsionally coupled buildings. These buildings require three dimensional modelling and analysis. In spite of much recent research and some successful applications of vibration based damage detection methods to civil structures in recent years, the applications to asymmetric buildings has been a challenging task for structural engineers. There has been relatively little research on detecting and locating damage specific to torsionally coupled asymmetric buildings. This paper aims to compare the difference in vibration behaviour between symmetric and asymmetric buildings and then use the vibration characteristics for predicting damage in them. The need for developing a special method to detect damage in asymmetric buildings thus becomes evident. Towards this end, this paper modifies the traditional modal strain energy based damage index by decomposing the mode shapes into their lateral and vertical components and to form component specific damage indices. The improved approach is then developed by combining the modified strain energy based damage indices with the modal flexibility method which was modified to suit three dimensional structures to form a new damage indicator. The procedure is illustrated through numerical studies conducted on three dimensional five-story symmetric and asymmetric frame structures with the same layout, after validating the modelling techniques through experimental testing of a laboratory scale asymmetric building model. Vibration parameters obtained from finite element analysis of the intact and damaged building models are then applied into the proposed algorithms for detecting and locating the single and multiple damages in these buildings. The results obtained from a number of different damage scenarios confirm the feasibility of the proposed vibration based damage detection method for three dimensional asymmetric buildings.

Wireless ultrasonic wavefield imaging via laser for hidden damage detection inside a steel box girder bridge

NASA Astrophysics Data System (ADS)

An, Yun-Kyu; Song, Homin; Sohn, Hoon

2014-09-01

This paper presents a wireless ultrasonic wavefield imaging (WUWI) technique for detecting hidden damage inside a steel box girder bridge. The proposed technique allows (1) complete wireless excitation of piezoelectric transducers and noncontact sensing of the corresponding responses using laser beams, (2) autonomous damage visualization without comparing against baseline data previously accumulated from the pristine condition of a target structure and (3) robust damage diagnosis even for real structures with complex structural geometries. First, a new WUWI hardware system was developed by integrating optoelectronic-based signal transmitting and receiving devices and a scanning laser Doppler vibrometer. Next, a damage visualization algorithm, self-referencing f-k filter (SRF), was introduced to isolate and visualize only crack-induced ultrasonic modes from measured ultrasonic wavefield images. Finally, the performance of the proposed technique was validated through hidden crack visualization at a decommissioned Ramp-G Bridge in South Korea. The experimental results reveal that the proposed technique instantaneously detects and successfully visualizes hidden cracks even in the complex structure of a real bridge.

Demyelinating and ischemic brain diseases: detection algorithm through regular magnetic resonance images

NASA Astrophysics Data System (ADS)

Castillo, D.; Samaniego, René; Jiménez, Y.; Cuenca, L.; Vivanco, O.; Rodríguez-Álvarez, M. J.

2017-09-01

This work presents the advance to development of an algorithm for automatic detection of demyelinating lesions and cerebral ischemia through magnetic resonance images, which have contributed in paramount importance in the diagnosis of brain diseases. The sequences of images to be used are T1, T2, and FLAIR. Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers; and therefore this deterioration is the cause of serious pathologies such as multiple sclerosis (MS), leukodystrophy, disseminated acute encephalomyelitis. Cerebral or cerebrovascular ischemia is the interruption of the blood supply to the brain, thus interrupting; the flow of oxygen and nutrients needed to maintain the functioning of brain cells. The algorithm allows the differentiation between these lesions.

A Bayesian state-space approach for damage detection and classification

NASA Astrophysics Data System (ADS)

Dzunic, Zoran; Chen, Justin G.; Mobahi, Hossein; Büyüköztürk, Oral; Fisher, John W.

2017-11-01

The problem of automatic damage detection in civil structures is complex and requires a system that can interpret collected sensor data into meaningful information. We apply our recently developed switching Bayesian model for dependency analysis to the problems of damage detection and classification. The model relies on a state-space approach that accounts for noisy measurement processes and missing data, which also infers the statistical temporal dependency between measurement locations signifying the potential flow of information within the structure. A Gibbs sampling algorithm is used to simultaneously infer the latent states, parameters of the state dynamics, the dependence graph, and any changes in behavior. By employing a fully Bayesian approach, we are able to characterize uncertainty in these variables via their posterior distribution and provide probabilistic estimates of the occurrence of damage or a specific damage scenario. We also implement a single class classification method which is more realistic for most real world situations where training data for a damaged structure is not available. We demonstrate the methodology with experimental test data from a laboratory model structure and accelerometer data from a real world structure during different environmental and excitation conditions.

Post-Disaster Damage Assessment Through Coherent Change Detection on SAR Imagery

NASA Astrophysics Data System (ADS)

Guida, L.; Boccardo, P.; Donevski, I.; Lo Schiavo, L.; Molinari, M. E.; Monti-Guarnieri, A.; Oxoli, D.; Brovelli, M. A.

2018-04-01

Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Structural health monitoring using DOG multi-scale space: an approach for analyzing damage characteristics

NASA Astrophysics Data System (ADS)

Guo, Tian; Xu, Zili

2018-03-01

Measurement noise is inevitable in practice; thus, it is difficult to identify defects, cracks or damage in a structure while suppressing noise simultaneously. In this work, a novel method is introduced to detect multiple damage in noisy environments. Based on multi-scale space analysis for discrete signals, a method for extracting damage characteristics from the measured displacement mode shape is illustrated. Moreover, the proposed method incorporates a data fusion algorithm to further eliminate measurement noise-based interference. The effectiveness of the method is verified by numerical and experimental methods applied to different structural types. The results demonstrate that there are two advantages to the proposed method. First, damage features are extracted by the difference of the multi-scale representation; this step is taken such that the interference of noise amplification can be avoided. Second, a data fusion technique applied to the proposed method provides a global decision, which retains the damage features while maximally eliminating the uncertainty. Monte Carlo simulations are utilized to validate that the proposed method has a higher accuracy in damage detection.

Fatigue crack detection and identification by the elastic wave propagation method

NASA Astrophysics Data System (ADS)

Stawiarski, Adam; Barski, Marek; Pająk, Piotr

2017-05-01

In this paper the elastic wave propagation phenomenon was used to detect the initiation of the fatigue damage in isotropic plate with a circular hole. The safety and reliability of structures mostly depend on the effectiveness of the monitoring methods. The Structural Health Monitoring (SHM) system based on the active pitch-catch measurement technique was proposed. The piezoelectric (PZT) elements was used as an actuators and sensors in the multipoint measuring system. The comparison of the intact and defected structures has been used by damage detection algorithm. One part of the SHM system has been responsible for detection of the fatigue crack initiation. The second part observed the evolution of the damage growth and assess the size of the defect. The numerical results of the wave propagation phenomenon has been used to present the effectiveness and accuracy of the proposed method. The preliminary experimental analysis has been carried out during the tension test of the aluminum plate with a circular hole to determine the efficiency of the measurement technique.

Curvature methods of damage detection using digital image correlation

NASA Astrophysics Data System (ADS)

Helfrick, Mark N.; Niezrecki, Christopher; Avitabile, Peter

2009-03-01

Analytical models have shown that local damage in a structure can be detected by studying changes in the curvature of the structure's displaced shape while under an applied load. In order for damage to be detected, located, and quantified using curvature methods, a spatially dense set of measurement points is required on the structure of interest and the change in curvature must be measurable. Experimental testing done to validate the theory is often plagued by sparse data sets and experimental noise. Furthermore, the type of load, the location and severity of the damage, and the mechanical properties (material and geometry) of the structure have a significant effect on how much the curvature will change. Within this paper, three-dimensional (3D) Digital Image Correlation (DIC) as one possible method for detecting damage through curvature methods is investigated. 3D DIC is a non-contacting full-field measurement technique which uses a stereo pair of digital cameras to capture surface shape. This approach allows for an extremely dense data set across the entire visible surface of an object. A test is performed to validate the approach on an aluminum cantilever beam. A dynamic load is applied to the beam which allows for measurements to be made of the beam's response at each of its first three resonant frequencies, corresponding to the first three bending modes of the structure. DIC measurements are used with damage detection algorithms to predict damage location with varying levels of damage inflicted in the form of a crack with a prescribed depth. The testing demonstrated that this technique will likely only work with structures where a large displaced shape is easily achieved and in cases where the damage is relatively severe. Practical applications and limitations of the technique are discussed.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

NASA Astrophysics Data System (ADS)

Dushyanth, N. D.; Suma, M. N.; Latte, Mrityanjaya V.

2016-03-01

Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damage-sensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm × 300 mm × 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.

Self-learning health monitoring algorithm in composite structures

NASA Astrophysics Data System (ADS)

Grassia, Luigi; Iannone, Michele; Califano, America; D'Amore, Alberto

2018-02-01

The paper describes a system that it is able of monitoring the health state of a composite structure in real time. The hardware of the system consists of a wire of strain sensors connected to a control unit. The software of the system elaborates the strain data and in real time is able to detect the presence of an eventual damage of the structures monitored with the strain sensors. The algorithm requires as input only the strains of the monitored structured measured on real time, i.e. those strains coming from the deformations of the composite structure due to the working loads. The health monitoring system does not require any additional device to interrogate the structure as often used in the literature, instead it is based on a self-learning procedure. The strain data acquired when the structure is healthy are used to set up the correlations between the strain in different positions of structure by means of neural network. Once the correlations between the strains in different position have been set up, these correlations act as a fingerprint of the healthy structure. In case of damage the correlation between the strains in the position of the structure near the damage will change due to the change of the stiffness of the structure caused by the damage. The developed software is able to recognize the change of the transfer function between the strains and consequently is able to detect the damage.

Artificial intelligence techniques for ground test monitoring of rocket engines

NASA Technical Reports Server (NTRS)

Ali, Moonis; Gupta, U. K.

1990-01-01

An expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.

Guided Lamb wave based 2-D spiral phased array for structural health monitoring of thin panel structures

NASA Astrophysics Data System (ADS)

Yoo, Byungseok

2011-12-01

In almost all industries of mechanical, aerospace, and civil engineering fields, structural health monitoring (SHM) technology is essentially required for providing the reliable information of structural integrity of safety-critical structures, which can help reduce the risk of unexpected and sometimes catastrophic failures, and also offer cost-effective inspection and maintenance of the structures. State of the art SHM research on structural damage diagnosis is focused on developing global and real-time technologies to identify the existence, location, extent, and type of damage. In order to detect and monitor the structural damage in plate-like structures, SHM technology based on guided Lamb wave (GLW) interrogation is becoming more attractive due to its potential benefits such as large inspection area coverage in short time, simple inspection mechanism, and sensitivity to small damage. However, the GLW method has a few critical issues such as dispersion nature, mode conversion and separation, and multiple-mode existence. Phased array technique widely used in all aspects of civil, military, science, and medical industry fields may be employed to resolve the drawbacks of the GLW method. The GLW-based phased array approach is able to effectively examine and analyze complicated structural vibration responses in thin plate structures. Because the phased sensor array operates as a spatial filter for the GLW signals, the array signal processing method can enhance a desired signal component at a specific direction while eliminating other signal components from other directions. This dissertation presents the development, the experimental validation, and the damage detection applications of an innovative signal processing algorithm based on two-dimensional (2-D) spiral phased array in conjunction with the GLW interrogation technique. It starts with general backgrounds of SHM and the associated technology including the GLW interrogation method. Then, it is focused on the fundamentals of the GLW-based phased array approach and the development of an innovative signal processing algorithm associated with the 2-D spiral phased sensor array. The SHM approach based on array responses determined by the proposed phased array algorithm implementation is addressed. The experimental validation of the GLW-based 2-D spiral phased array technology and the associated damage detection applications to thin isotropic plate and anisotropic composite plate structures are presented.

Experimental damage detection of wind turbine blade using thin film sensor array

NASA Astrophysics Data System (ADS)

Downey, Austin; Laflamme, Simon; Ubertini, Filippo; Sarkar, Partha

2017-04-01

Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade's surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage.

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

NASA Astrophysics Data System (ADS)

Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

2011-11-01

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

Automated laser-based barely visible impact damage detection in honeycomb sandwich composite structures

NASA Astrophysics Data System (ADS)

Girolamo, D.; Girolamo, L.; Yuan, F. G.

2015-03-01

Nondestructive evaluation (NDE) for detection and quantification of damage in composite materials is fundamental in the assessment of the overall structural integrity of modern aerospace systems. Conventional NDE systems have been extensively used to detect the location and size of damages by propagating ultrasonic waves normal to the surface. However they usually require physical contact with the structure and are time consuming and labor intensive. An automated, contactless laser ultrasonic imaging system for barely visible impact damage (BVID) detection in advanced composite structures has been developed to overcome these limitations. Lamb waves are generated by a Q-switched Nd:YAG laser, raster scanned by a set of galvano-mirrors over the damaged area. The out-of-plane vibrations are measured through a laser Doppler Vibrometer (LDV) that is stationary at a point on the corner of the grid. The ultrasonic wave field of the scanned area is reconstructed in polar coordinates and analyzed for high resolution characterization of impact damage in the composite honeycomb panel. Two methodologies are used for ultrasonic wave-field analysis: scattered wave field analysis (SWA) and standing wave energy analysis (SWEA) in the frequency domain. The SWA is employed for processing the wave field and estimate spatially dependent wavenumber values, related to discontinuities in the structural domain. The SWEA algorithm extracts standing waves trapped within damaged areas and, by studying the spectrum of the standing wave field, returns high fidelity damage imaging. While the SWA can be used to locate the impact damage in the honeycomb panel, the SWEA produces damage images in good agreement with X-ray computed tomographic (X-ray CT) scans. The results obtained prove that the laser-based nondestructive system is an effective alternative to overcome limitations of conventional NDI technologies.

Model-Based Fault Tolerant Control

NASA Technical Reports Server (NTRS)

Kumar, Aditya; Viassolo, Daniel

2008-01-01

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS).

PubMed

Jiang, Baofeng; Jia, Pengjiao; Zhao, Wen; Wang, Wentao

2018-01-01

This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique.

Acoustic emission beamforming for enhanced damage detection

NASA Astrophysics Data System (ADS)

McLaskey, Gregory C.; Glaser, Steven D.; Grosse, Christian U.

2008-03-01

As civil infrastructure ages, the early detection of damage in a structure becomes increasingly important for both life safety and economic reasons. This paper describes the analysis procedures used for beamforming acoustic emission techniques as well as the promising results of preliminary experimental tests on a concrete bridge deck. The method of acoustic emission offers a tool for detecting damage, such as cracking, as it occurs on or in a structure. In order to gain meaningful information from acoustic emission analyses, the damage must be localized. Current acoustic emission systems with localization capabilities are very costly and difficult to install. Sensors must be placed throughout the structure to ensure that the damage is encompassed by the array. Beamforming offers a promising solution to these problems and permits the use of wireless sensor networks for acoustic emission analyses. Using the beamforming technique, the azmuthal direction of the location of the damage may be estimated by the stress waves impinging upon a small diameter array (e.g. 30mm) of acoustic emission sensors. Additional signal discrimination may be gained via array processing techniques such as the VESPA process. The beamforming approach requires no arrival time information and is based on very simple delay and sum beamforming algorithms which can be easily implemented on a wireless sensor or mote.

Comparison of two optimization algorithms for fuzzy finite element model updating for damage detection in a wind turbine blade

NASA Astrophysics Data System (ADS)

Turnbull, Heather; Omenzetter, Piotr

2018-03-01

vDifficulties associated with current health monitoring and inspection practices combined with harsh, often remote, operational environments of wind turbines highlight the requirement for a non-destructive evaluation system capable of remotely monitoring the current structural state of turbine blades. This research adopted a physics based structural health monitoring methodology through calibration of a finite element model using inverse techniques. A 2.36m blade from a 5kW turbine was used as an experimental specimen, with operational modal analysis techniques utilised to realize the modal properties of the system. Modelling the experimental responses as fuzzy numbers using the sub-level technique, uncertainty in the response parameters was propagated back through the model and into the updating parameters. Initially, experimental responses of the blade were obtained, with a numerical model of the blade created and updated. Deterministic updating was carried out through formulation and minimisation of a deterministic objective function using both firefly algorithm and virus optimisation algorithm. Uncertainty in experimental responses were modelled using triangular membership functions, allowing membership functions of updating parameters (Young's modulus and shear modulus) to be obtained. Firefly algorithm and virus optimisation algorithm were again utilised, however, this time in the solution of fuzzy objective functions. This enabled uncertainty associated with updating parameters to be quantified. Varying damage location and severity was simulated experimentally through addition of small masses to the structure intended to cause a structural alteration. A damaged model was created, modelling four variable magnitude nonstructural masses at predefined points and updated to provide a deterministic damage prediction and information in relation to the parameters uncertainty via fuzzy updating.

Real Time Fatigue Damage Growth Assessment of a Composite Three-Stringer Panel Using Passive Thermography

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Burke, Eric R.; Horne, Michael R.; Bly, James B.

2015-01-01

Fatigue testing of advanced composite structures is critical to validate both structural designs and damage prediction models. In-situ inspection methods are necessary to track damage onset and growth as a function of load cycles. Passive thermography is a large area, noncontact inspection technique that is used to detect composite damage onset and growth in real time as a function of fatigue cycles. The thermal images are acquired in synchronicity to the applied compressive load using a dual infrared camera acquisition system for full (front and back) coverage. Image processing algorithms are investigated to increase defect contrast areas. The thermal results are compared to non-immersion ultrasound inspections and acoustic emission data.

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

NASA Astrophysics Data System (ADS)

Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

2017-04-01

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

NASA Astrophysics Data System (ADS)

Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

2018-01-01

Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a 99% of confidence.

Improvement of Forest Fire Detection Algorithm Using Brightness Temperature Lapse Rate Correction in HIMAWARI-8 IR Channels: Application to the 6 may 2017 Samcheok City, Korea

NASA Astrophysics Data System (ADS)

Park, S. H.; Park, W.; Jung, H. S.

2018-04-01

Forest fires are a major natural disaster that destroys a forest area and a natural environment. In order to minimize the damage caused by the forest fire, it is necessary to know the location and the time of day and continuous monitoring is required until fire is fully put out. We have tried to improve the forest fire detection algorithm by using a method to reduce the variability of surrounding pixels. We focused that forest areas of East Asia, part of the Himawari-8 AHI coverage, are mostly located in mountainous areas. The proposed method was applied to the forest fire detection in Samcheok city, Korea on May 6 to 10, 2017.

Sensitivity of PZT Impedance Sensors for Damage Detection of Concrete Structures

PubMed Central

Yang, Yaowen; Hu, Yuhang; Lu, Yong

2008-01-01

Piezoelectric ceramic Lead Zirconate Titanate (PZT) based electro-mechanical impedance (EMI) technique for structural health monitoring (SHM) has been successfully applied to various engineering systems. However, fundamental research work on the sensitivity of the PZT impedance sensors for damage detection is still in need. In the traditional EMI method, the PZT electro-mechanical (EM) admittance (inverse of the impedance) is used as damage indicator, which is difficult to specify the effect of damage on structural properties. This paper uses the structural mechanical impedance (SMI) extracted from the PZT EM admittance signature as the damage indicator. A comparison study on the sensitivity of the EM admittance and the structural mechanical impedance to the damages in a concrete structure is conducted. Results show that the SMI is more sensitive to the damage than the EM admittance thus a better indicator for damage detection. Furthermore, this paper proposes a dynamic system consisting of a number of single-degree-of-freedom elements with mass, spring and damper components to model the SMI. A genetic algorithm is employed to search for the optimal value of the unknown parameters in the dynamic system. An experiment is carried out on a two-storey concrete frame subjected to base vibrations that simulate earthquake. A number of PZT sensors are regularly arrayed and bonded to the frame structure to acquire PZT EM admittance signatures. The relationship between the damage index and the distance of the PZT sensor from the damage is studied. Consequently, the sensitivity of the PZT sensors is discussed and their sensing region in concrete is derived. PMID:27879711

Adaptive algorithm of selecting optimal variant of errors detection system for digital means of automation facility of oil and gas complex

NASA Astrophysics Data System (ADS)

Poluyan, A. Y.; Fugarov, D. D.; Purchina, O. A.; Nesterchuk, V. V.; Smirnova, O. V.; Petrenkova, S. B.

2018-05-01

To date, the problems associated with the detection of errors in digital equipment (DE) systems for the automation of explosive objects of the oil and gas complex are extremely actual. Especially this problem is actual for facilities where a violation of the accuracy of the DE will inevitably lead to man-made disasters and essential material damage, at such facilities, the diagnostics of the accuracy of the DE operation is one of the main elements of the industrial safety management system. In the work, the solution of the problem of selecting the optimal variant of the errors detection system of errors detection by a validation criterion. Known methods for solving these problems have an exponential valuation of labor intensity. Thus, with a view to reduce time for solving the problem, a validation criterion is compiled as an adaptive bionic algorithm. Bionic algorithms (BA) have proven effective in solving optimization problems. The advantages of bionic search include adaptability, learning ability, parallelism, the ability to build hybrid systems based on combining. [1].

Sensitivity of PZT Impedance Sensors for Damage Detection of Concrete Structures.

PubMed

Yang, Yaowen; Hu, Yuhang; Lu, Yong

2008-01-21

Piezoelectric ceramic Lead Zirconate Titanate (PZT) based electro-mechanicalimpedance (EMI) technique for structural health monitoring (SHM) has been successfullyapplied to various engineering systems. However, fundamental research work on thesensitivity of the PZT impedance sensors for damage detection is still in need. In thetraditional EMI method, the PZT electro-mechanical (EM) admittance (inverse of theimpedance) is used as damage indicator, which is difficult to specify the effect of damage onstructural properties. This paper uses the structural mechanical impedance (SMI) extractedfrom the PZT EM admittance signature as the damage indicator. A comparison study on thesensitivity of the EM admittance and the structural mechanical impedance to the damages ina concrete structure is conducted. Results show that the SMI is more sensitive to the damagethan the EM admittance thus a better indicator for damage detection. Furthermore, this paperproposes a dynamic system consisting of a number of single-degree-of-freedom elementswith mass, spring and damper components to model the SMI. A genetic algorithm isemployed to search for the optimal value of the unknown parameters in the dynamic system.An experiment is carried out on a two-storey concrete frame subjected to base vibrations thatsimulate earthquake. A number of PZT sensors are regularly arrayed and bonded to the framestructure to acquire PZT EM admittance signatures. The relationship between the damageindex and the distance of the PZT sensor from the damage is studied. Consequently, thesensitivity of the PZT sensors is discussed and their sensing region in concrete is derived.

Fiber optic sensor for continuous health monitoring in CFRP composite materials

NASA Astrophysics Data System (ADS)

Rippert, Laurent; Papy, Jean-Michel; Wevers, Martine; Van Huffel, Sabine

2002-07-01

An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.

Detection of Non-Symmetrical Damage in Smart Plate-Like Structures

NASA Technical Reports Server (NTRS)

Blanks, H. T.; Emeric, P. R.

1998-01-01

A two-dimensional model for in-plane vibrations of a cantilever plate with a non-symmetrical damage is used in the context of defect identification in materials with piezoelectric ceramic patches bonded to their surface. These patches can act both as actuators and sensors in a self-analyzing fashion, which is a characteristic of smart materials. A Galerkin method is used to approximate the dynamic response of these structures. The natural frequency shifts due to the damage are estimated numerically and compared to experimental data obtained from tests on cantilever aluminum plate-like structures damaged at different locations with defects of different depths. The damage location and extent are determined by an enhanced least square identification method. Efficacy of the frequency shift based algorithms is demonstrated using experimental data.

Structural damage detection for in-service highway bridge under operational and environmental variability

NASA Astrophysics Data System (ADS)

Jin, Chenhao; Li, Jingcheng; Jang, Shinae; Sun, Xiaorong; Christenson, Richard

2015-03-01

Structural health monitoring has drawn significant attention in the past decades with numerous methodologies and applications for civil structural systems. Although many researchers have developed analytical and experimental damage detection algorithms through vibration-based methods, these methods are not widely accepted for practical structural systems because of their sensitivity to uncertain environmental and operational conditions. The primary environmental factor that influences the structural modal properties is temperature. The goal of this article is to analyze the natural frequency-temperature relationships and detect structural damage in the presence of operational and environmental variations using modal-based method. For this purpose, correlations between natural frequency and temperature are analyzed to select proper independent variables and inputs for the multiple linear regression model and neural network model. In order to capture the changes of natural frequency, confidence intervals to detect the damages for both models are generated. A long-term structural health monitoring system was installed on an in-service highway bridge located in Meriden, Connecticut to obtain vibration and environmental data. Experimental testing results show that the variability of measured natural frequencies due to temperature is captured, and the temperature-induced changes in natural frequencies have been considered prior to the establishment of the threshold in the damage warning system. This novel approach is applicable for structural health monitoring system and helpful to assess the performance of the structure for bridge management and maintenance.

Damage mapping in structural health monitoring using a multi-grid architecture

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

Mathews, V. John

2015-03-31

This paper presents a multi-grid architecture for tomography-based damage mapping of composite aerospace structures. The system employs an array of piezo-electric transducers bonded on the structure. Each transducer may be used as an actuator as well as a sensor. The structure is excited sequentially using the actuators and the guided waves arriving at the sensors in response to the excitations are recorded for further analysis. The sensor signals are compared to their baseline counterparts and a damage index is computed for each actuator-sensor pair. These damage indices are then used as inputs to the tomographic reconstruction system. Preliminary damage mapsmore » are reconstructed on multiple coordinate grids defined on the structure. These grids are shifted versions of each other where the shift is a fraction of the spatial sampling interval associated with each grid. These preliminary damage maps are then combined to provide a reconstruction that is more robust to measurement noise in the sensor signals and the ill-conditioned problem formulation for single-grid algorithms. Experimental results on a composite structure with complexity that is representative of aerospace structures included in the paper demonstrate that for sufficiently high sensor densities, the algorithm of this paper is capable of providing damage detection and characterization with accuracy comparable to traditional C-scan and A-scan-based ultrasound non-destructive inspection systems quickly and without human supervision.« less

A haptic-inspired audio approach for structural health monitoring decision-making

NASA Astrophysics Data System (ADS)

Mao, Zhu; Todd, Michael; Mascareñas, David

2015-03-01

Haptics is the field at the interface of human touch (tactile sensation) and classification, whereby tactile feedback is used to train and inform a decision-making process. In structural health monitoring (SHM) applications, haptic devices have been introduced and applied in a simplified laboratory scale scenario, in which nonlinearity, representing the presence of damage, was encoded into a vibratory manual interface. In this paper, the "spirit" of haptics is adopted, but here ultrasonic guided wave scattering information is transformed into audio (rather than tactile) range signals. After sufficient training, the structural damage condition, including occurrence and location, can be identified through the encoded audio waveforms. Different algorithms are employed in this paper to generate the transformed audio signals and the performance of each encoding algorithms is compared, and also compared with standard machine learning classifiers. In the long run, the haptic decision-making is aiming to detect and classify structural damages in a more rigorous environment, and approaching a baseline-free fashion with embedded temperature compensation.

Locating and decoding barcodes in fuzzy images captured by smart phones

NASA Astrophysics Data System (ADS)

Deng, Wupeng; Hu, Jiwei; Liu, Quan; Lou, Ping

2017-07-01

With the development of barcodes for commercial use, people's requirements for detecting barcodes by smart phone become increasingly pressing. The low quality of barcode image captured by mobile phone always affects the decoding and recognition rates. This paper focuses on locating and decoding EAN-13 barcodes in fuzzy images. We present a more accurate locating algorithm based on segment length and high fault-tolerant rate algorithm for decoding barcodes. Unlike existing approaches, location algorithm is based on the edge segment length of EAN -13 barcodes, while our decoding algorithm allows the appearance of fuzzy region in barcode image. Experimental results are performed on damaged, contaminated and scratched digital images, and provide a quite promising result for EAN -13 barcode location and decoding.

Reliability based impact localization in composite panels using Bayesian updating and the Kalman filter

NASA Astrophysics Data System (ADS)

Morse, Llewellyn; Sharif Khodaei, Zahra; Aliabadi, M. H.

2018-01-01

In this work, a reliability based impact detection strategy for a sensorized composite structure is proposed. Impacts are localized using Artificial Neural Networks (ANNs) with recorded guided waves due to impacts used as inputs. To account for variability in the recorded data under operational conditions, Bayesian updating and Kalman filter techniques are applied to improve the reliability of the detection algorithm. The possibility of having one or more faulty sensors is considered, and a decision fusion algorithm based on sub-networks of sensors is proposed to improve the application of the methodology to real structures. A strategy for reliably categorizing impacts into high energy impacts, which are probable to cause damage in the structure (true impacts), and low energy non-damaging impacts (false impacts), has also been proposed to reduce the false alarm rate. The proposed strategy involves employing classification ANNs with different features extracted from captured signals used as inputs. The proposed methodologies are validated by experimental results on a quasi-isotropic composite coupon impacted with a range of impact energies.

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

PubMed Central

Jiang, Baofeng; Jia, Pengjiao; Zhao, Wen; Wang, Wentao

2018-01-01

This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique. PMID:29293593

Effects on Diagnostic Parameters After Removing Additional Synchronous Gear Meshes

NASA Technical Reports Server (NTRS)

Decker, Harry J.

2003-01-01

Gear cracks are typically difficult to diagnose with sufficient time before catastrophic damage occurs. Significant damage must be present before algorithms appear to be able to detect the damage. Frequently there are multiple gear meshes on a single shaft. Since they are all synchronous with the shaft frequency, the commonly used synchronous averaging technique is ineffective in removing other gear mesh effects. Carefully applying a filter to these extraneous gear mesh frequencies can reduce the overall vibration signal and increase the accuracy of commonly used vibration metrics. The vibration signals from three seeded fault tests were analyzed using this filtering procedure. Both the filtered and unfiltered vibration signals were then analyzed using commonly used fault detection metrics and compared. The tests were conducted on aerospace quality spur gears in a test rig. The tests were conducted at speeds ranging from 2500 to 5000 revolutions per minute and torques from 184 to 228 percent of design load. The inability to detect these cracks with high confidence results from the high loading which is causing fast fracture as opposed to stable crack growth. The results indicate that these techniques do not currently produce an indication of damage that significantly exceeds experimental scatter.

Accurate Damage Location in Complex Composite Structures and Industrial Environments using Acoustic Emission

NASA Astrophysics Data System (ADS)

Eaton, M.; Pearson, M.; Lee, W.; Pullin, R.

2015-07-01

The ability to accurately locate damage in any given structure is a highly desirable attribute for an effective structural health monitoring system and could help to reduce operating costs and improve safety. This becomes a far greater challenge in complex geometries and materials, such as modern composite airframes. The poor translation of promising laboratory based SHM demonstrators to industrial environments forms a barrier to commercial up take of technology. The acoustic emission (AE) technique is a passive NDT method that detects elastic stress waves released by the growth of damage. It offers very sensitive damage detection, using a sparse array of sensors to detect and globally locate damage within a structure. However its application to complex structures commonly yields poor accuracy due to anisotropic wave propagation and the interruption of wave propagation by structural features such as holes and thickness changes. This work adopts an empirical mapping technique for AE location, known as Delta T Mapping, which uses experimental training data to account for such structural complexities. The technique is applied to a complex geometry composite aerospace structure undergoing certification testing. The component consists of a carbon fibre composite tube with varying wall thickness and multiple holes, that was loaded under bending. The damage location was validated using X-ray CT scanning and the Delta T Mapping technique was shown to improve location accuracy when compared with commercial algorithms. The onset and progression of damage were monitored throughout the test and used to inform future design iterations.

TPS In-Flight Health Monitoring Project Progress Report

NASA Technical Reports Server (NTRS)

Kostyk, Chris; Richards, Lance; Hudston, Larry; Prosser, William

2007-01-01

Progress in the development of new thermal protection systems (TPS) is reported. New approaches use embedded lightweight, sensitive, fiber optic strain and temperature sensors within the TPS. Goals of the program are to develop and demonstrate a prototype TPS health monitoring system, develop a thermal-based damage detection algorithm, characterize limits of sensor/system performance, and develop ea methodology transferable to new designs of TPS health monitoring systems. Tasks completed during the project helped establish confidence in understanding of both test setup and the model and validated system/sensor performance in a simple TPS structure. Other progress included complete initial system testing, commencement of the algorithm development effort, generation of a damaged thermal response characteristics database, initial development of a test plan for integration testing of proven FBG sensors in simple TPS structure, and development of partnerships to apply the technology.

A novel approach for detection of anomalies using measurement data of the Ironton-Russell bridge

NASA Astrophysics Data System (ADS)

Zhang, Fan; Norouzi, Mehdi; Hunt, Victor; Helmicki, Arthur

2015-04-01

Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.

Application of wavefield imaging to characterize scattering from artificial and impact damage in composite laminate panels

NASA Astrophysics Data System (ADS)

Williams, Westin B.; Michaels, Thomas E.; Michaels, Jennifer E.

2018-04-01

Composite materials used for aerospace applications are highly susceptible to impacts, which can result in barely visible delaminations. Reliable and fast detection of such damage is needed before structural failures occur. One approach is to use ultrasonic guided waves generated from a sparse array consisting of permanently mounted or embedded transducers for performing structural health monitoring. This array can detect introduction of damage after baseline subtraction, and also provide localization and characterization information via the minimum variance imaging algorithm. Imaging performance can vary considerably depending upon where damage is located with respect to the array; however, prior work has shown that knowledge of expected scattering can improve imaging consistency for artificial damage at various locations. In this study, anisotropic material attenuation and wave speed are estimated as a function of propagation angle using wavefield data recorded along radial lines at multiple angles with respect to an omnidirectional guided wave source. Additionally, full wavefield data are recorded before and after the introduction of artificial and impact damage so that wavefield baseline subtraction may be applied. 3-D filtering techniques are then used to reduce noise and isolate scattered waves. A model for estimating scattering of a circular defect is developed and scattering estimates for both artificial and impact damage are presented and compared.

Helicopter rotor blade frequency evolution with damage growth and signal processing

NASA Astrophysics Data System (ADS)

Roy, Niranjan; Ganguli, Ranjan

2005-05-01

Structural damage in materials evolves over time due to growth of fatigue cracks in homogenous materials and a complicated process of matrix cracking, delamination, fiber breakage and fiber matrix debonding in composite materials. In this study, a finite element model of the helicopter rotor blade is used to analyze the effect of damage growth on the modal frequencies in a qualitative manner. Phenomenological models of material degradation for homogenous and composite materials are used. Results show that damage can be detected by monitoring changes in lower as well as higher mode flap (out-of-plane bending), lag (in-plane bending) and torsion rotating frequencies, especially for composite materials where the onset of the last stage of damage of fiber breakage is most critical. Curve fits are also proposed for mathematical modeling of the relationship between rotating frequencies and cycles. Finally, since operational data are noisy and also contaminated with outliers, denoising algorithms based on recursive median filters and radial basis function neural networks and wavelets are studied and compared with a moving average filter using simulated data for improved health-monitoring application. A novel recursive median filter is designed using integer programming through genetic algorithm and is found to have comparable performance to neural networks with much less complexity and is better than wavelet denoising for outlier removal. This filter is proposed as a tool for denoising time series of damage indicators.

Improving the resolution for Lamb wave testing via a smoothed Capon algorithm

NASA Astrophysics Data System (ADS)

Cao, Xuwei; Zeng, Liang; Lin, Jing; Hua, Jiadong

2018-04-01

Lamb wave testing is promising for damage detection and evaluation in large-area structures. The dispersion of Lamb waves is often unavoidable, restricting testing resolution and making the signal hard to interpret. A smoothed Capon algorithm is proposed in this paper to estimate the accurate path length of each wave packet. In the algorithm, frequency domain whitening is firstly used to obtain the transfer function in the bandwidth of the excitation pulse. Subsequently, wavenumber domain smoothing is employed to reduce the correlation between wave packets. Finally, the path lengths are determined by distance domain searching based on the Capon algorithm. Simulations are applied to optimize the number of smoothing times. Experiments are performed on an aluminum plate consisting of two simulated defects. The results demonstrate that spatial resolution is improved significantly by the proposed algorithm.

Compressive sensing for efficient health monitoring and effective damage detection of structures

NASA Astrophysics Data System (ADS)

Jayawardhana, Madhuka; Zhu, Xinqun; Liyanapathirana, Ranjith; Gunawardana, Upul

2017-02-01

Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) - a very recent development - introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

NASA Astrophysics Data System (ADS)

Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

2015-08-01

For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes

NASA Astrophysics Data System (ADS)

Amezquita-Sanchez, Juan P.; Valtierra-Rodriguez, Martin; Perez-Ramirez, Carlos A.; Camarena-Martinez, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2017-07-01

Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.

Characterization of Ultrasound Energy Diffusion Due to Small-Size Damage on an Aluminum Plate Using Piezoceramic Transducers

PubMed Central

Lu, Guangtao; Feng, Qian; Li, Yourong; Wang, Hao; Song, Gangbing

2017-01-01

During the propagation of ultrasonic waves in structures, there is usually energy loss due to ultrasound energy diffusion and dissipation. The aim of this research is to characterize the ultrasound energy diffusion that occurs due to small-size damage on an aluminum plate using piezoceramic transducers, for the future purpose of developing a damage detection algorithm. The ultrasonic energy diffusion coefficient is related to the damage distributed in the medium. Meanwhile, the ultrasonic energy dissipation coefficient is related to the inhomogeneity of the medium. Both are usually employed to describe the characteristics of ultrasound energy diffusion. The existence of multimodes of Lamb waves in metallic plate structures results in the asynchronous energy transport of different modes. The mode of Lamb waves has a great influence on ultrasound energy diffusion as a result, and thus has to be chosen appropriately. In order to study the characteristics of ultrasound energy diffusion in metallic plate structures, an experimental setup of an aluminum plate with a through-hole, whose diameter varies from 0.6 mm to 1.2 mm, is used as the test specimen with the help of piezoceramic transducers. The experimental results of two categories of damages at different locations reveal that the existence of damage changes the energy transport between the actuator and the sensor. Also, when there is only one dominate mode of Lamb wave excited in the structure, the ultrasound energy diffusion coefficient decreases approximately linearly with the diameter of the simulated damage. Meanwhile, the ultrasonic energy dissipation coefficient increases approximately linearly with the diameter of the simulated damage. However, when two or more modes of Lamb waves are excited, due to the existence of different group velocities between the different modes, the energy transport of the different modes is asynchronous, and the ultrasonic energy diffusion is not strictly linear with the size of the damage. Therefore, it is recommended that only one dominant mode of Lamb wave should be excited during the characterization process, in order to ensure that the linear relationship between the damage size and the characteristic parameters is maintained. In addition, the findings from this paper demonstrate the potential of developing future damage detection algorithms using the linear relationships between damage size and the ultrasound energy diffusion coefficient or ultrasonic energy dissipation coefficient when a single dominant mode is excited. PMID:29207530

Signal processing techniques for damage detection with piezoelectric wafer active sensors and embedded ultrasonic structural radar

NASA Astrophysics Data System (ADS)

Yu, Lingyu; Bao, Jingjing; Giurgiutiu, Victor

2004-07-01

Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.

On-line damage detection in rotating machinery

NASA Astrophysics Data System (ADS)

Alkhalifa, Tareq Jawad

This work is concerned with a set of techniques to detect internal defects in uniform circular discs (rotors). An internal defect is intentionally manufactured in stereolithographic discs by a rapid prototyping process using cured resin SL 5170 material. The analysis and results presented here are limited to a uniform circular disc, with internal defects, mounted on a uniform flexible circular shaft. The setup is comprised of a Bently Nevada rotor kit connected to a data acquisition system. The rotor consists of a disc and shaft that is supported by journal bearings and is coupled to a motor by a rubber joint. Damage produces localized changes in the strain energy, which is quantified to characterize the damage. Based on previous research, the Strain Energy Damage Index (SEDI) is utilized to localize the damage due to strain energy differences between damaged and undamaged modes. To accomplish the objective, this work covers three types of analysis: finite element analysis, vibration analysis, and experimental modal analysis. Finite element analysis (using SDRC Ideas software) is performed to develop a multi-degree-of-freedom (MDOF) rotor system with internal damage, and its dynamic characteristics are investigated. The analysis is performed for two different types damage cases: radial damage and circular damage. Parametric study for radial damage and random noise to undamaged disc have been investigated to predict the effect of noise in the damage detection. The developed on-line damage detection technique for rotating equipment incorporates and couples both vibration analysis and experimental modal analysis. The dynamic investigation of the rotating discs (with and without defect) is conducted by vibration signal analysis (using proximity sensors, data acquisition and LabView). The vibration analysis provides a unique vibration signature for the damaged disc, which indicates the existence of the damage. The vibration data are acquired at different running speeds (1000, 2500, 5000 rpm). Then the dynamic investigation of non-rotating discs (with and without defect) is conducted by experimental modal analysis (using STAR software). While the vibration analysis detects and indicates the existence of damage while the disc is rotating, experimental modal analysis (using STAR and MATLAB software) provides the localization of damage through the modal parameters for a non-rotating disc. Both of the experimental diagnostic algorithms are based on measurement of the dynamic behavior of the damaged disc. The results are compared with the reference, or baseline, one, obtained initially for an undamaged disc. (Abstract shortened by UMI.)

Methods and Piezoelectric Imbedded Sensors for Damage Detection in Composite Plates Under Ambient and Cryogenic Conditions

NASA Technical Reports Server (NTRS)

Engberg, Robert; Ooi, Teng K.

2004-01-01

New methods for structural health monitoring are being assessed, especially in high-performance, extreme environment, safety-critical applications. One such application is for composite cryogenic fuel tanks. The work presented here attempts to characterize and investigate the feasibility of using imbedded piezoelectric sensors to detect cracks and delaminations under cryogenic and ambient conditions. A variety of damage detection methods and different Sensors are employed in the different composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency, impedance measurements, and pulse echoing techniques of the sensors are employed and compared. Statistical and analytic techniques are then used to determine which method is most desirable for a specific type of damage. These results are furthermore compared with previous work using externally mounted sensors. Results and optimized methods from this work can then be incorporated into a larger composite structure to validate and assess its structural health. This could prove to be important in the development and qualification of any 2" generation reusable launch vehicle using composites as a structural element.

Dynamics and detection of laser induced microbubbles in the retinal pigment epithelium (RPE)

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Ptaszynski, Lars; Stoehr, Hardo; Brinkmann, Ralf

2007-07-01

Selective Retina Treatment (SRT) is a new method to treat eye diseases associated with disorders of the RPE. Selective RPE cell damage is achieved by applying a train of 1.7 μs laser pulses at 527 nm. The treatment of retinal diseases as e.g. diabetic maculopathy (DMP), is currently investigated within clinical studies, however 200 ns pulse durations are under investigation. Transient micro bubbles in the retinal pigment epithelium (RPE) are expected to be the origin of cell damage due to irradiation with laser pulses shorter than 50 μs. The bubbles emerge at the strongly absorbing RPE melanosomes. Cell membrane disruption caused by the transient associated volume increase is expected to be the origin of the angiographically observed RPE leakage. We investigate micro bubble formation and dynamics in porcine RPE using pulse durations of 150 ns. A laser interferometry system at 830 nm with the aim of an online dosimetry control for SRT was developed. Bubble formation was detected interferometrically and by fast flash photography. A correlation to cell damage observed with a vitality stain is found. A bubble detection algorithm is presented.

Comparing the ISO-recommended and the cumulative data-reduction algorithms in S-on-1 laser damage test by a reverse approach method

NASA Astrophysics Data System (ADS)

Zorila, Alexandru; Stratan, Aurel; Nemes, George

2018-01-01

We compare the ISO-recommended (the standard) data-reduction algorithm used to determine the surface laser-induced damage threshold of optical materials by the S-on-1 test with two newly suggested algorithms, both named "cumulative" algorithms/methods, a regular one and a limit-case one, intended to perform in some respects better than the standard one. To avoid additional errors due to real experiments, a simulated test is performed, named the reverse approach. This approach simulates the real damage experiments, by generating artificial test-data of damaged and non-damaged sites, based on an assumed, known damage threshold fluence of the target and on a given probability distribution function to induce the damage. In this work, a database of 12 sets of test-data containing both damaged and non-damaged sites was generated by using four different reverse techniques and by assuming three specific damage probability distribution functions. The same value for the threshold fluence was assumed, and a Gaussian fluence distribution on each irradiated site was considered, as usual for the S-on-1 test. Each of the test-data was independently processed by the standard and by the two cumulative data-reduction algorithms, the resulting fitted probability distributions were compared with the initially assumed probability distribution functions, and the quantities used to compare these algorithms were determined. These quantities characterize the accuracy and the precision in determining the damage threshold and the goodness of fit of the damage probability curves. The results indicate that the accuracy in determining the absolute damage threshold is best for the ISO-recommended method, the precision is best for the limit-case of the cumulative method, and the goodness of fit estimator (adjusted R-squared) is almost the same for all three algorithms.

A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge

NASA Astrophysics Data System (ADS)

Hester, David; González, Arturo

2017-06-01

Given the large number of bridges that currently have no instrumentation, there are obvious advantages in monitoring the condition of a bridge by analysing the response of a vehicle crossing it. As a result, the last two decades have seen a rise in the research attempting to solve the problem of identifying damage in a bridge from vehicle measurements. This paper examines the theoretical feasibility and practical limitations of a drive-by system in identifying damage associated to localised stiffness losses. First, the nature of the damage feature that is sought within the vehicle response needs to be characterized. For this purpose, the total vehicle response is considered to be made of 'static' and 'dynamic' components, and where the bridge has experienced a localised loss in stiffness, an additional 'damage' component. Understanding the nature of this 'damage' component is crucial to have an informed discussion on how damage can be identified and localised. Leveraging this new understanding, the authors propose a wavelet-based drive-by algorithm. By comparing the effect of the 'damage' component to other key effects defining the measurements such as 'vehicle speed', the 'road profile' and 'noise' on a wavelet contour plot, it is possible to establish if there is a frequency range where drive-by can be successful. The algorithm uses then specific frequency bands to improve the sensitivity to damage with respect to limitations imposed by Vehicle-Bridge vibrations. Recommendations on the selection of the mother wavelet and frequency band are provided. Finally, the paper discusses the impact of noise and road profile on the ability of the approach to identify damage and how periodic measurements can be effective at monitoring localised stiffness changes.

Building damage assessment using airborne lidar

NASA Astrophysics Data System (ADS)

Axel, Colin; van Aardt, Jan

2017-10-01

The assessment of building damage following a natural disaster is a crucial step in determining the impact of the event itself and gauging reconstruction needs. Automatic methods for deriving damage maps from remotely sensed data are preferred, since they are regarded as being rapid and objective. We propose an algorithm for performing unsupervised building segmentation and damage assessment using airborne light detection and ranging (lidar) data. Local surface properties, including normal vectors and curvature, were used along with region growing to segment individual buildings in lidar point clouds. Damaged building candidates were identified based on rooftop inclination angle, and then damage was assessed using planarity and point height metrics. Validation of the building segmentation and damage assessment techniques were performed using airborne lidar data collected after the Haiti earthquake of 2010. Building segmentation and damage assessment accuracies of 93.8% and 78.9%, respectively, were obtained using lidar point clouds and expert damage assessments of 1953 buildings in heavily damaged regions. We believe this research presents an indication of the utility of airborne lidar remote sensing for increasing the efficiency and speed at which emergency response operations are performed.

An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images.

PubMed

Medhi, Jyoti Prakash; Dandapat, Samarendra

2016-07-01

Prolonged diabetes causes severe damage to the vision through leakage of blood and blood constituents over the retina. The effect of the leakage becomes more threatening when these abnormalities involve the macula. This condition is known as diabetic maculopathy and it leads to blindness, if not treated in time. Early detection and proper diagnosis can help in preventing this irreversible damage. To achieve this, the possible way is to perform retinal screening at regular intervals. But the ratio of ophthalmologists to patients is very small and the process of evaluation is time consuming. Here, the automatic methods for analyzing retinal/fundus images prove handy and help the ophthalmologists to screen at a faster rate. Motivated from this aspect, an automated method for detection and analysis of diabetic maculopathy is proposed in this work. The method is implemented in two stages. The first stage involves preprocessing required for preparing the image for further analysis. During this stage the input image is enhanced and the optic disc is masked to avoid false detection during bright lesion identification. The second stage is maculopathy detection and its analysis. Here, the retinal lesions including microaneurysms, hemorrhages and exudates are identified by processing the green and hue plane color images. The macula and the fovea locations are determined using intensity property of processed red plane image. Different circular regions are thereafter marked in the neighborhood of the macula. The presence of lesions in these regions is identified to confirm positive maculopathy. Later, the information is used for evaluating its severity. The principal advantage of the proposed algorithm is, utilization of the relation of blood vessels with optic disc and macula, which enhances the detection process. Proper usage of various color plane information sequentially enables the algorithm to perform better. The method is tested on various publicly available databases consisting of both normal and maculopathy images. The algorithm detects fovea with an accuracy of 98.92% when applied on 1374 images. The average specificity and sensitivity of the proposed method for maculopathy detection are obtained as 98.05% and 98.86% respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comparative evaluation of piezoelectric sensors for acoustic emission-based impact location estimation and damage classification in composite structures

NASA Astrophysics Data System (ADS)

Uprety, Bibhisha; Kim, Sungwon; Mathews, V. John; Adams, Daniel O.

2015-03-01

Acoustic Emission (AE) based Structural Health Monitoring (SHM) is of great interest for detecting impact damage in composite structures. Within the aerospace industry the need to detect and locate these events, even when no visible damage is present, is important both from the maintenance and design perspectives. In this investigation, four commercially available piezoelectric sensors were evaluated for usage in an AE-based SHM system. Of particular interest was comparing the acoustic response of the candidate piezoelectric sensors for impact location estimations as well as damage classification resulting from the impact in fiber-reinforced composite structures. Sensor assessment was performed based on response signal characterization and performance for active testing at 300 kHz and steel-ball drop testing using both aluminum and carbon/epoxy composite plates. Wave mode velocities calculated from the measured arrival times were found to be in good agreement with predictions obtained using both the Disperse code and finite element analysis. Differences in the relative strength of the received wave modes, the overall signal strengths and signal-to-noise ratios were observed through the use of both active testing as well as passive steel-ball drop testing. Further comparative is focusing on assessing AE sensor performance for use in impact location estimation algorithms as well as detecting and classifying damage produced in composite structures due to impact events.

Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin

PubMed Central

2014-01-01

Background Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage. Methods A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm’s robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods. Results Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage. Conclusions Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues. PMID:24521154

Model-based damage evaluation of layered CFRP structures

NASA Astrophysics Data System (ADS)

Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.

2015-03-01

An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.

On effectiveness of network sensor-based defense framework

NASA Astrophysics Data System (ADS)

Zhang, Difan; Zhang, Hanlin; Ge, Linqiang; Yu, Wei; Lu, Chao; Chen, Genshe; Pham, Khanh

2012-06-01

Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring, detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework, we also implement the statistical-based detection and sequential testing-based detection techniques and compare their respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed framework for combating cyber attacks.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

PubMed Central

Camacho Navarro, Jhonatan; Ruiz, Magda; Villamizar, Rodolfo; Mujica, Luis

2018-01-01

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks. PMID:29762505

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment.

PubMed

Camacho Navarro, Jhonatan; Ruiz, Magda; Villamizar, Rodolfo; Mujica, Luis; Quiroga, Jabid

2018-05-15

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.

Mexican Seismic Alert System's SAS-I algorithm review considering strong earthquakes felt in Mexico City since 1985

NASA Astrophysics Data System (ADS)

Cuellar Martinez, A.; Espinosa Aranda, J.; Suarez, G.; Ibarrola Alvarez, G.; Ramos Perez, S.; Camarillo Barranco, L.

2013-05-01

The Seismic Alert System of Mexico (SASMEX) uses three algorithms for alert activation that involve the distance between the seismic sensing field station (FS) and the city to be alerted; and the forecast for earthquake early warning activation in the cities integrated to the system, for example in Mexico City, the earthquakes occurred with the highest accelerations, were originated in the Pacific Ocean coast, whose distance this seismic region and the city, favors the use of algorithm called Algorithm SAS-I. This algorithm, without significant changes since its beginning in 1991, employs the data that generate one or more FS during P wave detection until S wave detection plus a period equal to the time employed to detect these phases; that is the double S-P time, called 2*(S-P). In this interval, the algorithm performs an integration process of quadratic samples from FS which uses a triaxial accelerometer to get two parameters: amplitude and growth rate measured until 2*(S-P) time. The parameters in SAS-I are used in a Magnitude classifier model, which was made from Guerrero Coast earthquakes time series, with reference to Mb magnitude mainly. This algorithm activates a Public or Preventive Alert if the model predicts whether Strong or Moderate earthquake. The SAS-I algorithm has been operating for over 23 years in the subduction zone of the Pacific Coast of Mexico, initially in Guerrero and followed by Oaxaca; and since March 2012 in the seismic region of Pacific covering the coasts among Jalisco, Colima, Michoacan, Guerrero and Oaxaca, where this algorithm has issued 16 Public Alert and 62 Preventive Alerts to the Mexico City where its soil conditions increase damages by earthquake such as the occurred in September 1985. This work shows the review of the SAS-I algorithm and possible alerts that it could generate from major earthquakes recordings detected by FS or seismometers near the earthquakes, coming from Pacific Ocean Coast whose have been felt in Mexico City, in order to observe the performance SAS-I algorithm.

Impact source localisation in aerospace composite structures

NASA Astrophysics Data System (ADS)

De Simone, Mario Emanuele; Ciampa, Francesco; Boccardi, Salvatore; Meo, Michele

2017-12-01

The most commonly encountered type of damage in aircraft composite structures is caused by low-velocity impacts due to foreign objects such as hail stones, tool drops and bird strikes. Often these events can cause severe internal material damage that is difficult to detect and may lead to a significant reduction of the structure’s strength and fatigue life. For this reason there is an urgent need to develop structural health monitoring systems able to localise low-velocity impacts in both metallic and composite components as they occur. This article proposes a novel monitoring system for impact localisation in aluminium and composite structures, which is able to determine the impact location in real-time without a-priori knowledge of the mechanical properties of the material. This method relies on an optimal configuration of receiving sensors, which allows linearization of well-known nonlinear systems of equations for the estimation of the impact location. The proposed algorithm is based on the time of arrival identification of the elastic waves generated by the impact source using the Akaike Information Criterion. The proposed approach was demonstrated successfully on both isotropic and orthotropic materials by using a network of closely spaced surface-bonded piezoelectric transducers. The results obtained show the validity of the proposed algorithm, since the impact sources were detected with a high level of accuracy. The proposed impact detection system overcomes current limitations of other methods and can be retrofitted easily on existing aerospace structures allowing timely detection of an impact event.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Damage identification of a TLP floating wind turbine by meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.

2015-12-01

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

NASA Astrophysics Data System (ADS)

Mjachina, Ksenya; Hu, Zhiyong; Chibilyev, Alexander

2018-01-01

Oil production in a steppe region disturbs the landscape and damages the steppe ecosystem. The objective of this research was to detect areas damaged by oil production in an oil field within the Russian Volga-Ural steppe region using winter Landsat imagery. We developed a practicable and effective approach using winter snow season multispectral Landsat satellite imagery. To this end, we applied seven algorithms of spectral or texture-based transformation: K-means, maximum likelihood estimation, topsoil grain size index, soil brightness, normalized differential snow index, tasselled cap, and co-occurrence measures. The co-occurrence texture measure variance shows the optimal result of identifying damaged areas. The unique feature of our method is that it can differentiate damaged areas from the bare soil of cropland within a cold steppe region where the area damaged by oil production is mixed with bare (fallow) croplands that have a polygonal shape similar to well pads. Such similarities can lead to confusion in object-based classification. Using the co-occurrence measures, we found that from 1988 to 2015, damaged area is nearly three times as big in the peak period of the oil field development (2001 and 2009) as in 1988. Landscape fragmentation also peaked in 2001 and 2009. Our approach for this project is useful and cost effective regular monitoring of damages from oil production for both the Volga-Ural steppe region and other cold steppe regions.

An Augmented Reality Endoscope System for Ureter Position Detection.

PubMed

Yu, Feng; Song, Enmin; Liu, Hong; Li, Yunlong; Zhu, Jun; Hung, Chih-Cheng

2018-06-25

Iatrogenic injury of ureter in the clinical operation may cause the serious complication and kidney damage. To avoid such a medical accident, it is necessary to provide the ureter position information to the doctor. For the detection of ureter position, an ureter position detection and display system with the augmented ris proposed to detect the ureter that is covered by human tissue. There are two key issues which should be considered in this new system. One is how to detect the covered ureter that cannot be captured by the electronic endoscope and the other is how to display the ureter position that provides stable and high-quality images. Simultaneously, any delayed processing of the system should disturb the surgery. The aided hardware detection method and target detection algorithms are proposed in this system. To mark the ureter position, a surface-lighting plastic optical fiber (POF) with the encoded light-emitting diode (LED) light is used to indicate the ureter position. The monochrome channel filtering algorithm (MCFA) is proposed to locate the ureter region more precisely. The ureter position is extracted using the proposed automatic region growing algorithm (ARGA) that utilizes the statistical information of the monochrome channel for the selection of growing seed point. In addition, according to the pulse signal of encoded light, the recognition of bright and dark frames based on the aided hardware (BDAH) is proposed to expedite the processing speed. Experimental results demonstrate that the proposed endoscope system can identify 92.04% ureter region in average.

Portable Wideband Microwave Imaging System for Intracranial Hemorrhage Detection Using Improved Back-projection Algorithm with Model of Effective Head Permittivity

PubMed Central

Mobashsher, Ahmed Toaha; Mahmoud, A.; Abbosh, A. M.

2016-01-01

Intracranial hemorrhage is a medical emergency that requires rapid detection and medication to restrict any brain damage to minimal. Here, an effective wideband microwave head imaging system for on-the-spot detection of intracranial hemorrhage is presented. The operation of the system relies on the dielectric contrast between healthy brain tissues and a hemorrhage that causes a strong microwave scattering. The system uses a compact sensing antenna, which has an ultra-wideband operation with directional radiation, and a portable, compact microwave transceiver for signal transmission and data acquisition. The collected data is processed to create a clear image of the brain using an improved back projection algorithm, which is based on a novel effective head permittivity model. The system is verified in realistic simulation and experimental environments using anatomically and electrically realistic human head phantoms. Quantitative and qualitative comparisons between the images from the proposed and existing algorithms demonstrate significant improvements in detection and localization accuracy. The radiation and thermal safety of the system are examined and verified. Initial human tests are conducted on healthy subjects with different head sizes. The reconstructed images are statistically analyzed and absence of false positive results indicate the efficacy of the proposed system in future preclinical trials. PMID:26842761

Portable Wideband Microwave Imaging System for Intracranial Hemorrhage Detection Using Improved Back-projection Algorithm with Model of Effective Head Permittivity

NASA Astrophysics Data System (ADS)

Mobashsher, Ahmed Toaha; Mahmoud, A.; Abbosh, A. M.

2016-02-01

Intracranial hemorrhage is a medical emergency that requires rapid detection and medication to restrict any brain damage to minimal. Here, an effective wideband microwave head imaging system for on-the-spot detection of intracranial hemorrhage is presented. The operation of the system relies on the dielectric contrast between healthy brain tissues and a hemorrhage that causes a strong microwave scattering. The system uses a compact sensing antenna, which has an ultra-wideband operation with directional radiation, and a portable, compact microwave transceiver for signal transmission and data acquisition. The collected data is processed to create a clear image of the brain using an improved back projection algorithm, which is based on a novel effective head permittivity model. The system is verified in realistic simulation and experimental environments using anatomically and electrically realistic human head phantoms. Quantitative and qualitative comparisons between the images from the proposed and existing algorithms demonstrate significant improvements in detection and localization accuracy. The radiation and thermal safety of the system are examined and verified. Initial human tests are conducted on healthy subjects with different head sizes. The reconstructed images are statistically analyzed and absence of false positive results indicate the efficacy of the proposed system in future preclinical trials.

Structural damage diagnostics via wave propagation-based filtering techniques

NASA Astrophysics Data System (ADS)

Ayers, James T., III

Structural health monitoring (SHM) of aerospace components is a rapidly emerging field due in part to commercial and military transport vehicles remaining in operation beyond their designed life cycles. Damage detection strategies are sought that provide real-time information of the structure's integrity. One approach that has shown promise to accurately identify and quantify structural defects is based on guided ultrasonic wave (GUW) inspections, where low amplitude attenuation properties allow for long range and large specimen evaluation. One drawback to GUWs is that they exhibit a complex multi-modal response, such that each frequency corresponds to at least two excited modes, and thus intelligent signal processing is required for even the simplest of structures. In addition, GUWs are dispersive, whereby the wave velocity is a function of frequency, and the shape of the wave packet changes over the spatial domain, requiring sophisticated detection algorithms. Moreover, existing damage quantification measures are typically formulated as a comparison of the damaged to undamaged response, which has proven to be highly sensitive to changes in environment, and therefore often unreliable. As a response to these challenges inherent to GUW inspections, this research develops techniques to locate and estimate the severity of the damage. Specifically, a phase gradient based localization algorithm is introduced to identify the defect position independent of excitation frequency and damage size. Mode separation through the filtering technique is central in isolating and extracting single mode components, such as reflected, converted, and transmitted modes that may arise from the incident wave impacting a damage. Spatially-integrated single and multiple component mode coefficients are also formulated with the intent to better characterize wave reflections and conversions and to increase the signal to noise ratios. The techniques are applied to damaged isotropic finite element plate models and experimental data obtained from Scanning Laser Doppler Vibrometry tests. Numerical and experimental parametric studies are conducted, and the current strengths and weaknesses of the proposed approaches are discussed. In particular, limitations to the damage profiling characterization are shown for low ultrasonic frequency regimes, whereas the multiple component mode conversion coefficients provide excellent noise mitigation. Multiple component estimation relies on an experimental technique developed for the estimation of Lamb wave polarization using a 1D Laser Vibrometer. Lastly, suggestions are made to apply the techniques to more structurally complex geometries.

Impedance-based structural health monitoring of wind turbine blades

NASA Astrophysics Data System (ADS)

Pitchford, Corey; Grisso, Benjamin L.; Inman, Daniel J.

2007-04-01

Wind power is a fast-growing source of non-polluting, renewable energy with vast potential. However, current wind turbine technology must be improved before the potential of wind power can be fully realized. Wind turbine blades are one of the key components in improving this technology. Blade failure is very costly because it can damage other blades, the wind turbine itself, and possibly other wind turbines. A successful damage detection system incorporated into wind turbines could extend blade life and allow for less conservative designs. A damage detection method which has shown promise on a wide variety of structures is impedance-based structural health monitoring. The technique utilizes small piezoceramic (PZT) patches attached to a structure as self-sensing actuators to both excite the structure with high-frequency excitations, and monitor any changes in structural mechanical impedance. By monitoring the electrical impedance of the PZT, assessments can be made about the integrity of the mechanical structure. Recently, advances in hardware systems with onboard computing, including actuation and sensing, computational algorithms, and wireless telemetry, have improved the accessibility of the impedance method for in-field measurements. This paper investigates the feasibility of implementing such an onboard system inside of turbine blades as an in-field method of damage detection. Viability of onboard detection is accomplished by running a series of tests to verify the capability of the method on an actual wind turbine blade section from an experimental carbon/glass/balsa composite blade developed at Sandia National Laboratories.

Structural health monitoring approach for detecting ice accretion on bridge cable using the Haar Wavelet Transform

NASA Astrophysics Data System (ADS)

Andre, Julia; Kiremidjian, Anne; Liao, Yizheng; Georgakis, Christos; Rajagopal, Ram

2016-04-01

Ice accretion on cables of bridge structures poses serious risk to the structure as well as to vehicular traffic when the ice falls onto the road. Detection of ice formation, quantification of the amount of ice accumulated, and prediction of icefalls will increase the safety and serviceability of the structure. In this paper, an ice accretion detection algorithm is presented based on the Continuous Wavelet Transform (CWT). In the proposed algorithm, the acceleration signals obtained from bridge cables are transformed using wavelet method. The damage sensitive features (DSFs) are defined as a function of the wavelet energy at specific wavelet scales. It is found that as ice accretes on the cables, the mass of cable increases, thus changing the wavelet energies. Hence, the DSFs can be used to track the change of cables mass. To validate the proposed algorithm, we use the data collected from a laboratory experiment conducted at the Technical University of Denmark (DTU). In this experiment, a cable was placed in a wind tunnel as ice volume grew progressively. Several accelerometers were installed at various locations along the testing cable to collect vibration signals.

Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

NASA Astrophysics Data System (ADS)

Mustapha, S.; Braytee, A.; Ye, L.

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Structural health monitoring of inflatable structures for MMOD impacts

NASA Astrophysics Data System (ADS)

Anees, Muhammad; Gbaguidi, Audrey; Kim, Daewon; Namilae, Sirish

2017-04-01

Inflatable structures for space habitat are highly prone to damage caused by micrometeoroid and orbital debris impacts. Although the structures are effectively shielded against these impacts through multiple layers of impact resistant materials, there is a necessity for a health monitoring system to monitor the structural integrity and damage state within the structures. Assessment of damage is critical for the safety of personnel in the space habitat, as well as predicting the repair needs and the remaining useful life of the habitat. In this paper, we propose a unique impact detection and health monitoring system based on hybrid nanocomposite sensors. The sensors are composed of two fillers, carbon nanotubes and coarse graphene platelets with an epoxy matrix material. The electrical conductivity of these flexible nanocomposite sensors is highly sensitive to strains as well as presence of any holes and damage in the structure. The sensitivity of the sensors to the presence of 3mm holes due to an event of impact is evaluated using four point probe electrical resistivity measurements. An array of these sensors when sandwiched between soft good layers in a space habitat can act as a damage detection layer for inflatable structures. An algorithm is developed to determine the event of impact, its severity and location on the sensing layer for active health monitoring.

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

PubMed

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

PubMed Central

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-01-01

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. PMID:28773003

Damage detection and quantification in a structural model under seismic excitation using time-frequency analysis

NASA Astrophysics Data System (ADS)

Chan, Chun-Kai; Loh, Chin-Hsiung; Wu, Tzu-Hsiu

2015-04-01

In civil engineering, health monitoring and damage detection are typically carry out by using a large amount of sensors. Typically, most methods require global measurements to extract the properties of the structure. However, some sensors, like LVDT, cannot be used due to in situ limitation so that the global deformation remains unknown. An experiment is used to demonstrate the proposed algorithms: a one-story 2-bay reinforce concrete frame under weak and strong seismic excitation. In this paper signal processing techniques and nonlinear identification are used and applied to the response measurements of seismic response of reinforced concrete structures subject to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of RC frame using both input and output response data or output only measurement. From the signal-based damage identification method, which include the enhancement of time-frequency analysis of acceleration responses and the estimation of permanent deformation using directly from acceleration response data. Finally, local deformation measurement from dense optical tractor is also use to quantify the damage of the RC frame structure.

Damage Identification in Beam Structure using Spatial Continuous Wavelet Transform

NASA Astrophysics Data System (ADS)

Janeliukstis, R.; Rucevskis, S.; Wesolowski, M.; Kovalovs, A.; Chate, A.

2015-11-01

In this paper the applicability of spatial continuous wavelet transform (CWT) technique for damage identification in the beam structure is analyzed by application of different types of wavelet functions and scaling factors. The proposed method uses exclusively mode shape data from the damaged structure. To examine limitations of the method and to ascertain its sensitivity to noisy experimental data, several sets of simulated data are analyzed. Simulated test cases include numerical mode shapes corrupted by different levels of random noise as well as mode shapes with different number of measurement points used for wavelet transform. A broad comparison of ability of different wavelet functions to detect and locate damage in beam structure is given. Effectiveness and robustness of the proposed algorithms are demonstrated experimentally on two aluminum beams containing single mill-cut damage. The modal frequencies and the corresponding mode shapes are obtained via finite element models for numerical simulations and by using a scanning laser vibrometer with PZT actuator as vibration excitation source for the experimental study.

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks.

PubMed

Feng, Lei; Zhu, Susu; Lin, Fucheng; Su, Zhenzhu; Yuan, Kangpei; Zhao, Yiying; He, Yong; Zhang, Chu

2018-06-15

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Passive imaging based multi-cue hazard detection spacecraft safe landing

NASA Technical Reports Server (NTRS)

Huertas, Andres; Cheng, Yang; Madison, Richard

2006-01-01

Accurate assessment of potentially damaging ground hazards during the spacecraft EDL (Entry, Descent and Landing) phase is crucial to insure a high probability of safe landing. A lander that encounters a large rock, falls off a cliff, or tips over on a steep slope can sustain mission ending damage. Guided entry is expected to shrink landing ellipses from 100-300 km to -10 km radius for the second generation landers as early as 2009. Regardless of size and location, however, landing ellipses will almost always contain hazards such as craters, discontinuities, steep slopes, and large rocks. It is estimated that an MSL (Mars Science Laboratory)-sized lander should detect and avoid 16- 150m diameter craters, vertical drops similar to the edges of 16m or 3.75m diameter crater, for high and low altitude HAD (Hazard Detection and Avoidance) respectively. It should also be able to detect slopes 20' or steeper, and rocks 0.75m or taller. In this paper we will present a passive imaging based, multi-cue hazard detection and avoidance (HDA) system suitable for Martian and other lander missions. This is the first passively imaged HDA system that seamlessly integrates multiple algorithm-crater detection, slope estimation, rock detection and texture analysis, and multicues- crater morphology, rock distribution, to detect these hazards in real time.

Nondestructive testing of delaminated interfaces between two materials using electromagnetic interrogation

NASA Astrophysics Data System (ADS)

Cakoni, Fioralba; de Teresa, Irene; Monk, Peter

2018-06-01

We consider the problem of detecting whether two materials that should be in contact have separated or delaminated using electromagnetic radiation. The interface damage is modeled as a thin opening between two materials of different electromagnetic properties. To derive a reconstruction algorithm that focuses on testing for the delamination at the interface between the two materials, we use the approximate asymptotic model for the forward problem derived in de Teresa (2017 PhD Thesis University of Delaware). In this model, the differential equations in the small opening are replaced by approximate transmission conditions for the electromagnetic fields across the interface. We also assume that the undamaged or background state is known and it is desired to find where the delamination has opened. We adapt the linear sampling method to this configuration in order to locate the damaged part of the interface from a knowledge of the scattered field and the undamaged configuration, but without needing to know the electromagnetic properties of the opening. Numerical examples are presented to validate our algorithm.

Effects of Acoustic Impulses on the Middle Ear

DTIC Science & Technology

2016-10-01

development of a MEMC detection algorithm for use with the National Health and Nutrition Examination Survey (NHANES) impedance traces. The second specific...Flamme GA, Deiters KK, Tasko SM, Ahroon WA (under review). Acoustic reflexes are common but not pervasive: Evidence from the National Health and Nutrition ...of new (or revising existing) damage risk criteria and health hazard assessment methods for exposure to high-level acoustic impulses such as

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Numerical modeling of the load effect on PZT-induced guided wave for load compensation of damage detection

NASA Astrophysics Data System (ADS)

Sun, Hu; Zhang, Aijia; Wang, Yishou; Qing, Xinlin P.

2017-04-01

Guided wave-based structural health monitoring (SHM) has been given considerable attention and widely studied for large-scale aircraft structures. Nevertheless, it is difficult to apply SHM systems on board or online, for which one of the most serious reasons is the environmental influence. Load is one fact that affects not only the host structure, in which guided wave propagates, but also the PZT, by which guided wave is transmitted and received. In this paper, numerical analysis using finite element method is used to study the load effect on guided wave acquired by PZT. The static loads with different grades are considered to analyze its effect on guided wave signals that PZT transmits and receives. Based on the variation trend of guided waves versus load, a load compensation method is developed to eliminate effects of load in the process of damage detection. The probabilistic reconstruction algorithm based on the signal variation of transmitter-receiver path is employed to identify the damage. Numerical tests is conducted to verify the feasibility and effectiveness of the given method.

Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection

NASA Astrophysics Data System (ADS)

Magalhães, F.; Cunha, A.; Caetano, E.

2012-04-01

In order to evaluate the usefulness of approaches based on modal parameters tracking for structural health monitoring of bridges, in September of 2007, a dynamic monitoring system was installed in a concrete arch bridge at the city of Porto, in Portugal. The implementation of algorithms to perform the continuous on-line identification of modal parameters based on structural responses to ambient excitation (automated Operational Modal Analysis) has permitted to create a very complete database with the time evolution of the bridge modal characteristics during more than 2 years. This paper describes the strategy that was followed to minimize the effects of environmental and operational factors on the bridge natural frequencies, enabling, in a subsequent stage, the identification of structural anomalies. Alternative static and dynamic regression models are tested and complemented by a Principal Components Analysis. Afterwards, the identification of damages is tried with control charts. At the end, it is demonstrated that the adopted processing methodology permits the detection of realistic damage scenarios, associated with frequency shifts around 0.2%, which were simulated with a numerical model.

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Kim, Heung Soo; Sohn, Jung Woo

2018-03-01

Separation along the interfaces of layers (delamination) is a principal mode of failure in laminated composites and its detection is of prime importance for structural integrity of composite materials. In this work, structural vibration response is employed to detect and classify delaminations in piezo-bonded laminated composites. Improved layerwise theory and finite element method are adopted to develop the electromechanically coupled governing equation of a smart composite laminate with and without delaminations. Transient responses of the healthy and damaged structures are obtained through a surface bonded piezoelectric sensor by solving the governing equation in the time domain. Wavelet packet transform (WPT) and linear discriminant analysis (LDA) are employed to extract discriminative features from the structural vibration response of the healthy and delaminated structures. Dendrogram-based support vector machine (DSVM) is used to classify the discriminative features. The confusion matrix of the classification algorithm provided physically consistent results.

Advances in Micromechanics Modeling of Composites Structures for Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Moncada, Albert

Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focuses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors.

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

A topology visualization early warning distribution algorithm for large-scale network security incidents.

PubMed

He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

2013-01-01

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Detection of exudates in fundus imagery using a constant false-alarm rate (CFAR) detector

NASA Astrophysics Data System (ADS)

Khanna, Manish; Kapoor, Elina

2014-05-01

Diabetic retinopathy is the leading cause of blindness in adults in the United States. The presence of exudates in fundus imagery is the early sign of diabetic retinopathy so detection of these lesions is essential in preventing further ocular damage. In this paper we present a novel technique to automatically detect exudates in fundus imagery that is robust against spatial and temporal variations of background noise. The detection threshold is adjusted dynamically, based on the local noise statics around the pixel under test in order to maintain a pre-determined, constant false alarm rate (CFAR). The CFAR detector is often used to detect bright targets in radar imagery where the background clutter can vary considerably from scene to scene and with angle to the scene. Similarly, the CFAR detector addresses the challenge of detecting exudate lesions in RGB and multispectral fundus imagery where the background clutter often exhibits variations in brightness and texture. These variations present a challenge to common, global thresholding detection algorithms and other methods. Performance of the CFAR algorithm is tested against a publicly available, annotated, diabetic retinopathy database and preliminary testing suggests that performance of the CFAR detector proves to be superior to techniques such as Otsu thresholding.

A fundamental reconsideration of the CRASH3 damage analysis algorithm: the case against uniform ubiquitous linearity between BEV, peak collision force magnitude, and residual damage depth.

PubMed

Singh, Jai

2013-01-01

The objective of this study was a thorough reconsideration, within the framework of Newtonian mechanics and work-energy relationships, of the empirically interpreted relationships employed within the CRASH3 damage analysis algorithm in regards to linearity between barrier equivalent velocity (BEV) or peak collision force magnitude and residual damage depth. The CRASH3 damage analysis algorithm was considered, first in terms of the cases of collisions that produced no residual damage, in order to properly explain the damage onset speed and crush resistance terms. Under the modeling constraints of the collision partners representing a closed system and the a priori assumption of linearity between BEV or peak collision force magnitude and residual damage depth, the equations for the sole realistic model were derived. Evaluation of the work-energy relationships for collisions at or below the elastic limit revealed that the BEV or peak collision force magnitude relationships are bifurcated based upon the residual damage depth. Rather than being additive terms from the linear curve fits employed in the CRASH3 damage analysis algorithm, the Campbell b 0 and CRASH3 AL terms represent the maximum values that can be ascribed to the BEV or peak collision force magnitude, respectively, for collisions that produce zero residual damage. Collisions resulting in the production of non-zero residual damage depth already account for the surpassing of the elastic limit during closure and therefore the secondary addition of the elastic limit terms represents a double accounting of the same. This evaluation shows that the current energy absorbed formulation utilized in the CRASH3 damage analysis algorithm extraneously includes terms associated with the A and G stiffness coefficients. This sole realistic model, however, is limited, secondary to reducing the coefficient of restitution to a constant value for all cases in which the residual damage depth is nonzero. Linearity between BEV or peak collision force magnitude and residual damage depth may be applicable for particular ranges of residual damage depth for any given region of any given vehicle. Within the modeling construct employed by the CRASH3 damage algorithm, the case of uniform and ubiquitous linearity cannot be supported. Considerations regarding the inclusion of internal work recovered and restitution for modeling the separation phase change in velocity magnitude should account for not only the effects present during the evaluation of a vehicle-to-vehicle collision of interest but also to the approach taken for modeling the force-deflection response for each collision partner.

Hierarchical detection of red lesions in retinal images by multiscale correlation filtering

NASA Astrophysics Data System (ADS)

Zhang, Bob; Wu, Xiangqian; You, Jane; Li, Qin; Karray, Fakhri

2009-02-01

This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge (ROC) competition website and we conclude our method to be effective and efficient.

Detecting Structural Failures Via Acoustic Impulse Responses

NASA Technical Reports Server (NTRS)

Bayard, David S.; Joshi, Sanjay S.

1995-01-01

Advanced method of acoustic pulse reflectivity testing developed for use in determining sizes and locations of failures within structures. Used to detect breaks in electrical transmission lines, detect faults in optical fibers, and determine mechanical properties of materials. In method, structure vibrationally excited with acoustic pulse (a "ping") at one location and acoustic response measured at same or different location. Measured acoustic response digitized, then processed by finite-impulse-response (FIR) filtering algorithm unique to method and based on acoustic-wave-propagation and -reflection properties of structure. Offers several advantages: does not require training, does not require prior knowledge of mathematical model of acoustic response of structure, enables detection and localization of multiple failures, and yields data on extent of damage at each location.

LS-DYNA Simulation of Hemispherical-punch Stamping Process Using an Efficient Algorithm for Continuum Damage Based Elastoplastic Constitutive Equation

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

Salajegheh, Nima; Abedrabbo, Nader; Pourboghrat, Farhang

An efficient integration algorithm for continuum damage based elastoplastic constitutive equations is implemented in LS-DYNA. The isotropic damage parameter is defined as the ratio of the damaged surface area over the total cross section area of the representative volume element. This parameter is incorporated into the integration algorithm as an internal variable. The developed damage model is then implemented in the FEM code LS-DYNA as user material subroutine (UMAT). Pure stretch experiments of a hemispherical punch are carried out for copper sheets and the results are compared against the predictions of the implemented damage model. Evaluation of damage parameters ismore » carried out and the optimized values that correctly predicted the failure in the sheet are reported. Prediction of failure in the numerical analysis is performed through element deletion using the critical damage value. The set of failure parameters which accurately predict the failure behavior in copper sheets compared to experimental data is reported as well.« less

A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

NASA Astrophysics Data System (ADS)

Vickers, H.; Eckerstorfer, M.; Malnes, E.; Larsen, Y.; Hindberg, H.

2016-11-01

Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

N-Scan®: New Vibro-Modulation System for Crack Detection, Monitoring and Characterization

NASA Astrophysics Data System (ADS)

Zagrai, Andrei; Donskoy, Dimitri; Lottiaux, Jean-Louis

2004-02-01

In recent years, an innovative vibro-modulation technique has been introduced for the detection of contact-type interfaces such as cracks, debondings, and delaminations. The technique utilizes the effect of nonlinear interaction of ultrasound and vibrations at the interface of the defect. Vibration varies the contact area of the interface, modulating a passing ultrasonic wave. The modulation manifests itself as additional side-band spectral components with the combination frequencies in the spectrum of the received signal. The presence of these components allows for the detection and differentiation of the contact-type defects from other structural and material inhomogeneities. The vibro-modulation technique has been implemented in the N-SCAN® damage detection system providing a cost effective solution for the complex NDT problems. N-SCAN® proved to be very effective for damage detection and characterization in structures and structural components of simple and complex geometries made of steel, aluminum, composites, and other materials. Examples include 24 foot-long gun barrels, stainless steel pipes used in nuclear power plants, aluminum automotive parts, steel train couplers, etc. This paper describes the basic principles of the nonlinear vibro-modulation NDE technique, some theoretical background for nonlinear interaction, and justification of signal processing algorithms. The laboratory experiment is presented for a set of specimens with the calibrated cracks and the quantitative characterization of fatigue damage is given in terms of a modulation index. The paper also discusses examples of practical implementation and application of the technique.

Behavioral pattern identification for structural health monitoring in complex systems

NASA Astrophysics Data System (ADS)

Gupta, Shalabh

Estimation of structural damage and quantification of structural integrity are critical for safe and reliable operation of human-engineered complex systems, such as electromechanical, thermofluid, and petrochemical systems. Damage due to fatigue crack is one of the most commonly encountered sources of structural degradation in mechanical systems. Early detection of fatigue damage is essential because the resulting structural degradation could potentially cause catastrophic failures, leading to loss of expensive equipment and human life. Therefore, for reliable operation and enhanced availability, it is necessary to develop capabilities for prognosis and estimation of impending failures, such as the onset of wide-spread fatigue crack damage in mechanical structures. This dissertation presents information-based online sensing of fatigue damage using the analytical tools of symbolic time series analysis ( STSA). Anomaly detection using STSA is a pattern recognition method that has been recently developed based upon a fixed-structure, fixed-order Markov chain. The analysis procedure is built upon the principles of Symbolic Dynamics, Information Theory and Statistical Pattern Recognition. The dissertation demonstrates real-time fatigue damage monitoring based on time series data of ultrasonic signals. Statistical pattern changes are measured using STSA to monitor the evolution of fatigue damage. Real-time anomaly detection is presented as a solution to the forward (analysis) problem and the inverse (synthesis) problem. (1) the forward problem - The primary objective of the forward problem is identification of the statistical changes in the time series data of ultrasonic signals due to gradual evolution of fatigue damage. (2) the inverse problem - The objective of the inverse problem is to infer the anomalies from the observed time series data in real time based on the statistical information generated during the forward problem. A computer-controlled special-purpose fatigue test apparatus, equipped with multiple sensing devices (e.g., ultrasonics and optical microscope) for damage analysis, has been used to experimentally validate the STSA method for early detection of anomalous behavior. The sensor information is integrated with a software module consisting of the STSA algorithm for real-time monitoring of fatigue damage. Experiments have been conducted under different loading conditions on specimens constructed from the ductile aluminium alloy 7075 - T6. The dissertation has also investigated the application of the STSA method for early detection of anomalies in other engineering disciplines. Two primary applications include combustion instability in a generic thermal pulse combustor model and whirling phenomenon in a typical misaligned shaft.

A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Xiao, Gang

2011-08-01

Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

NASA Astrophysics Data System (ADS)

Shi, Binkai; Qiao, Pizhong

2018-03-01

Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks

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

Chin, George; Choudhury, Sutanay; Feo, John T.

2014-06-30

The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could providemore » cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.« less

Numerical analysis of PZT rebar active sensing system for structural health monitoring of RC structure

NASA Astrophysics Data System (ADS)

Wu, F.; Yi, J.; Li, W. J.

2014-03-01

An active sensing diagnostic system for reinforced concrete SHM has been under investigation. Test results show that the system can detect the damage of the structure. To fundamentally understand the damage algorithm and therefore to establish a robust diagnostic method, accurate Finite Element Analysis (FEA) for the system becomes essential. For the system, a rebar with surface bonded PZT under a transient wave load was simulated and analyzed using commercial FEA software. A detailed 2D axi-symmetric model for a rebar attaching PZT was first established. The model simulates the rebar with wedges, an epoxy adhesive layer, as well as a PZT layer. PZT material parameter transformation with high order tensors was discussed due to the format differences between IEEE Standard and ANSYS. The selection of material properties such as Raleigh damping coefficients was discussed. The direct coupled-field analysis type was selected during simulation. The results from simulation matched well with the experimental data. Further simulation for debonding damage detection for concrete beam with the PZT rebar has been performed. And the numerical results have been validated with test results too. The good consistency between two proves that the numerical models were reasonably accurate. Further system optimization has been performed based on these models. By changing PZT layout and size, the output signals could be increased with magnitudes. And the damage detection signals have been found to be increased exponentially with the debonding size of the rebar.

Interfacial damage identification of steel and concrete composite beams based on piezoceramic wave method.

PubMed

Yan, Shi; Dai, Yong; Zhao, Putian; Liu, Weiling

2018-01-01

Steel-concrete composite structures are playing an increasingly important role in economic construction because of a series of advantages of great stiffness, good seismic performance, steel material saving, cost efficiency, convenient construction, etc. However, in service process, due to the long-term effects of environmental impacts and dynamic loading, interfaces of a composite structure might generate debonding cracks, relative slips or separations, and so on, lowering the composite effect of the composite structure. In this paper, the piezoceramics (PZT) are used as transducers to perform experiments on interface debonding slips and separations of composite beams, respectively, aimed at proposing an interface damage identification model and a relevant damage detection innovation method based on PZT wave technology. One part of various PZT patches was embedded in concrete as "smart aggregates," and another part of the PZT patches was pasted on the surface of the steel beam flange, forming a sensor array. A push-out test for four specimens was carried out and experimental results showed that, under the action of the external loading, the received signal amplitudes will increasingly decrease with increase of debonding slips along the interface. The proposed signal energy-based interface damage detection algorithm is highly efficient in surface state evaluations of composite beams.

A feature-preserving hair removal algorithm for dermoscopy images.

PubMed

Abbas, Qaisar; Garcia, Irene Fondón; Emre Celebi, M; Ahmad, Waqar

2013-02-01

Accurate segmentation and repair of hair-occluded information from dermoscopy images are challenging tasks for computer-aided detection (CAD) of melanoma. Currently, many hair-restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesion's pattern. In this article, a novel hair-restoration algorithm is presented, which has a capability to preserve the skin lesion features such as color and texture and able to segment both dark and light hairs. Our algorithm is based on three major steps: the rough hairs are segmented using a matched filtering with first derivative of gaussian (MF-FDOG) with thresholding that generate strong responses for both dark and light hairs, refinement of hairs by morphological edge-based techniques, which are repaired through a fast marching inpainting method. Diagnostic accuracy (DA) and texture-quality measure (TQM) metrics are utilized based on dermatologist-drawn manual hair masks that were used as a ground truth to evaluate the performance of the system. The hair-restoration algorithm is tested on 100 dermoscopy images. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation (PDE), and (iii) exemplar-based repairing techniques. Among different hair detection and removal techniques, our proposed algorithm obtained the highest value of DA: 93.3% and TQM: 90%. The experimental results indicate that the proposed algorithm is highly accurate, robust and able to restore hair pixels without damaging the lesion texture. This method is fully automatic and can be easily integrated into a CAD system. © 2011 John Wiley & Sons A/S.

Structural Health Monitoring for Impact Damage in Composite Structures.

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

Roach, Dennis P.; Raymond Bond; Doug Adams

Composite structures are increasing in prevalence throughout the aerospace, wind, defense, and transportation industries, but the many advantages of these materials come with unique challenges, particularly in inspecting and repairing these structures. Because composites of- ten undergo sub-surface damage mechanisms which compromise the structure without a clear visual indication, inspection of these components is critical to safely deploying composite re- placements to traditionally metallic structures. Impact damage to composites presents one of the most signi fi cant challenges because the area which is vulnerable to impact damage is generally large and sometimes very dif fi cult to access. This workmore » seeks to further evolve iden- ti fi cation technology by developing a system which can detect the impact load location and magnitude in real time, while giving an assessment of the con fi dence in that estimate. Fur- thermore, we identify ways by which impact damage could be more effectively identi fi ed by leveraging impact load identi fi cation information to better characterize damage. The impact load identi fi cation algorithm was applied to a commercial scale wind turbine blade, and results show the capability to detect impact magnitude and location using a single accelerometer, re- gardless of sensor location. A technique for better evaluating the uncertainty of the impact estimates was developed by quantifying how well the impact force estimate meets the assump- tions underlying the force estimation technique. This uncertainty quanti fi cation technique was found to reduce the 95% con fi dence interval by more than a factor of two for impact force estimates showing the least uncertainty, and widening the 95% con fi dence interval by a fac- tor of two for the most uncertain force estimates, avoiding the possibility of understating the uncertainty associated with these estimates. Linear vibration based damage detection tech- niques were investigated in the context of structural stiffness reductions and impact damage. A method by which the sensitivity to damage could be increased for simple structures was presented, and the challenges of applying that technique to a more complex structure were identi fi ed. The structural dynamic changes in a weak adhesive bond were investigated, and the results showed promise for identifying weak bonds that show little or no static reduction in stiffness. To address these challenges in identifying highly localized impact damage, the possi- bility of detecting damage through nonlinear dynamic characteristics was also identi fi ed, with a proposed technique which would leverage impact location estimates to enable the detection of impact damage. This nonlinear damage identi fi cation concept was evaluated on a composite panel with a substructure disbond, and the results showed that the nonlinear dynamics at the damage site could be observed without a baseline healthy reference. By further developing impact load identi fi cation technology and combining load and damage estimation techniques into an integrated solution, the challenges associated with impact detection in composite struc- tures can be effectively solved, thereby reducing costs, improving safety, and enhancing the operational readiness and availability of high value assets.« less

A diabetic retinopathy detection method using an improved pillar K-means algorithm.

PubMed

Gogula, Susmitha Valli; Divakar, Ch; Satyanarayana, Ch; Rao, Allam Appa

2014-01-01

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

On the performance of SART and ART algorithms for microwave imaging

NASA Astrophysics Data System (ADS)

Aprilliyani, Ria; Prabowo, Rian Gilang; Basari

2018-02-01

The development of advanced technology leads to the change of human lifestyle in current society. One of the disadvantage impact is arising the degenerative diseases such as cancers and tumors, not just common infectious diseases. Every year, victims of cancers and tumors grow significantly leading to one of the death causes in the world. In early stage, cancer/tumor does not have definite symptoms, but it will grow abnormally as tissue cells and damage normal tissue. Hence, early cancer detection is required. Some common diagnostics modalities such as MRI, CT and PET are quite difficult to be operated in home or mobile environment such as ambulance. Those modalities are also high cost, unpleasant, complex, less safety and harder to move. Hence, this paper proposes a microwave imaging system due to its portability and low cost. In current study, we address on the performance of simultaneous algebraic reconstruction technique (SART) algorithm that was applied in microwave imaging. In addition, SART algorithm performance compared with our previous work on algebraic reconstruction technique (ART), in order to have performance comparison, especially in the case of reconstructed image quality. The result showed that by applying SART algorithm on microwave imaging, suspicious cancer/tumor can be detected with better image quality.

Non-supervised method for early forest fire detection and rapid mapping

NASA Astrophysics Data System (ADS)

Artés, Tomás; Boca, Roberto; Liberta, Giorgio; San-Miguel, Jesús

2017-09-01

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 5 - Phase 2 Implementation of the Concept Demonstrator

NASA Technical Reports Server (NTRS)

Batten, Adam; Dunlop, John; Edwards, Graeme; Farmer, Tony; Gaffney, Bruce; Hedley, Mark; Hoschke, Nigel; Isaacs, Peter; Johnson, Mark; Lewis, Chris;

Multiple damage identification on a wind turbine blade using a structural neural system

NASA Astrophysics Data System (ADS)

Kirikera, Goutham R.; Schulz, Mark J.; Sundaresan, Mannur J.

2007-04-01

A large number of sensors are required to perform real-time structural health monitoring (SHM) to detect acoustic emissions (AE) produced by damage growth on large complicated structures. This requires a large number of high sampling rate data acquisition channels to analyze high frequency signals. To overcome the cost and complexity of having such a large data acquisition system, a structural neural system (SNS) was developed. The SNS reduces the required number of data acquisition channels and predicts the location of damage within a sensor grid. The sensor grid uses interconnected sensor nodes to form continuous sensors. The combination of continuous sensors and the biomimetic parallel processing of the SNS tremendously reduce the complexity of SHM. A wave simulation algorithm (WSA) was developed to understand the flexural wave propagation in composite structures and to utilize the code for developing the SNS. Simulation of AE responses in a plate and comparison with experimental results are shown in the paper. The SNS was recently tested by a team of researchers from University of Cincinnati and North Carolina A&T State University during a quasi-static proof test of a 9 meter long wind turbine blade at the National Renewable Energy Laboratory (NREL) test facility in Golden, Colorado. Twelve piezoelectric sensor nodes were used to form four continuous sensors to monitor the condition of the blade during the test. The four continuous sensors are used as inputs to the SNS. There are only two analog output channels of the SNS, and these signals are digitized and analyzed in a computer to detect damage. In the test of the wind turbine blade, multiple damages were identified and later verified by sectioning of the blade. The results of damage identification using the SNS during this proof test will be shown in this paper. Overall, the SNS is very sensitive and can detect damage on complex structures with ribs, joints, and different materials, and the system relatively inexpensive and simple to implement on large structures.

Process and Structural Health Monitoring of Composite Structures with Embedded Fiber Optic Sensors and Piezoelectric Transducers

NASA Astrophysics Data System (ADS)

Keulen, Casey James

Advanced composite materials are becoming increasingly more valuable in a plethora of engineering applications due to properties such as tailorability, low specific strength and stiffness and resistance to fatigue and corrosion. Compared to more traditional metallic and ceramic materials, advanced composites such as carbon, aramid or glass reinforced plastic are relatively new and still require research to optimize their capabilities. Three areas that composites stand to benefit from improvement are processing, damage detection and life prediction. Fiber optic sensors and piezoelectric transducers show great potential for advances in these areas. This dissertation presents the research performed on improving the efficiency of advanced composite materials through the use of embedded fiber optic sensors and surface mounted piezoelectric transducers. Embedded fiber optic sensors are used to detect the presence of resin during the injection stage of resin transfer molding, monitor the degree of cure and predict the remaining useful life while in service. A sophisticated resin transfer molding apparatus was developed with the ability of embedding fiber optics into the composite and a glass viewing window so that resin flow sensors could be verified visually. A novel technique for embedding optical fiber into both 2- and 3-D structures was developed. A theoretical model to predict the remaining useful life was developed and a systematic test program was conducted to verify this model. A network of piezoelectric transducers was bonded to a composite panel in order to develop a structural health monitoring algorithm capable of detecting and locating damage in a composite structure. A network configuration was introduced that allows for a modular expansion of the system to accommodate larger structures and an algorithm based on damage progression history was developed to implement the network. The details and results of this research are contained in four manuscripts that are included in Appendices A-D while the body of the dissertation provides background information and a summary of the results.

Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging.

PubMed

Dabbah, M A; Graham, J; Petropoulos, I N; Tavakoli, M; Malik, R A

2011-10-01

Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation. Copyright © 2011 Elsevier B.V. All rights reserved.

Prostate lesion detection and localization based on locality alignment discriminant analysis

NASA Astrophysics Data System (ADS)

Lin, Mingquan; Chen, Weifu; Zhao, Mingbo; Gibson, Eli; Bastian-Jordan, Matthew; Cool, Derek W.; Kassam, Zahra; Chow, Tommy W. S.; Ward, Aaron; Chiu, Bernard

2017-03-01

Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Damage identification on spatial Timoshenko arches by means of genetic algorithms

NASA Astrophysics Data System (ADS)

Greco, A.; D'Urso, D.; Cannizzaro, F.; Pluchino, A.

2018-05-01

In this paper a procedure for the dynamic identification of damage in spatial Timoshenko arches is presented. The proposed approach is based on the calculation of an arbitrary number of exact eigen-properties of a damaged spatial arch by means of the Wittrick and Williams algorithm. The proposed damage model considers a reduction of the volume in a part of the arch, and is therefore suitable, differently than what is commonly proposed in the main part of the dedicated literature, not only for concentrated cracks but also for diffused damaged zones which may involve a loss of mass. Different damage scenarios can be taken into account with variable location, intensity and extension of the damage as well as number of damaged segments. An optimization procedure, aiming at identifying which damage configuration minimizes the difference between its eigen-properties and a set of measured modal quantities for the structure, is implemented making use of genetic algorithms. In this context, an initial random population of chromosomes, representing different damage distributions along the arch, is forced to evolve towards the fittest solution. Several applications with different, single or multiple, damaged zones and boundary conditions confirm the validity and the applicability of the proposed procedure even in presence of instrumental errors on the measured data.

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

Impact of target organ damage assessment in the evaluation of global risk in patients with essential hypertension.

PubMed

Viazzi, Francesca; Leoncini, Giovanna; Parodi, Denise; Ratto, Elena; Vettoretti, Simone; Vaccaro, Valentina; Parodi, Angelica; Falqui, Valeria; Tomolillo, Cinzia; Deferrari, Giacomo; Pontremoli, Roberto

2005-03-01

Accurate assessment of cardiovascular risk is a key step toward optimizing the treatment of hypertensive patients. We analyzed the impact and cost-effectiveness of routine, thorough assessment of target organ damage (TOD) in evaluating risk profile in hypertension. A total of 380 never-treated patients with essential hypertension underwent routine work-up plus evaluation of albuminuria and ultrasonography of cardiac and vascular structures. The impact of these tests on risk stratification, as indicated by European Society of Hypertension-European Society of Cardiology guidelines, was assessed in light of their cost and sensitivity. The combined use of all of these tests greatly improved the detection of TOD, therefore leading to the identification of a higher percentage of patients who were at high/very high risk, as compared with those who were detected by routine clinical work-up (73% instead of 42%; P < 0.0001). Different signs of TOD only partly cluster within the same subgroup of patients; thus, all three tests should be performed to maximize the sensitivity of the evaluation process. The diagnostic algorithm yielding the lowest cost per detected case of TOD is the search for microalbuminuria, followed by echocardiography and then carotid ultrasonography. Adopting lower cut-off values to define microalbuminuria allows us to optimize further the cost-effectiveness of diagnostic algorithms. In conclusion, because of its low cost and widespread availability, measuring albuminuria is an attractive and cost-effective screening test that is especially suitable as the first step in the large-scale diagnostic work-up of hypertensive patients.

Using ACIS on the Chandra X-ray Observatory as a Particle Radiation Monitor II

NASA Technical Reports Server (NTRS)

Grant, C. E.; Ford, P. G.; Bautz, M. W.; ODell, S. L.

2012-01-01

The Advanced CCD Imaging Spectrometer is an instrument on the Chandra X-ray Observatory. CCDs are vulnerable to radiation damage, particularly by soft protons in the radiation belts and solar storms. The Chandra team has implemented procedures to protect ACIS during high-radiation events including autonomous protection triggered by an on-board radiation monitor. Elevated temperatures have reduced the effectiveness of the on-board monitor. The ACIS team has developed an algorithm which uses data from the CCDs themselves to detect periods of high radiation and a flight software patch to apply this algorithm is currently active on-board the instrument. In this paper, we explore the ACIS response to particle radiation through comparisons to a number of external measures of the radiation environment. We hope to better understand the efficiency of the algorithm as a function of the flux and spectrum of the particles and the time-profile of the radiation event.

Automated Studies of Continuing Current in Lightning Flashes

NASA Astrophysics Data System (ADS)

Martinez-Claros, Jose

Continuing current (CC) is a continuous luminosity in the lightning channel that lasts longer than 10 ms following a lightning return stroke to ground. Lightning flashes following CC are associated with direct damage to power lines and are thought to be responsible for causing lightning-induced forest fires. The development of an algorithm that automates continuing current detection by combining NLDN (National Lightning Detection Network) and LEFA (Langmuir Electric Field Array) datasets for CG flashes will be discussed. The algorithm was applied to thousands of cloud-to-ground (CG) flashes within 40 km of Langmuir Lab, New Mexico measured during the 2013 monsoon season. It counts the number of flashes in a single minute of data and the number of return strokes of an individual lightning flash; records the time and location of each return stroke; performs peak analysis on E-field data, and uses the slope of interstroke interval (ISI) E-field data fits to recognize whether continuing current (CC) exists within the interval. Following CC detection, duration and magnitude are measured. The longest observed C in 5588 flashes was 631 ms. The performance of the algorithm (vs. human judgement) was checked on 100 flashes. At best, the reported algorithm is "correct" 80% of the time, where correct means that multiple stations agree with each other and with a human on both the presence and duration of CC. Of the 100 flashes that were validated against human judgement, 62% were hybrid. Automated analysis detects the first but misses the second return stroke in many cases where the second return stroke is followed by long CC. This problem is also present in human interpretation of field change records.

Accelerated Aging Experiments for Prognostics of Damage Growth in Composite Materials

NASA Technical Reports Server (NTRS)

Saxena, Abhinav; Goebel, Kai Frank; Larrosa, Cecilia C.; Janapati, Vishnuvardhan; Roy, Surajit; Chang, Fu-Kuo

2011-01-01

Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.

[Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

PubMed

Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

2015-11-01

Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect feature of 86 Hawthorn could be recognized. Lastly, the detect precision of bruised, insect damage and two-defect samples is 95.65%, 86.67% and 100%, respectively. This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise, insect damage, calyx, and stem-end in hawthorn fruit in qualitative analysis and feature detection which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.

Random, double- and single-strand DNA breaks can be differentiated in the method of Comet assay by the shape of the comet image.

PubMed

Georgieva, Milena; Zagorchev, Plamen; Miloshev, George

2015-10-01

Comet assay is an invaluable tool in DNA research. It is widely used to detect DNA damage as an indicator of exposure to genotoxic stress. A canonical set of parameters and specialized software programs exist for Comet assay data quantification and analysis. None of them so far has proven its potential to employ a computer-based algorithm for assessment of the shape of the comet as an indicator of the exact mechanism by which the studied genotoxins cut in the molecule of DNA. Here, we present 14 unique measurements of the comet image based on the comet morphology. Their mathematical derivation and statistical analysis allowed precise description of the shape of the comet image which in turn discriminated the cause of genotoxic stress. This algorithm led to the development of the "CometShape" software which allowed easy discrimination among different genotoxins depending on the type of DNA damage they induce. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Collision Detection for Underwater ROV Manipulator Systems

PubMed Central

Rossi, Matija; Dooly, Gerard; Toal, Daniel

2018-01-01

Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations. PMID:29642396

Collision Detection for Underwater ROV Manipulator Systems.

PubMed

Sivčev, Satja; Rossi, Matija; Coleman, Joseph; Omerdić, Edin; Dooly, Gerard; Toal, Daniel

2018-04-06

Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations.

Methodologies for Adaptive Flight Envelope Estimation and Protection

NASA Technical Reports Server (NTRS)

Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

2009-01-01

This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

Seismic damage diagnosis of a masonry building using short-term damping measurements

NASA Astrophysics Data System (ADS)

Kouris, Leonidas Alexandros S.; Penna, Andrea; Magenes, Guido

2017-04-01

It is of considerable importance to perform dynamic identification and detect damage in existing structures. This paper describes a new and practical method for damage diagnosis of masonry buildings requiring minimum computational effort. The method is based on the relative variation of modal damping and validated against experimental data from a full scale two storey shake table test. The experiment involves a building subjected to uniaxial vibrations of progressively increasing intensity at the facilities of EUCENTRE laboratory (Pavia, Italy) up to a near collapse damage state. Five time-histories are applied scaling the Montenegro (1979) accelerogram. These strong motion tests are preceded by random vibration tests (RVT's) which are used to perform modal analysis. Two deterministic methods are applied: the single degree of freedom (SDOF) assumption together with the peak-picking method in the discrete frequency domain and the Eigen realisation algorithm with data correlations (ERA-DC) in the discrete time domain. Regarding the former procedure, some improvements are incorporated to locate rigorously the natural frequencies and estimate the modal damping. The progressive evolution of the modal damping is used as a key indicator to characterise damage on the building. Modal damping is connected to the structural mass and stiffness. A square integrated but only with two components expression for proportional (classical) damping is proposed to fit better with the experimental measurements of modal damping ratios. Using this Rayleigh order formulation the contribution of each of the damping components is evaluated. The stiffness component coefficient is proposed as an effective index to detect damage and quantify its intensity.

Toward automated selective retina treatment (SRT): an optical microbubble detection technique

NASA Astrophysics Data System (ADS)

Seifert, Eric; Park, Young-Gun; Theisen-Kunde, Dirk; Roh, Young-Jung; Brinkmann, Ralf

2018-02-01

Selective retina therapy (SRT) is an ophthalmological laser technique, targeting the retinal pigment epithelium (RPE) with repetitive microsecond laser pulses, while causing no thermal damage to the neural retina, the photoreceptors as well as the choroid. The RPE cells get damaged mechanically by microbubbles originating, at the intracellular melanosomes. Beneficial effects of SRT on Central Serous Retinopathy (CSR) and Diabetic Macula Edema (DME) have already been shown. Variations in the transmission of the anterior eye media and pigmentation variation of RPE yield in intra- and inter- individual thresholds of the pulse energy required for selective RPE damage. Those selective RPE lesions are not visible. Thus, dosimetry-systems, designed to detect microbubbles as an indicator for RPE cell damage, are demanded elements to facilitate SRT application. Therefore, a technique based on the evaluation of backscattered treatment light has been developed. Data of 127 spots, acquired during 10 clinical treatments of CSR patients, were assigned to a RPE cell damage class, validated by fluorescence angiography (FLA). An algorithm has been designed to match the FLA based information. A sensitivity of 0.9 with a specificity close to 1 is achieved. The data can be processed within microseconds. Thus, the process can be implemented in existing SRT lasers with an automatic pulse wise increasing energy and an automatic irradiation ceasing ability to enable automated treatment close above threshold to prevent adverse effects caused by too high pulse energy. Alternatively, a guidance procedure, informing the treating clinician about the adequacy of the actual settings, is possible.

Characterization of Aircraft Structural Damage Using Guided Wave Based Finite Element Analysis for In-Flight Structural Health Management

NASA Technical Reports Server (NTRS)

Seshadri, Banavara R.; Krishnamurthy, Thiagarajan; Ross, Richard W.

2016-01-01

The development of multidisciplinary Integrated Vehicle Health Management (IVHM) tools will enable accurate detection, diagnosis and prognosis of damage under normal and adverse conditions during flight. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and structural damage conditions. A major concern is the growth of undetected damage/cracks due to fatigue and low velocity foreign object impact that can reach a critical size during flight, resulting in loss of control of the aircraft. To avoid unstable catastrophic propagation of damage during a flight, load levels must be maintained that are below the load-carrying capacity for damaged aircraft structures. Hence, a capability is needed for accurate real-time predictions of safe load carrying capacity for aircraft structures with complex damage configurations. In the present work, a procedure is developed that uses guided wave responses to interrogate damage. As the guided wave interacts with damage, the signal attenuates in some directions and reflects in others. This results in a difference in signal magnitude as well as phase shifts between signal responses for damaged and undamaged structures. Accurate estimation of damage size and location is made by evaluating the cumulative signal responses at various pre-selected sensor locations using a genetic algorithm (GA) based optimization procedure. The damage size and location is obtained by minimizing the difference between the reference responses and the responses obtained by wave propagation finite element analysis of different representative cracks, geometries and sizes.

Identification of damage in composite structures using Gaussian mixture model-processed Lamb waves

NASA Astrophysics Data System (ADS)

Wang, Qiang; Ma, Shuxian; Yue, Dong

2018-04-01

Composite materials have comprehensively better properties than traditional materials, and therefore have been more and more widely used, especially because of its higher strength-weight ratio. However, the damage of composite structures is usually varied and complicated. In order to ensure the security of these structures, it is necessary to monitor and distinguish the structural damage in a timely manner. Lamb wave-based structural health monitoring (SHM) has been proved to be effective in online structural damage detection and evaluation; furthermore, the characteristic parameters of the multi-mode Lamb wave varies in response to different types of damage in the composite material. This paper studies the damage identification approach for composite structures using the Lamb wave and the Gaussian mixture model (GMM). The algorithm and principle of the GMM, and the parameter estimation, is introduced. Multi-statistical characteristic parameters of the excited Lamb waves are extracted, and the parameter space with reduced dimensions is adopted by principal component analysis (PCA). The damage identification system using the GMM is then established through training. Experiments on a glass fiber-reinforced epoxy composite laminate plate are conducted to verify the feasibility of the proposed approach in terms of damage classification. The experimental results show that different types of damage can be identified according to the value of the likelihood function of the GMM.

Probing glaucoma visual damage by rarebit perimetry.

PubMed

Brusini, P; Salvetat, M L; Parisi, L; Zeppieri, M

2005-02-01

To compare rarebit perimetry (RBP) with standard achromatic perimetry (SAP) in detecting early glaucomatous functional damage. 43 patients with ocular hypertension (OH), 39 with early primary open angle glaucoma (POAG), and 41 controls were considered. Visual fields were assessed using the Humphrey field analyser (HFA) 30-2 and RBP tests. Differences among the groups were evaluated using Student-Newman-Keuls and chi(2) tests. Correlation between HFA and RBP parameters was assessed using the Pearson's correlation coefficients and regression analysis. Sensitivity and specificity of RBP in detecting early glaucomatous visual damage were calculated with different algorithms. RBP-mean hit rate (MHR) was respectively 88.6% (SD 4.8%) in controls; 79.1% (10.9%) in the OH group; 64.3% (13.8%) in the POAG group (differences statistically significant). Good correlation in the POAG group was found between HFA-mean deviation and RBP-MHR. Largest AROC (0.95) and optimal sensitivity (97.4%) were obtained when an abnormal RBP test was defined as having (at least 1): MHR <80%; >15 areas with a non-hit rate of >10%; > or =2 areas with a non-hit rate of >50%; at least one area with a non-hit rate of > or =70%. The RBP appeared to be a rapid, comfortable, and easily available perimetric test (requiring only a PC device), showing a high sensitivity and specificity in detecting early glaucomatous visual field defects.

Detection of hail signatures from single-polarization C-band radar reflectivity

NASA Astrophysics Data System (ADS)

Kunz, Michael; Kugel, Petra I. S.

2015-02-01

Five different criteria that estimate hail signatures from single-polarization radar data are statistically evaluated over a 15-year period by categorical verification against loss data provided by a building insurance company. The criteria consider different levels or thresholds of radar reflectivity, some of them complemented by estimates of the 0 °C level or cloud top temperature. Applied to reflectivity data from a single C-band radar in southwest Germany, it is found that all criteria are able to reproduce most of the past damage-causing hail events. However, the criteria substantially overestimate hail occurrence by up to 80%, mainly due to the verification process using damage data. Best results in terms of highest Heidke Skill Score HSS or Critical Success Index CSI are obtained for the Hail Detection Algorithm (HDA) and the Probability of Severe Hail (POSH). Radar-derived hail probability shows a high spatial variability with a maximum on the lee side of the Black Forest mountains and a minimum in the broad Rhine valley.

Orion MPCV Touchdown Detection Threshold Development and Testing

NASA Technical Reports Server (NTRS)

Daum, Jared; Gay, Robert

2013-01-01

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology.

PubMed

Liu, Ruiling; Zhong, Dexing; Lyu, Hongqiang; Han, Jiuqiang

2016-08-25

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm × 0.4 mm and the precision of dimension measurement is about 40-50 μm. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production.

Frequency Response Function Based Damage Identification for Aerospace Structures

NASA Astrophysics Data System (ADS)

Oliver, Joseph Acton

Structural health monitoring technologies continue to be pursued for aerospace structures in the interests of increased safety and, when combined with health prognosis, efficiency in life-cycle management. The current dissertation develops and validates damage identification technology as a critical component for structural health monitoring of aerospace structures and, in particular, composite unmanned aerial vehicles. The primary innovation is a statistical least-squares damage identification algorithm based in concepts of parameter estimation and model update. The algorithm uses frequency response function based residual force vectors derived from distributed vibration measurements to update a structural finite element model through statistically weighted least-squares minimization producing location and quantification of the damage, estimation uncertainty, and an updated model. Advantages compared to other approaches include robust applicability to systems which are heavily damped, large, and noisy, with a relatively low number of distributed measurement points compared to the number of analytical degrees-of-freedom of an associated analytical structural model (e.g., modal finite element model). Motivation, research objectives, and a dissertation summary are discussed in Chapter 1 followed by a literature review in Chapter 2. Chapter 3 gives background theory and the damage identification algorithm derivation followed by a study of fundamental algorithm behavior on a two degree-of-freedom mass-spring system with generalized damping. Chapter 4 investigates the impact of noise then successfully proves the algorithm against competing methods using an analytical eight degree-of-freedom mass-spring system with non-proportional structural damping. Chapter 5 extends use of the algorithm to finite element models, including solutions for numerical issues, approaches for modeling damping approximately in reduced coordinates, and analytical validation using a composite sandwich plate model. Chapter 6 presents the final extension to experimental systems-including methods for initial baseline correlation and data reduction-and validates the algorithm on an experimental composite plate with impact damage. The final chapter deviates from development and validation of the primary algorithm to discuss development of an experimental scaled-wing test bed as part of a collaborative effort for developing structural health monitoring and prognosis technology. The dissertation concludes with an overview of technical conclusions and recommendations for future work.

An automatic damage detection algorithm based on the Short Time Impulse Response Function

NASA Astrophysics Data System (ADS)

Auletta, Gianluca; Carlo Ponzo, Felice; Ditommaso, Rocco; Iacovino, Chiara

2016-04-01

Structural Health Monitoring together with all the dynamic identification techniques and damage detection techniques are increasing in popularity in both scientific and civil community in last years. The basic idea arises from the observation that spectral properties, described in terms of the so-called modal parameters (eigenfrequencies, mode shapes, and modal damping), are functions of the physical properties of the structure (mass, energy dissipation mechanisms and stiffness). Damage detection techniques traditionally consist in visual inspection and/or non-destructive testing. A different approach consists in vibration based methods detecting changes of feature related to damage. Structural damage exhibits its main effects in terms of stiffness and damping variation. Damage detection approach based on dynamic monitoring of structural properties over time has received a considerable attention in recent scientific literature. We focused the attention on the structural damage localization and detection after an earthquake, from the evaluation of the mode curvature difference. The methodology is based on the acquisition of the structural dynamic response through a three-directional accelerometer installed on the top floor of the structure. It is able to assess the presence of any damage on the structure providing also information about the related position and severity of the damage. The procedure is based on a Band-Variable Filter, (Ditommaso et al., 2012), used to extract the dynamic characteristics of systems that evolve over time by acting simultaneously in both time and frequency domain. In this paper using a combined approach based on the Fourier Transform and on the seismic interferometric analysis, an useful tool for the automatic fundamental frequency evaluation of nonlinear structures has been proposed. Moreover, using this kind of approach it is possible to improve some of the existing methods for the automatic damage detection providing stable results also during the strong motion phase. This approach helps to overcome the limitation derived from the use of techniques based on simple Fourier Transform that provide good results when the response of the monitored system is stationary, but fails when the system exhibits a non-stationary behaviour. The main advantage derived from the use of the proposed approach for Structural Health Monitoring is based on the simplicity of the interpretation of the nonlinear variations of the fundamental frequency. The proposed methodology has been tested on numerical models of reinforced concrete structures designed for only gravity loads without and with the presence of infill panels. In order to verify the effectiveness of the proposed approach for the automatic evaluation of the fundamental frequency over time, the results of an experimental campaign of shaking table tests conducted at the seismic laboratory of University of Basilicata (SISLAB) have been used. Acknowledgements This study was partially funded by the Italian Civil Protection Department within the project DPC-RELUIS 2015 - RS4 ''Seismic observatory of structures and health monitoring''. References Ditommaso, R., Mucciarelli, M., Ponzo, F.C. (2012) Analysis of non-stationary structural systems by using a band-variable filter. Bulletin of Earthquake Engineering. DOI: 10.1007/s10518-012-9338-y.

An expert system to perform on-line controller restructuring for abrupt model changes

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.

1990-01-01

Work in progress on an expert system used to reconfigure and tune airframe/engine control systems on-line in real time in response to battle damage or structural failures is presented. The closed loop system is monitored constantly for changes in structure and performance, the detection of which prompts the expert system to choose and apply a particular control restructuring algorithm based on the type and severity of the damage. Each algorithm is designed to handle specific types of failures and each is applicable only in certain situations. The expert system uses information about the system model to identify the failure and to select the technique best suited to compensate for it. A depth-first search is used to find a solution. Once a new controller is designed and implemented it must be tuned to recover the original closed-loop handling qualities and responsiveness from the degraded system. Ideally, the pilot should not be able to tell the difference between the original and redesigned systems. The key is that the system must have inherent redundancy so that degraded or missing capabilities can be restored by creative use of alternate functionalities. With enough redundancy in the control system, minor battle damage affecting individual control surfaces or actuators, compressor efficiency, etc., can be compensated for such that the closed-loop performance in not noticeably altered. The work is applied to a Black Hawk/T700 system.

Identification of modal strains using sub-microstrain FBG data and a novel wavelength-shift detection algorithm

NASA Astrophysics Data System (ADS)

Anastasopoulos, Dimitrios; Moretti, Patrizia; Geernaert, Thomas; De Pauw, Ben; Nawrot, Urszula; De Roeck, Guido; Berghmans, Francis; Reynders, Edwin

2017-03-01

The presence of damage in a civil structure alters its stiffness and consequently its modal characteristics. The identification of these changes can provide engineers with useful information about the condition of a structure and constitutes the basic principle of the vibration-based structural health monitoring. While eigenfrequencies and mode shapes are the most commonly monitored modal characteristics, their sensitivity to structural damage may be low relative to their sensitivity to environmental influences. Modal strains or curvatures could offer an attractive alternative but current measurement techniques encounter difficulties in capturing the very small strain (sub-microstrain) levels occurring during ambient, or operational excitation, with sufficient accuracy. This paper investigates the ability to obtain sub-microstrain accuracy with standard fiber-optic Bragg gratings using a novel optical signal processing algorithm that identifies the wavelength shift with high accuracy and precision. The novel technique is validated in an extensive experimental modal analysis test on a steel I-beam which is instrumented with FBG sensors at its top and bottom flange. The raw wavelength FBG data are processed into strain values using both a novel correlation-based processing technique and a conventional peak tracking technique. Subsequently, the strain time series are used for identifying the beam's modal characteristics. Finally, the accuracy of both algorithms in identification of modal characteristics is extensively investigated.

Acoustic emission-based sensor analysis and damage classification for structural health monitoring of composite structures

NASA Astrophysics Data System (ADS)

Uprety, Bibhisha

Within the aerospace industry the need to detect and locate impact events, even when no visible damage is present, is important both from the maintenance and design perspectives. This research focused on the use of Acoustic Emission (AE) based sensing technologies to identify impact events and characterize damage modes in composite structures for structural health monitoring. Six commercially available piezoelectric AE sensors were evaluated for use with impact location estimation algorithms under development at the University of Utah. Both active and passive testing were performed to estimate the time of arrival and plate wave mode velocities for impact location estimation. Four sensors were recommended for further comparative investigations. Furthermore, instrumented low-velocity impact experiments were conducted on quasi-isotropic carbon/epoxy composite laminates to initiate specific types of damage: matrix cracking, delamination and fiber breakage. AE signal responses were collected during impacting and the test panels were ultrasonically C-scanned after impact to identify the internal damage corresponding to the AE signals. Matrix cracking and delamination damage produced using more compliant test panels and larger diameter impactor were characterized by lower frequency signals while fiber breakage produced higher frequency responses. The results obtained suggest that selected characteristics of sensor response signals can be used both to determine whether damage is produced during impacting and to characterize the types of damage produced in an impacted composite structure.

The segmentation of Thangka damaged regions based on the local distinction

NASA Astrophysics Data System (ADS)

Xuehui, Bi; Huaming, Liu; Xiuyou, Wang; Weilan, Wang; Yashuai, Yang

2017-01-01

Damaged regions must be segmented before digital repairing Thangka cultural relics. A new segmentation algorithm based on local distinction is proposed for segmenting damaged regions, taking into account some of the damaged area with a transition zone feature, as well as the difference between the damaged regions and their surrounding regions, combining local gray value, local complexity and local definition-complexity (LDC). Firstly, calculate the local complexity and normalized; secondly, calculate the local definition-complexity and normalized; thirdly, calculate the local distinction; finally, set the threshold to segment local distinction image, remove the over segmentation, and get the final segmentation result. The experimental results show that our algorithm is effective, and it can segment the damaged frescoes and natural image etc.

Feasibility of Twitter Based Earthquake Characterization From Analysis of 32 Million Tweets: There's Got to be a Pony in Here Somewhere!

NASA Astrophysics Data System (ADS)

Earle, P. S.; Guy, M. R.; Smoczyk, G. M.; Horvath, S. R.; Jessica, T. S.; Bausch, D. B.

2014-12-01

The U.S. Geological Survey (USGS) operates a real-time system that detects earthquakes using only data from Twitter—a service for sending and reading public text-based messages of up to 140 characters. The detector algorithm scans for significant increases in tweets containing the word "earthquake" in several languages and sends internal alerts with the detection time, representative tweet texts, and the location of the population center where most of the tweets originated. It has been running in real-time for over two years and finds, on average, two or three felt events per day, with a false detection rate of 9%. The main benefit of the tweet-based detections is speed, with most detections occurring between 20 and 120 seconds after the earthquake origin time. This is considerably faster than seismic detections in poorly instrumented regions of the world. The detections have reasonable coverage of populated areas globally. The number of Twitter-based detections is small compared to the number of earthquakes detected seismically, and only a rough location and qualitative assessment of shaking can be determined based on Tweet data alone. However, the Twitter-based detections are generally caused by widely felt events in populated urban areas that are of more immediate interest than those with no human impact. We will present a technical overview of the system and investigate the potential for rapid characterization of earthquake damage and effects using the 32 million "earthquake" tweets that the system has so far amassed. Initial results show potential for a correlation between characteristic responses and shaking level. For example, tweets containing the word "terremoto" were common following the MMI VII shaking produced by the April 1, 2014 M8.2 Iquique, Chile earthquake whereas a widely-tweeted deep-focus M5.2 north of Santiago, Chile on April 4, 2014 produced MMI VI shaking and almost exclusively "temblor" tweets. We are also investigating the use of other social media such as Instagram to obtain rapid images of earthquake-related damage. An Instagram search following the damaging M6.9 earthquake near the Mexico, Guatemala boarder on July 7, 2014 reveled half a dozen unconfirmed images of damage; the first posted 15 minutes after the event.

Design of a sensor network for structural health monitoring of a full-scale composite horizontal tail

NASA Astrophysics Data System (ADS)

Gao, Dongyue; Wang, Yishou; Wu, Zhanjun; Rahim, Gorgin; Bai, Shengbao

2014-05-01

The detection capability of a given structural health monitoring (SHM) system strongly depends on its sensor network placement. In order to minimize the number of sensors while maximizing the detection capability, optimal design of the PZT sensor network placement is necessary for structural health monitoring (SHM) of a full-scale composite horizontal tail. In this study, the sensor network optimization was simplified as a problem of determining the sensor array placement between stiffeners to achieve the desired the coverage rate. First, an analysis of the structural layout and load distribution of a composite horizontal tail was performed. The constraint conditions of the optimal design were presented. Then, the SHM algorithm of the composite horizontal tail under static load was proposed. Based on the given SHM algorithm, a sensor network was designed for the full-scale composite horizontal tail structure. Effective profiles of cross-stiffener paths (CRPs) and uncross-stiffener paths (URPs) were estimated by a Lamb wave propagation experiment in a multi-stiffener composite specimen. Based on the coverage rate and the redundancy requirements, a seven-sensor array-network was chosen as the optimal sensor network for each airfoil. Finally, a preliminary SHM experiment was performed on a typical composite aircraft structure component. The reliability of the SHM result for a composite horizontal tail structure under static load was validated. In the result, the red zone represented the delamination damage. The detection capability of the optimized sensor network was verified by SHM of a full-scale composite horizontal tail; all the diagnosis results were obtained in two minutes. The result showed that all the damage in the monitoring region was covered by the sensor network.

A Simplified Algorithm for Statistical Investigation of Damage Spreading

NASA Astrophysics Data System (ADS)

Gecow, Andrzej

2009-04-01

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method—function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

Analytical and numerical analysis of frictional damage in quasi brittle materials

NASA Astrophysics Data System (ADS)

Zhu, Q. Z.; Zhao, L. Y.; Shao, J. F.

2016-07-01

Frictional sliding and crack growth are two main dissipation processes in quasi brittle materials. The frictional sliding along closed cracks is the origin of macroscopic plastic deformation while the crack growth induces a material damage. The main difficulty of modeling is to consider the inherent coupling between these two processes. Various models and associated numerical algorithms have been proposed. But there are so far no analytical solutions even for simple loading paths for the validation of such algorithms. In this paper, we first present a micro-mechanical model taking into account the damage-friction coupling for a large class of quasi brittle materials. The model is formulated by combining a linear homogenization procedure with the Mori-Tanaka scheme and the irreversible thermodynamics framework. As an original contribution, a series of analytical solutions of stress-strain relations are developed for various loading paths. Based on the micro-mechanical model, two numerical integration algorithms are exploited. The first one involves a coupled friction/damage correction scheme, which is consistent with the coupling nature of the constitutive model. The second one contains a friction/damage decoupling scheme with two consecutive steps: the friction correction followed by the damage correction. With the analytical solutions as reference results, the two algorithms are assessed through a series of numerical tests. It is found that the decoupling correction scheme is efficient to guarantee a systematic numerical convergence.

Smart acoustic emission system for wireless monitoring of concrete structures

NASA Astrophysics Data System (ADS)

Yoon, Dong-Jin; Kim, Young-Gil; Kim, Chi-Yeop; Seo, Dae-Cheol

2008-03-01

Acoustic emission (AE) has emerged as a powerful nondestructive tool to detect preexisting defects or to characterize failure mechanisms. Recently, this technique or this kind of principle, that is an in-situ monitoring of inside damages of materials or structures, becomes increasingly popular for monitoring the integrity of large structures. Concrete is one of the most widely used materials for constructing civil structures. In the nondestructive evaluation point of view, a lot of AE signals are generated in concrete structures under loading whether the crack development is active or not. Also, it was required to find a symptom of damage propagation before catastrophic failure through a continuous monitoring. Therefore we have done a practical study in this work to fabricate compact wireless AE sensor and to develop diagnosis system. First, this study aims to identify the differences of AE event patterns caused by both real damage sources and the other normal sources. Secondly, it was focused to develop acoustic emission diagnosis system for assessing the deterioration of concrete structures such as a bridge, dame, building slab, tunnel etc. Thirdly, the wireless acoustic emission system was developed for the application of monitoring concrete structures. From the previous laboratory study such as AE event patterns analysis under various loading conditions, we confirmed that AE analysis provided a promising approach for estimating the condition of damage and distress in concrete structures. In this work, the algorithm for determining the damage status of concrete structures was developed and typical criteria for decision making was also suggested. For the future application of wireless monitoring, a low energy consumable, compact, and robust wireless acoustic emission sensor module was developed and applied to the concrete beam for performance test. Finally, based on the self-developed diagnosis algorithm and compact wireless AE sensor, new AE system for practical AE diagnosis was demonstrated for assessing the conditions of damage and distress in concrete structures.

High-throughput discovery of rare human nucleotide polymorphisms by Ecotilling

PubMed Central

Till, Bradley J.; Zerr, Troy; Bowers, Elisabeth; Greene, Elizabeth A.; Comai, Luca; Henikoff, Steven

2006-01-01

Human individuals differ from one another at only ∼0.1% of nucleotide positions, but these single nucleotide differences account for most heritable phenotypic variation. Large-scale efforts to discover and genotype human variation have been limited to common polymorphisms. However, these efforts overlook rare nucleotide changes that may contribute to phenotypic diversity and genetic disorders, including cancer. Thus, there is an increasing need for high-throughput methods to robustly detect rare nucleotide differences. Toward this end, we have adapted the mismatch discovery method known as Ecotilling for the discovery of human single nucleotide polymorphisms. To increase throughput and reduce costs, we developed a universal primer strategy and implemented algorithms for automated band detection. Ecotilling was validated by screening 90 human DNA samples for nucleotide changes in 5 gene targets and by comparing results to public resequencing data. To increase throughput for discovery of rare alleles, we pooled samples 8-fold and found Ecotilling to be efficient relative to resequencing, with a false negative rate of 5% and a false discovery rate of 4%. We identified 28 new rare alleles, including some that are predicted to damage protein function. The detection of rare damaging mutations has implications for models of human disease. PMID:16893952

A Coupled/Uncoupled Computational Scheme for Deformation and Fatigue Damage Analysis of Unidirectional Metal-Matrix Composites

NASA Technical Reports Server (NTRS)

Wilt, Thomas E.; Arnold, Steven M.; Saleeb, Atef F.

1997-01-01

A fatigue damage computational algorithm utilizing a multiaxial, isothermal, continuum-based fatigue damage model for unidirectional metal-matrix composites has been implemented into the commercial finite element code MARC using MARC user subroutines. Damage is introduced into the finite element solution through the concept of effective stress that fully couples the fatigue damage calculations with the finite element deformation solution. Two applications using the fatigue damage algorithm are presented. First, an axisymmetric stress analysis of a circumferentially reinforced ring, wherein both the matrix cladding and the composite core were assumed to behave elastic-perfectly plastic. Second, a micromechanics analysis of a fiber/matrix unit cell using both the finite element method and the generalized method of cells (GMC). Results are presented in the form of S-N curves and damage distribution plots.

Development of Self-Powered Wireless Structural Health Monitoring (SHM) for Wind Turbine Blades

NASA Astrophysics Data System (ADS)

Lim, Dong-Won

Wind turbine blade failure can lead to unexpected power interruptions. Monitoring wind turbine blades is important to ensure seamless electricity delivery from power generation to consumers. Structural health monitoring (SHM) enables early recognition of structural problems so that the safety and reliability of operation can be enhanced. This dissertation focuses on the development of a wireless SHM system for wind turbine blades. The sensor is comprised of a piezoelectric energy harvester (EH) and a telemetry unit. The sensor node is mounted on the blade surface. As the blade rotates, the blade flexes, and the energy harvester captures the strain energy on the blade surface. Once sufficient electricity is captured, a pulse is sent from the sensing node to a gateway. Then, a central monitoring algorithm processes a series of pulses received from all three blades. This wireless SHM, which uses commercially available components, can be retrofitted to existing turbines. The harvested energy for sensing can be estimated in terms of two factors: the available strain energy and conversion efficiency. The available strain energy was evaluated using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulator. The conversion efficiency was studied analytically and experimentally. An experimental set-up was designed to mimic the expected strain frequency and amplitude for rotor blades. From a series of experiments, the efficiency of a piezoelectric EH at a typical rotor speed (0.2 Hz) was approximately 0.5%. The power requirement for sending one measurement (280 muJ) can be achieved in 10 minutes. Designing a detection algorithm is challenging due to this low sampling rate. A new sensing approach-the timing of pulses from the transmitter-was introduced. This pulse timing, which is tied to the charging time, is indicative of the structural health. The SHM system exploits the inherent triple redundancy of the three blades. The timing data of the three blades are compared to discern an outlier, corresponding to a damaged blade. Two types of post-processing of pulses were investigated: (1) comparing the ratios of signal timings (i.e. transmission ratio); and (2) comparing the difference between signal timings (i.e. residuals). For either method, damage is indicated when the energy ratio or residual exceeds a threshold level. When residuals are used to detect damage, performance measures such as the false alarm rate and detection probability can also be imposed. The SHM algorithms were evaluated using strain energy data from a 2.5 MW wind turbine.

Modeling Soft Tissue Damage and Failure Using a Combined Particle/Continuum Approach.

PubMed

Rausch, M K; Karniadakis, G E; Humphrey, J D

2017-02-01

Biological soft tissues experience damage and failure as a result of injury, disease, or simply age; examples include torn ligaments and arterial dissections. Given the complexity of tissue geometry and material behavior, computational models are often essential for studying both damage and failure. Yet, because of the need to account for discontinuous phenomena such as crazing, tearing, and rupturing, continuum methods are limited. Therefore, we model soft tissue damage and failure using a particle/continuum approach. Specifically, we combine continuum damage theory with Smoothed Particle Hydrodynamics (SPH). Because SPH is a meshless particle method, and particle connectivity is determined solely through a neighbor list, discontinuities can be readily modeled by modifying this list. We show, for the first time, that an anisotropic hyperelastic constitutive model commonly employed for modeling soft tissue can be conveniently implemented within a SPH framework and that SPH results show excellent agreement with analytical solutions for uniaxial and biaxial extension as well as finite element solutions for clamped uniaxial extension in 2D and 3D. We further develop a simple algorithm that automatically detects damaged particles and disconnects the spatial domain along rupture lines in 2D and rupture surfaces in 3D. We demonstrate the utility of this approach by simulating damage and failure under clamped uniaxial extension and in a peeling experiment of virtual soft tissue samples. In conclusion, SPH in combination with continuum damage theory may provide an accurate and efficient framework for modeling damage and failure in soft tissues.

Modeling Soft Tissue Damage and Failure Using a Combined Particle/Continuum Approach

PubMed Central

Rausch, M. K.; Karniadakis, G. E.; Humphrey, J. D.

2016-01-01

Biological soft tissues experience damage and failure as a result of injury, disease, or simply age; examples include torn ligaments and arterial dissections. Given the complexity of tissue geometry and material behavior, computational models are often essential for studying both damage and failure. Yet, because of the need to account for discontinuous phenomena such as crazing, tearing, and rupturing, continuum methods are limited. Therefore, we model soft tissue damage and failure using a particle/continuum approach. Specifically, we combine continuum damage theory with Smoothed Particle Hydrodynamics (SPH). Because SPH is a meshless particle method, and particle connectivity is determined solely through a neighbor list, discontinuities can be readily modeled by modifying this list. We show, for the first time, that an anisotropic hyperelastic constitutive model commonly employed for modeling soft tissue can be conveniently implemented within a SPH framework and that SPH results show excellent agreement with analytical solutions for uniaxial and biaxial extension as well as finite element solutions for clamped uniaxial extension in 2D and 3D. We further develop a simple algorithm that automatically detects damaged particles and disconnects the spatial domain along rupture lines in 2D and rupture surfaces in 3D. We demonstrate the utility of this approach by simulating damage and failure under clamped uniaxial extension and in a peeling experiment of virtual soft tissue samples. In conclusion, SPH in combination with continuum damage theory may provide an accurate and efficient framework for modeling damage and failure in soft tissues. PMID:27538848

A coupled/uncoupled deformation and fatigue damage algorithm utilizing the finite element method

NASA Technical Reports Server (NTRS)

Wilt, Thomas E.; Arnold, Steven M.

1994-01-01

A fatigue damage computational algorithm utilizing a multiaxial, isothermal, continuum based fatigue damage model for unidirectional metal matrix composites has been implemented into the commercial finite element code MARC using MARC user subroutines. Damage is introduced into the finite element solution through the concept of effective stress which fully couples the fatigue damage calculations with the finite element deformation solution. An axisymmetric stress analysis was performed on a circumferentially reinforced ring, wherein both the matrix cladding and the composite core were assumed to behave elastic-perfectly plastic. The composite core behavior was represented using Hill's anisotropic continuum based plasticity model, and similarly, the matrix cladding was represented by an isotropic plasticity model. Results are presented in the form of S-N curves and damage distribution plots.

Development of a Near Real-Time Hail Damage Swath Identification Algorithm for Vegetation

NASA Technical Reports Server (NTRS)

Bell, Jordan R.; Molthan, Andrew L.; Schultz, Kori A.; McGrath, Kevin M.; Burks, Jason E.

2015-01-01

Every year in the Midwest and Great Plains, widespread greenness forms in conjunction with the latter part of the spring-summer growing season. This prevalent greenness forms as a result of the high concentration of agricultural areas having their crops reach their maturity before the fall harvest. This time of year also coincides with an enhanced hail frequency for the Great Plains (Cintineo et al. 2012). These severe thunderstorms can bring damaging winds and large hail that can result in damage to the surface vegetation. The spatial extent of the damage can relatively small concentrated area or be a vast swath of damage that is visible from space. These large areas of damage have been well documented over the years. In the late 1960s aerial photography was used to evaluate crop damage caused by hail. As satellite remote sensing technology has evolved, the identification of these hail damage streaks has increased. Satellites have made it possible to view these streaks in additional spectrums. Parker et al. (2005) documented two streaks using the Moderate Resolution Imaging Spectroradiometer (MODIS) that occurred in South Dakota. He noted the potential impact that these streaks had on the surface temperature and associated surface fluxes that are impacted by a change in temperature. Gallo et al. (2012) examined at the correlation between radar signatures and ground observations from storms that produced a hail damage swath in Central Iowa also using MODIS. Finally, Molthan et al. (2013) identified hail damage streaks through MODIS, Landsat-7, and SPOT observations of different resolutions for the development of a potential near-real time applications. The manual analysis of hail damage streaks in satellite imagery is both tedious and time consuming, and may be inconsistent from event to event. This study focuses on development of an objective and automatic algorithm to detect these areas of damage in a more efficient and timely manner. This study utilizes the MODIS sensor aboard the NASA Aqua satellite. Aqua was chosen due to an afternoon orbit over the United States when land surface temperatures are relatively warm and improve the contrast between damaged and undamaged areas. This orbit is also similar to the orbit of the Suomi-National Polar-orbiting Partnership (NPP) satellite. The Suomi NPP satellite hosts the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, which is the next generation of a MODIS-like sensor in polar orbit.

A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology

PubMed Central

Liu, Ruiling; Zhong, Dexing; Lyu, Hongqiang; Han, Jiuqiang

2016-01-01

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm × 0.4 mm and the precision of dimension measurement is about 40–50 μm. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production. PMID:27571078

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Icing detection from geostationary satellite data using machine learning approaches

NASA Astrophysics Data System (ADS)

Lee, J.; Ha, S.; Sim, S.; Im, J.

2015-12-01

Icing can cause a significant structural damage to aircraft during flight, resulting in various aviation accidents. Icing studies have been typically performed using two approaches: one is a numerical model-based approach and the other is a remote sensing-based approach. The model based approach diagnoses aircraft icing using numerical atmospheric parameters such as temperature, relative humidity, and vertical thermodynamic structure. This approach tends to over-estimate icing according to the literature. The remote sensing-based approach typically uses meteorological satellite/ground sensor data such as Geostationary Operational Environmental Satellite (GOES) and Dual-Polarization radar data. This approach detects icing areas by applying thresholds to parameters such as liquid water path and cloud optical thickness derived from remote sensing data. In this study, we propose an aircraft icing detection approach which optimizes thresholds for L1B bands and/or Cloud Optical Thickness (COT) from Communication, Ocean and Meteorological Satellite-Meteorological Imager (COMS MI) and newly launched Himawari-8 Advanced Himawari Imager (AHI) over East Asia. The proposed approach uses machine learning algorithms including decision trees (DT) and random forest (RF) for optimizing thresholds of L1B data and/or COT. Pilot Reports (PIREPs) from South Korea and Japan were used as icing reference data. Results show that RF produced a lower false alarm rate (1.5%) and a higher overall accuracy (98.8%) than DT (8.5% and 75.3%), respectively. The RF-based approach was also compared with the existing COMS MI and GOES-R icing mask algorithms. The agreements of the proposed approach with the existing two algorithms were 89.2% and 45.5%, respectively. The lower agreement with the GOES-R algorithm was possibly due to the high uncertainty of the cloud phase product from COMS MI.

Chaotic Image Encryption of Regions of Interest

NASA Astrophysics Data System (ADS)

Xiao, Di; Fu, Qingqing; Xiang, Tao; Zhang, Yushu

Since different regions of an image have different importance, therefore only the important information of the image regions, which the users are really interested in, needs to be encrypted and protected emphatically in some special multimedia applications. However, the regions of interest (ROI) are always some irregular parts, such as the face and the eyes. Assuming the bulk data in transmission without being damaged, we propose a chaotic image encryption algorithm for ROI. ROI with irregular shapes are chosen and detected arbitrarily. Then the chaos-based image encryption algorithm with scrambling, S-box and diffusion parts is used to encrypt the ROI. Further, the whole image is compressed with Huffman coding. At last, a message authentication code (MAC) of the compressed image is generated based on chaotic maps. The simulation results show that the encryption algorithm has a good security level and can resist various attacks. Moreover, the compression method improves the storage and transmission efficiency to some extent, and the MAC ensures the integrity of the transmission data.

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

Ushizima, Daniela; Perciano, Talita; Krishnan, Harinarayan

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks andmore » fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.« less

Manifold learning-based subspace distance for machinery damage assessment

NASA Astrophysics Data System (ADS)

Sun, Chuang; Zhang, Zhousuo; He, Zhengjia; Shen, Zhongjie; Chen, Binqiang

2016-03-01

Damage assessment is very meaningful to keep safety and reliability of machinery components, and vibration analysis is an effective way to carry out the damage assessment. In this paper, a damage index is designed by performing manifold distance analysis on vibration signal. To calculate the index, vibration signal is collected firstly, and feature extraction is carried out to obtain statistical features that can capture signal characteristics comprehensively. Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace. The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment. Finally, Grassmann distance between manifold subspaces is defined as a damage index. The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold. The defined damage index is applied to damage assessment of a rotor and the bearing, and the result validates its effectiveness for damage assessment of machinery component.

Nondestructive inspection of graphite-epoxy laminates for heat damage using DRIFT and LPF spectroscopies

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

Powell, G.L.; Smyrl, N.R.; Janke, C.J.

The effect of heat damage on polymer matrix composites (PMC) used in aircraft structures presents a unique problem for nondestructive testing (ND) in that damage may result as a combination of thermally cycling the PMC above the glass transition temperature of the polymer and oxidative degradation of the polymer or the polymer-fiber interface. The usual techniques for the detection of voids and flaws by radiographic, ultrasonic, and thermal imaging techniques play an important role in this ND problem. However, heat damage may result in loss of strength in these materials without producing physical flaws (cracks and delaminations) big enough tomore » be detected. Diffuse reflectance Fourier transform infrared (DRIFT) and laser pumped fluorescence (LPF) measurements previously obtained on IM6/3501-6 laminate panels were re-evaluated to improve these techniques for the nondestructive inspection of aircraft. A more robust algorithm for relating flexural strength to changes in DRIFT spectra related to oxidation is presented and used to interpret previously reported evacuable cell DRIFT measurements. Recent advances in DRIFT technology are described which include an evacuable cell with a hemispherical window for oxidation kinetics studies, and the development of a portable DRIFT spectrometer that was used to make measurements on an aircraft. The use of a 633-nm helium-neon laser for LPF is reported as a means for rapidly relating both fluorescence intensity and spectral distribution to flexural strength.« less

Development and Application of an Objective Tracking Algorithm for Tropical Cyclones over the North-West Pacific purely based on Wind Speeds

NASA Astrophysics Data System (ADS)

Befort, Daniel J.; Kruschke, Tim; Leckebusch, Gregor C.

2017-04-01

Tropical Cyclones over East Asia have huge socio-economic impacts due to their strong wind fields and large rainfall amounts. Especially, the most severe events are associated with huge economic losses, e.g. Typhoon Herb in 1996 is related to overall losses exceeding 5 billion US (Munich Re, 2016). In this study, an objective tracking algorithm is applied to JRA55 reanalysis data from 1979 to 2014 over the Western North Pacific. For this purpose, a purely wind based algorithm, formerly used to identify extra-tropical wind storms, has been further developed. The algorithm is based on the exceedance of the local 98th percentile to define strong wind fields in gridded climate data. To be detected as a tropical cyclone candidate, the following criteria must be fulfilled: 1) the wind storm must exist for at least eight 6-hourly time steps and 2) the wind field must exceed a minimum size of 130.000km2 for each time step. The usage of wind information is motivated to focus on damage related events, however, a pre-selection based on the affected region is necessary to remove events of extra-tropical nature. Using IBTrACS Best Tracks for validation, it is found that about 62% of all detected tropical cyclone events in JRA55 reanalysis can be matched to an observed best track. As expected the relative amount of matched tracks increases with the wind intensity of the event, with a hit rate of about 98% for Violent Typhoons, above 90% for Very Strong Typhoons and about 75% for Typhoons. Overall these results are encouraging as the parameters used to detect tropical cyclones in JRA55, e.g. minimum area, are also suitable to detect TCs in most CMIP5 simulations and will thus allow estimates of potential future changes.

Classification of cucumber green mottle mosaic virus (CGMMV) infected watermelon seeds using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Lee, Hoonsoo; Lim, Hyoun-Sub; Cho, Byoung-Kwan

2016-05-01

The Cucumber Green Mottle Mosaic Virus (CGMMV) is a globally distributed plant virus. CGMMV-infected plants exhibit severe mosaic symptoms, discoloration, and deformation. Therefore, rapid and early detection of CGMMV infected seeds is very important for preventing disease damage and yield losses. Raman spectroscopy was investigated in this study as a potential tool for rapid, accurate, and nondestructive detection of infected seeds. Raman spectra of healthy and infected seeds were acquired in the 400 cm-1 to 1800 cm-1 wavenumber range and an algorithm based on partial least-squares discriminant analysis was developed to classify infected and healthy seeds. The classification model's accuracies for calibration and prediction data sets were 100% and 86%, respectively. Results showed that the Raman spectroscopic technique has good potential for nondestructive detection of virus-infected seeds.

Spectral algorithm for non-destructive damage localisation: Application to an ancient masonry arch model

NASA Astrophysics Data System (ADS)

Masciotta, Maria-Giovanna; Ramos, Luís F.; Lourenço, Paulo B.; Vasta, Marcello

2017-02-01

Structural monitoring and vibration-based damage identification methods are fundamental tools for condition assessment and early-stage damage identification, especially when dealing with the conservation of historical constructions and the maintenance of strategic civil structures. However, although the substantial advances in the field, several issues must still be addressed to broaden the application range of such tools and to assert their reliability. This study deals with the experimental validation of a novel method for non-destructive damage identification purposes. This method is based on the use of spectral output signals and has been recently validated by the authors through a numerical simulation. After a brief insight into the basic principles of the proposed approach, the spectral-based technique is applied to identify the experimental damage induced on a masonry arch through statically increasing loading. Once the direct and cross spectral density functions of the nodal response processes are estimated, the system's output power spectrum matrix is built and decomposed in eigenvalues and eigenvectors. The present study points out how the extracted spectral eigenparameters contribute to the damage analysis allowing to detect the occurrence of damage and to locate the target points where the cracks appear during the experimental tests. The sensitivity of the spectral formulation to the level of noise in the modal data is investigated and discussed. As a final evaluation criterion, the results from the spectrum-driven method are compared with the ones obtained from existing non-model based damage identification methods.

Combined distributed and concentrated transducer network for failure indication

NASA Astrophysics Data System (ADS)

Ostachowicz, Wieslaw; Wandowski, Tomasz; Malinowski, Pawel

2010-03-01

In this paper algorithm for discontinuities localisation in thin panels made of aluminium alloy is presented. Mentioned algorithm uses Lamb wave propagation methods for discontinuities localisation. Elastic waves were generated and received using piezoelectric transducers. They were arranged in concentrated arrays distributed on the specimen surface. In this way almost whole specimen could be monitored using this combined distributed-concentrated transducer network. Excited elastic waves propagate and reflect from panel boundaries and discontinuities existing in the panel. Wave reflection were registered through the piezoelectric transducers and used in signal processing algorithm. Proposed processing algorithm consists of two parts: signal filtering and extraction of obstacles location. The first part was used in order to enhance signals by removing noise from them. Second part allowed to extract features connected with wave reflections from discontinuities. Extracted features damage influence maps were a basis to create damage influence maps. Damage maps indicated intensity of elastic wave reflections which corresponds to obstacles coordinates. Described signal processing algorithms were implemented in the MATLAB environment. It should be underlined that in this work results based only on experimental signals were presented.

Pansharpening Techniques to Detect Mass Monument Damaging in Iraq

NASA Astrophysics Data System (ADS)

Baiocchi, V.; Bianchi, A.; Maddaluno, C.; Vidale, M.

2017-05-01

The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons of safety. For the same reasons, it's not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology, for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in unsafe area, is reported. The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of damages using VHR images.

Comparison of human and algorithmic target detection in passive infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Hutchinson, Meredith

2003-09-01

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

Acoustic Emission Source Location Using a Distributed Feedback Fiber Laser Rosette

PubMed Central

Huang, Wenzhu; Zhang, Wentao; Li, Fang

2013-01-01

This paper proposes an approach for acoustic emission (AE) source localization in a large marble stone using distributed feedback (DFB) fiber lasers. The aim of this study is to detect damage in structures such as those found in civil applications. The directional sensitivity of DFB fiber laser is investigated by calculating location coefficient using a method of digital signal analysis. In this, autocorrelation is used to extract the location coefficient from the periodic AE signal and wavelet packet energy is calculated to get the location coefficient of a burst AE source. Normalization is processed to eliminate the influence of distance and intensity of AE source. Then a new location algorithm based on the location coefficient is presented and tested to determine the location of AE source using a Delta (Δ) DFB fiber laser rosette configuration. The advantage of the proposed algorithm over the traditional methods based on fiber Bragg Grating (FBG) include the capability of: having higher strain resolution for AE detection and taking into account two different types of AE source for location. PMID:24141266

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

PubMed Central

Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra

2015-01-01

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. PMID:25633599

Planetary Transmission Diagnostics

NASA Technical Reports Server (NTRS)

Lewicki, David G. (Technical Monitor); Samuel, Paul D.; Conroy, Joseph K.; Pines, Darryll J.

2004-01-01

This report presents a methodology for detecting and diagnosing gear faults in the planetary stage of a helicopter transmission. This diagnostic technique is based on the constrained adaptive lifting algorithm. The lifting scheme, developed by Wim Sweldens of Bell Labs, is a time domain, prediction-error realization of the wavelet transform that allows for greater flexibility in the construction of wavelet bases. Classic lifting analyzes a given signal using wavelets derived from a single fundamental basis function. A number of researchers have proposed techniques for adding adaptivity to the lifting scheme, allowing the transform to choose from a set of fundamental bases the basis that best fits the signal. This characteristic is desirable for gear diagnostics as it allows the technique to tailor itself to a specific transmission by selecting a set of wavelets that best represent vibration signals obtained while the gearbox is operating under healthy-state conditions. However, constraints on certain basis characteristics are necessary to enhance the detection of local wave-form changes caused by certain types of gear damage. The proposed methodology analyzes individual tooth-mesh waveforms from a healthy-state gearbox vibration signal that was generated using the vibration separation (synchronous signal-averaging) algorithm. Each waveform is separated into analysis domains using zeros of its slope and curvature. The bases selected in each analysis domain are chosen to minimize the prediction error, and constrained to have the same-sign local slope and curvature as the original signal. The resulting set of bases is used to analyze future-state vibration signals and the lifting prediction error is inspected. The constraints allow the transform to effectively adapt to global amplitude changes, yielding small prediction errors. However, local wave-form changes associated with certain types of gear damage are poorly adapted, causing a significant change in the prediction error. The constrained adaptive lifting diagnostic algorithm is validated using data collected from the University of Maryland Transmission Test Rig and the results are discussed.

Study on on-machine defects measuring system on high power laser optical elements

NASA Astrophysics Data System (ADS)

Luo, Chi; Shi, Feng; Lin, Zhifan; Zhang, Tong; Wang, Guilin

2017-10-01

The influence of surface defects on high power laser optical elements will cause some harm to the performances of imaging system, including the energy consumption and the damage of film layer. To further increase surface defects on high power laser optical element, on-machine defects measuring system was investigated. Firstly, the selection and design are completed by the working condition analysis of the on-machine defects detection system. By designing on processing algorithms to realize the classification recognition and evaluation of surface defects. The calibration experiment of the scratch was done by using the self-made standard alignment plate. Finally, the detection and evaluation of surface defects of large diameter semi-cylindrical silicon mirror are realized. The calibration results show that the size deviation is less than 4% that meet the precision requirement of the detection of the defects. Through the detection of images the on-machine defects detection system can realize the accurate identification of surface defects.

Microwave hematoma detector

DOEpatents

Haddad, Waleed S.; Trebes, James E.; Matthews, Dennis L.

2001-01-01

The Microwave Hematoma Detector is a non-invasive device designed to detect and localize blood pooling and clots near the outer surface of the body. While being geared towards finding sub-dural and epi-dural hematomas, the device can be used to detect blood pooling anywhere near the surface of the body. Modified versions of the device can also detect pneumothorax, organ hemorrhage, atherosclerotic plaque in the carotid arteries, evaluate perfusion (blood flow) at or near the body surface, body tissue damage at or near the surface (especially for burn assessment) and be used in a number of NDE applications. The device is based on low power pulsed microwave technology combined with a specialized antenna, signal processing/recognition algorithms and a disposable cap worn by the patient which will facilitate accurate mapping of the brain and proper function of the instrument. The invention may be used for rapid, non-invasive detection of sub-dural or epi-dural hematoma in human or animal patients, detection of hemorrhage within approximately 5 cm of the outer surface anywhere on a patient's body.

Design and analysis of compound flexible skin based on deformable honeycomb

NASA Astrophysics Data System (ADS)

Zou, Tingting; Zhou, Li

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

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.

Characterizing Hypervelocity Impact (HVI)-Induced Pitting Damage Using Active Guided Ultrasonic Waves: From Linear to Nonlinear

PubMed Central

Liu, Menglong; Wang, Kai; Lissenden, Cliff J.; Wang, Qiang; Zhang, Qingming; Long, Renrong; Su, Zhongqing; Cui, Fangsen

2017-01-01

Hypervelocity impact (HVI), ubiquitous in low Earth orbit with an impacting velocity in excess of 1 km/s, poses an immense threat to the safety of orbiting spacecraft. Upon penetration of the outer shielding layer of a typical two-layer shielding system, the shattered projectile, together with the jetted materials of the outer shielding material, subsequently impinge the inner shielding layer, to which pitting damage is introduced. The pitting damage includes numerous craters and cracks disorderedly scattered over a wide region. Targeting the quantitative evaluation of this sort of damage (multitudinous damage within a singular inspection region), a characterization strategy, associating linear with nonlinear features of guided ultrasonic waves, is developed. Linear-wise, changes in the signal features in the time domain (e.g., time-of-flight and energy dissipation) are extracted, for detecting gross damage whose characteristic dimensions are comparable to the wavelength of the probing wave; nonlinear-wise, changes in the signal features in the frequency domain (e.g., second harmonic generation), which are proven to be more sensitive than their linear counterparts to small-scale damage, are explored to characterize HVI-induced pitting damage scattered in the inner layer. A numerical simulation, supplemented with experimental validation, quantitatively reveals the accumulation of nonlinearity of the guided waves when the waves traverse the pitting damage, based on which linear and nonlinear damage indices are proposed. A path-based rapid imaging algorithm, in conjunction with the use of the developed linear and nonlinear indices, is developed, whereby the HVI-induced pitting damage is characterized in images in terms of the probability of occurrence. PMID:28772908

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Science Goals and Preliminary Analysis

NASA Astrophysics Data System (ADS)

Shields, C. A.; Rutz, J. J.; Wehner, M. F.; Ralph, F. M.; Leung, L. R.

2017-12-01

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is a community effort whose purpose is to quantify uncertainties in atmospheric river (AR) research solely due to different identification and tracking techniques. Atmospheric rivers transport significant amounts of moisture in long, narrow filamentary bands, typically travelling from the subtropics to the mid-latitudes. They are an important source of regional precipitation impacting local hydroclimate, and in extreme cases, cause severe flooding and infrastructure damage in local communities. Our understanding of ARs, from forecast skill to future climate projections, all hinge on how we define ARs. By comparing a diverse set of detection algorithms, the uncertainty in our definition of ARs, (including statistics and climatology), and the implications of those uncertainties, can be analyzed and quantified. ARTMIP is divided into two broad phases that aim to answer science questions impacted by choice of detection algorithm. How robust are AR metrics such as climatology, storm duration, and relationship to extreme precipitation? How are the AR metrics in future climate projections impacted by choice of algorithm? Some algorithms rely on threshold values for water vapor. In a warmer world, the background state, by definition, is moister due to the Clausius-Clapeyron relationship, and could potentially skew results. Can uncertainty bounds be accurately placed on each metric? Tier 1 participants will apply their algorithms to a high resolution common dataset (MERRA2) and provide the greater group AR metrics (frequency, location, duration, etc). Tier 2 research will encompass sensitivity studies regarding resolution, reanalysis choice, and future climate change scenarios. ARTMIP is currently in the Tier 1 Phase and will begin Tier 2 in 2018. Preliminary metrics and analysis from Tier 1 will be presented.

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.

Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

NASA Astrophysics Data System (ADS)

Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

2013-12-01

Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can be paired with infrastructure overlays, allowing emergency response teams to identify sites that may have been exposed to damage. The faults will also be incorporated into a database for future integration into fault models and earthquake simulations, improving future earthquake hazard assessment. As new faults are mapped, they will further understanding of the complex fault systems and earthquake hazards within the seismically dynamic state of California.

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

NASA Astrophysics Data System (ADS)

Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene

2016-07-01

Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Hyperspectral depth-profiling with deep Raman spectroscopy for detecting chemicals in building materials.

PubMed

Cho, Youngho; Song, Si Won; Sung, Jiha; Jeong, Young-Su; Park, Chan Ryang; Kim, Hyung Min

2017-09-25

Toxic chemicals inside building materials have long-term harmful effects on human bodies. To prevent secondary damage caused by the evaporation of latent chemicals, it is necessary to detect the chemicals inside building materials at an early stage. Deep Raman spectroscopy is a potential candidate for on-site detection because it can provide molecular information about subsurface components. However, it is very difficult to spectrally distinguish the Raman signal of the internal chemicals from the background signal of the surrounding materials and to acquire the geometric information of chemicals. In this study, we developed hyperspectral wide-depth spatially offset Raman spectroscopy coupled with a data processing algorithm to identify toxic chemicals, such as chemical warfare agent (CWA) simulants in building materials. Furthermore, the spatial distribution of the chemicals and the thickness of the building material were also measured from one-dimensional (1D) spectral variation.

Application of Remote Sensing in Building Damages Assessment after Moderate and Strong Earthquake

NASA Astrophysics Data System (ADS)

Tian, Y.; Zhang, J.; Dou, A.

2003-04-01

- Earthquake is a main natural disaster in modern society. However, we still cannot predict the time and place of its occurrence accurately. Then it is of much importance to survey the damages information when an earthquake occurs, which can help us to mitigate losses and implement fast damage evaluation. In this paper, we use remote sensing techniques for our purposes. Remotely sensed satellite images often view a large scale of land at a time. There are several kinds of satellite images, which of different spatial and spectral resolutions. Landsat-4/5 TM sensor can view ground at 30m resolution, while Landsat-7 ETM Plus has a resolution of 15m in panchromatic waveband. SPOT satellite can provide images with higher resolutions. Those images obtained pre- and post-earthquake can help us greatly in identifying damages of moderate and large-size buildings. In this paper, we bring forward a method to implement quick damages assessment by analyzing both pre- and post-earthquake satellite images. First, those images are geographically registered together with low RMS (Root Mean Square) error. Then, we clip out residential areas by overlaying images with existing vector layers through Geographic Information System (GIS) software. We present a new change detection algorithm to quantitatively identify damages degree. An empirical or semi-empirical model is then established by analyzing the real damage degree and changes of pixel values of the same ground objects. Experimental result shows that there is a good linear relationship between changes of pixel values and ground damages, which proves the potentials of remote sensing in post-quake fast damage assessment. Keywords: Damages Assessment, Earthquake Hazard, Remote Sensing

Local-based damage detection of cyclically loaded bridge piers using wireless sensing units

NASA Astrophysics Data System (ADS)

Hou, Tsung-Chin; Lynch, Jerome P.; Parra-Montesinos, Gustavo

2005-05-01

Concrete bridge piers are a common structural element employed in the design of bridges and elevated roadways. In order to ensure adequate behavior under earthquake-induced displacements, extensive reinforcement detailing in the form of closely spaced ties or spirals is necessary, leading to congestion problems and difficulties during concrete casting. Further, costly repairs are often necessary in bridge piers after a major earthquake which in some cases involve the total or partial shutdown of the bridge. In order to increase the damage tolerance while relaxing the transverse reinforcement requirements of bridge piers, the use of high-performance fiber reinforced cementitious composites (HPFRCC) in earthquake-resistant bridge piers is explored. HPFRCCs are a relatively new class of cementitious material for civil structures with tensile strain-hardening behavior and high damage tolerance. To monitor the behavior of this new class of material in the field, low-cost wireless monitoring technologies will be adopted to provide HPFRCC structural elements the capability to accurately monitor their performance and health. In particular, the computational core of a wireless sensing unit can be harnessed to screen HPFRCC components for damage in real-time. A seismic damage index initially proposed for flexure dominated reinforced concrete elements is modified to serve as an algorithmic tool for the rapid assessment of damage (due to flexure and shear) in HPFRCC bridge piers subjected to large shear reversals. Traditional and non-traditional sensor strategies of an HPFRCC bridge pier are proposed to optimize the correlation between the proposed damage index model and the damage observed in a circular pier test specimen. Damage index models are shown to be a sufficiently accurate rough measure of the degree of local-area damage that can then be wirelessly communicated to bridge officials.

Finding Snowmageddon: Detecting and quantifying northeastern U.S. snowstorms in a multi-decadal global climate ensemble

NASA Astrophysics Data System (ADS)

Zarzycki, C. M.

2017-12-01

The northeastern coast of the United States is particularly vulnerable to impacts from extratropical cyclones during winter months, which produce heavy precipitation, high winds, and coastal flooding. These impacts are amplified by the proximity of major population centers to common storm tracks and include risks to health and welfare, massive transportation disruption, lost spending productivity, power outages, and structural damage. Historically, understanding regional snowfall in climate models has generally centered around seasonal mean climatologies even though major impacts typically occur at the scales of hours to days. To quantify discrete snowstorms at the event level, we describe a new objective detection algorithm for gridded data based on the Regional Snowfall Index (RSI) produced by NOAA's National Centers for Environmental Information. The algorithm uses 6-hourly precipitation to collocate storm-integrated snowfall with population density to produce a distribution of snowstorms with societally relevant impacts. The algorithm is tested on the Community Earth System Model (CESM) Large Ensemble Project (LENS) data. Present day distributions of snowfall events is well-replicated within the ensemble. We discuss classification sensitivities to assumptions made in determining precipitation phase and snow water equivalent. We also explore projected reductions in mid-century and end-of-century snowstorms due to changes in snowfall rates and precipitation phase, as well as highlight potential improvements in storm representation from refined horizontal resolution in model simulations.

Management of hypertension at the community level in sub-Saharan Africa (SSA): towards a rational use of available resources.

PubMed

Twagirumukiza, M; Van Bortel, L M

2011-01-01

Hypertension is emerging in many developing nations as a leading cause of cardiovascular mortality, morbidity and disability in adults. In sub-Saharan African (SSA) countries it has specificities such as occurring in young and active adults, resulting in severe complications dominated by heart failure and taking place in limited-resource settings in which an individual's access to treatment (affordability) is very limited. Within this context of restrained economic conditions, the greatest gains for SSA in controlling the hypertension epidemic lie in its prevention. Attempts should be made to detect hypertensive patients early before irreversible organ damage becomes apparent, and to provide them with the best possible and affordable non-pharmacological and pharmacological treatment. Therefore, efforts should be made for detection and early management at the community level. In this context, a standardized algorithm of management can help in the rational use of available resources. Although many international and regional guidelines have been published, they cannot apply to SSA settings because the economy of the countries and affordability of the patients do not allow access to advocated treatment. In addition, none of them suggest a clear algorithm of management for limited-resource settings at the community level. In line with available data and analysing existing guidelines, a practical algorithm for management of hypertension at the community level, including treatment affordability, has been suggested in the present work.

Proposed health state awareness of helicopter blades using an artificial neural network strategy

NASA Astrophysics Data System (ADS)

Lee, Andrew; Habtour, Ed; Gadsden, S. A.

2016-05-01

Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

Rapid and Reliable Damage Proxy Map from InSAR Coherence

NASA Technical Reports Server (NTRS)

Yun, Sang-Ho; Fielding, Eric; Simons, Mark; Agram, Piyush; Rosen, Paul; Owen, Susan; Webb, Frank

2012-01-01

Future radar satellites will visit SoCal within a day after a disaster event. Data acquisition latency in 2015-2020 is 8 to approx. 15 hours. Data transfer latency that often involves human/agency intervention far exceeds the data acquisition latency. Need interagency cooperation to establish automatic pipeline for data transfer. The algorithm is tested with ALOS PALSAR data of Pasadena, California. Quantitative quality assessment is being pursued: Meeting with Pasadena City Hall computer engineers for a complete list of demolition/construction project 1. Estimate the probability of detection and probability of false alarm 2. Estimate the optimal threshold value.

An experimental validation of a statistical-based damage detection approach.

DOT National Transportation Integrated Search

2011-01-01

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to : autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and : predicted beha...

Embedded fiber optic sensors for monitoring processing, quality and structural health of resin transfer molded components

NASA Astrophysics Data System (ADS)

Keulen, C.; Rocha, B.; Yildiz, M.; Suleman, A.

2011-07-01

Due to their small size and flexibility fiber optics can be embedded into composite materials with little negative effect on strength and reliability of the host material. Fiber optic sensors such as Fiber Bragg Gratings (FBG) or Etched Fiber Sensors (EFS) can be used to detect a number of relevant parameters such as flow, degree of cure, quality and structural health throughout the life of a composite component. With a detection algorithm these embedded sensors can be used to detect damage in real time while the component remains in service. This paper presents the research being conducted on the use of fiber optic sensors for process and Structural Health Monitoring (SHM) of Resin Transfer Molded (RTM) composite structures. Fiber optic sensors are used at all life stages of an RTM composite panel. A laboratory scale RTM apparatus was developed with the capability of visually monitoring the resin filling process. A technique for embedding fiber optic sensors with this apparatus has also been developed. Both FBGs and EFSs have been embedded in composite panels using the apparatus. EFSs to monitor the fabrication process, specifically resin flow have been embedded and shown to be capable of detecting the presence of resin at various locations as it is injected into the mold. Simultaneously these sensors were multiplexed on the same fiber with FBGs, which have the ability to measure strain. Since multiple sensors can be multiplexed on a single fiber the number of ingress/egress locations required per sensor can be significantly reduced. To characterize the FBGs for strain detection tensile test specimens with embedded FBG sensors have been produced. These specimens have been instrumented with a resistive strain gauge for benchmarking. Both specimens and embedded sensors were characterized through tensile testing. Furthermore FBGs have been embedded into composite panels in a manner that is conducive to detection of Lamb waves generated with a centrally located PZT. To sense Lamb waves a high speed, high precision sensing technique is required to acquire data from embedded FBGs due to the high velocities and small strain amplitudes of these guided waves. A technique based on a filter consisting of a tunable FBG was developed. Since this filter is not dependant on moving parts, tests executed with this filter concluded with the detection of Lamb waves, removing the influence of temperature and operational strains. A damage detection algorithm was developed to detect and localize cracks and delaminations.

Medium change based image estimation from application of inverse algorithms to coda wave measurements

NASA Astrophysics Data System (ADS)

Zhan, Hanyu; Jiang, Hanwan; Jiang, Ruinian

2018-03-01

Perturbations worked as extra scatters will cause coda waveform distortions; thus, coda wave with long propagation time and traveling path are sensitive to micro-defects in strongly heterogeneous media such as concretes. In this paper, we conduct varied external loads on a life-size concrete slab which contains multiple existing micro-cracks, and a couple of sources and receivers are installed to collect coda wave signals. The waveform decorrelation coefficients (DC) at different loads are calculated for all available source-receiver pair measurements. Then inversions of the DC results are applied to estimate the associated distribution density values in three-dimensional regions through kernel sensitivity model and least-square algorithms, which leads to the images indicating the micro-cracks positions. This work provides an efficiently non-destructive approach to detect internal defects and damages of large-size concrete structures.

SU-G-JeP4-03: Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis

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

Kotoku, J; Kumagai, S; Nakabayashi, S

Purpose: The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy. Here, we propose an automatic anomaly detection method using singular value decomposition analysis. Methods: The anomaly detection procedure consists of four parts:1) measurement of normal respiratory motion data of a patient2) calculation of a trajectory matrix representing normal time-series feature3) real-time monitoring and calculation of a trajectory matrix of real-time data.4) calculation of an anomaly score from the similarity of the two feature matrices. Patient motion was observed by a marker-less tracking system using a depthmore » camera. Results: Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application. Conclusion: Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful. This work was supported by JSPS KAKENHI Grant Number 15K08703.« less

Extended Kalman filtering for the detection of damage in linear mechanical structures

NASA Astrophysics Data System (ADS)

Liu, X.; Escamilla-Ambrosio, P. J.; Lieven, N. A. J.

2009-09-01

This paper addresses the problem of assessing the location and extent of damage in a vibrating structure by means of vibration measurements. Frequency domain identification methods (e.g. finite element model updating) have been widely used in this area while time domain methods such as the extended Kalman filter (EKF) method, are more sparsely represented. The difficulty of applying EKF in mechanical system damage identification and localisation lies in: the high computational cost, the dependence of estimation results on the initial estimation error covariance matrix P(0), the initial value of parameters to be estimated, and on the statistics of measurement noise R and process noise Q. To resolve these problems in the EKF, a multiple model adaptive estimator consisting of a bank of EKF in modal domain was designed, each filter in the bank is based on different P(0). The algorithm was iterated by using the weighted global iteration method. A fuzzy logic model was incorporated in each filter to estimate the variance of the measurement noise R. The application of the method is illustrated by simulated and real examples.

An intelligent stand-alone ultrasonic device for monitoring local structural damage: implementation and preliminary experiments

NASA Astrophysics Data System (ADS)

Pertsch, Alexander; Kim, Jin-Yeon; Wang, Yang; Jacobs, Laurence J.

2011-01-01

Continuous structural health monitoring has the potential to significantly improve the safety management of aged, in-service civil structures. In particular, monitoring of local damage growth at hot-spot areas can help to prevent disastrous structural failures. Although ultrasonic nondestructive evaluation (NDE) has proved to be effective in monitoring local damage growth, conventional equipment and devices are usually bulky and only suitable for scheduled human inspections. The objective of this research is to harness the latest developments in embedded hardware and wireless communication for developing a stand-alone, compact ultrasonic device. The device is directed at the continuous structural health monitoring of civil structures. Relying on battery power, the device possesses the functionalities of high-speed actuation, sensing, signal processing, and wireless communication. Integrated with contact ultrasonic transducers, the device can generate 1 MHz Rayleigh surface waves in a steel specimen and measure response waves. An envelope detection algorithm based on the Hilbert transform is presented for efficiently determining the peak values of the response signals, from which small surface cracks are successfully identified.

Structural Health Management of Damaged Aircraft Structures Using the Digital Twin Concept

NASA Technical Reports Server (NTRS)

Seshadri, Banavara R.; Krishnamurthy, Thiagarajan

2017-01-01

The development of multidisciplinary integrated Structural Health Management (SHM) tools will enable accurate detection, and prognosis of damaged aircraft under normal and adverse conditions during flight. As part of the digital twin concept, methodologies are developed by using integrated multiphysics models, sensor information and input data from an in-service vehicle to mirror and predict the life of its corresponding physical twin. SHM tools are necessary for both damage diagnostics and prognostics for continued safe operation of damaged aircraft structures. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and structural damage conditions. A major concern in these structures is the growth of undetected damage/cracks due to fatigue and low velocity foreign object impact that can reach a critical size during flight, resulting in loss of control of the aircraft. To avoid unstable, catastrophic propagation of damage during a flight, load levels must be maintained that are below a reduced load-carrying capacity for continued safe operation of an aircraft. Hence, a capability is needed for accurate real-time predictions of damage size and safe load carrying capacity for structures with complex damage configurations. In the present work, a procedure is developed that uses guided wave responses to interrogate damage. As the guided wave interacts with damage, the signal attenuates in some directions and reflects in others. This results in a difference in signal magnitude as well as phase shifts between signal responses for damaged and undamaged structures. Accurate estimation of damage size, location, and orientation is made by evaluating the cumulative signal responses at various pre-selected sensor locations using a genetic algorithm (GA) based optimization procedure. The damage size, location, and orientation is obtained by minimizing the difference between the reference responses and the responses obtained by wave propagation finite element analysis of different representative cracks, geometries, and sizes.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

PubMed

Hoang, Toan Minh; Baek, Na Rae; Cho, Se Woon; Kim, Ki Wan; Park, Kang Ryoung

2017-10-28

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

Optical diagnosis of the progression and reversal of CCl4-induced liver injury in rodent model using minimally invasive autofluorescence spectroscopy.

PubMed

Nazeer, Shaiju S; Sandhyamani, S; Jayasree, Ramapurath S

2015-06-07

Worldwide, liver cancer is the fifth most common cancer in men and seventh most common cancer in women. Intoxicant-induced liver injury is one of the major causes for severe structural damage with fibrosis and functional derangement of the liver leading to cancer in its later stages. This report focuses on the minimally invasive autofluorescence spectroscopic (AFS) studies on intoxicant, carbon tetrachloride (CCl4)-induced liver damage in a rodent model. Different stages of liver damage, including the reversed stage, on stoppage of the intoxicant are examined. Emission from prominent fluorophores, such as collagen, nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD), and variations in redox ratio have been studied. A direct correlation between the severity of the disease and the levels of collagen and redox ratio was observed. On withdrawal of the intoxicant, a gradual reversal of the disease to normal conditions was observed as indicated by the decrease in collagen levels and redox ratio. Multivariate statistical techniques and principal component analysis followed by linear discriminant analysis (PC-LDA) were used to develop diagnostic algorithms for distinguishing different stages of the liver disease based on spectral features. The PC-LDA modeling on a minimally invasive AFS dataset yielded diagnostic sensitivities of 93%, 87% and 87% and specificities of 90%, 98% and 98% for pairwise classification among normal, fibrosis, cirrhosis and reversal conditions. We conclude that AFS along with PC-LDA algorithm has the potential for rapid and accurate minimally invasive diagnosis and detection of structural changes due to liver injury resulting from various intoxicants.

Quantitative evaluation for small surface damage based on iterative difference and triangulation of 3D point cloud

NASA Astrophysics Data System (ADS)

Zhang, Yuyan; Guo, Quanli; Wang, Zhenchun; Yang, Degong

2018-03-01

This paper proposes a non-contact, non-destructive evaluation method for the surface damage of high-speed sliding electrical contact rails. The proposed method establishes a model of damage identification and calculation. A laser scanning system is built to obtain the 3D point cloud data of the rail surface. In order to extract the damage region of the rail surface, the 3D point cloud data are processed using iterative difference, nearest neighbours search and a data registration algorithm. The curvature of the point cloud data in the damage region is mapped to RGB color information, which can directly reflect the change trend of the curvature of the point cloud data in the damage region. The extracted damage region is divided into three prism elements by a method of triangulation. The volume and mass of a single element are calculated by the method of geometric segmentation. Finally, the total volume and mass of the damage region are obtained by the principle of superposition. The proposed method is applied to several typical injuries and the results are discussed. The experimental results show that the algorithm can identify damage shapes and calculate damage mass with milligram precision, which are useful for evaluating the damage in a further research stage.

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

Detecting unresolved binary stars in Euclid VIS images

NASA Astrophysics Data System (ADS)

Kuntzer, T.; Courbin, F.

2017-10-01

Measuring a weak gravitational lensing signal to the level required by the next generation of space-based surveys demands exquisite reconstruction of the point-spread function (PSF). However, unresolved binary stars can significantly distort the PSF shape. In an effort to mitigate this bias, we aim at detecting unresolved binaries in realistic Euclid stellar populations. We tested methods in numerical experiments where (I) the PSF shape is known to Euclid requirements across the field of view; and (II) the PSF shape is unknown. We drew simulated catalogues of PSF shapes for this proof-of-concept paper. Following the Euclid survey plan, the objects were observed four times. We propose three methods to detect unresolved binary stars. The detection is based on the systematic and correlated biases between exposures of the same object. One method is a simple correlation analysis, while the two others use supervised machine-learning algorithms (random forest and artificial neural network). In both experiments, we demonstrate the ability of our methods to detect unresolved binary stars in simulated catalogues. The performance depends on the level of prior knowledge of the PSF shape and the shape measurement errors. Good detection performances are observed in both experiments. Full complexity, in terms of the images and the survey design, is not included, but key aspects of a more mature pipeline are discussed. Finding unresolved binaries in objects used for PSF reconstruction increases the quality of the PSF determination at arbitrary positions. We show, using different approaches, that we are able to detect at least binary stars that are most damaging for the PSF reconstruction process. The code corresponding to the algorithms used in this work and all scripts to reproduce the results are publicly available from a GitHub repository accessible via http://lastro.epfl.ch/software

A numerical model for predicting crack path and modes of damage in unidirectional metal matrix composites

NASA Technical Reports Server (NTRS)

Bakuckas, J. G.; Tan, T. M.; Lau, A. C. W.; Awerbuch, J.

1993-01-01

A finite element-based numerical technique has been developed to simulate damage growth in unidirectional composites. This technique incorporates elastic-plastic analysis, micromechanics analysis, failure criteria, and a node splitting and node force relaxation algorithm to create crack surfaces. Any combination of fiber and matrix properties can be used. One of the salient features of this technique is that damage growth can be simulated without pre-specifying a crack path. In addition, multiple damage mechanisms in the forms of matrix cracking, fiber breakage, fiber-matrix debonding and plastic deformation are capable of occurring simultaneously. The prevailing failure mechanism and the damage (crack) growth direction are dictated by the instantaneous near-tip stress and strain fields. Once the failure mechanism and crack direction are determined, the crack is advanced via the node splitting and node force relaxation algorithm. Simulations of the damage growth process in center-slit boron/aluminum and silicon carbide/titanium unidirectional specimens were performed. The simulation results agreed quite well with the experimental observations.

Robust Maneuvering Envelope Estimation Based on Reachability Analysis in an Optimal Control Formulation

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas; Schuet, Stefan R.; Wheeler, Kevin; Acosta, Diana; Kaneshige, John

2013-01-01

This paper discusses an algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. Starting with an optimal control formulation, the optimization problem can be rewritten as a Hamilton- Jacobi-Bellman equation. This equation can be solved by level set methods. This approach has been applied on an aircraft example involving structural airframe damage. Monte Carlo validation tests have confirmed that this approach is successful in estimating the safe maneuvering envelope for damaged aircraft.

Developmental approach towards high resolution optical coherence tomography for glaucoma diagnostics

NASA Astrophysics Data System (ADS)

Kemper, Björn; Ketelhut, Steffi; Heiduschka, Peter; Thorn, Marie; Larsen, Michael; Schnekenburger, Jürgen

2018-02-01

Glaucoma is caused by a pathological rise in the intraocular pressure, which results in a progressive loss of vision by a damage to retinal cells and the optical nerve head. Early detection of pressure-induced damage is thus essential for the reduction of eye pressure and to prevent severe incapacity or blindness. Within the new European Project GALAHAD (Glaucoma Advanced, Label free High Resolution Automated OCT Diagnostics), we will develop a new low-cost and high-resolution OCT system for the early detection of glaucoma. The device is designed to improve diagnosis based on a new system of optical coherence tomography. Although OCT systems are at present available in ophthalmology centres, high-resolution devices are extremely expensive. The novelty of the new Galahad system is its super wideband light source to achieve high image resolution at a reasonable cost. Proof of concept experiments with cell and tissue Glaucoma test standards and animal models are planned for the test of the new optical components and new algorithms performance for the identification of Glaucoma associated cell and tissue structures. The intense training of the software systems with various samples should result in a increased sensitivity and specificity of the OCT software system.

On damage diagnosis for a wind turbine blade using pattern recognition

NASA Astrophysics Data System (ADS)

Dervilis, N.; Choi, M.; Taylor, S. G.; Barthorpe, R. J.; Park, G.; Farrar, C. R.; Worden, K.

2014-03-01

With the increased interest in implementation of wind turbine power plants in remote areas, structural health monitoring (SHM) will be one of the key cards in the efficient establishment of wind turbines in the energy arena. Detection of blade damage at an early stage is a critical problem, as blade failure can lead to a catastrophic outcome for the entire wind turbine system. Experimental measurements from vibration analysis were extracted from a 9 m CX-100 blade by researchers at Los Alamos National Laboratory (LANL) throughout a full-scale fatigue test conducted at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC). The blade was harmonically excited at its first natural frequency using a Universal Resonant EXcitation (UREX) system. In the current study, machine learning algorithms based on Artificial Neural Networks (ANNs), including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs. This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring.

PubMed

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-05-21

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

PubMed Central

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-01-01

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. PMID:29883411

Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility

PubMed Central

Cao, Hongyou; Liu, Quanmin; Wahab, Magd Abdel

2017-01-01

Output-based structural damage detection is becoming increasingly appealing due to its potential in real engineering applications without any restriction regarding excitation measurements. A new transmissibility-based damage detection approach is presented in this study by combining transmissibility with correlation analysis in order to strengthen its performance in discriminating damaged from undamaged scenarios. From this perspective, damage detection strategies are hereafter established by constructing damage-sensitive indicators from a derived transmissibility. A cantilever beam is numerically analyzed to verify the feasibility of the proposed damage detection procedure, and an ASCE (American Society of Civil Engineers) benchmark is henceforth used in the validation for its application in engineering structures. The results of both studies reveal a good performance of the proposed methodology in identifying damaged states from intact states. The comparison between the proposed indicator and the existing indicator also affirms its applicability in damage detection, which might be adopted in further structural health monitoring systems as a discrimination criterion. This study contributed an alternative criterion for transmissibility-based damage detection in addition to the conventional ones. PMID:28773218

Wavelet subspace decomposition of thermal infrared images for defect detection in artworks

NASA Astrophysics Data System (ADS)

Ahmad, M. Z.; Khan, A. A.; Mezghani, S.; Perrin, E.; Mouhoubi, K.; Bodnar, J. L.; Vrabie, V.

2016-07-01

Health of ancient artworks must be routinely monitored for their adequate preservation. Faults in these artworks may develop over time and must be identified as precisely as possible. The classical acoustic testing techniques, being invasive, risk causing permanent damage during periodic inspections. Infrared thermometry offers a promising solution to map faults in artworks. It involves heating the artwork and recording its thermal response using infrared camera. A novel strategy based on pseudo-random binary excitation principle is used in this work to suppress the risks associated with prolonged heating. The objective of this work is to develop an automatic scheme for detecting faults in the captured images. An efficient scheme based on wavelet based subspace decomposition is developed which favors identification of, the otherwise invisible, weaker faults. Two major problems addressed in this work are the selection of the optimal wavelet basis and the subspace level selection. A novel criterion based on regional mutual information is proposed for the latter. The approach is successfully tested on a laboratory based sample as well as real artworks. A new contrast enhancement metric is developed to demonstrate the quantitative efficiency of the algorithm. The algorithm is successfully deployed for both laboratory based and real artworks.

Self-recovery reversible image watermarking algorithm

PubMed Central

Sun, He; Gao, Shangbing; Jin, Shenghua

2018-01-01

The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration. PMID:29920528

Infrared machine vision system for the automatic detection of olive fruit quality.

PubMed

Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

2013-11-15

External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

Distributed road assessment system

DOEpatents

Beer, N. Reginald; Paglieroni, David W

2014-03-25

A system that detects damage on or below the surface of a paved structure or pavement is provided. A distributed road assessment system includes road assessment pods and a road assessment server. Each road assessment pod includes a ground-penetrating radar antenna array and a detection system that detects road damage from the return signals as the vehicle on which the pod is mounted travels down a road. Each road assessment pod transmits to the road assessment server occurrence information describing each occurrence of road damage that is newly detected on a current scan of a road. The road assessment server maintains a road damage database of occurrence information describing the previously detected occurrences of road damage. After the road assessment server receives occurrence information for newly detected occurrences of road damage for a portion of a road, the road assessment server determines which newly detected occurrences correspond to which previously detected occurrences of road damage.

Post-hurricane forest damage assessment using satellite remote sensing

Treesearch

W. Wang; J.J. Qu; X. Hao; Y. Liu; J.A. Stanturf

2010-01-01

This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was...

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

NASA Astrophysics Data System (ADS)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

Linear feature detection algorithm for astronomical surveys - I. Algorithm description

NASA Astrophysics Data System (ADS)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

An efficient parallel termination detection algorithm

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

Baker, A. H.; Crivelli, S.; Jessup, E. R.

2004-05-27

Information local to any one processor is insufficient to monitor the overall progress of most distributed computations. Typically, a second distributed computation for detecting termination of the main computation is necessary. In order to be a useful computational tool, the termination detection routine must operate concurrently with the main computation, adding minimal overhead, and it must promptly and correctly detect termination when it occurs. In this paper, we present a new algorithm for detecting the termination of a parallel computation on distributed-memory MIMD computers that satisfies all of those criteria. A variety of termination detection algorithms have been devised. Ofmore » these, the algorithm presented by Sinha, Kale, and Ramkumar (henceforth, the SKR algorithm) is unique in its ability to adapt to the load conditions of the system on which it runs, thereby minimizing the impact of termination detection on performance. Because their algorithm also detects termination quickly, we consider it to be the most efficient practical algorithm presently available. The termination detection algorithm presented here was developed for use in the PMESC programming library for distributed-memory MIMD computers. Like the SKR algorithm, our algorithm adapts to system loads and imposes little overhead. Also like the SKR algorithm, ours is tree-based, and it does not depend on any assumptions about the physical interconnection topology of the processors or the specifics of the distributed computation. In addition, our algorithm is easier to implement and requires only half as many tree traverses as does the SKR algorithm. This paper is organized as follows. In section 2, we define our computational model. In section 3, we review the SKR algorithm. We introduce our new algorithm in section 4, and prove its correctness in section 5. We discuss its efficiency and present experimental results in section 6.« less

Radar Detection of Marine Mammals

DTIC Science & Technology

2011-09-30

BFT-BPT algorithm for use with our radar data. This track - before - detect algorithm had been effective in enhancing small but persistent signatures in...will be possible with the detect before track algorithm. 4 We next evaluated the track before detect algorithm, the BFT-BPT, on the CEDAR data

Peripheral intravenous and central catheter algorithm: a proactive quality initiative.

PubMed

Wilder, Kerry A; Kuehn, Susan C; Moore, James E

2014-12-01

Peripheral intravenous (PIV) infiltrations causing tissue damage is a global issue surrounded by situations that make vascular access decisions difficult. The purpose of this quality improvement project was to develop an algorithm and assess its effectiveness in reducing PIV infiltrations in neonates. The targeted subjects were all infants in our neonatal intensive care unit (NICU) with a PIV catheter. We completed a retrospective chart review of the electronic medical record to collect 4th quarter 2012 baseline data. Following adoption of the algorithm, we also performed a daily manual count of all PIV catheters in the 1st and 2nd quarters 2013. Daily PIV days were defined as follows: 1 patient with a PIV catheter equals 1 PIV day. An infant with 2 PIV catheters in place was counted as 2 PIV days. Our rate of infiltration or tissue damage was determined by counting the number of events and dividing by the number of PIV days. The rate of infiltration or tissue damage was reported as the number of events per 100 PIV days. The number of infiltrations and PIV catheters was collected from the electronic medical record and also verified manually by daily assessment after adoption of the algorithm. To reduce the rate of PIV infiltrations leading to grade 4 infiltration and tissue damage by at least 30% in the NICU population. Incidence of PIV infiltrations/100 catheter days. The baseline rate for total infiltrations increased slightly from 5.4 to 5.68/100 PIV days (P = .397) for the NICU. We attributed this increase to heightened awareness and better reporting. Grade 4 infiltrations decreased from 2.8 to 0.83/100 PIV catheter days (P = .00021) after the algorithm was implemented. Tissue damage also decreased from 0.68 to 0.3/100 PIV days (P = .11). Statistical analysis used the Fisher exact test and reported as statistically significant at P < .05. Our findings suggest that utilization of our standardized decision pathway was instrumental in providing guidance for problem solving related to vascular access decisions. We feel this contributed to the overall reduction in grade 4 intravenous infiltration and tissue damage rates. Grade 4 infiltration reductions were highly statistically significant (P = .00021).

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.

Efficient audio signal processing for embedded systems

NASA Astrophysics Data System (ADS)

Chiu, Leung Kin

As mobile platforms continue to pack on more computational power, electronics manufacturers start to differentiate their products by enhancing the audio features. However, consumers also demand smaller devices that could operate for longer time, hence imposing design constraints. In this research, we investigate two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound ”richer" and "fuller." Piezoelectric speakers have a small form factor but exhibit poor response in the low-frequency region. In the algorithm, we combine psychoacoustic bass extension and dynamic range compression to improve the perceived bass coming out from the tiny speakers. We also developed an audio energy reduction algorithm for loudspeaker power management. The perceptually transparent algorithm extends the battery life of mobile devices and prevents thermal damage in speakers. This method is similar to audio compression algorithms, which encode audio signals in such a ways that the compression artifacts are not easily perceivable. Instead of reducing the storage space, however, we suppress the audio contents that are below the hearing threshold, therefore reducing the signal energy. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The system is an example of an analog-to-information converter. The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. In this classifier architecture, we combine simple "base" analog classifiers to form a strong one. We also designed the circuits to implement the AdaBoost-based analog classifier.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Damage Detection Sensor System for Aerospace and Multiple Applications

NASA Technical Reports Server (NTRS)

Williams, M.; Lewis, M.; Gibson, T.; Medelius, P.; Lane, J.

2017-01-01

The damage detection sensory system is an intelligent damage detection ‘skin’ that can be embedded into rigid or flexible structures, providing a lightweight capability for in-situ health monitoring for applications such as spacecraft, expandable or inflatable structures, extravehicular activities (EVA) suits, smart wearables, and other applications where diagnostic impact damage monitoring might be critical. The sensor systems can be customized for detecting location, damage size, and depth, with velocity options and can be designed for particular environments for monitoring of impact or physical damage to a structure. The operation of the sensor detection system is currently based on the use of parallel conductive traces placed on a firm or flexible surface. Several detection layers can be implemented, where alternate layers are arranged in orthogonal direction with respect to the adjacent layers allowing for location and depth calculations. Increased flexibility of the damage detection sensor system designs will also be introduced.

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

Structural health monitoring of cylindrical bodies under impulsive hydrodynamic loading by distributed FBG strain measurements

NASA Astrophysics Data System (ADS)

Fanelli, Pierluigi; Biscarini, Chiara; Jannelli, Elio; Ubertini, Filippo; Ubertini, Stefano

2017-02-01

Various mechanical, ocean, aerospace and civil engineering problems involve solid bodies impacting the water surface and often result in complex coupled dynamics, characterized by impulsive loading conditions, high amplitude vibrations and large local deformations. Monitoring in such problems for purposes such as remaining fatigue life estimation and real time damage detection is a technical and scientific challenge of primary concern in this context. Open issues include the need for developing distributed sensing systems able to operate at very high acquisition frequencies, to be utilized to study rapidly varying strain fields, with high resolution and very low noise, while scientific challenges mostly relate to the definition of appropriate signal processing and modeling tools enabling the extraction of useful information from distributed sensing signals. Building on previous work by some of the authors, we propose an enhanced method for real time deformed shape reconstruction using distributed FBG strain measurements in curved bodies subjected to impulsive loading and we establish a new framework for applying this method for structural health monitoring purposes, as the main focus of the work. Experiments are carried out on a cylinder impacting the water at various speeds, proving improved performance in displacement reconstruction of the enhanced method compared to its previous version. A numerical study is then carried out considering the same physical problem with different delamination damages affecting the body. The potential for detecting, localizing and quantifying this damage using the reconstruction algorithm is thoroughly investigated. Overall, the results presented in the paper show the potential of distributed FBG strain measurements for real time structural health monitoring of curved bodies under impulsive hydrodynamic loading, defining damage sensitive features in terms of strain or displacement reconstruction errors at selected locations along the structure.

Experimental validation of a structural damage detection method based on marginal Hilbert spectrum

NASA Astrophysics Data System (ADS)

Banerji, Srishti; Roy, Timir B.; Sabamehr, Ardalan; Bagchi, Ashutosh

2017-04-01

Structural Health Monitoring (SHM) using dynamic characteristics of structures is crucial for early damage detection. Damage detection can be performed by capturing and assessing structural responses. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Signal processing is an important tool for the processing of the collected data to diagnose anomalies in structural behavior. The vibration signature of the structure varies with damage. In order to attain effective damage detection, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) detects location and severity of the damage. The present work investigates damage detection analytically and experimentally by employing MHS. The testing of this methodology for different damage scenarios of a frame structure resulted in its accurate damage identification. The sensitivity of Hilbert Spectral Analysis (HSA) is assessed with varying frequencies and damage locations by means of calculating Damage Indices (DI) from the Hilbert spectrum curves of the undamaged and damaged structures.

Assessment of landscape change associated with tropical cyclone phenomena in Baja California Sur, Mexico, using satellite remote sensing

NASA Astrophysics Data System (ADS)

Martinez-Gutierrez, Genaro

Baja California Sur (Mexico), as well as mainland Mexico, is affected by tropical cyclone storms, which originate in the eastern north Pacific. Historical records show that Baja has been damaged by intense summer storms. An arid to semiarid climate characterizes the study area, where precipitation mainly occurs during the summer and winter seasons. Natural and anthropogenic changes have impacted the landscape of southern Baja. The present research documents the effects of tropical storms over the southern region of Baja California for a period of approximately twenty-six years. The goal of the research is to demonstrate how remote sensing can be used to detect the important effects of tropical storms including: (a) evaluation of change detection algorithms, and (b) delineating changes to the landscape including coastal modification, fluvial erosion and deposition, vegetation change, river avulsion using change detection algorithms. Digital image processing methods with temporal Landsat satellite remotely sensed data from the North America Landscape Characterization archive (NALC), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) images were used to document the landscape change. Two image processing methods were tested including Image differencing (ID), and Principal Component Analysis (PCA). Landscape changes identified with the NALC archive and TM images showed that the major changes included a rapid change of land use in the towns of San Jose del Cabo and Cabo San Lucas between 1973 and 1986. The features detected using the algorithms included flood deposits within the channels of active streams, erosion banks, and new channels caused by channel avulsion. Despite the 19 year period covered by the NALC data and approximately 10 year intervals between acquisition dates, there were changed features that could be identified in the images. The TM images showed that flooding from Hurricane Isis (1998) produced new large deposits within the stream channels. This research has shown that remote sensing based change detection can delineate the effects of flooding on the landscape at scales down to the nominal resolution of the sensor. These findings indicate that many other applications for change detection are both viable and important. These include disaster response, flood hazard planning, geomorphic studies, water supply management in deserts.

Surgical screw segmentation for mobile C-arm CT devices

NASA Astrophysics Data System (ADS)

Görres, Joseph; Brehler, Michael; Franke, Jochen; Wolf, Ivo; Vetter, Sven Y.; Grützner, Paul A.; Meinzer, Hans-Peter; Nabers, Diana

2014-03-01

Calcaneal fractures are commonly treated by open reduction and internal fixation. An anatomical reconstruction of involved joints is mandatory to prevent cartilage damage and premature arthritis. In order to avoid intraarticular screw placements, the use of mobile C-arm CT devices is required. However, for analyzing the screw placement in detail, a time-consuming human-computer interaction is necessary to navigate through 3D images and therefore to view a single screw in detail. Established interaction procedures of repeatedly positioning and rotating sectional planes are inconvenient and impede the intraoperative assessment of the screw positioning. To simplify the interaction with 3D images, we propose an automatic screw segmentation that allows for an immediate selection of relevant sectional planes. Our algorithm consists of three major steps. At first, cylindrical characteristics are determined from local gradient structures with the help of RANSAC. In a second step, a DBScan clustering algorithm is applied to group similar cylinder characteristics. Each detected cluster represents a screw, whose determined location is then refined by a cylinder-to-image registration in a third step. Our evaluation with 309 screws in 50 images shows robust and precise results. The algorithm detected 98% (303) of the screws correctly. Thirteen clusters led to falsely identified screws. The mean distance error for the screw tip was 0.8 +/- 0.8 mm and for the screw head 1.2 +/- 1 mm. The mean orientation error was 1.4 +/- 1.2 degrees.

A multi-damages identification method for cantilever beam based on mode shape curvatures and Kriging surrogate model

NASA Astrophysics Data System (ADS)

Xie, Fengle; Jiang, Zhansi; Jiang, Hui

2018-05-01

This paper presents a multi-damages identification method for Cantilever Beam. First, the damage location is identified by using the mode shape curvatures. Second, samples of varying damage severities at the damage location and their corresponding natural frequencies are used to construct the initial Kriging surrogate model. Then a particle swarm optimization (PSO) algorithm is employed to identify the damage severities based on Kriging surrogate model. The simulation study of a double-damaged cantilever beam demonstrated that the proposed method is effective.

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

Elmagarmid, A.K.

The availability of distributed data bases is directly affected by the timely detection and resolution of deadlocks. Consequently, mechanisms are needed to make deadlock detection algorithms resilient to failures. Presented first is a centralized algorithm that allows transactions to have multiple requests outstanding. Next, a new distributed deadlock detection algorithm (DDDA) is presented, using a global detector (GD) to detect global deadlocks and local detectors (LDs) to detect local deadlocks. This algorithm essentially identifies transaction-resource interactions that m cause global (multisite) deadlocks. Third, a deadlock detection algorithm utilizing a transaction-wait-for (TWF) graph is presented. It is a fully disjoint algorithmmore » that allows multiple outstanding requests. The proposed algorithm can achieve improved overall performance by using multiple disjoint controllers coupled with the two-phase property while maintaining the simplicity of centralized schemes. Fourth, an algorithm that combines deadlock detection and avoidance is given. This algorithm uses concurrent transaction controllers and resource coordinators to achieve maximum distribution. The language of CSP is used to describe this algorithm. Finally, two efficient deadlock resolution protocols are given along with some guidelines to be used in choosing a transaction for abortion.« less

Coupling Between Doppler Radar Signatures and Tornado Damage Tracks

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew L.; Carey, Lawrence; Carcione, Brian; Smith, Matthew; Schultz, Elise V.; Schultz, Christopher; Lafontaine, Frank

2011-01-01

On April 27, 2011, the southeastern United States was raked with several episodes of severe weather. Numerous tornadoes caused extensive damage, and tragically, the deaths of over 300 people. In Alabama alone, there were 61 confirmed tornados, 4 of them produced EF5 damage, and several were on the ground an hour or more with continuous damage tracks exceeding 80km. The use of Doppler radars covering the region provided reflectivity and velocity signatures that allowed forecasters to monitors the severe storms from beginning to end issuing hundreds of severe weather warnings throughout the day. Meteorologists from the the NWS performed extensive surveys to assess the intensity, duration, and ground track of tornadoes reported during the event. Survey activities included site visits to the affected locations, analysis of radar and satellite data, aerial surveys, and interviews with eyewitnesses. Satellite data from NASA's MODIS and ASTER instruments played a helpful role in determining the location of tornado damage paths and in the assessment. High resolution multispectral and temporal composites helped forecasters corroborate their damage assessments, determine starting and ending points for tornado touchdowns, and helped to provide forecasters with a better big-picture view of the damage region. The imagery also helped to separate damage from the April 27th tornados from severe weather that occurred earlier that month. In a post analysis of the outbreak, tornado damage path signatures observed in the NASA satellite data have been correlated to "debris ball" signatures in the NWS Doppler radars and a special ARMOR dual-polarization radar operated by the University of Alabama Huntsville during the event. The Doppler radar data indicates a circular enhanced reflectivity signal and rotational couplet in the radial velocity likely associated with the tornado that is spatially correlated with the damage tracks in the observed satellite data. An algorithm to detect and isolate the "debris ball" from precipitation signatures in the dual polarization radar data has been developed and verified using the NASA damage track data.

Hybrid Bearing Prognostic Test Rig

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Certo, Joseph M.; Handschuh, Robert F.; Dimofte, Florin

2005-01-01

The NASA Glenn Research Center has developed a new Hybrid Bearing Prognostic Test Rig to evaluate the performance of sensors and algorithms in predicting failures of rolling element bearings for aeronautics and space applications. The failure progression of both conventional and hybrid (ceramic rolling elements, metal races) bearings can be tested from fault initiation to total failure. The effects of different lubricants on bearing life can also be evaluated. Test conditions monitored and recorded during the test include load, oil temperature, vibration, and oil debris. New diagnostic research instrumentation will also be evaluated for hybrid bearing damage detection. This paper summarizes the capabilities of this new test rig.

Damage detection of an in-service condensation pipeline joint

NASA Astrophysics Data System (ADS)

Briand, Julie; Rezaei, Davood; Taheri, Farid

2010-04-01

The early detection of damage in structural or mechanical systems is of vital importance. With early detection, the damage may be repaired before the integrity of the system is jeopardized, resulting in monetary losses, loss of life or limb, and environmental impacts. Among the various types of structural health monitoring techniques, vibration-based methods are of significant interest since the damage location does not need to be known beforehand, making it a more versatile approach. The non-destructive damage detection method used for the experiments herein is a novel vibration-based method which uses an index called the EMD Energy Damage Index, developed with the aim of providing improved qualitative results compared to those methods currently available. As part of an effort to establish the integrity and limitation of this novel damage detection method, field testing was completed on a mechanical pipe joint on a condensation line, located in the physical plant of Dalhousie University. Piezoceramic sensors, placed at various locations around the joint were used to monitor the free vibration of the pipe imposed through the use of an impulse hammer. Multiple damage progression scenarios were completed, each having a healthy state and multiple damage cases. Subsequently, the recorded signals from the healthy and damaged joint were processed through the EMD Energy Damage Index developed in-house in an effort to detect the inflicted damage. The proposed methodology successfully detected the inflicted damages. In this paper, the effects of impact location, sensor location, frequency bandwidth, intrinsic mode functions, and boundary conditions are discussed.

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.

PubMed

Yang, Chao; He, Zengyou; Yu, Weichuan

2009-01-06

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods. In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data. The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.

Synchronous parallel spatially resolved stochastic cluster dynamics

DOE PAGES

Dunn, Aaron; Dingreville, Rémi; Martínez, Enrique; ...

2016-04-23

In this work, a spatially resolved stochastic cluster dynamics (SRSCD) model for radiation damage accumulation in metals is implemented using a synchronous parallel kinetic Monte Carlo algorithm. The parallel algorithm is shown to significantly increase the size of representative volumes achievable in SRSCD simulations of radiation damage accumulation. Additionally, weak scaling performance of the method is tested in two cases: (1) an idealized case of Frenkel pair diffusion and annihilation, and (2) a characteristic example problem including defect cluster formation and growth in α-Fe. For the latter case, weak scaling is tested using both Frenkel pair and displacement cascade damage.more » To improve scaling of simulations with cascade damage, an explicit cascade implantation scheme is developed for cases in which fast-moving defects are created in displacement cascades. For the first time, simulation of radiation damage accumulation in nanopolycrystals can be achieved with a three dimensional rendition of the microstructure, allowing demonstration of the effect of grain size on defect accumulation in Frenkel pair-irradiated α-Fe.« less

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

Gecow, Andrzej

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead ofmore » a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method--function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.« less

Identification of damage in plates using full-field measurement with a continuously scanning laser Doppler vibrometer system

NASA Astrophysics Data System (ADS)

Chen, Da-Ming; Xu, Y. F.; Zhu, W. D.

2018-05-01

An effective and reliable damage identification method for plates with a continuously scanning laser Doppler vibrometer (CSLDV) system is proposed. A new constant-speed scan algorithm is proposed to create a two-dimensional (2D) scan trajectory and automatically scan a whole plate surface. Full-field measurement of the plate can be achieved by applying the algorithm to the CSLDV system. Based on the new scan algorithm, the demodulation method is extended from one dimension for beams to two dimensions for plates to obtain a full-field operating deflection shape (ODS) of the plate from velocity response measured by the CSLDV system. The full-field ODS of an associated undamaged plate is obtained by using polynomials with proper orders to fit the corresponding full-field ODS from the demodulation method. A curvature damage index (CDI) using differences between curvatures of ODSs (CODSs) associated with ODSs that are obtained by the demodulation method and the polynomial fit is proposed to identify damage. An auxiliary CDI obtained by averaging CDIs at different excitation frequencies is defined to further assist damage identification. An experiment of an aluminum plate with damage in the form of 10.5% thickness reduction in a damage area of 0.86% of the whole scan area is conducted to investigate the proposed method. Six frequencies close to natural frequencies of the plate and one randomly selected frequency are used as sinusoidal excitation frequencies. Two 2D scan trajectories, i.e., a horizontally moving 2D scan trajectory and a vertically moving 2D scan trajectory, are used to obtain ODSs, CODSs, and CDIs of the plate. The damage is successfully identified near areas with consistently high values of CDIs at different excitation frequencies along the two 2D scan trajectories; the damage area is also identified by auxiliary CDIs.

Experimental Validation of Model Updating and Damage Detection via Eigenvalue Sensitivity Methods with Artificial Boundary Conditions

DTIC Science & Technology

2017-09-01

VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS by Matthew D. Bouwense...VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS 5. FUNDING NUMBERS 6. AUTHOR...unlimited. EXPERIMENTAL VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Bal, A.; Alam, M. S.; Aslan, M. S.

2006-05-01

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Automatic detection system of shaft part surface defect based on machine vision

NASA Astrophysics Data System (ADS)

Jiang, Lixing; Sun, Kuoyuan; Zhao, Fulai; Hao, Xiangyang

2015-05-01

Surface physical damage detection is an important part of the shaft parts quality inspection and the traditional detecting methods are mostly human eye identification which has many disadvantages such as low efficiency, bad reliability. In order to improve the automation level of the quality detection of shaft parts and establish its relevant industry quality standard, a machine vision inspection system connected with MCU was designed to realize the surface detection of shaft parts. The system adopt the monochrome line-scan digital camera and use the dark-field and forward illumination technology to acquire images with high contrast; the images were segmented to Bi-value images through maximum between-cluster variance method after image filtering and image enhancing algorithms; then the mainly contours were extracted based on the evaluation criterion of the aspect ratio and the area; then calculate the coordinates of the centre of gravity of defects area, namely locating point coordinates; At last, location of the defects area were marked by the coding pen communicated with MCU. Experiment show that no defect was omitted and false alarm error rate was lower than 5%, which showed that the designed system met the demand of shaft part on-line real-time detection.

Automatic thermographic image defect detection of composites

NASA Astrophysics Data System (ADS)

Luo, Bin; Liebenberg, Bjorn; Raymont, Jeff; Santospirito, SP

2011-05-01

Detecting defects, and especially reliably measuring defect sizes, are critical objectives in automatic NDT defect detection applications. In this work, the Sentence software is proposed for the analysis of pulsed thermography and near IR images of composite materials. Furthermore, the Sentence software delivers an end-to-end, user friendly platform for engineers to perform complete manual inspections, as well as tools that allow senior engineers to develop inspection templates and profiles, reducing the requisite thermographic skill level of the operating engineer. Finally, the Sentence software can also offer complete independence of operator decisions by the fully automated "Beep on Defect" detection functionality. The end-to-end automatic inspection system includes sub-systems for defining a panel profile, generating an inspection plan, controlling a robot-arm and capturing thermographic images to detect defects. A statistical model has been built to analyze the entire image, evaluate grey-scale ranges, import sentencing criteria and automatically detect impact damage defects. A full width half maximum algorithm has been used to quantify the flaw sizes. The identified defects are imported into the sentencing engine which then sentences (automatically compares analysis results against acceptance criteria) the inspection by comparing the most significant defect or group of defects against the inspection standards.

[A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].

PubMed

Jing, Xueping; Zheng, Xiujuan; Song, Shaoli; Liu, Kai

2017-12-01

Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

NASA Astrophysics Data System (ADS)

Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

2018-03-01

False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources.

Line Scanning Thermography for Rapid Nondestructive Inspection of Large Scale Composites

NASA Astrophysics Data System (ADS)

Chung, S.; Ley, O.; Godinez, V.; Bandos, B.

2011-06-01

As next generation structures are utilizing larger amounts of composite materials, a rigorous and reliable method is needed to inspect these structures in order to prevent catastrophic failure and extend service life. Current inspection methods, such as ultrasonic, generally require extended down time and man hours as they are typically carried out via point-by-point measurements. A novel Line Scanning Thermography (LST) System has been developed for the non-contact, large-scale field inspection of composite structures with faster scanning times than conventional thermography systems. LST is a patented dynamic thermography technique where the heat source and thermal camera move in tandem, which allows the continuous scan of long surfaces without the loss of resolution. The current system can inspect an area of 10 in2 per 1 second, and has a resolution of 0.05×0.03 in2. Advanced data gathering protocols have been implemented for near-real time damage visualization and post-analysis algorithms for damage interpretation. The system has been used to successfully detect defects (delamination, dry areas) in fiber-reinforced composite sandwich panels for Navy applications, as well as impact damage in composite missile cases and armor ceramic panels.

Imaging strategy for infants with urinary tract infection: a new algorithm.

PubMed

Preda, Iulian; Jodal, Ulf; Sixt, Rune; Stokland, Eira; Hansson, Sverker

2011-03-01

We analyzed clinical data for prediction of permanent renal damage in infants with first time urinary tract infection. This population based, prospective, 3-year study included 161 male and 129 female consecutive infants with first time urinary tract infection. Ultrasonography and dimercapto-succinic acid scintigraphy were performed as acute investigations and voiding cystourethrography within 2 months. Late scintigraphy was performed after 1 year in infants with abnormality on the first dimercapto-succinic acid scan or recurrent febrile urinary tract infections. End point was renal damage on the late scan. A total of 270 patients had end point data available, of whom 70 had renal damage and 200 did not. Final kidney status was associated with C-reactive protein, serum creatinine, temperature, leukocyturia, non-Escherichia coli bacteria, anteroposterior diameter on ultrasound and recurrent febrile urinary tract infections. In stepwise multiple regression analysis C-reactive protein, creatinine, leukocyturia, anteroposterior diameter and non-E.coli bacteria were independent predictors of permanent renal damage. C-reactive protein 70 mg/l or greater combined with anteroposterior diameter 10 mm or greater had sensitivity of 87% and specificity of 59% for renal damage. An algorithm for imaging of infants with first time urinary tract infection based on these results would have eliminated 126 acute dimercapto-succinic acid scans compared to our study protocol, while missing 9 patients with permanent renal damage. C-reactive protein can be used as a predictor of permanent renal damage in infants with urinary tract infection and together with anteroposterior diameter serves as a basis for an imaging algorithm. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Flexible, multi-measurement guided wave damage detection under varying temperatures

NASA Astrophysics Data System (ADS)

Douglass, Alexander C. S.; Harley, Joel B.

2018-04-01

Temperature compensation in structural health monitoring helps identify damage in a structure by removing data variations due to environmental conditions, such as temperature. Stretch-based methods are one of the most commonly used temperature compensation methods. To account for variations in temperature, stretch-based methods optimally stretch signals in time to optimally match a measurement to a baseline. All of the data is then compared with the single baseline to determine the presence of damage. Yet, for these methods to be effective, the measurement and the baseline must satisfy the inherent assumptions of the temperature compensation method. In many scenarios, these assumptions are wrong, the methods generate error, and damage detection fails. To improve damage detection, a multi-measurement damage detection method is introduced. By using each measurement in the dataset as a baseline, error caused by imperfect temperature compensation is reduced. The multi-measurement method increases the detection effectiveness of our damage metric, or damage indicator, over time and reduces the presence of additional peaks caused by temperature that could be mistaken for damage. By using many baselines, the variance of the damage indicator is reduced and the effects from damage are amplified. Notably, the multi-measurement improves damage detection over single-measurement methods. This is demonstrated through an increase in the maximum of our damage signature from 0.55 to 0.95 (where large values, up to a maximum of one, represent a statistically significant change in the data due to damage).

Heat shock protein expression as guidance for the therapeutic window of retinal laser therapy

NASA Astrophysics Data System (ADS)

Wang, Jenny; Huie, Philip; Dalal, Roopa; Lee, Seungjun; Tan, Gavin; Lee, Daeyoung; Lavinksy, Daniel; Palanker, Daniel

2016-03-01

Unlike conventional photocoagulation, non-damaging retinal laser therapy (NRT) limits laser-induced heating to stay below the retinal damage threshold and therefore requires careful dosimetry. Without the adverse effects associated with photocoagulation, NRT can be applied to critical areas of the retina and repeatedly to manage chronic disorders. Although the clinical benefits of NRT have been demonstrated, the mechanism of therapeutic effect and width of the therapeutic window below damage threshold are not well understood. Here, we measure activation of heat shock response via laser-induced hyperthermia as one indication of cellular response. A 577 nm laser is used with the Endpoint Management (EpM) user interface, a titration algorithm, to set experimental pulse energies relative to a barely visible titration lesion. Live/dead staining and histology show that the retinal damage threshold in rabbits is at 40% of titration energy on EpM scale. Heat shock protein 70 (HSP70) expression in the retinal pigment epithelium (RPE) was detected by whole-mount immunohistochemistry after different levels of laser treatment. We show HSP70 expression in the RPE beginning at 25% of titration energy indicating that there is a window for NRT between 25% and 40% with activation of the heat shock protein expression in response to hyperthermia. HSP70 expression is also seen at the perimeter of damaging lesions, as expected based on a computational model of laser heating. Expression area for each pulse energy setting varied between laser spots due to pigmentation changes, indicating the relatively narrow window of non-damaging activation and highlighting the importance of proper titration.

Evaluation of three automatic oxygen therapy control algorithms on ventilated low birth weight neonates.

PubMed

Morozoff, Edmund P; Smyth, John A

2009-01-01

Neonates with under developed lungs often require oxygen therapy. During the course of oxygen therapy, elevated levels of blood oxygenation, hyperoxemia, must be avoided or the risk of chronic lung disease or retinal damage is increased. Low levels of blood oxygen, hypoxemia, may lead to permanent brain tissue damage and, in some cases, mortality. A closed loop controller that automatically administers oxygen therapy using 3 algorithms - state machine, adaptive model, and proportional integral derivative (PID) - is applied to 7 ventilated low birth weight neonates and compared to manual oxygen therapy. All 3 automatic control algorithms demonstrated their ability to improve manual oxygen therapy by increasing periods of normoxemia and reducing the need for manual FiO(2) adjustments. Of the three control algorithms, the adaptive model showed the best performance with 0.25 manual adjustments per hour and 73% time spent within target +/- 3% SpO(2).

Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ross, Kenton W.; Graham, William D.

2006-01-01

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.

The application of data mining and cloud computing techniques in data-driven models for structural health monitoring

NASA Astrophysics Data System (ADS)

Khazaeli, S.; Ravandi, A. G.; Banerji, S.; Bagchi, A.

2016-04-01

Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.

Structural damage identification using damping: a compendium of uses and features

NASA Astrophysics Data System (ADS)

Cao, M. S.; Sha, G. G.; Gao, Y. F.; Ostachowicz, W.

2017-04-01

The vibration responses of structures under controlled or ambient excitation can be used to detect structural damage by correlating changes in structural dynamic properties extracted from responses with damage. Typical dynamic properties refer to modal parameters: natural frequencies, mode shapes, and damping. Among these parameters, natural frequencies and mode shapes have been investigated extensively for their use in damage characterization by associating damage with reduction in local stiffness of structures. In contrast, the use of damping as a dynamic property to represent structural damage has not been comprehensively elucidated, primarily due to the complexities of damping measurement and analysis. With advances in measurement technologies and analysis tools, the use of damping to identify damage is becoming a focus of increasing attention in the damage detection community. Recently, a number of studies have demonstrated that damping has greater sensitivity for characterizing damage than natural frequencies and mode shapes in various applications, but damping-based damage identification is still a research direction ‘in progress’ and is not yet well resolved. This situation calls for an overall survey of the state-of-the-art and the state-of-the-practice of using damping to detect structural damage. To this end, this study aims to provide a comprehensive survey of uses and features of applying damping in structural damage detection. First, we present various methods for damping estimation in different domains including the time domain, the frequency domain, and the time-frequency domain. Second, we investigate the features and applications of damping-based damage detection methods on the basis of two predominant infrastructure elements, reinforced concrete structures and fiber-reinforced composites. Third, we clarify the influential factors that can impair the capability of damping to characterize damage. Finally, we recommend future research directions for advancing damping-based damage detection. This work holds the promise of (a) helping researchers identify crucial components in damping-based damage detection theories, methods, and technologies, and (b) leading practitioners to better implement damping-based structural damage identification.

Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

PubMed

Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

2017-07-01

Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

Processing Ocean Images to Detect Large Drift Nets

NASA Technical Reports Server (NTRS)

Veenstra, Tim

2009-01-01

A computer program processes the digitized outputs of a set of downward-looking video cameras aboard an aircraft flying over the ocean. The purpose served by this software is to facilitate the detection of large drift nets that have been lost, abandoned, or jettisoned. The development of this software and of the associated imaging hardware is part of a larger effort to develop means of detecting and removing large drift nets before they cause further environmental damage to the ocean and to shores on which they sometimes impinge. The software is capable of near-realtime processing of as many as three video feeds at a rate of 30 frames per second. After a user sets the parameters of an adjustable algorithm, the software analyzes each video stream, detects any anomaly, issues a command to point a high-resolution camera toward the location of the anomaly, and, once the camera has been so aimed, issues a command to trigger the camera shutter. The resulting high-resolution image is digitized, and the resulting data are automatically uploaded to the operator s computer for analysis.

Shielded loaded bowtie antenna incorporating the presence of paving structure for improved GPR pipe detection

NASA Astrophysics Data System (ADS)

Seyfried, Daniel; Jansen, Ronald; Schoebel, Joerg

2014-12-01

In civil engineering Ground Penetrating Radar becomes more and more a considerable tool for nondestructive testing and exploration of the underground. For example, the detection of existence of utilization pipe networks prior to construction works or detection of damaged spot beneath a paved street is a highly advantageous application. However, different surface conditions as well as ground bounce reflection and antenna cross-talk may seriously affect the detection capability of the entire radar system. Therefore, proper antenna design is an essential part in order to obtain radar data of high quality. In this paper we redesign a given loaded bowtie antenna in order to reduce strong and unwanted signal contributions such as ground bounce reflection and antenna cross-talk. During the optimization process we also review all parameters of our existing antenna in order to maximize energy transfer into ground. The entire process incorporating appropriate simulations along with running measurements on our GPR test site where we buried different types of pipes and cables for testing and developing radar hardware and software algorithms under quasi-real conditions is described in this paper.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

PubMed Central

Hoang, Toan Minh; Baek, Na Rae; Cho, Se Woon; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods. PMID:29143764

Identification of Surface and Near Surface Defects and Damage Evaluation by Laser Speckle Techniques

NASA Technical Reports Server (NTRS)

Gowda, Chandrakanth H.

2001-01-01

As a part of the grant activity, a laboratory was established within the Department of Electrical Engineering for the study for measurements of surface defects and damage evaluation. This facility has been utilized for implementing several algorithms for accurate measurements of defects. Experiments were conducted using simulated images and multiple images were fused to achieve accurate measurements. During the nine months of the grants when the principal investigator was transferred in my name, experiments were conducted using simulated synthetic aperture radar (SAR) images. This proved useful when several algorithms were used on images of smooth objects with minor deformalities. Given the time constraint, the derived algorithms could not be applied to actual images of smooth objects with minor abnormalities.

Change Detection Processing Chain Dedicated to Sentinel Data Time Series. Application to Forest and Water Bodies Monitoring

NASA Astrophysics Data System (ADS)

Perez Saavedra, L.-M.; Mercier, G.; Yesou, H.; Liege, F.; Pasero, G.

2016-08-01

The Copernicus program of ESA and European commission (6 Sentinels Missions, among them Sentinel-1 with Synthetic Aperture Radar sensor and Sentinel-2 with 13-band 10 to 60 meter resolution optical sensors), offers a new opportunity to Earth Observation with high temporal acquisition capability ( 12 days repetitiveness and 5 days in some geographic areas of the world) with high spatial resolution.Due to these high temporal and spatial resolutions, it opens new challenges in several fields such as image processing, new algorithms for Time Series and big data analysis. In addition, these missions will be able to analyze several topics of earth temporal evolution such as crop vegetation, water bodies, Land use and Land Cover (LULC), sea and ice information, etc. This is particularly useful for end users and policy makers to detect early signs of damages, vegetation illness, flooding areas, etc.From the state of the art, one can find algorithms and methods that use a bi-date comparison for change detection [1-3] or time series analysis. Actually, these methods are essentially used for target detection or for abrupt change detection that requires 2 observations only.A Hölder means-based change detection technique has been proposed in [2,3] for high resolution radar images. This so-called MIMOSA technique has been mainly dedicated to man-made change detection in urban areas and CARABAS - II project by using a couple of SAR images. An extension to multitemporal change detection technique has been investigated but its application to land use and cover changes still has to be validated.The Hölder Hp is a Time Series pixel by pixel feature extraction and is defined by:H𝑝[X]=[1/n∑ⁿᵢ₌1 Xᴾᵢ]1/p p∈R Hp[X] : N images * S Bandes * t datesn is the number of images in the time series. N > 2Hp (X) is continuous and monotonic increasing in p for - ∞ < p < ∞

Low-complexity R-peak detection in ECG signals: a preliminary step towards ambulatory fetal monitoring.

PubMed

Rooijakkers, Michiel; Rabotti, Chiara; Bennebroek, Martijn; van Meerbergen, Jef; Mischi, Massimo

2011-01-01

Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Health Monitoring for Airframe Structural Characterization

NASA Technical Reports Server (NTRS)

Munns, Thomas E.; Kent, Renee M.; Bartolini, Antony; Gause, Charles B.; Borinski, Jason W.; Dietz, Jason; Elster, Jennifer L.; Boyd, Clark; Vicari, Larry; Ray, Asok;

Driver face tracking using semantics-based feature of eyes on single FPGA

NASA Astrophysics Data System (ADS)

Yu, Ying-Hao; Chen, Ji-An; Ting, Yi-Siang; Kwok, Ngaiming

2017-06-01

Tracking driver's face is one of the essentialities for driving safety control. This kind of system is usually designed with complicated algorithms to recognize driver's face by means of powerful computers. The design problem is not only about detecting rate but also from parts damages under rigorous environments by vibration, heat, and humidity. A feasible strategy to counteract these damages is to integrate entire system into a single chip in order to achieve minimum installation dimension, weight, power consumption, and exposure to air. Meanwhile, an extraordinary methodology is also indispensable to overcome the dilemma of low-computing capability and real-time performance on a low-end chip. In this paper, a novel driver face tracking system is proposed by employing semantics-based vague image representation (SVIR) for minimum hardware resource usages on a FPGA, and the real-time performance is also guaranteed at the same time. Our experimental results have indicated that the proposed face tracking system is viable and promising for the smart car design in the future.

Analysis on Behaviour of Wavelet Coefficient during Fault Occurrence in Transformer

NASA Astrophysics Data System (ADS)

Sreewirote, Bancha; Ngaopitakkul, Atthapol

2018-03-01

The protection system for transformer has play significant role in avoiding severe damage to equipment when disturbance occur and ensure overall system reliability. One of the methodology that widely used in protection scheme and algorithm is discrete wavelet transform. However, characteristic of coefficient under fault condition must be analyzed to ensure its effectiveness. So, this paper proposed study and analysis on wavelet coefficient characteristic when fault occur in transformer in both high- and low-frequency component from discrete wavelet transform. The effect of internal and external fault on wavelet coefficient of both fault and normal phase has been taken into consideration. The fault signal has been simulate using transmission connected to transformer experimental setup on laboratory level that modelled after actual system. The result in term of wavelet coefficient shown a clearly differentiate between wavelet characteristic in both high and low frequency component that can be used to further design and improve detection and classification algorithm that based on discrete wavelet transform methodology in the future.

Multi-physics modeling of multifunctional composite materials for damage detection

NASA Astrophysics Data System (ADS)

Sujidkul, Thanyawalai

This study presents a modeling of multifunction composite materials for damage detection with its verification and validation to mechanical behavior predictions of Carbon Fibre Reinforced Polymer composites (CFRPs), CFRPs laminated composites, and woven SiC/SiC matrix composites that are subjected to fracture damage. Advantages of those materials are low cost, low density, high strength-to-weight ratio, and comparable specific tensile properties, the special of SiC/SiC is good environmental stability at high temperature. Resulting in, the composite has been used for many important structures such as helicopter rotors, aerojet engines, gas turbines, hot control surfaces, sporting goods, and windmill blades. Damage or material defect detection in a mechanical component can provide vital information for the prediction of remaining useful life, which will result in the prevention of catastrophic failures. Thus the understanding of the mechanical behavior have been challenge to the prevent damage and failure of composites in different scales. The damage detection methods in composites have been investigated widely in recent years. Non-destructive techniques are the traditional methods to detect the damage such as X-ray, acoustic emission and thermography. However, due to the invisible damage in composite can be occurred, to prevent the failure in composites. The developments of damage detection methods have been considered. Due to carbon fibers are conductive materials, in resulting CFRPs can be self-sensing to detect damage. As is well known, the electrical resistance has been shown to be a sensitive measure of internal damage, and also this work study in thermal resistance can detect damage in composites. However, there is a few number of different micromechanical modeling schemes has been proposed in the published literature for various types of composites. This works will provide with a numerical, analytical, and theoretical failure models in different damages to predict the mechanical damage behavior with electrical properties and thermal properties.

NASA airborne radar wind shear detection algorithm and the detection of wet microbursts in the vicinity of Orlando, Florida

NASA Technical Reports Server (NTRS)

Britt, Charles L.; Bracalente, Emedio M.

1992-01-01

The algorithms used in the NASA experimental wind shear radar system for detection, characterization, and determination of windshear hazard are discussed. The performance of the algorithms in the detection of wet microbursts near Orlando is presented. Various suggested algorithms that are currently being evaluated using the flight test results from Denver and Orlando are reviewed.

Locating damage using integrated global-local approach with wireless sensing system and single-chip impedance measurement device.

PubMed

Lin, Tzu-Hsuan; Lu, Yung-Chi; Hung, Shih-Lin

2014-01-01

This study developed an integrated global-local approach for locating damage on building structures. A damage detection approach with a novel embedded frequency response function damage index (NEFDI) was proposed and embedded in the Imote2.NET-based wireless structural health monitoring (SHM) system to locate global damage. Local damage is then identified using an electromechanical impedance- (EMI-) based damage detection method. The electromechanical impedance was measured using a single-chip impedance measurement device which has the advantages of small size, low cost, and portability. The feasibility of the proposed damage detection scheme was studied with reference to a numerical example of a six-storey shear plane frame structure and a small-scale experimental steel frame. Numerical and experimental analysis using the integrated global-local SHM approach reveals that, after NEFDI indicates the approximate location of a damaged area, the EMI-based damage detection approach can then identify the detailed damage location in the structure of the building.

Social Media as Seismic Networks for the Earthquake Damage Assessment

NASA Astrophysics Data System (ADS)

Meletti, C.; Cresci, S.; La Polla, M. N.; Marchetti, A.; Tesconi, M.

2014-12-01

The growing popularity of online platforms, based on user-generated content, is gradually creating a digital world that mirrors the physical world. In the paradigm of crowdsensing, the crowd becomes a distributed network of sensors that allows us to understand real life events at a quasi-real-time rate. The SoS-Social Sensing project [http://socialsensing.it/] exploits the opportunistic crowdsensing, involving users in the sensing process in a minimal way, for social media emergency management purposes in order to obtain a very fast, but still reliable, detection of emergency dimension to face. First of all we designed and implemented a decision support system for the detection and the damage assessment of earthquakes. Our system exploits the messages shared in real-time on Twitter. In the detection phase, data mining and natural language processing techniques are firstly adopted to select meaningful and comprehensive sets of tweets. Then we applied a burst detection algorithm in order to promptly identify outbreaking seismic events. Using georeferenced tweets and reported locality names, a rough epicentral determination is also possible. The results, compared to Italian INGV official reports, show that the system is able to detect, within seconds, events of a magnitude in the region of 3.5 with a precision of 75% and a recall of 81,82%. We then focused our attention on damage assessment phase. We investigated the possibility to exploit social media data to estimate earthquake intensity. We designed a set of predictive linear models and evaluated their ability to map the intensity of worldwide earthquakes. The models build on a dataset of almost 5 million tweets exploited to compute our earthquake features, and more than 7,000 globally distributed earthquakes data, acquired in a semi-automatic way from USGS, serving as ground truth. We extracted 45 distinct features falling into four categories: profile, tweet, time and linguistic. We run diagnostic tests and simulations on generated models to assess their significance and avoid overfitting. Overall results show a correlation between the messages shared in social media and intensity estimations based on online survey data (CDI).

An FP7 "Space" project: Aphorism "Advanced PRocedures for volcanic and Seismic Monitoring"

NASA Astrophysics Data System (ADS)

Di Iorio, A., Sr.; Stramondo, S.; Bignami, C.; Corradini, S.; Merucci, L.

2014-12-01

APHORISM project proposes the development and testing of two new methods to combine Earth Observation satellite data from different sensors, and ground data. The aim is to demonstrate that this two types of data, appropriately managed and integrated, can provide new improved GMES products useful for seismic and volcanic crisis management. The first method, APE - A Priori information for Earthquake damage mapping, concerns the generation of maps to address the detection and estimate of damage caused by a seism. The use of satellite data to investigate earthquake damages is not an innovative issue. We can find a wide literature and projects concerning such issue, but usually the approach is only based on change detection techniques and classifications algorithms. The novelty of APE relies on the exploitation of a priori information derived by InSAR time series to measure surface movements, shake maps obtained from seismological data, and vulnerability information. This a priori information is then integrated with change detection map to improve accuracy and to limit false alarms. The second method deals with volcanic crisis management. The method, MACE - Multi-platform volcanic Ash Cloud Estimation, concerns the exploitation of GEO (Geosynchronous Earth Orbit) sensor platform, LEO (Low Earth Orbit) satellite sensors and ground measures to improve the ash detection and retrieval and to characterize the volcanic ash clouds. The basic idea of MACE consists of an improvement of volcanic ash retrievals at the space-time scale by using both the LEO and GEO estimations and in-situ data. Indeed the standard ash thermal infrared retrieval is integrated with data coming from a wider spectral range from visible to microwave. The ash detection is also extended in case of cloudy atmosphere or steam plumes. APE and MACE methods have been defined in order to provide products oriented toward the next ESA Sentinels satellite missions.The project is funded under the European Union FP7 program and the Kick-Off meeting has been held at INGV premises in Rome on 18th December 2013.

Detection and Prediction of Hail Storms in Satellite Imagery using Deep Learning

NASA Astrophysics Data System (ADS)

Pullman, M.; Gurung, I.; Ramachandran, R.; Maskey, M.

2017-12-01

Natural hazards, such as damaging hail storms, dramatically disrupt both industry and agriculture, having significant socio-economic impacts in the United States. In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest 2016 weather phenomenon in the United States. The destructive nature and high cost of hail storms has driven research into the development of more accurate hail-prediction algorithms in an effort to mitigate societal impacts. Recently, weather forecasting efforts have turned to deep learning neural networks because neural networks can more effectively model complex, nonlinear, dynamical phenomenon that exist in large datasets through multiple stages of transformation and representation. In an effort to improve hail-prediction techniques, we propose a deep learning technique that leverages satellite imagery to detect and predict the occurrence of hail storms. The technique is applied to satellite imagery from 2006 to 2016 for the contiguous United States and incorporates hail reports obtained from the National Center for Environmental Information Storm Events Database for training and validation purposes. In this presentation, we describe a novel approach to predicting hail via a neural network model that creates a large labeled dataset of hail storms, the accuracy and results of the model, and its applications for improving hail forecasting.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

Clinical and public health implications of acute and early HIV detection and treatment: a scoping review.

PubMed

Rutstein, Sarah E; Ananworanich, Jintanat; Fidler, Sarah; Johnson, Cheryl; Sanders, Eduard J; Sued, Omar; Saez-Cirion, Asier; Pilcher, Christopher D; Fraser, Christophe; Cohen, Myron S; Vitoria, Marco; Doherty, Meg; Tucker, Joseph D

2017-06-28

The unchanged global HIV incidence may be related to ignoring acute HIV infection (AHI). This scoping review examines diagnostic, clinical, and public health implications of identifying and treating persons with AHI. We searched PubMed, in addition to hand-review of key journals identifying research pertaining to AHI detection and treatment. We focused on the relative contribution of AHI to transmission and the diagnostic, clinical, and public health implications. We prioritized research from low- and middle-income countries (LMICs) published in the last fifteen years. Extensive AHI research and limited routine AHI detection and treatment have begun in LMIC. Diagnostic challenges include ease-of-use, suitability for application and distribution in LMIC, and throughput for high-volume testing. Risk score algorithms have been used in LMIC to screen for AHI among individuals with behavioural and clinical characteristics more often associated with AHI. However, algorithms have not been implemented outside research settings. From a clinical perspective, there are substantial immunological and virological benefits to identifying and treating persons with AHI - evading the irreversible damage to host immune systems and seeding of viral reservoirs that occurs during untreated acute infection. The therapeutic benefits require rapid initiation of antiretrovirals, a logistical challenge in the absence of point-of-care testing. From a public health perspective, AHI diagnosis and treatment is critical to: decrease transmission via viral load reduction and behavioural interventions; improve pre-exposure prophylaxis outcomes by avoiding treatment initiation for HIV-seronegative persons with AHI; and, enhance partner services via notification for persons recently exposed or likely transmitting. There are undeniable clinical and public health benefits to AHI detection and treatment, but also substantial diagnostic and logistical barriers to implementation and scale-up. Effective early ART initiation may be critical for HIV eradication efforts, but widespread use in LMIC requires simple and accurate diagnostic tools. Implementation research is critical to facilitate sustainable integration of AHI detection and treatment into existing health systems and will be essential for prospective evaluation of testing algorithms, point-of-care diagnostics, and efficacious and effective first-line regimens.

Clinical and public health implications of acute and early HIV detection and treatment: a scoping review

PubMed Central

Rutstein, Sarah E.; Ananworanich, Jintanat; Fidler, Sarah; Johnson, Cheryl; Sanders, Eduard J.; Sued, Omar; Saez-Cirion, Asier; Pilcher, Christopher D.; Fraser, Christophe; Cohen, Myron S.; Vitoria, Marco; Doherty, Meg; Tucker, Joseph D.

2017-01-01

Abstract Introduction: The unchanged global HIV incidence may be related to ignoring acute HIV infection (AHI). This scoping review examines diagnostic, clinical, and public health implications of identifying and treating persons with AHI. Methods: We searched PubMed, in addition to hand-review of key journals identifying research pertaining to AHI detection and treatment. We focused on the relative contribution of AHI to transmission and the diagnostic, clinical, and public health implications. We prioritized research from low- and middle-income countries (LMICs) published in the last fifteen years. Results and Discussion: Extensive AHI research and limited routine AHI detection and treatment have begun in LMIC. Diagnostic challenges include ease-of-use, suitability for application and distribution in LMIC, and throughput for high-volume testing. Risk score algorithms have been used in LMIC to screen for AHI among individuals with behavioural and clinical characteristics more often associated with AHI. However, algorithms have not been implemented outside research settings. From a clinical perspective, there are substantial immunological and virological benefits to identifying and treating persons with AHI – evading the irreversible damage to host immune systems and seeding of viral reservoirs that occurs during untreated acute infection. The therapeutic benefits require rapid initiation of antiretrovirals, a logistical challenge in the absence of point-of-care testing. From a public health perspective, AHI diagnosis and treatment is critical to: decrease transmission via viral load reduction and behavioural interventions; improve pre-exposure prophylaxis outcomes by avoiding treatment initiation for HIV-seronegative persons with AHI; and, enhance partner services via notification for persons recently exposed or likely transmitting. Conclusions: There are undeniable clinical and public health benefits to AHI detection and treatment, but also substantial diagnostic and logistical barriers to implementation and scale-up. Effective early ART initiation may be critical for HIV eradication efforts, but widespread use in LMIC requires simple and accurate diagnostic tools. Implementation research is critical to facilitate sustainable integration of AHI detection and treatment into existing health systems and will be essential for prospective evaluation of testing algorithms, point-of-care diagnostics, and efficacious and effective first-line regimens. PMID:28691435

Automated Identification of MHD Mode Bifurcation and Locking in Tokamaks

NASA Astrophysics Data System (ADS)

Riquezes, J. D.; Sabbagh, S. A.; Park, Y. S.; Bell, R. E.; Morton, L. A.

2017-10-01

Disruption avoidance is critical in reactor-scale tokamaks such as ITER to maintain steady plasma operation and avoid damage to device components. A key physical event chain that leads to disruptions is the appearance of rotating MHD modes, their slowing by resonant field drag mechanisms, and their locking. An algorithm has been developed that automatically detects bifurcation of the mode toroidal rotation frequency due to loss of torque balance under resonant braking, and mode locking for a set of shots using spectral decomposition. The present research examines data from NSTX, NSTX-U and KSTAR plasmas which differ significantly in aspect ratio (ranging from A = 1.3 - 3.5). The research aims to examine and compare the effectiveness of different algorithms for toroidal mode number discrimination, such as phase matching and singular value decomposition approaches, and to examine potential differences related to machine aspect ratio (e.g. mode eigenfunction shape variation). Simple theoretical models will be compared to the dynamics found. Main goals are to detect or potentially forecast the event chain early during a discharge. This would serve as a cue to engage active mode control or a controlled plasma shutdown. Supported by US DOE Contracts DE-SC0016614 and DE-AC02-09CH11466.

Accurate permittivity measurements for microwave imaging via ultra-wideband removal of spurious reflectors.

PubMed

Pelletier, Mathew G; Viera, Joseph A; Wanjura, John; Holt, Greg

2010-01-01

The use of microwave imaging is becoming more prevalent for detection of interior hidden defects in manufactured and packaged materials. In applications for detection of hidden moisture, microwave tomography can be used to image the material and then perform an inverse calculation to derive an estimate of the variability of the hidden material, such internal moisture, thereby alerting personnel to damaging levels of the hidden moisture before material degradation occurs. One impediment to this type of imaging occurs with nearby objects create strong reflections that create destructive and constructive interference, at the receiver, as the material is conveyed past the imaging antenna array. In an effort to remove the influence of the reflectors, such as metal bale ties, research was conducted to develop an algorithm for removal of the influence of the local proximity reflectors from the microwave images. This research effort produced a technique, based upon the use of ultra-wideband signals, for the removal of spurious reflections created by local proximity reflectors. This improvement enables accurate microwave measurements of moisture in such products as cotton bales, as well as other physical properties such as density or material composition. The proposed algorithm was shown to reduce errors by a 4:1 ratio and is an enabling technology for imaging applications in the presence of metal bale ties.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

Low-complexity R-peak detection for ambulatory fetal monitoring.

PubMed

Rooijakkers, Michael J; Rabotti, Chiara; Oei, S Guid; Mischi, Massimo

2012-07-01

Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

Functional Optical Coherence Tomography Enables In Vivo Physiological Assessment of Retinal Rod and Cone Photoreceptors

PubMed Central

Zhang, Qiuxiang; Lu, Rongwen; Wang, Benquan; Messinger, Jeffrey D.; Curcio, Christine A.; Yao, Xincheng

2015-01-01

Transient intrinsic optical signal (IOS) changes have been observed in retinal photoreceptors, suggesting a unique biomarker for eye disease detection. However, clinical deployment of IOS imaging is challenging due to unclear IOS sources and limited signal-to-noise ratios (SNRs). Here, by developing high spatiotemporal resolution optical coherence tomography (OCT) and applying an adaptive algorithm for IOS processing, we were able to record robust IOSs from single-pass measurements. Transient IOSs, which might reflect an early stage of light phototransduction, are consistently observed in the photoreceptor outer segment almost immediately (<4 ms) after retinal stimulation. Comparative studies of dark- and light-adapted retinas have demonstrated the feasibility of functional OCT mapping of rod and cone photoreceptors, promising a new method for early disease detection and improved treatment of diseases such as age-related macular degeneration (AMD) and other eye diseases that can cause photoreceptor damage. PMID:25901915

Functional Optical Coherence Tomography Enables In Vivo Physiological Assessment of Retinal Rod and Cone Photoreceptors

NASA Astrophysics Data System (ADS)

Zhang, Qiuxiang; Lu, Rongwen; Wang, Benquan; Messinger, Jeffrey D.; Curcio, Christine A.; Yao, Xincheng

2015-04-01

Transient intrinsic optical signal (IOS) changes have been observed in retinal photoreceptors, suggesting a unique biomarker for eye disease detection. However, clinical deployment of IOS imaging is challenging due to unclear IOS sources and limited signal-to-noise ratios (SNRs). Here, by developing high spatiotemporal resolution optical coherence tomography (OCT) and applying an adaptive algorithm for IOS processing, we were able to record robust IOSs from single-pass measurements. Transient IOSs, which might reflect an early stage of light phototransduction, are consistently observed in the photoreceptor outer segment almost immediately (<4 ms) after retinal stimulation. Comparative studies of dark- and light-adapted retinas have demonstrated the feasibility of functional OCT mapping of rod and cone photoreceptors, promising a new method for early disease detection and improved treatment of diseases such as age-related macular degeneration (AMD) and other eye diseases that can cause photoreceptor damage.

A new real-time tsunami detection algorithm

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Pignagnoli, L.

2016-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of sea-bed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability, at low 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. Pressure data sets acquired by Bottom Pressure Recorders 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 which occurred at Haida Gwaii on October 28th, 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard the GEOSTAR stand-alone multidisciplinary abyssal observatory, deployed in the Gulf of Cadiz during the EC project NEAREST and on NEMO-SN1 cabled observatory deployed in the Western Ionian Sea, operational node of the European research infrastructure EMSO.

Dynamical structure and risk assessment of 20th Century Windstorms

NASA Astrophysics Data System (ADS)

Varino, Filipa; Philippe, Arbogast; Bruno, Joly; Gwendal, Rivière; Marie-Laure, Fandeur; Henry, Bovy; Jean-Baptiste, Granier; Mitchell-Wallace, Kirsten

2017-04-01

Windstorms play an important role in weather variability over western Europe. Strong winds associated with fronts and sting jets can lead to several social and economic damages. However, in addition to wind intensity, the displacement speed of the storm, its area and position are also important factors in determining loss. In this study we focus on windstorms associated with the highest damages of the 20th century, and we analyse whether the dynamical structure of the storm is related to its impact. First, we apply an extra-tropical storm tracking algorithm to the ECMWF ERA-20C reanalysis that covers the whole twentieth century and for the whole Northern Hemisphere. Secondly, using the same data, we compute the 3-hourly Loss and Meteorological index for 18 different European countries as in Pinto et al. (2012) with a 25km grid resolution. Thirdly, we develop a High-Loss Tracking Method that matches information from the Loss Index results and the trajectories tracked to systematically associate damages over a particular country to a particular storm. Such a combination provides information on the typical life cycle of storms that create strong damages over a particular country. Finally, only storms hitting France are considered. More than 1500 storms are detected over the whole period and their evolution is analyzed by performing various composites depending on their position relative to the jet stream and their region of impact.

Structural Damage Detection Using Virtual Passive Controllers

NASA Technical Reports Server (NTRS)

Lew, Jiann-Shiun; Juang, Jer-Nan

2001-01-01

This paper presents novel approaches for structural damage detection which uses the virtual passive controllers attached to structures, where passive controllers are energy dissipative devices and thus guarantee the closed-loop stability. The use of the identified parameters of various closed-loop systems can solve the problem that reliable identified parameters, such as natural frequencies of the open-loop system may not provide enough information for damage detection. Only a small number of sensors are required for the proposed approaches. The identified natural frequencies, which are generally much less sensitive to noise and more reliable than the identified natural frequencies, are used for damage detection. Two damage detection techniques are presented. One technique is based on the structures with direct output feedback controllers while the other technique uses the second-order dynamic feedback controllers. A least-squares technique, which is based on the sensitivity of natural frequencies to damage variables, is used for accurately identifying the damage variables.

A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

PubMed

Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

2010-10-30

Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. Copyright © 2010 Elsevier B.V. All rights reserved.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2018-02-01

To advance the concept of smart structures in large systems, such as wind turbines (WTs), it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimised for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialised for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimised one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale WT blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimised DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimised projection vector.

75 FR 27419 - Airworthiness Directives; BAE Systems (Operations) Limited Model BAe 146 and Avro 146-RJ70A, 146...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-17

... environmental and fatigue inspections would not have detected the corrosion or fatigue damage. Corrosion or fatigue damage in this area, if not detected and corrected, could lead to degradation of the structural... fatigue inspections would not have detected the corrosion or fatigue damage. Corrosion or fatigue damage...

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

DOEpatents

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

2015-07-28

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

Error detection method

DOEpatents

Olson, Eric J.

2013-06-11

An apparatus, program product, and method that run an algorithm on a hardware based processor, generate a hardware error as a result of running the algorithm, generate an algorithm output for the algorithm, compare the algorithm output to another output for the algorithm, and detect the hardware error from the comparison. The algorithm is designed to cause the hardware based processor to heat to a degree that increases the likelihood of hardware errors to manifest, and the hardware error is observable in the algorithm output. As such, electronic components may be sufficiently heated and/or sufficiently stressed to create better conditions for generating hardware errors, and the output of the algorithm may be compared at the end of the run to detect a hardware error that occurred anywhere during the run that may otherwise not be detected by traditional methodologies (e.g., due to cooling, insufficient heat and/or stress, etc.).

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

NASA Astrophysics Data System (ADS)

Axel, Colin

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

NASA Astrophysics Data System (ADS)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Lining seam elimination algorithm and surface crack detection in concrete tunnel lining

NASA Astrophysics Data System (ADS)

Qu, Zhong; Bai, Ling; An, Shi-Quan; Ju, Fang-Rong; Liu, Ling

2016-11-01

Due to the particularity of the surface of concrete tunnel lining and the diversity of detection environments such as uneven illumination, smudges, localized rock falls, water leakage, and the inherent seams of the lining structure, existing crack detection algorithms cannot detect real cracks accurately. This paper proposed an algorithm that combines lining seam elimination with the improved percolation detection algorithm based on grid cell analysis for surface crack detection in concrete tunnel lining. First, check the characteristics of pixels within the overlapping grid to remove the background noise and generate the percolation seed map (PSM). Second, cracks are detected based on the PSM by the accelerated percolation algorithm so that the fracture unit areas can be scanned and connected. Finally, the real surface cracks in concrete tunnel lining can be obtained by removing the lining seam and performing percolation denoising. Experimental results show that the proposed algorithm can accurately, quickly, and effectively detect the real surface cracks. Furthermore, it can fill the gap in the existing concrete tunnel lining surface crack detection by removing the lining seam.

Material damage modeling and detection in a thin metallic sheet and sandwich panel using passive acoustic transmission

NASA Astrophysics Data System (ADS)

Jiang, Hao

A method is developed for modeling, detecting, and locating material damage in homogeneous thin metallic sheets and sandwich panels. Analytical and numerical models are used along with non-contact, passive acoustic transmission measurements. It is shown that global and local damage mechanisms characterized by both material and geometrical changes in structural components can be detected using passive acoustic transmission measurements. Theoretical models of a flat sheet and sandwich panel are developed to describe the effects of global material damage due to density, modulus, or thickness changes on backplane radiated sound pressure level distributions. To describe the effects of local material damage, a three-segment stepped beam model and finite element beam, plate, and sandwich panel models are developed and analyzed using the acoustic transmission approach. It is shown that increases or decreases in transmitted sound energy occur behind a damaged material component that exhibits changes in thickness or other geometric or material properties. The damage due to thickness and density changes can be detected from the acoustic transmission, but modulus changes cannot. If the damage is located at an anti-node of a certain forced vibration pattern, the damage can be more readily observed in the data. Higher excitation frequencies within the operating spectrum are preferred to lower frequencies for damage detection. With the finite element beam, plate, and sandwich panel models, local damage detection has been performed in simulations. Experiments on a baffled homogeneous sheet and sandwich panel subjected to broadband acoustic energy show that transmitted intensity measurements with non-contact probes can be used to identify and locate material defects in the sheet and sandwich panel. Material damage is most readily identified where the changes in transmitted sound intensity are largest in the resonant frequency range of the panel. The three main contributions of this research are: (1) the use of non-contact sensing to detect global and localized damage in structural components; (2) the analytical and numerical modeling of material and geometrical damage mechanisms in structural components; and, (3) the experimental verification of acoustic transmission measurements for detecting both material and geometric damage mechanisms.

Probabilistic Damage Characterization Using the Computationally-Efficient Bayesian Approach

NASA Technical Reports Server (NTRS)

Warner, James E.; Hochhalter, Jacob D.

2016-01-01

This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2017-07-01

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Towards real time speckle controlled retinal photocoagulation

NASA Astrophysics Data System (ADS)

Bliedtner, Katharina; Seifert, Eric; Stockmann, Leoni; Effe, Lisa; Brinkmann, Ralf

2016-03-01

Photocoagulation is a laser treatment widely used for the therapy of several retinal diseases. Intra- and inter-individual variations of the ocular transmission, light scattering and the retinal absorption makes it impossible to achieve a uniform effective exposure and hence a uniform damage throughout the therapy. A real-time monitoring and control of the induced damage is highly requested. Here, an approach to realize a real time optical feedback using dynamic speckle analysis is presented. A 532 nm continuous wave Nd:YAG laser is used for coagulation. During coagulation, speckle dynamics are monitored by a coherent object illumination using a 633nm HeNe laser and analyzed by a CMOS camera with a frame rate up to 1 kHz. It is obvious that a control system needs to determine whether the desired damage is achieved to shut down the system in a fraction of the exposure time. Here we use a fast and simple adaption of the generalized difference algorithm to analyze the speckle movements. This algorithm runs on a FPGA and is able to calculate a feedback value which is correlated to the thermal and coagulation induced tissue motion and thus the achieved damage. For different spot sizes (50-200 μm) and different exposure times (50-500 ms) the algorithm shows the ability to discriminate between different categories of retinal pigment epithelial damage ex-vivo in enucleated porcine eyes. Furthermore in-vivo experiments in rabbits show the ability of the system to determine tissue changes in living tissue during coagulation.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

Detection of dominant flow and abnormal events in surveillance video

NASA Astrophysics Data System (ADS)

Kwak, Sooyeong; Byun, Hyeran

2011-02-01

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Spiral Bevel Gear Damage Detection Using Decision Fusion Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Handschuh, Robert F.; Afjeh, Abdollah A.

2002-01-01

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Rigs. Data was collected during experiments performed in this test rig when pitting damage occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears.

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

Liu, Nana; Rebentrost, Patrick

2018-04-01

Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

A Formally Verified Conflict Detection Algorithm for Polynomial Trajectories

NASA Technical Reports Server (NTRS)

Narkawicz, Anthony; Munoz, Cesar

2015-01-01

In air traffic management, conflict detection algorithms are used to determine whether or not aircraft are predicted to lose horizontal and vertical separation minima within a time interval assuming a trajectory model. In the case of linear trajectories, conflict detection algorithms have been proposed that are both sound, i.e., they detect all conflicts, and complete, i.e., they do not present false alarms. In general, for arbitrary nonlinear trajectory models, it is possible to define detection algorithms that are either sound or complete, but not both. This paper considers the case of nonlinear aircraft trajectory models based on polynomial functions. In particular, it proposes a conflict detection algorithm that precisely determines whether, given a lookahead time, two aircraft flying polynomial trajectories are in conflict. That is, it has been formally verified that, assuming that the aircraft trajectories are modeled as polynomial functions, the proposed algorithm is both sound and complete.

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.

Automatised data quality monitoring of the LHCb Vertex Locator

NASA Astrophysics Data System (ADS)

Bel, L.; Crocombe, A. Ch.; Gersabeck, M.; Pearce, A.; Majewski, M.; Szumlak, T.

2017-10-01

The LHCb Vertex Locator (VELO) is a silicon strip semiconductor detector operating at just 8mm distance to the LHC beams. Its 172,000 strips are read at a frequency of 1.1 MHz and processed by off-detector FPGAs followed by a PC cluster that reduces the event rate to about 10 kHz. During the second run of the LHC, which lasts from 2015 until 2018, the detector performance will undergo continued change due to radiation damage effects. This necessitates a detailed monitoring of the data quality to avoid adverse effects on the physics analysis performance. The VELO monitoring infrastructure has been re-designed compared to the first run of the LHC when it was based on manual checks. The new system is based around an automatic analysis framework, which monitors the performance of new data as well as long-term trends and using dedicated algorithms flags issues whenever they arise. The new analysis framework then analyses the plots that are produced by these algorithms. One of its tasks is to perform custom comparisons between the newly processed data and that from reference runs. The most-likely scenario in which this analysis would identify an issue is the parameters of the readout electronics no longer being optimal and requiring retuning. The data of the monitoring plots can be reduced further, e.g. by evaluating averages, and these quantities are input to long-term trending. This is used to detect slow variation of quantities, which are not detectable by the comparison of two nearby runs. Such gradual change is what is expected due to radiation damage effects. It is essential to detect these changes early such that measures can be taken, e.g. adjustments of the operating voltage, to prevent any impact on the quality of high-level quantities and thus on physics analyses. The plots as well as the analysis results and trends are made available through graphical user interfaces (GUIs). These GUIs are dynamically configured by a single configuration that determines the choice and arrangement of plots and trends and ensures a common look and feel.

Clustering analysis of moving target signatures

NASA Astrophysics Data System (ADS)

Martone, Anthony; Ranney, Kenneth; Innocenti, Roberto

2010-04-01

Previously, we developed a moving target indication (MTI) processing approach to detect and track slow-moving targets inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultra-wideband radar. Our MTI algorithms include change detection, automatic target detection (ATD), clustering, and tracking. The MTI algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the cluster algorithm is unknown and requires manual selection. A critical need exists to automate all aspects of the MTI processing formulation. In this paper, we investigate two techniques that automatically determine the number of clusters: the adaptive knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is based on a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both algorithms are used to analyze the false alarm and detection rates of three operational scenarios of personnel walking inside wood and cinderblock buildings.

DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

PubMed

Shuvaev, Sergey A; Lazutkin, Alexander A; Kedrov, Alexander V; Anokhin, Konstantin V; Enikolopov, Grigori N; Koulakov, Alexei A

2017-01-01

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Multi-object Detection and Discrimination Algorithms

DTIC Science & Technology

2015-03-26

with  an   algorithm  similar  to  a  depth-­‐first   search .   This  stage  of  the   algorithm  is  O(CN).  From...Multi-object Detection and Discrimination Algorithms This document contains an overview of research and work performed and published at the University...of Florida from October 1, 2009 to October 31, 2013 pertaining to proposal 57306CS: Multi-object Detection and Discrimination Algorithms

Video Shot Boundary Detection Using QR-Decomposition and Gaussian Transition Detection

NASA Astrophysics Data System (ADS)

Amiri, Ali; Fathy, Mahmood

2010-12-01

This article explores the problem of video shot boundary detection and examines a novel shot boundary detection algorithm by using QR-decomposition and modeling of gradual transitions by Gaussian functions. Specifically, the authors attend to the challenges of detecting gradual shots and extracting appropriate spatiotemporal features that affect the ability of algorithms to efficiently detect shot boundaries. The algorithm utilizes the properties of QR-decomposition and extracts a block-wise probability function that illustrates the probability of video frames to be in shot transitions. The probability function has abrupt changes in hard cut transitions, and semi-Gaussian behavior in gradual transitions. The algorithm detects these transitions by analyzing the probability function. Finally, we will report the results of the experiments using large-scale test sets provided by the TRECVID 2006, which has assessments for hard cut and gradual shot boundary detection. These results confirm the high performance of the proposed algorithm.

FRF-based structural damage detection of controlled buildings with podium structures: Experimental investigation

NASA Astrophysics Data System (ADS)

Xu, Y. L.; Huang, Q.; Zhan, S.; Su, Z. Q.; Liu, H. J.

2014-06-01

How to use control devices to enhance system identification and damage detection in relation to a structure that requires both vibration control and structural health monitoring is an interesting yet practical topic. In this study, the possibility of using the added stiffness provided by control devices and frequency response functions (FRFs) to detect damage in a building complex was explored experimentally. Scale models of a 12-storey main building and a 3-storey podium structure were built to represent a building complex. Given that the connection between the main building and the podium structure is most susceptible to damage, damage to the building complex was experimentally simulated by changing the connection stiffness. To simulate the added stiffness provided by a semi-active friction damper, a steel circular ring was designed and used to add the related stiffness to the building complex. By varying the connection stiffness using an eccentric wheel excitation system and by adding or not adding the circular ring, eight cases were investigated and eight sets of FRFs were measured. The experimental results were used to detect damage (changes in connection stiffness) using a recently proposed FRF-based damage detection method. The experimental results showed that the FRF-based damage detection method could satisfactorily locate and quantify damage.

Combining model based and data based techniques in a robust bridge health monitoring algorithm.

DOT National Transportation Integrated Search

2014-09-01

Structural Health Monitoring (SHM) aims to analyze civil, mechanical and aerospace systems in order to assess : incipient damage occurrence. In this project, we are concerned with the development of an algorithm within the : SHM paradigm for applicat...

Hail statistic in Western Europe based on a hyrid cell-tracking algorithm combining radar signals with hailstone observations

NASA Astrophysics Data System (ADS)

Fluck, Elody

2015-04-01

Hail statistic in Western Europe based on a hybrid cell-tracking algorithm combining radar signals with hailstone observations Elody Fluck¹, Michael Kunz¹ , Peter Geissbühler², Stefan P. Ritz² With hail damage estimated over Billions of Euros for a single event (e.g., hailstorm Andreas on 27/28 July 2013), hail constitute one of the major atmospheric risks in various parts of Europe. The project HAMLET (Hail Model for Europe) in cooperation with the insurance company Tokio Millennium Re aims at estimating hail probability, hail hazard and, combined with vulnerability, hail risk for several European countries (Germany, Switzerland, France, Netherlands, Austria, Belgium and Luxembourg). Hail signals are obtained from radar reflectivity since this proxy is available with a high temporal and spatial resolution using several hail proxies, especially radar data. The focus in the first step is on Germany and France for the periods 2005- 2013 and 1999 - 2013, respectively. In the next step, the methods will be transferred and extended to other regions. A cell-tracking algorithm TRACE2D was adjusted and applied to two dimensional radar reflectivity data from different radars operated by European weather services such as German weather service (DWD) and French weather service (Météo-France). Strong convective cells are detected by considering 3 connected pixels over 45 dBZ (Reflectivity Cores RCs) in a radar scan. Afterwards, the algorithm tries to find the same RCs in the next 5 minute radar scan and, thus, track the RCs centers over time and space. Additional information about hailstone diameters provided by ESWD (European Severe Weather Database) is used to determine hail intensity of the detected hail swaths. Maximum hailstone diameters are interpolated along and close to the individual hail tracks giving an estimation of mean diameters for the detected hail swaths. Furthermore, a stochastic event set is created by randomizing the parameters obtained from the tracking approach of the historical event catalogue (length, width, orientation, diameter). This stochastic event set will be used to quantify hail risk and to estimate probable maximum loss (e.g., PML200) for a given industry motor or property (building) portfolio.

Fast and accurate image recognition algorithms for fresh produce food safety sensing

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Kim, Moon S.; Chao, Kuanglin; Kang, Sukwon; Lefcourt, Alan M.

2011-06-01

This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

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

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using amore » combinatorial algorithm.« less

Gas leak detection in infrared video with background modeling

NASA Astrophysics Data System (ADS)

Zeng, Xiaoxia; Huang, Likun

2018-03-01

Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

NASA Astrophysics Data System (ADS)

Salamatova, T.; Zhukov, V.

2017-02-01

The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

PubMed

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-08-14

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Experimental Study on Damage Detection in Timber Specimens Based on an Electromechanical Impedance Technique and RMSD-Based Mahalanobis Distance

PubMed Central

Wang, Dansheng; Wang, Qinghua; Wang, Hao; Zhu, Hongping

2016-01-01

In the electromechanical impedance (EMI) method, the PZT patch performs the functions of both sensor and exciter. Due to the high frequency actuation and non-model based characteristics, the EMI method can be utilized to detect incipient structural damage. In recent years EMI techniques have been widely applied to monitor the health status of concrete and steel materials, however, studies on application to timber are limited. This paper will explore the feasibility of using the EMI technique for damage detection in timber specimens. In addition, the conventional damage index, namely root mean square deviation (RMSD) is employed to evaluate the level of damage. On that basis, a new damage index, Mahalanobis distance based on RMSD, is proposed to evaluate the damage severity of timber specimens. Experimental studies are implemented to detect notch and hole damage in the timber specimens. Experimental results verify the availability and robustness of the proposed damage index and its superiority over the RMSD indexes. PMID:27782088

Experimental Study on Damage Detection in Timber Specimens Based on an Electromechanical Impedance Technique and RMSD-Based Mahalanobis Distance.

PubMed

Wang, Dansheng; Wang, Qinghua; Wang, Hao; Zhu, Hongping

2016-10-22

In the electromechanical impedance (EMI) method, the PZT patch performs the functions of both sensor and exciter. Due to the high frequency actuation and non-model based characteristics, the EMI method can be utilized to detect incipient structural damage. In recent years EMI techniques have been widely applied to monitor the health status of concrete and steel materials, however, studies on application to timber are limited. This paper will explore the feasibility of using the EMI technique for damage detection in timber specimens. In addition, the conventional damage index, namely root mean square deviation (RMSD) is employed to evaluate the level of damage. On that basis, a new damage index, Mahalanobis distance based on RMSD, is proposed to evaluate the damage severity of timber specimens. Experimental studies are implemented to detect notch and hole damage in the timber specimens. Experimental results verify the availability and robustness of the proposed damage index and its superiority over the RMSD indexes.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Computational Modeling System for Deformation and Failure in Polycrystalline Metals

DTIC Science & Technology

2009-03-29

FIB/EHSD 3.3 The Voronoi Cell FEM for Micromechanical Modeling 3.4 VCFEM for Microstructural Damage Modeling 3.5 Adaptive Multiscale Simulations...accurate and efficient image-based micromechanical finite element model, for crystal plasticity and damage , incorporating real morphological and...topology with evolving strain localization and damage . (v) Development of multi-scaling algorithms in the time domain for compression and localization in

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Vibration-based damage detection in a concrete beam under temperature variations using AR models and state-space approaches

NASA Astrophysics Data System (ADS)

Clément, A.; Laurens, S.

2011-07-01

The Structural Health Monitoring of civil structures subjected to ambient vibrations is very challenging. Indeed, the variations of environmental conditions and the difficulty to characterize the excitation make the damage detection a hard task. Auto-regressive (AR) models coefficients are often used as damage sensitive feature. The presented work proposes a comparison of the AR approach with a state-space feature formed by the Jacobian matrix of the dynamical process. Since the detection of damage can be formulated as a novelty detection problem, Mahalanobis distance is applied to track new points from an undamaged reference collection of feature vectors. Data from a concrete beam subjected to temperature variations and damaged by several static loading are analyzed. It is observed that the damage sensitive features are effectively sensitive to temperature variations. However, the use of the Mahalanobis distance makes possible the detection of cracking with both of them. Early damage (before cracking) is only revealed by the AR coefficients with a good sensibility.

Optical imaging modalities: From design to diagnosis of skin cancer

NASA Astrophysics Data System (ADS)

Korde, Vrushali Raj

This study investigates three high resolution optical imaging modalities to better detect and diagnose skin cancer. The ideal high resolution optical imaging system can visualize pre-malignant tissue growth non-invasively with resolution comparable to histology. I examined 3 modalities which approached this goal. The first method examined was high magnification microscopy of thin stained tissue sections, together with a statistical analysis of nuclear chromatin patterns termed Karyometry. This method has subcellular resolution, but it necessitates taking a biopsy at the desired tissue site and imaging the tissue ex-vivo. My part of this study was to develop an automated nuclear segmentation algorithm to segment cell nuclei in skin histology images for karyometric analysis. The results of this algorithm were compared to hand segmented cell nuclei in the same images, and it was concluded that the automated segmentations can be used for karyometric analysis. The second optical imaging modality I investigated was Optical Coherence Tomography (OCT). OCT is analogous to ultrasound, in which sound waves are delivered into the body and the echo time and reflected signal magnitude are measured. Due to the fast speed of light and detector temporal integration times, low coherence interferometry is needed to gate the backscattered light. OCT acquires cross sectional images, and has an axial resolution of 1-15 mum (depending on the source bandwidth) and a lateral resolution of 10-20 mum (depending on the sample arm optics). While it is not capable of achieving subcellular resolution, it is a non-invasive imaging modality. OCT was used in this study to evaluate skin along a continuum from normal to sun damaged to precancer. I developed algorithms to detect statistically significant differences between images of sun protected and sun damaged skin, as well as between undiseased and precancerous skin. An Optical Coherence Microscopy (OCM) endoscope was developed in the third portion of this study. OCM is a high resolution en-face imaging modality. It is a hybrid system that combines the principles of confocal microscopy with coherence gating to provide an increased imaging depth. It can also be described as an OCT system with a high NA objective. Similar to OCT, the axial resolution is determined by the source center wavelength and bandwidth. The NA of the sample arm optics determines the lateral resolution, usually on the order of 1-5 mum. My effort on this system was to develop a handheld endoscope. To my knowledge, an OCM endoscope has not been developed prior to this work. An image of skin was taken as a proof of concept. This rigid handheld OCM endoscope will be useful for applications ranging from minimally invasive surgical imaging to non-invasively assessing dysplasia and sun damage in skin.

Follow-Up Genotoxic Study: Chromosome Damage Two and Six Years after Exposure to the Prestige Oil Spill

PubMed Central

Hildur, Kristin; Templado, Cristina; Zock, Jan-Paul; Giraldo, Jesús; Pozo-Rodríguez, Francisco; Frances, Alexandra; Monyarch, Gemma; Rodríguez-Trigo, Gema; Rodriguez-Rodriguez, Emma; Souto, Ana; Gómez, Federico P.; Antó, Josep M.; Barberà, Joan Albert; Fuster, Carme

2015-01-01

Background The north-west coast of Spain was heavily contaminated by the Prestige oil spill, in 2002. Individuals who participated in the clean-up tasks showed increased chromosome damage two years after exposure. Long-term clinical implications of chromosome damage are still unknown. Objective To realize a follow-up genotoxic study to detect whether the chromosome damage persisted six years after exposure to the oil. Design Follow-up study. Setting Fishermen cooperatives in coastal villages. Participants Local fishermen who were highly exposed (n = 52) and non-exposed (n = 23) to oil seven years after the spill. Measurements Chromosome damage in circulating lymphocytes. Results Chromosome damage in exposed individuals persists six years after oil exposure, with a similar incidence than those previously detected four years before. A surprising increase in chromosome damage in non-exposed individual was found six years after Prestige spill vs. those detected two years after the exposure. Limitations The sample size and the possibility of some kind of selection bias should be considered. Genotoxic results cannot be extrapolated to the approximately 300,000 individuals who participated occasionally in clean-up tasks. Conclusion The persistence of chromosome damage detected in exposed individuals six years after oil exposure seems to indicate that the cells of the bone marrow are affected. A surprising increase in chromosome damage in non-exposed individuals detected in the follow-up study suggests an indirect exposition of these individuals to some oil compounds or to other toxic agents during the last four years. More long-term studies are needed to confirm the presence of chromosome damage in exposed and non-exposed fishermen due to the association between increased chromosomal damage and increased risk of cancer. Understanding and detecting chromosome damage is important for detecting cancer in its early stages. The present work is the first follow-up cytogenetic study carried out in lymphocytes to determine genotoxic damage evolution between two and six years after oil exposure in same individuals. PMID:26221948

Firefighting and Emergency Response Study of Advanced Composites Aircraft; Objective 1: Composite Material Damage in Minor Aircraft Fires

DTIC Science & Technology

2013-05-18

26 4.3. 1-D Heat Transfer Model with Pyrolysis and Thermal Damage...Improvements and Added Features ........................................................................31 4.3.4. Pyrolysis Model Calibration... Pyrolysis Model ................................................32 Figure 25. Updated Heat Transfer Algorithm Flow Chart

Comparison of human observer and algorithmic target detection in nonurban forward-looking infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.

2005-07-01

We have performed an experiment that compares the performance of human observers with that of a robust algorithm for the detection of targets in difficult, nonurban forward-looking infrared imagery. Our purpose was to benchmark the comparison and document performance differences for future algorithm improvement. The scale-insensitive detection algorithm, used as a benchmark by the Night Vision Electronic Sensors Directorate for algorithm evaluation, employed a combination of contrastlike features to locate targets. Detection receiver operating characteristic curves and observer-confidence analyses were used to compare human and algorithmic responses and to gain insight into differences. The test database contained ground targets, in natural clutter, whose detectability, as judged by human observers, ranged from easy to very difficult. In general, as compared with human observers, the algorithm detected most of the same targets, but correlated confidence with correct detections poorly and produced many more false alarms at any useful level of performance. Though characterizing human performance was not the intent of this study, results suggest that previous observational experience was not a strong predictor of human performance, and that combining individual human observations by majority vote significantly reduced false-alarm rates.

Multi-Agent Patrolling under Uncertainty and Threats.

PubMed

Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D

2015-01-01

We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Damage Assessment Map from Interferometric Coherence

NASA Astrophysics Data System (ADS)

Yun, S.; Fielding, E. J.; Simons, M.; Rosen, P. A.; Owen, S. E.; Webb, F.

2010-12-01

Large earthquakes cause buildings to collapse, which often claims the lives of many. For example, 2010 Haiti earthquake killed about 230,000 people, with about 280,000 buildings collapsed or severely damaged. When a major earthquake hits an urban area, one of the most critical information for rescue operations is rapid and accurate assessment of building-collapse areas. From a study on 2003 Bam earthquake in Iran, interferometric coherence was proved useful for earthquake damage assessment (Fielding et al., 2005) when similar perpendicular baselines can be found for pre- and coseismic interferometric pairs and when there is little temporal and volume decorrelation. In this study we develop a new algorithm to create a more robust and accurate damage assessment map using interferometric coherence despite different interferometric baselines and with other decorrelation sources. We test the algorithm on a building block that recently underwent demolition, which is a proxy for building collapse due to earthquakes, for new construction in the City of Pasadena, California. The size of the building block is about 150 m E-W and 300 m N-S, and the demolition project started on April 23, 2007 and continued until January 22, 2008. After we process Japanese L-band ALOS PALSAR data with ROI_PAC, an interferometric coherence map that spans the demolition period is registered to a coherence map before the demolition, and the relative bias of the coherence values are removed, then a causality constraint is applied to enhance the change due to demolition. The results show clear change in coherence at the demolition site. We improve the signal-to-noise ratio of the coherence change at the demolition site from 17.3 (for simple difference) to 44.6 (with the new algorithm). The damage assessment map algorithm will become more useful with the emergence of InSAR missions with more frequent data acquisition, such as Sentinel-1 and DESDynI.

Flood damage estimation of companies: A comparison of Stage-Damage-Functions and Random Forests

NASA Astrophysics Data System (ADS)

Sieg, Tobias; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno

2017-04-01

The development of appropriate flood damage models plays an important role not only for the damage assessment after an event but also to develop adaptation and risk mitigation strategies. So called Stage-Damage-Functions (SDFs) are often applied as a standard approach to estimate flood damage. These functions assign a certain damage to the water depth depending on the use or other characteristics of the exposed objects. Recent studies apply machine learning algorithms like Random Forests (RFs) to model flood damage. These algorithms usually consider more influencing variables and promise to depict a more detailed insight into the damage processes. In addition they provide an inherent validation scheme. Our study focuses on direct, tangible damage of single companies. The objective is to model and validate the flood damage suffered by single companies with SDFs and RFs. The data sets used are taken from two surveys conducted after the floods in the Elbe and Danube catchments in the years 2002 and 2013 in Germany. Damage to buildings (n = 430), equipment (n = 651) as well as goods and stock (n = 530) are taken into account. The model outputs are validated via a comparison with the actual flood damage acquired by the surveys and subsequently compared with each other. This study investigates the gain in model performance with the use of additional data and the advantages and disadvantages of the RFs compared to SDFs. RFs show an increase in model performance with an increasing amount of data records over a comparatively large range, while the model performance of the SDFs is already saturated for a small set of records. In addition, the RFs are able to identify damage influencing variables, which improves the understanding of damage processes. Hence, RFs can slightly improve flood damage predictions and provide additional insight into the underlying mechanisms compared to SDFs.

A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

PubMed

Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

2015-05-01

A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices.

Application of crowdsourced hail data and damage information for hail risk assessment in the province of Styria, Austria

NASA Astrophysics Data System (ADS)

Tani, Satyanarayana; Rechberger, Andreas; Süsser Rechberger, Barbara; Teschl, Reinhard; Paulitsch, Helmut

2017-04-01

Hail storm damage is a major concern to the farmers in the province of Styria, Austria. Each year severe hail storms are causing damages to crops, resulting in losses of millions of euros. High spatial and timely ground truth information of the hail event and crop damage measurements are essential for better hail risk assessment. Usually, hail pad networks and visual damage surveys are used to collect the hail data and corresponding damage information. However, these hail pad networks are expensive and need laborious maintenance. The traditional crop damage assessment approaches are very labour-intensive and time-consuming. The advancements in information and communication technology (ICT) and the power of citizen based crowdsourcing data, will help to overcome these problems and ultimately provide a platform for data collection. A user-friendly and bilingual web interface was developed to collect hail data and crop damage information in the province of Styria, Austria. The dynamic web interface was developed using HTML5, JavaScript, and PHP7 combined with a MySQL database back-end. OpenStreetMap was integrated into the web interface and tile server optimised for an easy identification of geolocation information. The user needs an internet connection to transfer the data through smartphone or computer. Crowdsourced data will be quality tested and evaluated with 3D single polarisation C-band weather radar data to remove potential false reports. Further, the relationship between the reported hail events and radar-based hail detection algorithms (Waldvogel and Auer) and derived hail signature information intended for crop hail risk assessment will be investigated. The details about the web interface tool, application and verification methods to collect, analyse, and integrate different data sets are given. Further, the high spatial risk assessment information is communicated to support risk management policy.

A vibro-haptic human-machine interface for structural health monitoring

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

Mascarenas, David; Plont, Crystal; Brown, Christina

The structural health monitoring (SHM) community’s goal has been to endow physical systems with a nervous system not unlike those commonly found in living organisms. Typically the SHM community has attempted to do this by instrumenting structures with a variety of sensors, and then applying various signal processing and classification procedures to the data in order to detect the presence of damage, the location of damage, the severity of damage, and to estimate the remaining useful life of the structure. This procedure has had some success, but we are still a long way from achieving the performance of nervous systemsmore » found in biology. This is primarily because contemporary classification algorithms do not have the performance required. In many cases expert judgment is superior to automated classification. This work introduces a new paradigm. We propose interfacing the human nervous system to the distributed sensor network located on the structure and developing new techniques to enable human-machine cooperation. Results from the field of sensory substitution suggest this should be possible. This study investigates a vibro-haptic human-machine interface for SHM. The investigation was performed using a surrogate three-story structure. The structure features three nonlinearity-inducing bumpers to simulate damage. Accelerometers are placed on each floor to measure the response of the structure to a harmonic base excitation. The accelerometer measurements are preprocessed. As a result, the preprocessed data is then encoded encoded as a vibro-tactile stimulus. Human subjects were then subjected to the vibro-tactile stimulus and asked to characterize the damage in the structure.« less

A vibro-haptic human-machine interface for structural health monitoring

DOE PAGES

Mascarenas, David; Plont, Crystal; Brown, Christina; ...

2014-11-01

The structural health monitoring (SHM) community’s goal has been to endow physical systems with a nervous system not unlike those commonly found in living organisms. Typically the SHM community has attempted to do this by instrumenting structures with a variety of sensors, and then applying various signal processing and classification procedures to the data in order to detect the presence of damage, the location of damage, the severity of damage, and to estimate the remaining useful life of the structure. This procedure has had some success, but we are still a long way from achieving the performance of nervous systemsmore » found in biology. This is primarily because contemporary classification algorithms do not have the performance required. In many cases expert judgment is superior to automated classification. This work introduces a new paradigm. We propose interfacing the human nervous system to the distributed sensor network located on the structure and developing new techniques to enable human-machine cooperation. Results from the field of sensory substitution suggest this should be possible. This study investigates a vibro-haptic human-machine interface for SHM. The investigation was performed using a surrogate three-story structure. The structure features three nonlinearity-inducing bumpers to simulate damage. Accelerometers are placed on each floor to measure the response of the structure to a harmonic base excitation. The accelerometer measurements are preprocessed. As a result, the preprocessed data is then encoded encoded as a vibro-tactile stimulus. Human subjects were then subjected to the vibro-tactile stimulus and asked to characterize the damage in the structure.« less

AdaBoost-based algorithm for network intrusion detection.

PubMed

Hu, Weiming; Hu, Wei; Maybank, Steve

2008-04-01

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

QRS Detection Algorithm for Telehealth Electrocardiogram Recordings.

PubMed

Khamis, Heba; Weiss, Robert; Xie, Yang; Chang, Chan-Wei; Lovell, Nigel H; Redmond, Stephen J

2016-07-01

QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a "UNSW" QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan-Tompkins (PT) and Gutiérrez-Rivas (GR) algorithm. For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity (Se) and positive predictivity (+P) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher +P (98%, p < 0.05) than PT and GR. For 250 telehealth ECG records (unsupervised recordings; dry metal electrodes), the UNSW algorithm had 98% Se and 95% +P which was superior to PT (+P: p < 0.001) and GR (Se and +P: p < 0.001). This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. The UNSW algorithm could be used to manage increasing telehealth ECG analysis workloads.

Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography.

PubMed

Latha, Indu; Reichenbach, Stephen E; Tao, Qingping

2011-09-23

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method. Copyright © 2011 Elsevier B.V. All rights reserved.

Development of lightweight structural health monitoring systems for aerospace applications

NASA Astrophysics Data System (ADS)

Pearson, Matthew

This thesis investigates the development of structural health monitoring systems (SHM) for aerospace applications. The work focuses on each aspect of a SHM system covering novel transducer technologies and damage detection techniques to detect and locate damage in metallic and composite structures. Secondly the potential of energy harvesting and power arrangement methodologies to provide a stable power source is assessed. Finally culminating in the realisation of smart SHM structures. 1. Transducer Technology A thorough experimental study of low profile, low weight novel transducers not normally used for acoustic emission (AE) and acousto-ultrasonics (AU) damage detection was conducted. This included assessment of their performance when exposed to aircraft environments and feasibility of embedding these transducers in composites specimens in order to realise smart structures. 2. Damage Detection An extensive experimental programme into damage detection utilising AE and AU were conducted in both composites and metallic structures. These techniques were used to assess different damage mechanism within these materials. The same transducers were used for novel AE location techniques coupled with AU similarity assessment to successfully detect and locate damage in a variety of structures. 3. Energy Harvesting and Power Management Experimental investigations and numerical simulations were undertaken to assess the power generation levels of piezoelectric and thermoelectric generators for typical vibration and temperature differentials which exist in the aerospace environment. Furthermore a power management system was assessed to demonstrate the ability of the system to take the varying nature of the input power and condition it to a stable power source for a system. 4. Smart Structures The research conducted is brought together into a smart carbon fibre wing showcasing the novel embedded transducers for AE and AU damage detection and location, as well as vibration energy harvesting. A study into impact damage detection using the techniques showed the successful detection and location of damage. Also the feasibility of the embedded transducers for power generation was assessed..

Bio-ALIRT biosurveillance detection algorithm evaluation.

PubMed

Siegrist, David; Pavlin, J

2004-09-24

Early detection of disease outbreaks by a medical biosurveillance system relies on two major components: 1) the contribution of early and reliable data sources and 2) the sensitivity, specificity, and timeliness of biosurveillance detection algorithms. This paper describes an effort to assess leading detection algorithms by arranging a common challenge problem and providing a common data set. The objectives of this study were to determine whether automated detection algorithms can reliably and quickly identify the onset of natural disease outbreaks that are surrogates for possible terrorist pathogen releases, and do so at acceptable false-alert rates (e.g., once every 2-6 weeks). Historic de-identified data were obtained from five metropolitan areas over 23 months; these data included International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. An outbreak detection group identified and labeled two natural disease outbreaks in these data and provided them to analysts for training of detection algorithms. All outbreaks in the remaining test data were identified but not revealed to the detection groups until after their analyses. The algorithms established a probability of outbreak for each day's counts. The probability of outbreak was assessed as an "actual" alert for different false-alert rates. The best algorithms were able to detect all of the outbreaks at false-alert rates of one every 2-6 weeks. They were often able to detect for the same day human investigators had identified as the true start of the outbreak. Because minimal data exists for an actual biologic attack, determining how quickly an algorithm might detect such an attack is difficult. However, application of these algorithms in combination with other data-analysis methods to historic outbreak data indicates that biosurveillance techniques for analyzing syndrome counts can rapidly detect seasonal respiratory and gastrointestinal illness outbreaks. Further research is needed to assess the value of electronic data sources for predictive detection. In addition, simulations need to be developed and implemented to better characterize the size and type of biologic attack that can be detected by current methods by challenging them under different projected operational conditions.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm.

PubMed

Pandit, Diptangshu; Zhang, Li; Liu, Chengyu; Chattopadhyay, Samiran; Aslam, Nauman; Lim, Chee Peng

2017-06-01

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal. Copyright © 2017 Elsevier B.V. All rights reserved.

Design of an algorithm for autonomous docking with a freely tumbling target

NASA Astrophysics Data System (ADS)

Nolet, Simon; Kong, Edmund; Miller, David W.

2005-05-01

For complex unmanned docking missions, limited communication bandwidth and delays do not allow ground operators to have immediate access to all real-time state information and hence prevent them from playing an active role in the control loop. Advanced control algorithms are needed to make mission critical decisions to ensure safety of both spacecraft during close proximity maneuvers. This is especially true when unexpected contingencies occur. These algorithms will enable multiple space missions, including servicing of damaged spacecraft and missions to Mars. A key characteristic of spacecraft servicing missions is that the target spacecraft is likely to be freely tumbling due to various mechanical failures or fuel depletion. Very few technical references in the literature can be found on autonomous docking with a freely tumbling target and very few such maneuvers have been attempted. The MIT Space Systems Laboratory (SSL) is currently performing research on the subject. The objective of this research is to develop a control architecture that will enable safe and fuel-efficient docking of a thruster based spacecraft with a freely tumbling target in presence of obstacles and contingencies. The approach is to identify, select and implement state estimation, fault detection, isolation and recovery, optimal path planning and thruster management algorithms that, once properly integrated, can accomplish such a maneuver autonomously. Simulations and demonstrations on the SPHERES testbed developed by the MIT SSL will be executed to assess the performance of different combinations of algorithms. To date, experiments have been carried out at the MIT SSL 2-D Laboratory and at the NASA Marshall Space Flight Center (MSFC) flat floor.

Robots that can adapt like animals.

PubMed

Cully, Antoine; Clune, Jeff; Tarapore, Danesh; Mouret, Jean-Baptiste

2015-05-28

Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot 'think outside the box' to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot's prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

Robots that can adapt like animals

NASA Astrophysics Data System (ADS)

Cully, Antoine; Clune, Jeff; Tarapore, Danesh; Mouret, Jean-Baptiste

2015-05-01

Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot `think outside the box' to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot's prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

NASA Astrophysics Data System (ADS)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

STREAMFINDER - I. A new algorithm for detecting stellar streams

NASA Astrophysics Data System (ADS)

Malhan, Khyati; Ibata, Rodrigo A.

2018-07-01

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution, we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams, and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the European Space Agency/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia data set.

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

An Improved Harmonic Current Detection Method Based on Parallel Active Power Filter

NASA Astrophysics Data System (ADS)

Zeng, Zhiwu; Xie, Yunxiang; Wang, Yingpin; Guan, Yuanpeng; Li, Lanfang; Zhang, Xiaoyu

2017-05-01

Harmonic detection technology plays an important role in the applications of active power filter. The accuracy and real-time performance of harmonic detection are the precondition to ensure the compensation performance of Active Power Filter (APF). This paper proposed an improved instantaneous reactive power harmonic current detection algorithm. The algorithm uses an improved ip -iq algorithm which is combined with the moving average value filter. The proposed ip -iq algorithm can remove the αβ and dq coordinate transformation, decreasing the cost of calculation, simplifying the extraction process of fundamental components of load currents, and improving the detection speed. The traditional low-pass filter is replaced by the moving average filter, detecting the harmonic currents more precisely and quickly. Compared with the traditional algorithm, the THD (Total Harmonic Distortion) of the grid currents is reduced from 4.41% to 3.89% for the simulations and from 8.50% to 4.37% for the experiments after the improvement. The results show the proposed algorithm is more accurate and efficient.

A Space Object Detection Algorithm using Fourier Domain Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Becker, D.; Cain, S.

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This paper explores the potential for detection performance advantages when operating in the Fourier domain of long exposure images of small and/or dim space objects from ground based telescopes. A binary hypothesis test is developed based on the joint probability distribution function of the image under the hypothesis that an object is present and under the hypothesis that the image only contains background noise. The detection algorithm tests each pixel point of the Fourier transformed images to make the determination if an object is present based on the criteria threshold found in the likelihood ratio test. Using simulated data, the performance of the Fourier domain detection algorithm is compared to the current algorithm used in space situational awareness applications to evaluate its value.

Effect of Non-rigid Registration Algorithms on Deformation Based Morphometry: A Comparative Study with Control and Williams Syndrome Subjects

PubMed Central

Han, Zhaoying; Thornton-Wells, Tricia A.; Dykens, Elisabeth M.; Gore, John C.; Dawant, Benoit M.

2014-01-01

Deformation Based Morphometry (DBM) is a widely used method for characterizing anatomical differences across groups. DBM is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a DBM atlas. Although several studies have compared non-rigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithms on group differences that may be uncovered through DBM. In this study, we compared group atlas creation and DBM results obtained with five well-established non-rigid registration algorithms using thirteen subjects with Williams Syndrome (WS) and thirteen Normal Control (NC) subjects. The five non-rigid registration algorithms include: (1) The Adaptive Bases Algorithm (ABA); (2) The Image Registration Toolkit (IRTK); (3) The FSL Nonlinear Image Registration Tool (FSL); (4) The Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. Results indicate that the choice of algorithm has little effect on the creation of group atlases. However, regions of differences between groups detected with DBM vary from algorithm to algorithm both qualitatively and quantitatively. The unique nature of the data set used in this study also permits comparison of visible anatomical differences between the groups and regions of difference detected by each algorithm. Results show that the interpretation of DBM results is difficult. Four out of the five algorithms we have evaluated detect bilateral differences between the two groups in the insular cortex, the basal ganglia, orbitofrontal cortex, as well as in the cerebellum. These correspond to differences that have been reported in the literature and that are visible in our samples. But our results also show that some algorithms detect regions that are not detected by the others and that the extent of the detected regions varies from algorithm to algorithm. These results suggest that using more than one algorithm when performing DBM studies would increase confidence in the results. Properties of the algorithms such as the similarity measure they maximize and the regularity of the deformation fields, as well as the location of differences detected with DBM, also need to be taken into account in the interpretation process. PMID:22459439

A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes

PubMed Central

Wang, Jianqiang; Sun, Xiaoyan; Guo, Junbin

2013-01-01

The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.

Accelerated damage visualization using binary search with fixed pitch-catch distance laser ultrasonic scanning

NASA Astrophysics Data System (ADS)

Park, Byeongjin; Sohn, Hoon

2017-07-01

Laser ultrasonic scanning, especially full-field wave propagation imaging, is attractive for damage visualization thanks to its noncontact nature, sensitivity to local damage, and high spatial resolution. However, its practicality is limited because scanning at a high spatial resolution demands a prohibitively long scanning time. Inspired by binary search, an accelerated damage visualization technique is developed to visualize damage with a reduced scanning time. The pitch-catch distance between the excitation point and the sensing point is also fixed during scanning to maintain a high signal-to-noise ratio (SNR) of measured ultrasonic responses. The approximate damage boundary is identified by examining the interactions between ultrasonic waves and damage observed at the scanning points that are sparsely selected by a binary search algorithm. Here, a time-domain laser ultrasonic response is transformed into a spatial ultrasonic domain response using a basis pursuit approach so that the interactions between ultrasonic waves and damage, such as reflections and transmissions, can be better identified in the spatial ultrasonic domain. Then, the area inside the identified damage boundary is visualized as damage. The performance of the proposed damage visualization technique is validated excusing a numerical simulation performed on an aluminum plate with a notch and experiments performed on an aluminum plate with a crack and a wind turbine blade with delamination. The proposed damage visualization technique accelerates the damage visualization process in three aspects: (1) the number of measurements that is necessary for damage visualization is dramatically reduced by a binary search algorithm; (2) the number of averaging that is necessary to achieve a high SNR is reduced by maintaining the wave propagation distance short; and (3) with the proposed technique, the same damage can be identified with a lower spatial resolution than the spatial resolution required by full-field wave propagation imaging.

Expert system constant false alarm rate processor

NASA Astrophysics Data System (ADS)

Baldygo, William J., Jr.; Wicks, Michael C.

1993-10-01

The requirements for high detection probability and low false alarm probability in modern wide area surveillance radars are rarely met due to spatial variations in clutter characteristics. Many filtering and CFAR detection algorithms have been developed to effectively deal with these variations; however, any single algorithm is likely to exhibit excessive false alarms and intolerably low detection probabilities in a dynamically changing environment. A great deal of research has led to advances in the state of the art in Artificial Intelligence (AI) and numerous areas have been identified for application to radar signal processing. The approach suggested here, discussed in a patent application submitted by the authors, is to intelligently select the filtering and CFAR detection algorithms being executed at any given time, based upon the observed characteristics of the interference environment. This approach requires sensing the environment, employing the most suitable algorithms, and applying an appropriate multiple algorithm fusion scheme or consensus algorithm to produce a global detection decision.

Toward an Objective Enhanced-V Detection Algorithm

NASA Technical Reports Server (NTRS)

Moses, John F.; Brunner,Jason C.; Feltz, Wayne F.; Ackerman, Steven A.; Moses, John F.; Rabin, Robert M.

2007-01-01

The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V signature, has been observed to occur during and preceding severe weather. This study describes an algorithmic approach to objectively detect overshooting tops, temperature couplets, and enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of temperature, temperature difference, and distance thresholds for the overshooting top and temperature couplet detection parts of the algorithm and consists of cross correlation statistics of pixels for the enhanced-V detection part of the algorithm. The effectiveness of the overshooting top and temperature couplet detection components of the algorithm is examined using GOES and MODIS image data for case studies in the 2003-2006 seasons. The main goal is for the algorithm to be useful for operations with future sensors, such as GOES-R.

Quantitative Damage Detection and Sparse Sensor Array Optimization of Carbon Fiber Reinforced Resin Composite Laminates for Wind Turbine Blade Structural Health Monitoring

PubMed Central

Li, Xiang; Yang, Zhibo; Chen, Xuefeng

2014-01-01

The active structural health monitoring (SHM) approach for the complex composite laminate structures of wind turbine blades (WTBs), addresses the important and complicated problem of signal noise. After illustrating the wind energy industry's development perspectives and its crucial requirement for SHM, an improved redundant second generation wavelet transform (IRSGWT) pre-processing algorithm based on neighboring coefficients is introduced for feeble signal denoising. The method can avoid the drawbacks of conventional wavelet methods that lose information in transforms and the shortcomings of redundant second generation wavelet (RSGWT) denoising that can lead to error propagation. For large scale WTB composites, how to minimize the number of sensors while ensuring accuracy is also a key issue. A sparse sensor array optimization of composites for WTB applications is proposed that can reduce the number of transducers that must be used. Compared to a full sixteen transducer array, the optimized eight transducer configuration displays better accuracy in identifying the correct position of simulated damage (mass of load) on composite laminates with anisotropic characteristics than a non-optimized array. It can help to guarantee more flexible and qualified monitoring of the areas that more frequently suffer damage. The proposed methods are verified experimentally on specimens of carbon fiber reinforced resin composite laminates. PMID:24763210

Comparison and validation of injury risk classifiers for advanced automated crash notification systems.

PubMed

Kusano, Kristofer; Gabler, Hampton C

2014-01-01

The odds of death for a seriously injured crash victim are drastically reduced if he or she received care at a trauma center. Advanced automated crash notification (AACN) algorithms are postcrash safety systems that use data measured by the vehicles during the crash to predict the likelihood of occupants being seriously injured. The accuracy of these models are crucial to the success of an AACN. The objective of this study was to compare the predictive performance of competing injury risk models and algorithms: logistic regression, random forest, AdaBoost, naïve Bayes, support vector machine, and classification k-nearest neighbors. This study compared machine learning algorithms to the widely adopted logistic regression modeling approach. Machine learning algorithms have not been commonly studied in the motor vehicle injury literature. Machine learning algorithms may have higher predictive power than logistic regression, despite the drawback of lacking the ability to perform statistical inference. To evaluate the performance of these algorithms, data on 16,398 vehicles involved in non-rollover collisions were extracted from the NASS-CDS. Vehicles with any occupants having an Injury Severity Score (ISS) of 15 or greater were defined as those requiring victims to be treated at a trauma center. The performance of each model was evaluated using cross-validation. Cross-validation assesses how a model will perform in the future given new data not used for model training. The crash ΔV (change in velocity during the crash), damage side (struck side of the vehicle), seat belt use, vehicle body type, number of events, occupant age, and occupant sex were used as predictors in each model. Logistic regression slightly outperformed the machine learning algorithms based on sensitivity and specificity of the models. Previous studies on AACN risk curves used the same data to train and test the power of the models and as a result had higher sensitivity compared to the cross-validated results from this study. Future studies should account for future data; for example, by using cross-validation or risk presenting optimistic predictions of field performance. Past algorithms have been criticized for relying on age and sex, being difficult to measure by vehicle sensors, and inaccuracies in classifying damage side. The models with accurate damage side and including age/sex did outperform models with less accurate damage side and without age/sex, but the differences were small, suggesting that the success of AACN is not reliant on these predictors.

Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks

PubMed Central

Talbot, Thomas R; Schaffner, William; Bloch, Karen C; Daniels, Titus L; Miller, Randolph A

2011-01-01

Objective The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners. Design Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. Measurements The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool. Results Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. Limitations Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance. Conclusion Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening. PMID:21606134

Evaluation schemes for video and image anomaly detection algorithms

NASA Astrophysics Data System (ADS)

Parameswaran, Shibin; Harguess, Josh; Barngrover, Christopher; Shafer, Scott; Reese, Michael

2016-05-01

Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.

One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms.

PubMed

Andersson, Richard; Larsson, Linnea; Holmqvist, Kenneth; Stridh, Martin; Nyström, Marcus

2017-04-01

Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484-2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

On the spot damage detection methodology for highway bridges during natural crises : tech transfer summary.

DOT National Transportation Integrated Search

2010-07-01

The objective of this work was to develop a : low-cost portable damage detection tool to : assess and predict damage areas in highway : bridges. : The proposed tool was based on standard : vibration-based damage identification (VBDI) : techniques but...

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images.

PubMed

Harati, Vida; Khayati, Rasoul; Farzan, Abdolreza

2011-07-01

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spot-shadowing optimization to mitigate damage growth in a high-energy-laser amplifier chain.

PubMed

Bahk, Seung-Whan; Zuegel, Jonathan D; Fienup, James R; Widmayer, C Clay; Heebner, John

2008-12-10

A spot-shadowing technique to mitigate damage growth in a high-energy laser is studied. Its goal is to minimize the energy loss and undesirable hot spots in intermediate planes of the laser. A nonlinear optimization algorithm solves for the complex fields required to mitigate damage growth in the National Ignition Facility amplifier chain. The method is generally applicable to any large fusion laser.

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

PubMed

Passman, Rod S; Rogers, John D; Sarkar, Shantanu; Reiland, Jerry; Reisfeld, Erin; Koehler, Jodi; Mittal, Suneet

2017-07-01

Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs). The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes. Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope. The original algorithm uses an auto-adjusting sensitivity threshold for R-wave sensing to detect tachycardia and avoid T-wave oversensing. In the enhanced algorithm, a second sensing threshold is used with a long blanking and fixed lower sensitivity threshold, looking for evidence of undersensed signals. Data reported includes percent change in appropriate and inappropriate bradycardia and pause detections as well as changes in episode detection sensitivity and positive predictive value with the enhanced algorithm. The validation data set, from 663 consecutive patients, consisted of 4904 (161 patients) bradycardia and 2582 (133 patients) pause episodes, of which 2976 (61%) and 996 (39%) were appropriately detected bradycardia and pause episodes. The enhanced algorithm reduced inappropriate bradycardia and pause episodes by 95% and 47%, respectively, with 1.7% and 0.6% reduction in appropriate episodes, respectively. The average episode positive predictive value improved by 62% (P < .001) for bradycardia detection and by 26% (P < .001) for pause detection, with an average relative sensitivity of 95% (P < .001) and 99% (P = .5), respectively. The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Adapting detection sensitivity based on evidence of irregular sinus arrhythmia to improve atrial fibrillation detection in insertable cardiac monitors.

PubMed

Pürerfellner, Helmut; Sanders, Prashanthan; Sarkar, Shantanu; Reisfeld, Erin; Reiland, Jerry; Koehler, Jodi; Pokushalov, Evgeny; Urban, Luboš; Dekker, Lukas R C

2017-10-03

Intermittent change in p-wave discernibility during periods of ectopy and sinus arrhythmia is a cause of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). To address this, we developed and validated an enhanced AF detection algorithm. Atrial fibrillation detection in Reveal LINQ ICM uses patterns of incoherence in RR intervals and absence of P-wave evidence over a 2-min period. The enhanced algorithm includes P-wave evidence during RR irregularity as evidence of sinus arrhythmia or ectopy to adaptively optimize sensitivity for AF detection. The algorithm was developed and validated using Holter data from the XPECT and LINQ Usability studies which collected surface electrocardiogram (ECG) and continuous ICM ECG over a 24-48 h period. The algorithm detections were compared with Holter annotations, performed by multiple reviewers, to compute episode and duration detection performance. The validation dataset comprised of 3187 h of valid Holter and LINQ recordings from 138 patients, with true AF in 37 patients yielding 108 true AF episodes ≥2-min and 449 h of AF. The enhanced algorithm reduced inappropriately detected episodes by 49% and duration by 66% with <1% loss in true episodes or duration. The algorithm correctly identified 98.9% of total AF duration and 99.8% of total sinus or non-AF rhythm duration. The algorithm detected 97.2% (99.7% per-patient average) of all AF episodes ≥2-min, and 84.9% (95.3% per-patient average) of detected episodes involved AF. An enhancement that adapts sensitivity for AF detection reduced inappropriately detected episodes and duration with minimal reduction in sensitivity. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Cardiology

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade

PubMed Central

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-01-01

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade. PMID:26287200

Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

NASA Astrophysics Data System (ADS)

Lin, Tzu-Kang; Fajri, Haikal

2017-03-01

Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.

A Green's Function Approach to Simulate DNA Damage by the Indirect Effect

NASA Technical Reports Server (NTRS)

Plante, Ianik; Cicinotta, Francis A.

2013-01-01

The DNA damage is of fundamental importance in the understanding of the effects of ionizing radiation. DNA is damaged by the direct effect of radiation (e.g. direct ionization) and by indirect effect (e.g. damage by.OH radicals created by the radiolysis of water). Despite years of research, many questions on the DNA damage by ionizing radiation remains. In the recent years, the Green's functions of the diffusion equation (GFDE) have been used extensively in biochemistry [1], notably to simulate biochemical networks in time and space [2]. In our future work on DNA damage, we wish to use an approach based on the GFDE to refine existing models on the indirect effect of ionizing radiation on DNA. To do so, we will use the code RITRACKS [3] developed at the NASA Johnson Space Center to simulate the radiation track structure and calculate the position of radiolytic species after irradiation. We have also recently developed an efficient Monte-Carlo sampling algorithm for the GFDE of reversible reactions with an intermediate state [4], which can be modified and adapted to simulate DNA damage by free radicals. To do so, we will use the known reaction rate constants between radicals (OH, eaq, H,...) and the DNA bases, sugars and phosphates and use the sampling algorithms to simulate the diffusion of free radicals and chemical reactions with DNA. These techniques should help the understanding of the contribution of the indirect effect in the formation of DNA damage and double-strand breaks.

A new hand-held microfluidic cytometer for evaluating irradiation damage by analysis of the damaged cells distribution.

PubMed

Wang, Junsheng; Fan, Zhiqiang; Zhao, Yile; Song, Younan; Chu, Hui; Song, Wendong; Song, Yongxin; Pan, Xinxiang; Sun, Yeqing; Li, Dongqing

2016-03-17

Space radiation brings uneven damages to cells. The detection of the distribution of cell damage plays a very important role in radiation medicine and the related research. In this paper, a new hand-held microfluidic flow cytometer was developed to evaluate the degree of radiation damage of cells. The device we propose overcomes the shortcomings (e.g., large volume and high cost) of commercial flow cytometers and can evaluate the radiation damage of cells accurately and quickly with potential for onsite applications. The distribution of radiation-damaged cells is analyzed by a simultaneous detection of immunofluorescence intensity of γ-H2AX and resistance pulse sensor (RPS) signal. The γ-H2AX fluorescence intensity provides information of the degree of radiation damage in cells. The ratio of the number of cells with γ-H2AX fluorescence signals to the total numbers of cells detected by RPS indicates the percentage of the cells that are damaged by radiation. The comparison experiment between the developed hand-held microfluidic flow cytometer and a commercial confocal microscope indicates a consistent and comparable detection performance.

A new hand-held microfluidic cytometer for evaluating irradiation damage by analysis of the damaged cells distribution

NASA Astrophysics Data System (ADS)

Wang, Junsheng; Fan, Zhiqiang; Zhao, Yile; Song, Younan; Chu, Hui; Song, Wendong; Song, Yongxin; Pan, Xinxiang; Sun, Yeqing; Li, Dongqing

2016-03-01

Space radiation brings uneven damages to cells. The detection of the distribution of cell damage plays a very important role in radiation medicine and the related research. In this paper, a new hand-held microfluidic flow cytometer was developed to evaluate the degree of radiation damage of cells. The device we propose overcomes the shortcomings (e.g., large volume and high cost) of commercial flow cytometers and can evaluate the radiation damage of cells accurately and quickly with potential for onsite applications. The distribution of radiation-damaged cells is analyzed by a simultaneous detection of immunofluorescence intensity of γ-H2AX and resistance pulse sensor (RPS) signal. The γ-H2AX fluorescence intensity provides information of the degree of radiation damage in cells. The ratio of the number of cells with γ-H2AX fluorescence signals to the total numbers of cells detected by RPS indicates the percentage of the cells that are damaged by radiation. The comparison experiment between the developed hand-held microfluidic flow cytometer and a commercial confocal microscope indicates a consistent and comparable detection performance.

Damage Detection Response Characteristics of Open Circuit Resonant (SansEC) Sensors

NASA Technical Reports Server (NTRS)

Dudley, Kenneth L.; Szatkowski, George N.; Smith, Laura J.; Koppen, Sandra V.; Ely, Jay J.; Nguyen, Truong X.; Wang, Chuantong; Ticatch, Larry A.; Mielnik, John J.

2013-01-01

The capability to assess the current or future state of the health of an aircraft to improve safety, availability, and reliability while reducing maintenance costs has been a continuous goal for decades. Many companies, commercial entities, and academic institutions have become interested in Integrated Vehicle Health Management (IVHM) and a growing effort of research into "smart" vehicle sensing systems has emerged. Methods to detect damage to aircraft materials and structures have historically relied on visual inspection during pre-flight or post-flight operations by flight and ground crews. More quantitative non-destructive investigations with various instruments and sensors have traditionally been performed when the aircraft is out of operational service during major scheduled maintenance. Through the use of reliable sensors coupled with data monitoring, data mining, and data analysis techniques, the health state of a vehicle can be detected in-situ. NASA Langley Research Center (LaRC) is developing a composite aircraft skin damage detection method and system based on open circuit SansEC (Sans Electric Connection) sensor technology. Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage structures, empennage structures, control surfaces and aircraft skins. SansEC sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect various types of damage in composite materials. The source cause of the in-service damage (lightning strike, impact damage, material fatigue, etc.) to the aircraft composite is not relevant. The sensor will detect damage independent of the cause. Damage in composite material is generally associated with a localized change in material permittivity and/or conductivity. These changes are sensed using SansEC. The unique electrical signatures (amplitude, frequency, bandwidth, and phase) are used for damage detection and diagnosis. An operational system and method would incorporate a SansEC sensor array on select areas of the aircraft exterior surfaces to form a "Smart skin" sensing surface. In this paper a new method and system for aircraft in-situ damage detection and diagnosis is presented. Experimental test results on seeded fault damage coupons and computational modeling simulation results are presented. NASA LaRC has demonstrated with individual sensors that SansEC sensors can be effectively used for in-situ composite damage detection of delamination, voids, fractures, and rips. Keywords: Damage Detection, Composites, Integrated Vehicle Health Monitoring (IVHM), Aviation Safety, SansEC Sensors

Stride search: A general algorithm for storm detection in high resolution climate data

DOE PAGES

Bosler, Peter Andrew; Roesler, Erika Louise; Taylor, Mark A.; ...

2015-09-08

This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared. The commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. Stride Search is designed to work at all latitudes, while grid point searches may fail in polar regions. Results from the two algorithms are compared for the application of tropicalmore » cyclone detection, and shown to produce similar results for the same set of storm identification criteria. The time required for both algorithms to search the same data set is compared. Furthermore, Stride Search's ability to search extreme latitudes is demonstrated for the case of polar low detection.« less

Evaluation of basal DNA damage and oxidative stress in Wistar rat leukocytes after exposure to microwave radiation.

PubMed

Garaj-Vrhovac, Vera; Gajski, Goran; Trosić, Ivancica; Pavicić, Ivan

2009-05-17

The aim of this study was to assess whether microwave-induced DNA damage is basal or it is also generated through reactive oxygen species (ROS) formation. After having irradiated Wistar rats with 915MHz microwave radiation, we assessed different DNA alterations in peripheral leukocytes using standard and formamidopyrimidine DNA-glycosylase (Fpg)-modified comet assay. The first is a sensitive tool for detecting primary DNA damage, and the second is much more specific for detecting oxidative damage. The animals were irradiated for 1h a day for 2 weeks at a field power density of 2.4W/m(2), and the whole-body average specific absorption rate (SAR) of 0.6W/kg. Both the standard and the Fpg-modified comet assay detected increased DNA damage in blood leukocytes of the exposed rats. The significant increase in Fpg-detected DNA damage in the exposed rats suggests that oxidative stress is likely to be responsible. DNA damage detected by the standard comet assay indicates that some other mechanisms may also be involved. In addition, both methods served proved sensitive enough to measure basal and oxidative DNA damage after long-term exposure to 915MHz microwave radiation in vivo.

Verification of Small Hole Theory for Application to Wire Chaffing Resulting in Shield Faults

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

2011-01-01

Our work is focused upon developing methods for wire chafe fault detection through the use of reflectometry to assess shield integrity. When shielded electrical aircraft wiring first begins to chafe typically the resulting evidence is small hole(s) in the shielding. We are focused upon developing algorithms and the signal processing necessary to first detect these small holes prior to incurring damage to the inner conductors. Our approach has been to develop a first principles physics model combined with probabilistic inference, and to verify this model with laboratory experiments as well as through simulation. Previously we have presented the electromagnetic small-hole theory and how it might be applied to coaxial cable. In this presentation, we present our efforts to verify this theoretical approach with high-fidelity electromagnetic simulations (COMSOL). Laboratory observations are used to parameterize the computationally efficient theoretical model with probabilistic inference resulting in quantification of hole size and location. Our efforts in characterizing faults in coaxial cable are subsequently leading to fault detection in shielded twisted pair as well as analysis of intermittent faulty connectors using similar techniques.

A scale-invariant keypoint detector in log-polar space

NASA Astrophysics Data System (ADS)

Tao, Tao; Zhang, Yun

2017-02-01

The scale-invariant feature transform (SIFT) algorithm is devised to detect keypoints via the difference of Gaussian (DoG) images. However, the DoG data lacks the high-frequency information, which can lead to a performance drop of the algorithm. To address this issue, this paper proposes a novel log-polar feature detector (LPFD) to detect scale-invariant blubs (keypoints) in log-polar space, which, in contrast, can retain all the image information. The algorithm consists of three components, viz. keypoint detection, descriptor extraction and descriptor matching. Besides, the algorithm is evaluated in detecting keypoints from the INRIA dataset by comparing with the SIFT algorithm and one of its fast versions, the speed up robust features (SURF) algorithm in terms of three performance measures, viz. correspondences, repeatability, correct matches and matching score.

CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

NASA Astrophysics Data System (ADS)

Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

2017-12-01

We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Tao; Qiu, Yue-hong

2015-07-01

Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

Online damage inspection of optics for ATP system

NASA Astrophysics Data System (ADS)

Chen, Jing; Jiang, Yu; Mao, Yao; Gan, Xun; Liu, Qiong

2016-09-01

In the Electro-Optical acquisition-tracking-pointing system (ATP), the optical components will be damaged with the several influencing factors. In this situation, the rate will increase sharply when the arrival of damage to some extent. As the complex processing techniques and long processing cycle of optical components, the damage will cause the great increase of the system development cost and cycle. Therefore, it is significant to detect the laser-induced damage in the ATP system. At present, the major research on the on-line damage detection technology of optical components is for the large optical system in the international. The relevant detection systems have complicated structures and many of components, and require enough installation space reserved, which do not apply for ATP system. To solve the problem mentioned before, This paper use a method based on machine vision to detect the damage on-line for the present ATP system. To start with, CCD and PC are used for image acquisition. Secondly, smoothing filters are used to restrain false damage points produced by noise. Then, with the shape feature included in the damage image, the OTSU Method which can define the best segmentation threshold automatically is used to achieve the goal to locate the damage regions. At last, we can supply some opinions for the lifetime of the optical components by analyzing the damage data, such as damage area, damage position. The method has the characteristics of few-detectors and simple-structures which can be installed without any changes of the original light path. With the method, experimental results show that it is stable and effective to achieve the goal of detecting the damage of optical components on-line in the ATP system.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network

PubMed Central

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection. PMID:26447696

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

PubMed

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Automatic target detection using binary template matching

NASA Astrophysics Data System (ADS)

Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

2005-03-01

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Robust automatic line scratch detection in films.

PubMed

Newson, Alasdair; Almansa, Andrés; Gousseau, Yann; Pérez, Patrick

2014-03-01

Line scratch detection in old films is a particularly challenging problem due to the variable spatiotemporal characteristics of this defect. Some of the main problems include sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. We propose a robust and automatic algorithm for frame-by-frame line scratch detection in old films, as well as a temporal algorithm for the filtering of false detections. In the frame-by-frame algorithm, we relax some of the hypotheses used in previous algorithms in order to detect a wider variety of scratches. This step's robustness and lack of external parameters is ensured by the combined use of an a contrario methodology and local statistical estimation. In this manner, over-detection in textured or cluttered areas is greatly reduced. The temporal filtering algorithm eliminates false detections due to thin vertical structures by exploiting the coherence of their motion with that of the underlying scene. Experiments demonstrate the ability of the resulting detection procedure to deal with difficult situations, in particular in the presence of noise, texture, and slanted or partial scratches. Comparisons show significant advantages over previous work.

Detection of Anomalous Machining Damages in Inconel 718 and TI 6-4 by Eddy Current Techniques

NASA Astrophysics Data System (ADS)

Lo, C. C. H.; Shimon, M.; Nakagawa, N.

2010-02-01

This paper reports on an eddy current (EC) study aimed at detecting anomalous machining damages in Inconel 718 and Ti 6-4 samples, including (i) surface discontinuities such as re-depositing of chips onto the machined surface, and (ii) microstructural damages manifested as a white surface layer and a subsurface layer of distorted grains, typically tens of microns thick. A series of pristine and machine-damaged coupons were studied by EC scans using a differential probe operated at 2 MHz to detect discontinuous surface anomalies, and by swept high frequency EC (SHFEC) measurements from 0.5 MHz to 65.5 MHz using proprietary detection coils to detect surface microstructural damages. In general, the EC c-scan data from machine-damaged surfaces show spatial variations with larger standard deviations than those from the undamaged surfaces. In some cases, the c-scan images exhibit characteristic bipolar indications in good spatial correlation with surface anomalies revealed by optical microscopy and laser profilometry. Results of the SHFEC measurements indicate a reduced near-surface conductivity of the damaged surfaces compared to the undamaged surfaces.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

DOE PAGES

Xu, W.; Zhu, W. D.; Smith, S. A.; ...

2016-03-18

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, a slope vibration shape is transformed to a multiscale slope vibration shape in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numericalmore » cases of damaged bars, and results show that multiscale slope vibration shapes can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.« less

Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery.

PubMed

Penza, Veronica; Du, Xiaofei; Stoyanov, Danail; Forgione, Antonello; Mattos, Leonardo S; De Momi, Elena

2018-04-01

Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time ( ≃ 4 min - containing the aforementioned events) with high precision (0.7) and recall (0.8) values, and with a recovery time after a failure between 1 and 8 frames in the worst case. Copyright © 2017 Elsevier B.V. All rights reserved.

Hazardous gas detection for FTIR-based hyperspectral imaging system using DNN and CNN

NASA Astrophysics Data System (ADS)

Kim, Yong Chan; Yu, Hyeong-Geun; Lee, Jae-Hoon; Park, Dong-Jo; Nam, Hyun-Woo

2017-10-01

Recently, a hyperspectral imaging system (HIS) with a Fourier Transform InfraRed (FTIR) spectrometer has been widely used due to its strengths in detecting gaseous fumes. Even though numerous algorithms for detecting gaseous fumes have already been studied, it is still difficult to detect target gases properly because of atmospheric interference substances and unclear characteristics of low concentration gases. In this paper, we propose detection algorithms for classifying hazardous gases using a deep neural network (DNN) and a convolutional neural network (CNN). In both the DNN and CNN, spectral signal preprocessing, e.g., offset, noise, and baseline removal, are carried out. In the DNN algorithm, the preprocessed spectral signals are used as feature maps of the DNN with five layers, and it is trained by a stochastic gradient descent (SGD) algorithm (50 batch size) and dropout regularization (0.7 ratio). In the CNN algorithm, preprocessed spectral signals are trained with 1 × 3 convolution layers and 1 × 2 max-pooling layers. As a result, the proposed algorithms improve the classification accuracy rate by 1.5% over the existing support vector machine (SVM) algorithm for detecting and classifying hazardous gases.

A Motion Detection Algorithm Using Local Phase Information

PubMed Central

Lazar, Aurel A.; Ukani, Nikul H.; Zhou, Yiyin

2016-01-01

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm. PMID:26880882

Detection of dechallenge in spontaneous reporting systems: a comparison of Bayes methods.

PubMed

Banu, A Bazila; Alias Balamurugan, S Appavu; Thirumalaikolundusubramanian, Ponniah

2014-01-01

Dechallenge is a response observed for the reduction or disappearance of adverse drug reactions (ADR) on withdrawal of a drug from a patient. Currently available algorithms to detect dechallenge have limitations. Hence, there is a need to compare available new methods. To detect dechallenge in Spontaneous Reporting Systems, data-mining algorithms like Naive Bayes and Improved Naive Bayes were applied for comparing the performance of the algorithms in terms of accuracy and error. Analyzing the factors of dechallenge like outcome and disease category will help medical practitioners and pharmaceutical industries to determine the reasons for dechallenge in order to take essential steps toward drug safety. Adverse drug reactions of the year 2011 and 2012 were downloaded from the United States Food and Drug Administration's database. The outcome of classification algorithms showed that Improved Naive Bayes algorithm outperformed Naive Bayes with accuracy of 90.11% and error of 9.8% in detecting the dechallenge. Detecting dechallenge for unknown samples are essential for proper prescription. To overcome the issues exposed by Naive Bayes algorithm, Improved Naive Bayes algorithm can be used to detect dechallenge in terms of higher accuracy and minimal error.

Detection and Tracking of Moving Objects with Real-Time Onboard Vision System

NASA Astrophysics Data System (ADS)

Erokhin, D. Y.; Feldman, A. B.; Korepanov, S. E.

2017-05-01

Detection of moving objects in video sequence received from moving video sensor is a one of the most important problem in computer vision. The main purpose of this work is developing set of algorithms, which can detect and track moving objects in real time computer vision system. This set includes three main parts: the algorithm for estimation and compensation of geometric transformations of images, an algorithm for detection of moving objects, an algorithm to tracking of the detected objects and prediction their position. The results can be claimed to create onboard vision systems of aircraft, including those relating to small and unmanned aircraft.

Research on improved edge extraction algorithm of rectangular piece

NASA Astrophysics Data System (ADS)

He, Yi-Bin; Zeng, Ya-Jun; Chen, Han-Xin; Xiao, San-Xia; Wang, Yan-Wei; Huang, Si-Yu

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Passive Impact Damage Detection of Fiber Glass Composite Panels

DTIC Science & Technology

2013-12-19

PASSIVE IMPACT DAMAGE DETECTION OF FIBER GLASS COMPOSITE PANELS. By BRUNO ZAMORANO-SENDEROS A dissertation...COVERED 04-11-2012 to 10-12-2013 4. TITLE AND SUBTITLE PASSIVE IMPACT DAMAGE DETECTION OF FIBER GLASS COMPOSITE PANELS 5a. CONTRACT NUMBER 5b...process. .................................... 31 Figure 3-8 Sensor attached to the fiber glass fabric

Heterogeneous Vision Data Fusion for Independently Moving Cameras

DTIC Science & Technology

2010-03-01

target detection , tracking , and identification over a large terrain. The goal of the project is to investigate and evaluate the existing image...fusion algorithms, develop new real-time algorithms for Category-II image fusion, and apply these algorithms in moving target detection and tracking . The...moving target detection and classification. 15. SUBJECT TERMS Image Fusion, Target Detection , Moving Cameras, IR Camera, EO Camera 16. SECURITY

Vibration characteristics and damage detection in a suspension bridge

NASA Astrophysics Data System (ADS)

Wickramasinghe, Wasanthi R.; Thambiratnam, David P.; Chan, Tommy H. T.; Nguyen, Theanh

2016-08-01

Suspension bridges are flexible and vibration sensitive structures that exhibit complex and multi-modal vibration. Due to this, the usual vibration based methods could face a challenge when used for damage detection in these structures. This paper develops and applies a mode shape component specific damage index (DI) to detect and locate damage in a suspension bridge with pre-tensioned cables. This is important as suspension bridges are large structures and damage in them during their long service lives could easily go un-noticed. The capability of the proposed vibration based DI is demonstrated through its application to detect and locate single and multiple damages with varied locations and severity in the cables of the suspension bridge. The outcome of this research will enhance the safety and performance of these bridges which play an important role in the transport network.

An Automated Energy Detection Algorithm Based on Consecutive Mean Excision

DTIC Science & Technology

2018-01-01

present in the RF spectrum. 15. SUBJECT TERMS RF spectrum, detection threshold algorithm, consecutive mean excision, rank order filter , statistical...Median 4 3.1.9 Rank Order Filter (ROF) 4 3.1.10 Crest Factor (CF) 5 3.2 Statistical Summary 6 4. Algorithm 7 5. Conclusion 8 6. References 9...energy detection algorithm based on morphological filter processing with a semi- disk structure. Adelphi (MD): Army Research Laboratory (US); 2018 Jan

Studying the effect of cracks on the ultrasonic wave propagation in a two dimensional gearbox finite element model

NASA Astrophysics Data System (ADS)

Ozevin, Didem; Fazel, Hossein; Cox, Justin; Hardman, William; Kessler, Seth S.; Timmons, Alan

2014-04-01

Gearbox components of aerospace structures are typically made of brittle materials with high fracture toughness, but susceptible to fatigue failure due to continuous cyclic loading. Structural Health Monitoring (SHM) methods are used to monitor the crack growth in gearbox components. Damage detection methodologies developed in laboratory-scale experiments may not represent the actual gearbox structural configuration, and are usually not applicable to real application as the vibration and wave properties depend on the material, structural layers and thicknesses. Also, the sensor types and locations are key factors for frequency content of ultrasonic waves, which are essential features for pattern recognition algorithm development in noisy environments. Therefore, a deterministic damage detection methodology that considers all the variables influencing the waveform signature should be considered in the preliminary computation before any experimental test matrix. In order to achieve this goal, we developed two dimensional finite element models of a gearbox cross section from front view and shaft section. The cross section model consists of steel revolving teeth, a thin layer of oil, and retention plate. An ultrasonic wave up to 1 MHz frequency is generated, and waveform histories along the gearbox are recorded. The received waveforms under pristine and cracked conditions are compared in order to analyze the crack influence on the wave propagation in gearbox, which can be utilized by both active and passive SHM methods.

Self-Sensing TDR with Micro-Strip Line

DTIC Science & Technology

2015-06-11

detect impact damage of a CFRP plate in the second year (Todoroki A, et al., Impact damage detection of a carbon- fibre -reinforced-polymer plate...inspection methods is self-sensing technology that uses carbon fibres as sensors [1]-[11]. The self-sensing technology applies electric current to the...Time Domain Reflectometry (TDR) for damage detection [15]-[17]. Authors have developed a self-sensing TDR for detection of fibre breakages using a

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

PubMed

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. 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.

Accurate Permittivity Measurements for Microwave Imaging via Ultra-Wideband Removal of Spurious Reflectors

PubMed Central

Pelletier, Mathew G.; Viera, Joseph A.; Wanjura, John; Holt, Greg

2010-01-01

The use of microwave imaging is becoming more prevalent for detection of interior hidden defects in manufactured and packaged materials. In applications for detection of hidden moisture, microwave tomography can be used to image the material and then perform an inverse calculation to derive an estimate of the variability of the hidden material, such internal moisture, thereby alerting personnel to damaging levels of the hidden moisture before material degradation occurs. One impediment to this type of imaging occurs with nearby objects create strong reflections that create destructive and constructive interference, at the receiver, as the material is conveyed past the imaging antenna array. In an effort to remove the influence of the reflectors, such as metal bale ties, research was conducted to develop an algorithm for removal of the influence of the local proximity reflectors from the microwave images. This research effort produced a technique, based upon the use of ultra-wideband signals, for the removal of spurious reflections created by local proximity reflectors. This improvement enables accurate microwave measurements of moisture in such products as cotton bales, as well as other physical properties such as density or material composition. The proposed algorithm was shown to reduce errors by a 4:1 ratio and is an enabling technology for imaging applications in the presence of metal bale ties. PMID:22163668

Using random forests to diagnose aviation turbulence.

PubMed

Williams, John K

Atmospheric turbulence poses a significant hazard to aviation, with severe encounters costing airlines millions of dollars per year in compensation, aircraft damage, and delays due to required post-event inspections and repairs. Moreover, attempts to avoid turbulent airspace cause flight delays and en route deviations that increase air traffic controller workload, disrupt schedules of air crews and passengers and use extra fuel. For these reasons, the Federal Aviation Administration and the National Aeronautics and Space Administration have funded the development of automated turbulence detection, diagnosis and forecasting products. This paper describes a methodology for fusing data from diverse sources and producing a real-time diagnosis of turbulence associated with thunderstorms, a significant cause of weather delays and turbulence encounters that is not well-addressed by current turbulence forecasts. The data fusion algorithm is trained using a retrospective dataset that includes objective turbulence reports from commercial aircraft and collocated predictor data. It is evaluated on an independent test set using several performance metrics including receiver operating characteristic curves, which are used for FAA turbulence product evaluations prior to their deployment. A prototype implementation fuses data from Doppler radar, geostationary satellites, a lightning detection network and a numerical weather prediction model to produce deterministic and probabilistic turbulence assessments suitable for use by air traffic managers, dispatchers and pilots. The algorithm is scheduled to be operationally implemented at the National Weather Service's Aviation Weather Center in 2014.

Real-Time Simulation for Verification and Validation of Diagnostic and Prognostic Algorithms

NASA Technical Reports Server (NTRS)

Aguilar, Robet; Luu, Chuong; Santi, Louis M.; Sowers, T. Shane

2005-01-01

To verify that a health management system (HMS) performs as expected, a virtual system simulation capability, including interaction with the associated platform or vehicle, very likely will need to be developed. The rationale for developing this capability is discussed and includes the limited capability to seed faults into the actual target system due to the risk of potential damage to high value hardware. The capability envisioned would accurately reproduce the propagation of a fault or failure as observed by sensors located at strategic locations on and around the target system and would also accurately reproduce the control system and vehicle response. In this way, HMS operation can be exercised over a broad range of conditions to verify that it meets requirements for accurate, timely response to actual faults with adequate margin against false and missed detections. An overview is also presented of a real-time rocket propulsion health management system laboratory which is available for future rocket engine programs. The health management elements and approaches of this lab are directly applicable for future space systems. In this paper the various components are discussed and the general fault detection, diagnosis, isolation and the response (FDIR) concept is presented. Additionally, the complexities of V&V (Verification and Validation) for advanced algorithms and the simulation capabilities required to meet the changing state-of-the-art in HMS are discussed.

The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.

USGS Publications Warehouse

Dominguez, Luis A.; Yildirim, Battalgazi; Husker, Allen L.; Cochran, Elizabeth S.; Christensen, Carl; Cruz-Atienza, Victor M.

2015-01-01

Each volunteer computer monitors ground motion and communicates using the Berkeley Open Infrastructure for Network Computing (BOINC, Anderson, 2004). Using a standard short‐term average, long‐term average (STLA) algorithm (Earle and Shearer, 1994; Cochran, Lawrence, Christensen, Chung, 2009; Cochran, Lawrence, Christensen, and Jakka, 2009), volunteer computer and sensor systems detect abrupt changes in the acceleration recordings. Each time a possible trigger signal is declared, a small package of information containing sensor and ground‐motion information is streamed to one of the QCN servers (Chung et al., 2011). Trigger signals, correlated in space and time, are then processed by the QCN server to look for potential earthquakes.

Autonomous Modal Identification of the Space Shuttle Tail Rudder

NASA Technical Reports Server (NTRS)

Pappa, Richard S.; James, George H., III; Zimmerman, David C.

1997-01-01

Autonomous modal identification automates the calculation of natural vibration frequencies, damping, and mode shapes of a structure from experimental data. This technology complements damage detection techniques that use continuous or periodic monitoring of vibration characteristics. The approach shown in the paper incorporates the Eigensystem Realization Algorithm (ERA) as a data analysis engine and an autonomous supervisor to condense multiple estimates of modal parameters using ERA's Consistent-Mode Indicator and correlation of mode shapes. The procedure was applied to free-decay responses of a Space Shuttle tail rudder and successfully identified the seven modes of the structure below 250 Hz. The final modal parameters are a condensed set of results for 87 individual ERA cases requiring approximately five minutes of CPU time on a DEC Alpha computer.

Automated crack detection in conductive smart-concrete structures using a resistor mesh model

NASA Astrophysics Data System (ADS)

Downey, Austin; D'Alessandro, Antonella; Ubertini, Filippo; Laflamme, Simon

2018-03-01

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a 500 × 500 × 50 mm3 reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure.

Texture orientation-based algorithm for detecting infrared maritime targets.

PubMed

Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai

2015-05-20

Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

Investigating prior probabilities in a multiple hypothesis test for use in space domain awareness

NASA Astrophysics Data System (ADS)

Hardy, Tyler J.; Cain, Stephen C.

2016-05-01

The goal of this research effort is to improve Space Domain Awareness (SDA) capabilities of current telescope systems through improved detection algorithms. Ground-based optical SDA telescopes are often spatially under-sampled, or aliased. This fact negatively impacts the detection performance of traditionally proposed binary and correlation-based detection algorithms. A Multiple Hypothesis Test (MHT) algorithm has been previously developed to mitigate the effects of spatial aliasing. This is done by testing potential Resident Space Objects (RSOs) against several sub-pixel shifted Point Spread Functions (PSFs). A MHT has been shown to increase detection performance for the same false alarm rate. In this paper, the assumption of a priori probability used in a MHT algorithm is investigated. First, an analysis of the pixel decision space is completed to determine alternate hypothesis prior probabilities. These probabilities are then implemented into a MHT algorithm, and the algorithm is then tested against previous MHT algorithms using simulated RSO data. Results are reported with Receiver Operating Characteristic (ROC) curves and probability of detection, Pd, analysis.

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

PubMed

Gregoire, John M; Dale, Darren; van Dover, R Bruce

2011-01-01

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

Robust crop and weed segmentation under uncontrolled outdoor illumination.

PubMed

Jeon, Hong Y; Tian, Lei F; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).

NASA Tech Briefs, January 2006

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: Semiautonomous Avionics-and-Sensors System for a UAV; Biomimetic/Optical Sensors for Detecting Bacterial Species; System Would Detect Foreign-Object Damage in Turbofan Engine; Detection of Water Hazards for Autonomous Robotic Vehicles; Fuel Cells Utilizing Oxygen From Air at Low Pressures; Hybrid Ion-Detector/Data-Acquisition System for a TOF-MS; Spontaneous-Desorption Ionizer for a TOF-MS; Equipment for On-Wafer Testing From 220 to 325 GHz; Computing Isentropic Flow Properties of Air/R-134a Mixtures; Java Mission Evaluation Workstation System; Using a Quadtree Algorithm To Assess Line of Sight; Software for Automated Generation of Cartesian Meshes; Optics Program Modified for Multithreaded Parallel Computing; Programs for Testing Processor-in-Memory Computing Systems; PVM Enhancement for Beowulf Multiple-Processor Nodes; Ion-Exclusion Chromatography for Analyzing Organics in Water; Selective Plasma Deposition of Fluorocarbon Films on SAMs; Water-Based Pressure-Sensitive Paints; System Finds Horizontal Location of Center of Gravity; Predicting Tail Buffet Loads of a Fighter Airplane; Water Containment Systems for Testing High-Speed Flywheels; Vapor-Compression Heat Pumps for Operation Aboard Spacecraft; Multistage Electrophoretic Separators; Recovering Residual Xenon Propellant for an Ion Propulsion System; Automated Solvent Seaming of Large Polyimide Membranes; Manufacturing Precise, Lightweight Paraboloidal Mirrors; Analysis of Membrane Lipids of Airborne Micro-Organisms; Noninvasive Diagnosis of Coronary Artery Disease Using 12-Lead High-Frequency Electrocardiograms; Dual-Laser-Pulse Ignition; Enhanced-Contrast Viewing of White-Hot Objects in Furnaces; Electrically Tunable Terahertz Quantum-Cascade Lasers; Few-Mode Whispering-Gallery-Mode Resonators; Conflict-Aware Scheduling Algorithm; and Real-Time Diagnosis of Faults Using a Bank of Kalman Filters.

NASA Tech Briefs, June 2012

NASA Technical Reports Server (NTRS)

2012-01-01

Topics covered include: iGlobe Interactive Visualization and Analysis of Spatial Data; Broad-Bandwidth FPGA-Based Digital Polyphase Spectrometer; Small Aircraft Data Distribution System; Earth Science Datacasting v2.0; Algorithm for Compressing Time-Series Data; Onboard Science and Applications Algorithm for Hyperspectral Data Reduction; Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data; Security Data Warehouse Application; Integrated Laser Characterization, Data Acquisition, and Command and Control Test System; Radiation-Hard SpaceWire/Gigabit Ethernet-Compatible Transponder; Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager; High-Voltage, Low-Power BNC Feedthrough Terminator; SpaceCube Mini; Dichroic Filter for Separating W-Band and Ka-Band; Active Mirror Predictive and Requirement Verification Software (AMP-ReVS); Navigation/Prop Software Suite; Personal Computer Transport Analysis Program; Pressure Ratio to Thermal Environments; Probabilistic Fatigue Damage Program (FATIG); ASCENT Program; JPL Genesis and Rapid Intensification Processes (GRIP) Portal; Data::Downloader; Fault Tolerance Middleware for a Multi-Core System; DspaceOgreTerrain 3D Terrain Visualization Tool; Trick Simulation Environment 07; Geometric Reasoning for Automated Planning; Water Detection Based on Color Variation; Single-Layer, All-Metal Patch Antenna Element with Wide Bandwidth; Scanning Laser Infrared Molecular Spectrometer (SLIMS); Next-Generation Microshutter Arrays for Large-Format Imaging and Spectroscopy; Detection of Carbon Monoxide Using Polymer-Composite Films with a Porphyrin-Functionalized Polypyrrole; Enhanced-Adhesion Multiwalled Carbon Nanotubes on Titanium Substrates for Stray Light Control; Three-Dimensional Porous Particles Composed of Curved, Two-Dimensional, Nano-Sized Layers for Li-Ion Batteries 23 Ultra-Lightweight; and Ultra-Lightweight Nanocomposite Foams and Sandwich Structures for Space Structure Applications.

Superior Rhythm Discrimination With the SmartShock Technology Algorithm　- Results of the Implantable Defibrillator With Enhanced Features and Settings for Reduction of Inaccurate Detection (DEFENSE) Trial.

PubMed

Oginosawa, Yasushi; Kohno, Ritsuko; Honda, Toshihiro; Kikuchi, Kan; Nozoe, Masatsugu; Uchida, Takayuki; Minamiguchi, Hitoshi; Sonoda, Koichiro; Ogawa, Masahiro; Ideguchi, Takeshi; Kizaki, Yoshihisa; Nakamura, Toshihiro; Oba, Kageyuki; Higa, Satoshi; Yoshida, Keiki; Tsunoda, Soichi; Fujino, Yoshihisa; Abe, Haruhiko

2017-08-25

Shocks delivered by implanted anti-tachyarrhythmia devices, even when appropriate, lower the quality of life and survival. The new SmartShock Technology ® (SST) discrimination algorithm was developed to prevent the delivery of inappropriate shock. This prospective, multicenter, observational study compared the rate of inaccurate detection of ventricular tachyarrhythmia using the SST vs. a conventional discrimination algorithm.Methods and Results:Recipients of implantable cardioverter defibrillators (ICD) or cardiac resynchronization therapy defibrillators (CRT-D) equipped with the SST algorithm were enrolled and followed up every 6 months. The tachycardia detection rate was set at ≥150 beats/min with the SST algorithm. The primary endpoint was the time to first inaccurate detection of ventricular tachycardia (VT) with conventional vs. the SST discrimination algorithm, up to 2 years of follow-up. Between March 2012 and September 2013, 185 patients (mean age, 64.0±14.9 years; men, 74%; secondary prevention indication, 49.5%) were enrolled at 14 Japanese medical centers. Inaccurate detection was observed in 32 patients (17.6%) with the conventional, vs. in 19 patients (10.4%) with the SST algorithm. SST significantly lowered the rate of inaccurate detection by dual chamber devices (HR, 0.50; 95% CI: 0.263-0.950; P=0.034). Compared with previous algorithms, the SST discrimination algorithm significantly lowered the rate of inaccurate detection of VT in recipients of dual-chamber ICD or CRT-D.

Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Afjeh, Abdollah A.

2002-01-01

A diagnostic tool for detecting damage to spur gears was developed. Two different measurement technologies, wear debris analysis and vibration, were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Test Rig. Experimental data were collected during experiments performed in this test rig with and without pitting. Results show combining the two measurement technologies improves the detection of pitting damage on spur gears.

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

PubMed

Li, Ye; Whelan, Michael; Hobbs, Leigh; Fan, Wen Qi; Fung, Cecilia; Wong, Kenny; Marchand-Austin, Alex; Badiani, Tina; Johnson, Ian

2016-06-27

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

Text Extraction from Scene Images by Character Appearance and Structure Modeling

PubMed Central

Yi, Chucai; Tian, Yingli

2012-01-01

In this paper, we propose a novel algorithm to detect text information from natural scene images. Scene text classification and detection are still open research topics. Our proposed algorithm is able to model both character appearance and structure to generate representative and discriminative text descriptors. The contributions of this paper include three aspects: 1) a new character appearance model by a structure correlation algorithm which extracts discriminative appearance features from detected interest points of character samples; 2) a new text descriptor based on structons and correlatons, which model character structure by structure differences among character samples and structure component co-occurrence; and 3) a new text region localization method by combining color decomposition, character contour refinement, and string line alignment to localize character candidates and refine detected text regions. We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm, including text classification, text detection, and character identification. The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene text classification and detection, and significantly outperforms the existing algorithms for character identification. PMID:23316111

Natural Inspired Intelligent Visual Computing and Its Application to Viticulture.

PubMed

Ang, Li Minn; Seng, Kah Phooi; Ge, Feng Lu

2017-05-23

This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

NASA Astrophysics Data System (ADS)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

NASA Astrophysics Data System (ADS)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms

NASA Astrophysics Data System (ADS)

Bedard, Noah D.; Sampat, Mehul P.; Stokes, Patrick A.; Markey, Mia K.

2006-03-01

In this paper we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.

A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

PubMed Central

Xu, Songhua; Krauthammer, Michael

2010-01-01

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. A C++ implementation of our algorithm is freely available through email request for academic use. PMID:20887803

Ozone damage detection in cantaloupe plants

NASA Technical Reports Server (NTRS)

Gausman, H. W.; Escobar, D. E.; Rodriguez, R. R.; Thomas, C. E.; Bowen, R. L.

1978-01-01

Ozone causes up to 90 percent of air pollution injury to vegetation in the United States; excess ozone affects plant growth and development and can cause undetected decrease in yields. Laboratory and field reflectance measurements showed that ozone-damaged cantaloupe (Cucumis melo L.) leaves had lower water contents and higher reflectance than did nondamaged leaves. Cantaloupe plants which were lightly, severely, and very severely ozone-damaged were distinguishable from nondamaged plants by reflectance measurements in the 1.35- to 2.5 micron near-infrared water absorption waveband. Ozone-damaged leaf areas were detected photographically 16 h before the damage was visible. Sensors are available for use with aircraft and spacecraft that possibly could be used routinely to detect ozone-damaged crops.

Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry

NASA Astrophysics Data System (ADS)

Yudovsky, Dmitry; Nouvong, Aksone; Schomacker, Kevin; Pilon, Laurent

2011-02-01

Foot ulceration remains a serious health concern for diabetic patients and has a major impact on the cost of diabetes treatment. Early detection and preventive care, such as offloading or improved hygiene, can greatly reduce the risk of further complications. We aim to assess the use of hyperspectral tissue oximetry in predicting the risk of diabetic foot ulcer formation. Tissue oximetry measurements are performed during several visits with hyperspectral imaging of the feet in type 1 and 2 diabetes mellitus subjects that are at risk for foot ulceration. The data are retrospectively analyzed at 21 sites that ulcerated during the course of our study and an ulceration prediction index is developed. Then, an image processing algorithm based on this index is implemented. This algorithm is able to predict tissue at risk of ulceration with a sensitivity and specificity of 95 and 80%, respectively, for images taken, on average, 58 days before tissue damage is apparent to the naked eye. Receiver operating characteristic analysis is also performed to give a range of sensitivity/specificity values resulting in a Q-value of 89%.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

PubMed

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-09-07

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

PubMed

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.