Linking multimetric and multivariate approaches to assess the ecological condition of streams.

PubMed

Collier, Kevin J

2009-10-01

Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil.

PubMed

Rauscher, Markus S; Tremmel, Anton J; Schardt, Michael; Koch, Alexander W

2017-02-18

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil

PubMed Central

Rauscher, Markus S.; Tremmel, Anton J.; Schardt, Michael; Koch, Alexander W.

2017-01-01

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit. PMID:28218701

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet.

PubMed

Potyrailo, Radislav A

2016-10-12

Modern gas monitoring scenarios for medical diagnostics, environmental surveillance, industrial safety, and other applications demand new sensing capabilities. This Review provides analysis of development of new generation of gas sensors based on the multivariable response principles. Design criteria of these individual sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with independent outputs to recognize these different gas responses. These new sensors quantify individual components in mixtures, reject interferences, and offer more stable response over sensor arrays. Such performance is attractive when selectivity advantages of classic gas chromatography, ion mobility, and mass spectrometry instruments are canceled by requirements for no consumables, low power, low cost, and unobtrusive form factors for Internet of Things, Industrial Internet, and other applications. This Review is concluded with a perspective for future needs in fundamental and applied aspects of gas sensing and with the 2025 roadmap for ubiquitous gas monitoring.

Coupling GIS and multivariate approaches to reference site selection for wadeable stream monitoring.

PubMed

Collier, Kevin J; Haigh, Andy; Kelly, Johlene

2007-04-01

Geographic Information System (GIS) was used to identify potential reference sites for wadeable stream monitoring, and multivariate analyses were applied to test whether invertebrate communities reflected a priori spatial and stream type classifications. We identified potential reference sites in segments with unmodified vegetation cover adjacent to the stream and in >85% of the upstream catchment. We then used various landcover, amenity and environmental impact databases to eliminate sites that had potential anthropogenic influences upstream and that fell into a range of access classes. Each site identified by this process was coded by four dominant stream classes and seven zones, and 119 candidate sites were randomly selected for follow-up assessment. This process yielded 16 sites conforming to reference site criteria using a conditional-probabilistic design, and these were augmented by an additional 14 existing or special interest reference sites. Non-metric multidimensional scaling (NMS) analysis of percent abundance invertebrate data indicated significant differences in community composition among some of the zones and stream classes identified a priori providing qualified support for this framework in reference site selection. NMS analysis of a range standardised condition and diversity metrics derived from the invertebrate data indicated a core set of 26 closely related sites, and four outliers that were considered atypical of reference site conditions and subsequently dropped from the network. Use of GIS linked to stream typology, available spatial databases and aerial photography greatly enhanced the objectivity and efficiency of reference site selection. The multi-metric ordination approach reduced variability among stream types and bias associated with non-random site selection, and provided an effective way to identify representative reference sites.

Survey of Condition Indicators for Condition Monitoring Systems (Open Access)

DTIC Science & Technology

2014-09-29

Hinesburg, Vermont, 05461, USA jz@renewablenrgsystems.com ABSTRACT Currently, the wind energy industry is swiftly changing its maintenance strategy...from schedule based maintenance to predictive based maintenance . Condition monitoring systems (CMS) play an important role in the predictive... maintenance cycle. As condition monitoring systems are being adopted by more and more OEM and O&M service providers from the wind energy industry, it is

A multivariate test of disease risk reveals conditions leading to disease amplification.

PubMed

Halliday, Fletcher W; Heckman, Robert W; Wilfahrt, Peter A; Mitchell, Charles E

2017-10-25

Theory predicts that increasing biodiversity will dilute the risk of infectious diseases under certain conditions and will amplify disease risk under others. Yet, few empirical studies demonstrate amplification. This contrast may occur because few studies have considered the multivariate nature of disease risk, which includes richness and abundance of parasites with different transmission modes. By combining a multivariate statistical model developed for biodiversity-ecosystem-multifunctionality with an extensive field manipulation of host (plant) richness, composition and resource supply to hosts, we reveal that (i) host richness alone could not explain most changes in disease risk, and (ii) shifting host composition allowed disease amplification, depending on parasite transmission mode. Specifically, as predicted from theory, the effect of host diversity on parasite abundance differed for microbes (more density-dependent transmission) and insects (more frequency-dependent transmission). Host diversity did not influence microbial parasite abundance, but nearly doubled insect parasite abundance, and this amplification effect was attributable to variation in host composition. Parasite richness was reduced by resource addition, but only in species-rich host communities. Overall, this study demonstrates that multiple drivers, related to both host community and parasite characteristics, can influence disease risk. Furthermore, it provides a framework for evaluating multivariate disease risk in other systems. © 2017 The Author(s).

Technology review: prototyping platforms for monitoring ambient conditions.

PubMed

Afolaranmi, Samuel Olaiya; Ramis Ferrer, Borja; Martinez Lastra, Jose Luis

2018-05-08

The monitoring of ambient conditions in indoor spaces is very essential owing to the amount of time spent indoors. Specifically, the monitoring of air quality is significant because contaminated air affects the health, comfort and productivity of occupants. This research work presents a technology review of prototyping platforms for monitoring ambient conditions in indoor spaces. It involves the research on sensors (for CO 2 , air quality and ambient conditions), IoT platforms, and novel and commercial prototyping platforms. The ultimate objective of this review is to enable the easy identification, selection and utilisation of the technologies best suited for monitoring ambient conditions in indoor spaces. Following the review, it is recommended to use metal oxide sensors, optical sensors and electrochemical sensors for IAQ monitoring (including NDIR sensors for CO 2 monitoring), Raspberry Pi for data processing, ZigBee and Wi-Fi for data communication, and ThingSpeak IoT platform for data storage, analysis and visualisation.

Infrared thermography for condition monitoring - A review

NASA Astrophysics Data System (ADS)

Bagavathiappan, S.; Lahiri, B. B.; Saravanan, T.; Philip, John; Jayakumar, T.

2013-09-01

Temperature is one of the most common indicators of the structural health of equipment and components. Faulty machineries, corroded electrical connections, damaged material components, etc., can cause abnormal temperature distribution. By now, infrared thermography (IRT) has become a matured and widely accepted condition monitoring tool where the temperature is measured in real time in a non-contact manner. IRT enables early detection of equipment flaws and faulty industrial processes under operating condition thereby, reducing system down time, catastrophic breakdown and maintenance cost. Last three decades witnessed a steady growth in the use of IRT as a condition monitoring technique in civil structures, electrical installations, machineries and equipment, material deformation under various loading conditions, corrosion damages and welding processes. IRT has also found its application in nuclear, aerospace, food, paper, wood and plastic industries. With the advent of newer generations of infrared camera, IRT is becoming a more accurate, reliable and cost effective technique. This review focuses on the advances of IRT as a non-contact and non-invasive condition monitoring tool for machineries, equipment and processes. Various conditions monitoring applications are discussed in details, along with some basics of IRT, experimental procedures and data analysis techniques. Sufficient background information is also provided for the beginners and non-experts for easy understanding of the subject.

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

Reusable rocket engine turbopump condition monitoring

NASA Technical Reports Server (NTRS)

Hampson, M. E.

1984-01-01

Significant improvements in engine readiness with reductions in maintenance costs and turn-around times can be achieved with an engine condition monitoring systems (CMS). The CMS provides health status of critical engine components, without disassembly, through monitoring with advanced sensors. Engine failure reports over 35 years were categorized into 20 different modes of failure. Rotor bearings and turbine blades were determined to be the most critical in limiting turbopump life. Measurement technologies were matched to each of the failure modes identified. Three were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiberoptic deflectometer (bearings), and the fiberoptic pyrometer (blades). Signal processing algorithms were evaluated for their ability to provide useful health data to maintenance personnel. Design modifications to the Space Shuttle Main Engine (SSME) high pressure turbopumps were developed to incorporate the sensors. Laboratory test fixtures have been designed for monitoring the rotor bearings and turbine blades in simulated turbopump operating conditions.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Noncontacting measurement technologies for space propulsion condition monitoring

NASA Technical Reports Server (NTRS)

Randall, M. R.; Barkhoudarian, S.; Collins, J. J.; Schwartzbart, A.

1987-01-01

This paper describes four noncontacting measurement technologies that can be used in a turbopump condition monitoring system. The isotope wear analyzer, fiberoptic deflectometer, brushless torque-meter, and fiberoptic pyrometer can be used to monitor component wear, bearing degradation, instantaneous shaft torque, and turbine blade cracking, respectively. A complete turbopump condition monitoring system including these four technologies could predict remaining component life, thus reducing engine operating costs and increasing reliability.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

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

Meier, David E.; Coble, Jamie B.; Jordan, David V.

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicatemore » changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.« less

Quaternion Based Thermal Condition Monitoring System

NASA Astrophysics Data System (ADS)

Wong, Wai Kit; Loo, Chu Kiong; Lim, Way Soong; Tan, Poi Ngee

In this paper, we will propose a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. In simulation, we also discover that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Beside that, log-polar mapping can have a two fold of data compression capability. Log-polar mapping can help smoother up the output correlation plane too, hence makes a better measurement way for PSR and p-values. Simulation results also show that the proposed system is an efficient machine condition monitoring system with accuracy more than 98%.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

Oxidation management of white wines using cyclic voltammetry and multivariate process monitoring.

PubMed

Martins, Rui C; Oliveira, Raquel; Bento, Fatima; Geraldo, Dulce; Lopes, Vitor V; Guedes de Pinho, Paula; Oliveira, Carla M; Silva Ferreira, Antonio C

2008-12-24

The development of a fingerprinting strategy capable to evaluate the "oxidation status" of white wines based on cyclic voltammetry is proposed here. It is known that the levels of specific antioxidants and redox mechanisms may be evaluated by cyclic voltammetry. This electrochemical technique was applied on two sets of samples. One group was composed of normal aged white wines and a second group obtained from a white wine forced aging protocol with different oxygen, SO(2), pH, and temperature regimens. A study of antioxidant additions, namely ascorbic acid, was also made in order to establish a statistical link between voltammogram fingerprints and chemical antioxidant substances. It was observed that the oxidation curve presented typical features, which enables sample discrimination according to age, oxygen consumption, and antioxidant additions. In fact, it was possible to place the results into four significant orthogonal directions, compressing 99.8% of nonrandom features. Attempts were made to make voltammogram fingerprinting a tool for monitoring oxidation management. For this purpose, a supervised multivariate control chart was developed using a control sample as reference. When white wines are plotted onto the chart, it is possible to monitor the oxidation status and to diagnose the effects of oxygen regimes and antioxidant activity. Finally, quantification of substances implicated in the oxidation process as reagents (antioxidants) and products (off-flavors) was tried using a supervised algorithmic the partial least square regression analysis. Good correlations (r > 0.93) were observed for ascorbic acid, Folin-Ciocalteu index, total SO(2), methional, and phenylacetaldehyde. These results show that cyclic voltammetry fingerprinting can be used to monitor and diagnose the effects of wine oxidation.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

PubMed

Eide, Ingvar; Westad, Frank

2018-01-01

A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

Wind Turbine Gearbox Oil Filtration and Condition Monitoring

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

Sheng, Shuangwen

This is an invited presentation for a pre-conference workshop, titled advances and opportunities in lubrication: wind turbine, at the 2015 Society of Tribologists and Lubrication Engineers (STLE) Tribology Frontiers Conference held in Denver, CO. It gives a brief overview of wind turbine gearbox oil filtration and condition monitoring by highlighting typical industry practices and challenges. The presentation starts with an introduction by covering recent growth of global wind industry, reliability challenges, benefits of oil filtration and condition monitoring, and financial incentives to conduct wind operation and maintenance research, which includes gearbox oil filtration and condition monitoring work presented herein. Then,more » the presentation moves on to oil filtration by stressing the benefits of filtration, discussing typical main- and offline-loop practices, highlighting important factors considered when specifying a filtration system, and illustrating real-world application challenges through a cold-start example. In the next section on oil condition monitoring, a discussion on oil sample analysis, oil debris monitoring, oil cleanliness measurements and filter analysis is given based on testing results mostly obtained by and at NREL, and by pointing out a few challenges with oil sample analysis. The presentation concludes with a brief touch on future research and development (R and D) opportunities. It is hoping that the information presented can inform the STLE community to start or redirect their R and D work to help the wind industry advance.« less

Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

NASA Astrophysics Data System (ADS)

Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

2013-04-01

Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be

Optical Spectroscopy and Multivariate Analysis for Biodosimetry and Monitoring of Radiation Injury to the Skin

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

Levitskaia, Tatiana G.; Bryan, Samuel A.; Creim, Jeffrey A.

2012-08-01

In the event of an intentional or accidental release of ionizing radiation in a densely populated area, timely assessment and triage of the general population for the radiation exposure is critical. In particular, a significant number of the victims may sustain cutaneous radiation injury, which increases the mortality and worsens the overall prognosis of the victims suffered from combined thermal/mechanical and radiation trauma. Diagnosis of the cutaneous radiation injury is challenging, and established methods largely rely on visual manifestations, presence of the skin contamination, and a high degree of recall by the victim. Availability of a high throughput non-invasive inmore » vivo biodosimetry tool for assessment of the radiation exposure of the skin is of particular importance for the timely diagnosis of the cutaneous injury. In the reported investigation, we have tested the potential of an optical reflectance spectroscopy for the evaluation of the radiation injury to the skin. This is technically attractive because optical spectroscopy relies on well-established and routinely used for various applications instrumentation, one example being pulse oximetry which uses selected wavelengths for the quantification of the blood oxygenation. Our method relies on a broad spectral region ranging from the locally absorbed, shallow-penetrating ultraviolet and visible (250 to 800 nm) to more deeply penetrating near-Infrared (800 – 1600 nm) light for the monitoring of multiple physiological changes in the skin upon irradiation. Chemometrics is a multivariate methodology that allows the information from entire spectral region to be used to generate predictive regression models. In this report we demonstrate that simple spectroscopic method, such as the optical reflectance spectroscopy, in combination with multivariate data analysis, offers the promise of rapid and non-invasive in vivo diagnosis and monitoring of the cutaneous radiation exposure, and is able accurately

Reusable rocket engine turbopump condition monitoring

NASA Technical Reports Server (NTRS)

Hampson, M. E.; Barkhoudarian, S.

1985-01-01

Significant improvements in engine readiness with attendant reductions in maintenance costs and turnaround times can be achieved with an engine condition monitoring system (CMS). The CMS provides real time health status of critical engine components, without disassembly, through component monitoring with advanced sensor technologies. Three technologies were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiber optic deflectometer (bearings), and the fiber optic pyrometer (blades). Signal processing algorithms were evaluated and ranked for their utility in providing useful component health data to unskilled maintenance personnel. Design modifications to current configuration Space Shuttle Main Engine (SSME) high pressure turbopumps and the MK48-F turbopump were developed to incorporate the sensors.

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

NASA Technical Reports Server (NTRS)

Smith, M. E.; Gevins, A.; Brown, H.; Karnik, A.; Du, R.

2001-01-01

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

Instantaneous angular speed monitoring of gearboxes under non-cyclic stationary load conditions

NASA Astrophysics Data System (ADS)

Stander, C. J.; Heyns, P. S.

2005-07-01

Recent developments in the condition monitoring and asset management market have led to the commercialisation of online vibration-monitoring systems. These systems are primarily utilised to monitor large mineral mining equipment such as draglines, continuous miners and hydraulic shovels. Online monitoring systems make diagnostic information continuously available for asset management, production outsourcing and maintenance alliances with equipment manufacturers. However, most online vibration-monitoring systems are based on conventional vibration-monitoring technologies, which are prone to giving false equipment deterioration warnings on gears that operate under fluctuating load conditions. A simplified mathematical model of a gear system was developed to illustrate the feasibility of monitoring the instantaneous angular speed (IAS) as a means of monitoring the condition of gears that are subjected to fluctuating load conditions. A distinction is made between cyclic stationary load modulation and non-cyclic stationary load modulation. It is shown that rotation domain averaging will suppress the modulation caused by non-cyclic stationary load conditions but will not suppress the modulation caused by cyclic stationary load conditions. An experimental investigation on a test rig indicated that the IAS of a gear shaft could be monitored with a conventional shaft encoder to indicate a deteriorating gear fault condition.

FT-IR/ATR univariate and multivariate calibration models for in situ monitoring of sugars in complex microalgal culture media.

PubMed

Girard, Jean-Michel; Deschênes, Jean-Sébastien; Tremblay, Réjean; Gagnon, Jonathan

2013-09-01

The objective of this work is to develop a quick and simple method for the in situ monitoring of sugars in biological cultures. A new technology based on Attenuated Total Reflectance-Fourier Transform Infrared (FT-IR/ATR) spectroscopy in combination with an external light guiding fiber probe was tested, first to build predictive models from solutions of pure sugars, and secondly to use those models to monitor the sugars in the complex culture medium of mixotrophic microalgae. Quantification results from the univariate model were correlated with the total dissolved solids content (R(2)=0.74). A vector normalized multivariate model was used to proportionally quantify the different sugars present in the complex culture medium and showed a predictive accuracy of >90% for sugars representing >20% of the total. This method offers an alternative to conventional sugar monitoring assays and could be used at-line or on-line in commercial scale production systems. Copyright © 2013 Elsevier Ltd. All rights reserved.

Electrical condition monitoring method for polymers

DOEpatents

Watkins, Jr. Kenneth S.; Morris, Shelby J.; Masakowski, Daniel D.; Wong, Ching Ping; Luo, Shijian

2010-02-16

An electrical condition monitoring method utilizes measurement of electrical resistivity of a conductive composite degradation sensor to monitor environmentally induced degradation of a polymeric product such as insulated wire and cable. The degradation sensor comprises a polymeric matrix and conductive filler. The polymeric matrix may be a polymer used in the product, or it may be a polymer with degradation properties similar to that of a polymer used in the product. The method comprises a means for communicating the resistivity to a measuring instrument and a means to correlate resistivity of the degradation sensor with environmentally induced degradation of the product.

Transient multivariable sensor evaluation

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

Vilim, Richard B.; Heifetz, Alexander

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Monitoring Thermal Conditions in Footwear

NASA Astrophysics Data System (ADS)

Silva-Moreno, Alejandra. A.; Lopez Vela, Martín; Alcalá Ochoa, Noe

2006-09-01

Thermal conditions inside the foot were evaluated on a volunteer subject. We have designed and constructed an electronic system which can monitors temperature and humidity of the foot inside the shoe. The data is stored in a battery-powered device for later uploading to a host computer for data analysis. The apparatus potentially can be used to provide feedback to patients who are prone to having skin breakdowns.

Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions.

PubMed

Xu, Xiaoya; Zhong, Miao; Wan, Jiafu; Yi, Minglun; Gao, Tiancheng

2016-10-01

In adverse working conditions, environmental parameters such as metallic dust, noise, and environmental temperature, directly affect the health condition of manufacturing workers. It is therefore important to implement health monitoring and management based on important physiological parameters (e.g., heart rate, blood pressure, and body temperature). In recent years, new technologies, such as body area networks, cloud computing, and smart clothing, have allowed the improvement of the quality of services. In this article, we first give five-layer architecture for health monitoring and management of manufacturing workers. Then, we analyze the system implementation process, including environmental data processing, physical condition monitoring and system services and management, and present the corresponding algorithms. Finally, we carry out an evaluation and analysis from the perspective of insurance and compensation for manufacturing workers in adverse working conditions. The proposed scheme will contribute to the improvement of workplace conditions, realize health monitoring and management, and protect the interests of manufacturing workers.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

PubMed Central

Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

2014-01-01

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

GEOGLAM Crop Monitor Assessment Tool: Developing Monthly Crop Condition Assessments

NASA Astrophysics Data System (ADS)

McGaughey, K.; Becker Reshef, I.; Barker, B.; Humber, M. L.; Nordling, J.; Justice, C. O.; Deshayes, M.

2014-12-01

The Group on Earth Observations (GEO) developed the Global Agricultural Monitoring initiative (GEOGLAM) to improve existing agricultural information through a network of international partnerships, data sharing, and operational research. This presentation will discuss the Crop Monitor component of GEOGLAM, which provides the Agricultural Market Information System (AMIS) with an international, multi-source, and transparent consensus assessment of crop growing conditions, status, and agro-climatic conditions likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soybean) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. This presentation will focus on the building of international networks, data collection, and data dissemination.

Wireless pilot monitoring system for extreme race conditions.

PubMed

Pino, Esteban J; Arias, Diego E; Aqueveque, Pablo; Melin, Pedro; Curtis, Dorothy W

2012-01-01

This paper presents the design and implementation of an assistive device to monitor car drivers under extreme conditions. In particular, this system is designed in preparation for the 2012 Atacama Solar Challenge to be held in the Chilean desert. Actual preliminary results show the feasibility of such a project including physiological and ambient sensors, real-time processing algorithms, wireless data transmission and a remote monitoring station. Implementation details and field results are shown along with a discussion of the main problems found in real-life telemetry monitoring.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Multivariate analysis of gamma spectra to characterize used nuclear fuel

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

Coble, Jamie; Orton, Christopher; Schwantes, Jon

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

REGIONAL MONITORING OF CORAL CONDITION IN THE FLORIDA KEYS

EPA Science Inventory

Fisher, William S. and Deborah L. Santavy. 2004. Regional Monitoring of Coral Condition in Florida Keys (Abstract). Presented at the Monitoring Science and Technology Symposium, 20-24 September 2004, Denver, CO. 1 p. (ERL,GB R1020).

Coral reefs have experienced unpreceden...

Condition Monitoring of Large-Scale Facilities

NASA Technical Reports Server (NTRS)

Hall, David L.

1999-01-01

This document provides a summary of the research conducted for the NASA Ames Research Center under grant NAG2-1182 (Condition-Based Monitoring of Large-Scale Facilities). The information includes copies of view graphs presented at NASA Ames in the final Workshop (held during December of 1998), as well as a copy of a technical report provided to the COTR (Dr. Anne Patterson-Hine) subsequent to the workshop. The material describes the experimental design, collection of data, and analysis results associated with monitoring the health of large-scale facilities. In addition to this material, a copy of the Pennsylvania State University Applied Research Laboratory data fusion visual programming tool kit was also provided to NASA Ames researchers.

Monitoring the condition of the fetus during delivery.

PubMed

Sarvilinna, Nanna; Isaksson, Camilla; Kokljuschkin, Henrica; Timonen, Susanna; Halmesmäki, Erja

Uterine contractions during delivery increase the resistance to flow in the blood vessels of the placenta and decreases placental blood circulation, possibly subjecting the fetus to hypoxia. Several methods have been developed for monitoring the condition of the fetus during delivery. Cardiotocography is used to monitor the fetus's heart rate and variability in relation to the mother's contractions. A change in cardiotocography recording due to stimulation of the presenting part is an indication of a healthy fetus. ST analysis of fetal ECG depicts the oxygenation of fetal cardiac muscle during delivery. In addition to cardiotocography and ST analysis, analysis of blood gases and lactate determination are used in assessing the condition of the fetus.

Wireless sensing system for bridge condition assessment and health monitoring

NASA Astrophysics Data System (ADS)

Gangone, Michael V.; Whelan, Matthew J.; Janoyan, Kerop D.

2009-03-01

Discussed in this paper is the deployment of a universal and low-cost dense wireless sensor system for structural monitoring, load rating and condition assessment of bridges. The wireless sensor system developed is designed specifically for diagnostic bridge monitoring, providing independent conditioning for both accelerometers and strain transducers in addition to high-rate wireless data transmission. The system was field deployed on a three span simply supported bridge superstructure, where strain and acceleration measurements were obtained simultaneously and in realtime at critical locations under several loading conditions, providing reliable quantitative information as to the actual performance level of the bridge. Monitoring was also conducted as the bridge was subjected to various controlled damage scenarios on the final day of testing. Select cases of detected damage using strain and modal based analysis are presented.

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Bridge condition assessment based on long-term strain monitoring

NASA Astrophysics Data System (ADS)

Sun, LiMin; Sun, Shouwang

2011-04-01

In consideration of the important role that bridges play as transportation infrastructures, their safety, durability and serviceability have always been deeply concerned. Structural Health Monitoring Systems (SHMS) have been installed to many long-span bridges to provide bridge engineers with the information needed in making rational decisions for maintenance. However, SHMS also confronted bridge engineers with the challenge of efficient use of monitoring data. Thus, methodologies which are robust to random disturbance and sensitive to damage become a subject on which many researches in structural condition assessment concentrate. In this study, an innovative probabilistic approach for condition assessment of bridge structures was proposed on the basis of long-term strain monitoring on steel girder of a cable-stayed bridge. First, the methodology of damage detection in the vicinity of monitoring point using strain-based indices was investigated. Then, the composition of strain response of bridge under operational loads was analyzed. Thirdly, the influence of temperature and wind on strains was eliminated and thus strain fluctuation under vehicle loads is obtained. Finally, damage evolution assessment was carried out based on the statistical characteristics of rain-flow cycles derived from the strain fluctuation under vehicle loads. The research conducted indicates that the methodology proposed is qualified for structural condition assessment so far as the following respects are concerned: (a) capability of revealing structural deterioration; (b) immunity to the influence of environmental variation; (c) adaptability to the random characteristic exhibited by long-term monitoring data. Further examination of the applicability of the proposed methodology in aging bridge may provide a more convincing validation.

Condition Monitoring of Helicopter Gearboxes by Embedded Sensing

NASA Technical Reports Server (NTRS)

Suryavanashi, Abhijit; Wang, Shengda; Gao, Robert; Danai, Kourosh; Lewicki, David G.

2002-01-01

Health of helicopter gearboxes is commonly assessed by monitoring the housing vibration, thus it is challenged by poor signal-to-noise ratio of the signal measured away from the source. It is hypothesized that vibration measurements from sensors placed inside the gearbox will be much clearer indicators of faults and will eliminate many of the difficulties faced by present condition monitoring systems. This paper outlines our approach to devising such a monitoring system. Several tasks have been outlined toward this objective and the strategy to address each has been described. Among the tasks are wireless sensor design, antenna design, and selection of sensor locations.

Noninvasive health condition monitoring device for workers at high altitudes conditions.

PubMed

Aqueveque, Pablo; Gutierrez, Cristopher; Saavedra, Francisco; Pino, Esteban J

2016-08-01

This work presents the design and implementation of a continuous monitoring device to control the health state of workers, for instance miners, at high altitudes. The extreme ambient conditions are harmful for peoples' health; therefore a continuous control of the workers' vital signs is necessary. The developed system includes physiological variables: electrocardiogram (ECG), respiratory activity and body temperature (BT), and ambient variables: ambient temperature (AT) and relative humidity (RH). The noninvasive sensors are incorporated in a t-shirt to deliver a functional device, and maximum comfort to the users. The device is able to continuously calculate heart rate (HR) and respiration rate (RR), and establish a wireless data transmission to a central monitoring station.

Electrical condition monitoring method for polymers

DOEpatents

Watkins, Jr., Kenneth S.; Morris, Shelby J [Hampton, VA; Masakowski, Daniel D [Worcester, MA; Wong, Ching Ping [Duluth, GA; Luo, Shijian [Boise, ID

2008-08-19

An electrical condition monitoring method utilizes measurement of electrical resistivity of an age sensor made of a conductive matrix or composite disposed in a polymeric structure such as an electrical cable. The conductive matrix comprises a base polymer and conductive filler. The method includes communicating the resistivity to a measuring instrument and correlating resistivity of the conductive matrix of the polymeric structure with resistivity of an accelerated-aged conductive composite.

Reusable rocket engine optical condition monitoring

NASA Technical Reports Server (NTRS)

Wyett, L.; Maram, J.; Barkhoudarian, S.; Reinert, J.

1987-01-01

Plume emission spectrometry and optical leak detection are described as two new applications of optical techniques to reusable rocket engine condition monitoring. Plume spectrometry has been used with laboratory flames and reusable rocket engines to characterize both the nominal combustion spectra and anomalous spectra of contaminants burning in these plumes. Holographic interferometry has been used to identify leaks and quantify leak rates from reusable rocket engine joints and welds.

A multivariate approach for a comparison of big data matrices. Case study: thermo-hygrometric monitoring inside the Carcer Tullianum (Rome) in the absence and in the presence of visitors.

PubMed

Visco, Giovanni; Plattner, Susanne H; Fortini, Patrizia; Sammartino, Mariapia

2017-06-01

In the last decades, the very fast improvement of the analytical instrumentation has led to the possibility of quickly and easily getting a lot of data; in turn, the need of advanced statistical methods suitable to extract the full information furnished by instruments has increased. Such kind of data treatments is particularly important in any case of continuous monitoring of one or more parameters, so the microclimate monitoring is a typical example for this application. Microclimate control is essential in the conservation of Cultural Heritage (CH), but decisions on optimal conservation parameters cannot base only on existing norms that do not take into account the environment's history. Often CH has survived for many centuries in conditions that must be considered risky but also a stable state (equilibrium) resulting from a long adaptation process during which a more or less heavy damage occurred to the materials. Any successive change of microclimate parameters has interrupted this equilibrium conditions and has induced further damage to material until a new equilibrium is reached; dimension and frequency of changes are proportional to the expected damage. This thermodynamic consideration provides the background for a CH conservation project based on microclimate control and highlights the importance of environmental monitoring for the identification of equilibrium parameters to be maintained. In 2010, we monitored the microclimate of an important historical building in Rome, the Mamertino Carcer, before its opening to visitors. One year later, we repeated the monitoring in the presence of visitors, and here, we present a careful choice of multivariate data treatments adopted for an enough, simple and immediate evaluation of the microclimatic changes; this allows an easier understanding also for persons with not too deep scientific background, such as Superintendents and, in turn, really useful information to provide suggestions for a conservation project

Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

NASA Astrophysics Data System (ADS)

Jaramillo, Víctor H.; Ottewill, James R.; Dudek, Rafał; Lepiarczyk, Dariusz; Pawlik, Paweł

2017-03-01

In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of

Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

PubMed

Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

2014-01-01

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers.

Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

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

Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.

2014-01-01

The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic usedmore » fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in

Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

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

Lines, Amanda M.; Adami, Susan R.; Sinkov, Sergey I.

Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example ofmore » a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.« less

Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

ERIC Educational Resources Information Center

Thompson, Bruce

Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

PubMed

Yotova, Galina I; Tsitouridou, Roxani; Tsakovski, Stefan L; Simeonov, Vasil D

2016-01-01

The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 μm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified. A specific element of the study is the effort to clarify the role of outliers in the data set. The reason for the appearance of outliers is strongly related to the atmospheric condition on the particular sampling days leading to enhanced concentration of pollutants (secondary emissions, sea sprays, road and soil dust, combustion processes) especially for ultra fine and coarse particles. It is also shown that three major sources affect the urban air quality of the location studied-sea sprays, mineral dust and anthropogenic influences (agricultural activity, combustion processes, and industrial sources). The level of impact is related to certain extent to the aerosol fraction size. The assessment of the meteorological conditions leads to defining of four downwind patterns affecting the air quality (Pelagic, Western and Central Europe, Eastern and Northeastern Europe and Africa and Southern Europe). Thus, the present study offers a complete urban air assessment taking into account the weather conditions, pollution sources and aerosol fractioning.

On the use of temperature for online condition monitoring of geared systems - A review

NASA Astrophysics Data System (ADS)

Touret, T.; Changenet, C.; Ville, F.; Lalmi, M.; Becquerelle, S.

2018-02-01

Gear unit condition monitoring is a key factor for mechanical system reliability management. When they are subjected to failure, gears and bearings may generate excessive vibration, debris and heat. Vibratory, acoustic or debris analyses are proven approaches to perform condition monitoring. An alternative to those methods is to use temperature as a condition indicator to detect gearbox failure. The review focuses on condition monitoring studies which use this thermal approach. According to the failure type and the measurement method, it exists a distinction whether it is contact (e.g. thermocouple) or non-contact temperature sensor (e.g. thermography). Capabilities and limitations of this approach are discussed. It is shown that the use of temperature for condition monitoring has a clear potential as an alternative to vibratory or acoustic health monitoring.

Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus.

PubMed

Scherdel, Pauline; Reynaud, Rachel; Pietrement, Christine; Salaün, Jean-François; Bellaïche, Marc; Arnould, Michel; Chevallier, Bertrand; Piloquet, Hugues; Jobez, Emmanuel; Cheymol, Jacques; Bichara, Emmanuelle; Heude, Barbara; Chalumeau, Martin

2017-01-01

Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization. To obtain a consensus about the priority target conditions for algorithms monitoring children's growth. We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating), with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists) as well as parent associations. We asked experts in the pilot (n = 11), reading (n = 8) and rating (n = 60) groups to complete the list of diagnostic classification of the European Society for Paediatric Endocrinology and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition. Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement). This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

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

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

40 CFR 141.625 - Conditions requiring increased monitoring.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 22 2010-07-01 2010-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts...

40 CFR 141.625 - Conditions requiring increased monitoring.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 24 2013-07-01 2013-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts...

40 CFR 141.625 - Conditions requiring increased monitoring.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 23 2014-07-01 2014-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts...

40 CFR 141.625 - Conditions requiring increased monitoring.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 23 2011-07-01 2011-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts...

40 CFR 141.625 - Conditions requiring increased monitoring.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 24 2012-07-01 2012-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts...

Comparison of different incubation conditions for microbiological environmental monitoring.

PubMed

Gordon, Oliver; Berchtold, Manfred; Staerk, Alexandra; Roesti, David

2014-01-01

Environmental monitoring represents an integral part of the microbiological quality control system of a pharmaceutical manufacturing operation. However, guidance documents differ regarding recommendation of a procedure, particularly regarding incubation time, incubation temperature, or nutrient media. Because of these discrepancies, many manufacturers decide for a particular environmental monitoring sample incubation strategy and support this decision with validation data. Such validations are typically laboratory-based in vitro studies, meaning that these are based on comparing incubation conditions and nutrient media through use of cultured microorganisms. An informal survey of the results of these in vitro studies performed at Novartis or European manufacturing sites of different pharmaceutical companies highlighted that no consensus regarding the optimal incubation conditions for microbial recovery existed. To address this question differently, we collected a significant amount of samples directly from air, inanimate surfaces, and personnel in pharmaceutical production and packaging rooms during manufacturing operation (in situ study). Samples were incubated under different conditions suggested in regulatory guidelines, and recovery of total aerobic microorganisms as well as moulds was assessed. We found the highest recovery of total aerobic count from areas with personnel flow using a general microbiological growth medium incubated at 30-35 °C. The highest recovery of moulds was obtained with mycological medium incubated at 20-25 °C. Single-plate strategies (two-temperature incubation or an intermediate incubation temperature of 25-30 °C) also yielded reasonable recovery of total aerobic count and moulds. However, recovery of moulds was found to be highly inefficient at 30-35 °C compared to lower incubation temperatures. This deficiency could not be rectified by subsequent incubation at 20-25 °C. A laboratory-based in vitro study performed in parallel was

Technical guide for monitoring selected conditions related to wilderness character

Treesearch

Peter Landres; Steve Boutcher; Liese Dean; Troy Hall; Tamara Blett; Terry Carlson; Ann Mebane; Carol Hardy; Susan Rinehart; Linda Merigliano; David N. Cole; Andy Leach; Pam Wright; Deb Bumpus

2009-01-01

The purpose of monitoring wilderness character is to improve wilderness stewardship by providing managers a tool to assess how selected actions and conditions related to wilderness character are changing over time. Wilderness character monitoring provides information to help answer two key questions about wilderness character and wilderness stewardship: 1. How is...

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

PubMed Central

Batal, Iyad; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Distributed condition monitoring techniques of optical fiber composite power cable in smart grid

NASA Astrophysics Data System (ADS)

Sun, Zhihui; Liu, Yuan; Wang, Chang; Liu, Tongyu

2011-11-01

Optical fiber composite power cable such as optical phase conductor (OPPC) is significant for the development of smart grid. This paper discusses the distributed cable condition monitoring techniques of the OPPC, which adopts embedded single-mode fiber as the sensing medium. By applying optical time domain reflection and laser Raman scattering, high-resolution spatial positioning and high-precision distributed temperature measurement is executed. And the OPPC cable condition parameters including temperature and its location, current carrying capacity, and location of fracture and loss can be monitored online. OPPC cable distributed condition monitoring experimental system is set up, and the main parts including pulsed fiber laser, weak Raman signal reception, high speed acquisition and cumulative average processing, temperature demodulation and current carrying capacity analysis are introduced. The distributed cable condition monitoring techniques of the OPPC is significant for power transmission management and security.

New Fast Beam Conditions Monitoring (BCM1F) system for CMS

NASA Astrophysics Data System (ADS)

Zagozdzinska, A. A.; Bell, A. J.; Dabrowski, A. E.; Hempel, M.; Henschel, H. M.; Karacheban, O.; Przyborowski, D.; Leonard, J. L.; Penno, M.; Pozniak, K. T.; Miraglia, M.; Lange, W.; Lohmann, W.; Ryjov, V.; Lokhovitskiy, A.; Stickland, D.; Walsh, R.

2016-01-01

The CMS Beam Radiation Instrumentation and Luminosity (BRIL) project is composed of several systems providing the experiment protection from adverse beam conditions while also measuring the online luminosity and beam background. Although the readout bandwidth of the Fast Beam Conditions Monitoring system (BCM1F—one of the faster monitoring systems of the CMS BRIL), was sufficient for the initial LHC conditions, the foreseen enhancement of the beams parameters after the LHC Long Shutdown-1 (LS1) imposed the upgrade of the system. This paper presents the new BCM1F, which is designed to provide real-time fast diagnosis of beam conditions and instantaneous luminosity with readout able to resolve the 25 ns bunch structure.

Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using Labview

NASA Astrophysics Data System (ADS)

Kong, Changduk; Kim, Keonwoo

2011-12-01

Recently, an aero gas turbine health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which occurs in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the online condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measured engine performance data and base performance results calculated by base engine performance model. This work aims to develop a GUI type on-line condition monitoring program for the PT6A-67 turboprop engine of a high altitude and long endurance operation UAV using LabVIEW. The base engine performance of the on-line condition monitoring program is simulated using component maps inversely generated from the limited performance deck data provided by engine manufacturer. The base engine performance simulation program is evaluated because analysis results by this program agree well with the performance deck data. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between clean engine performance results calculated by the base performance simulation program and measured engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed online monitoring system. For user friendly purpose, all monitoring program are coded by LabVIEW, and monitoring examples are demonstrated using the proposed GUI type on-condition monitoring program.

Optimization of Remediation Conditions using Vadose Zone Monitoring Technology

NASA Astrophysics Data System (ADS)

Dahan, O.; Mandelbaum, R.; Ronen, Z.

2010-12-01

Success of in-situ bio-remediation of the vadose zone depends mainly on the ability to change and control hydrological, physical and chemical conditions of subsurface. These manipulations enables the development of specific, indigenous, pollutants degrading bacteria or set the environmental conditions for seeded bacteria. As such, the remediation efficiency is dependent on the ability to implement optimal hydraulic and chemical conditions in deep sections of the vadose zone. Enhanced bioremediation of the vadose zone is achieved under field conditions through infiltration of water enriched with chemical additives. Yet, water percolation and solute transport in unsaturated conditions is a complex process and application of water with specific chemical conditions near land surface dose not necessarily result in promoting of desired chemical and hydraulic conditions in deeper sections of the vadose zone. A newly developed vadose-zone monitoring system (VMS) allows continuous monitoring of the hydrological and chemical properties of the percolating water along deep sections of the vadose zone. Implementation of the VMS at sites that undergoes active remediation provides real time information on the chemical and hydrological conditions in the vadose zone as the remediation process progresses. Manipulating subsurface conditions for optimal biodegradation of hydrocarbons is demonstrated through enhanced bio-remediation of the vadose zone at a site that has been contaminated with gasoline products in Tel Aviv. The vadose zone at the site is composed of 6 m clay layer overlying a sandy formation extending to the water table at depth of 20 m bls. The upper 5 m of contaminated soil were removed for ex-situ treatment, and the remaining 15 m vadose zone is treated in-situ through enhanced bioremedaition. Underground drip irrigation system was installed below the surface on the bottom of the excavation. Oxygen and nutrients releasing powder (EHCO, Adventus) was spread below the

Comparing methods suitable for monitoring marine mammals in low visibility conditions during seismic surveys.

PubMed

Verfuss, Ursula K; Gillespie, Douglas; Gordon, Jonathan; Marques, Tiago A; Miller, Brianne; Plunkett, Rachael; Theriault, James A; Tollit, Dominic J; Zitterbart, Daniel P; Hubert, Philippe; Thomas, Len

2018-01-01

Loud sound emitted during offshore industrial activities can impact marine mammals. Regulations typically prescribe marine mammal monitoring before and/or during these activities to implement mitigation measures that minimise potential acoustic impacts. Using seismic surveys under low visibility conditions as a case study, we review which monitoring methods are suitable and compare their relative strengths and weaknesses. Passive acoustic monitoring has been implemented as either a complementary or alternative method to visual monitoring in low visibility conditions. Other methods such as RADAR, active sonar and thermal infrared have also been tested, but are rarely recommended by regulatory bodies. The efficiency of the monitoring method(s) will depend on the animal behaviour and environmental conditions, however, using a combination of complementary systems generally improves the overall detection performance. We recommend that the performance of monitoring systems, over a range of conditions, is explored in a modelling framework for a variety of species. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

OTVE turbopump condition monitoring, task E.5

NASA Technical Reports Server (NTRS)

Coleman, Paul T.; Collins, J. J.

1989-01-01

Recent work has been carried out on development of isotope wear analysis and optical and eddy current technologies to provide bearing wear measurements and real time monitoring of shaft speed, shaft axial displacement and shaft orbit of the Orbit Transfer Vehicle hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mils by two fiberoptic deflectometers. Evaluation of eddy current probes showed that, in addition to measuring shaft orbital motion, they can be used to measure shaft speed without having to machine grooves on the shaft surface as is the usual practice for turbomachinery. The interim results of this condition monitoring effort are presented.

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

PubMed Central

Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

2014-01-01

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

A selection of forest condition indicators for monitoring

Treesearch

Kurt H. Riitters; B.E. Law; R.C. Kucera; A.L. Gallant; R.L. DeVelice; C.J. Palmer

1992-01-01

Regional monitoring and assessments of the health of forested ecosystems require indicators of forest conditions and environmental stresses. Indicator selections depend on objectives and the strategy for data collection and analysis. This paper recommends a set of indicators to signal changes in forest ecosystem distribution, productivity, and disturbance. Additional...

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Image edge detection based tool condition monitoring with morphological component analysis.

PubMed

Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

2017-07-01

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A real time study on condition monitoring of distribution transformer using thermal imager

NASA Astrophysics Data System (ADS)

Mariprasath, T.; Kirubakaran, V.

2018-05-01

The transformer is one of the critical apparatus in the power system. At any cost, a few minutes of outages harshly influence the power system. Hence, prevention-based maintenance technique is very essential. The continuous conditioning and monitoring technology significantly increases the life span of the transformer, as well as reduces the maintenance cost. Hence, conditioning and monitoring of transformer's temperature are very essential. In this paper, a critical review has been made on various conditioning and monitoring techniques. Furthermore, a new method, hot spot indication technique, is discussed. Also, transformer's operating condition is monitored by using thermal imager. From the thermal analysis, it is inferred that major hotspot locations are appearing at connection lead out; also, the bushing of the transformer is the very hottest spot in transformer, so monitoring the level of oil is essential. Alongside, real time power quality analysis has been carried out using the power analyzer. It shows that industrial drives are injecting current harmonics to the distribution network, which causes the power quality problem on the grid. Moreover, the current harmonic limit has exceeded the IEEE standard limit. Hence, the adequate harmonics suppression technique is need an hour.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Recommendations for strengthening the infrared technology component of any condition monitoring program

NASA Astrophysics Data System (ADS)

Nicholas, Jack R., Jr.; Young, R. K.

1999-03-01

This presentation provides insights of a long term 'champion' of many condition monitoring technologies and a Level III infra red thermographer. The co-authors present recommendations based on their observations of infra red and other components of predictive, condition monitoring programs in manufacturing, utility and government defense and energy activities. As predictive maintenance service providers, trainers, informal observers and formal auditors of such programs, the co-authors provide a unique perspective that can be useful to practitioners, managers and customers of advanced programs. Each has over 30 years experience in the field of machinery operation, maintenance, and support the origins of which can be traced to and through the demanding requirements of the U.S. Navy nuclear submarine forces. They have over 10 years each of experience with programs in many different countries on 3 continents. Recommendations are provided on the following: (1) Leadership and Management Support (For survival); (2) Life Cycle View (For establishment of a firm and stable foundation for a program); (3) Training and Orientation (For thermographers as well as operators, managers and others); (4) Analyst Flexibility (To innovate, explore and develop their understanding of machinery condition); (5) Reports and Program Justification (For program visibility and continued expansion); (6) Commitment to Continuous Improvement of Capability and Productivity (Through application of updated hardware and software); (7) Mutual Support by Analysts (By those inside and outside of the immediate organization); (8) Use of Multiple Technologies and System Experts to Help Define Problems (Through the use of correlation analysis of data from up to 15 technologies. An example correlation analysis table for AC and DC motors is provided.); (9) Root Cause Analysis (Allows a shift from reactive to proactive stance for a program); (10) Master Equipment Identification and Technology Application (To

On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters

NASA Astrophysics Data System (ADS)

Han, Fenghua; Xie, Feng

2017-07-01

In the process of cutting tools, it is very important to monitor the working state of the tools. On the basis of acceleration signal acquisition under the constant speed, time domain and frequency domain analysis of relevant indicators monitor the online of tool wear condition. The analysis results show that the method can effectively judge the tool wear condition in the process of machining. It has certain application value.

Progress toward an advanced condition monitoring system for reusable rocket engines

NASA Technical Reports Server (NTRS)

Maram, J.; Barkhoudarian, S.

1987-01-01

A new generation of advanced sensor technologies will allow the direct measurement of critical/degradable rocket engine components' health and the detection of degraded conditions before component deterioration affects engine performance, leading to substantial improvements in reusable engines' operation and maintenance. When combined with a computer-based engine condition-monitoring system, these sensors can furnish a continuously updated data base for the prediction of engine availability and advanced warning of emergent maintenance requirements. Attention is given to the case of a practical turbopump and combustion device diagnostic/prognostic health-monitoring system.

Wetlands monitoring - hydrological conditions and water quality in selected transects of Biebrza National Park.

NASA Astrophysics Data System (ADS)

Stelmaszczyk, Mateusz; Okruszko, Tomasz

2010-05-01

. Studied locations were covered mainly by Magnocaricion vegetation (e.g. Caricetum gracilis and Caricetum elatae), Molinio-Arrhenatheretea vegetation (Molinietum caeruleae), and Scheuchzerio-Caricetea nigrae vegetation (e.g. Caricetum lasiocarpae). In presented work authors show results of water quality measurements and monitoring of hydrological conditions, characterized by changes of groundwater table, period and size of inundation. During six years long monitoring period (2004 - 2009 hydrological years) there were observed high diversification of groundwater and surface water levels among locations. They fluctuate in some places from very low groundwater levels, observed in late summer and in early autumn (over 1 m beneath the ground), to levels reaching surface of the ground or laying nearly below it, occurring in winter and spring. There are also places where quite high inundations in winter and spring are observed. Collected chemical and hydrological data were statistically analyzed using STATISTICA 8 software with a use of one of the multivariate analysis - Principal Component Analysis (PCA) method. Owing to the usage of PCA analysis it was possible to define most important parameters characterizing habitats were occurs selected vegetation. The impact of hydrological conditions (presented as a main factor) on forming particular wetland plant communities can be discussed. Authors determine that some other factors (e.g. management) can be more responsible for occurrence of particular plant communities and their sustaining in good status in specific locations.

A remote condition monitoring system for wind-turbine based DG systems

NASA Astrophysics Data System (ADS)

Ma, X.; Wang, G.; Cross, P.; Zhang, X.

2012-05-01

In this paper, a remote condition monitoring system is proposed, which fundamentally consists of real-time monitoring modules on the plant side, a remote support centre and the communications between them. The paper addresses some of the key issues related on the monitoring system, including i) the implementation and configuration of a VPN connection, ii) an effective database system to be able to handle huge amount of monitoring data, and iii) efficient data mining techniques to convert raw data into useful information for plant assessment. The preliminary results have demonstrated that the proposed system is practically feasible and can be deployed to monitor the emerging new energy generation systems.

Surface Acoustic Wave (SAW) Resonators for Monitoring Conditioning Film Formation

PubMed Central

Hohmann, Siegfried; Kögel, Svea; Brunner, Yvonne; Schmieg, Barbara; Ewald, Christina; Kirschhöfer, Frank; Brenner-Weiß, Gerald; Länge, Kerstin

2015-01-01

We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA) and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM) sensor measurements, which confirmed the suitability of the SAW resonators for this application. PMID:26007735

Condition monitoring of Electric Components

NASA Astrophysics Data System (ADS)

Zaman, Ishtiaque

A universal non-intrusive model of a flexible antenna array is presented in this paper to monitor and identify the failures in electric machines. This adjustable antenna is designed to serve the purpose of condition monitoring of a vast range of electrical components including Induction Motor (IM), Printed Circuit Board (PCB), Synchronous Reluctance Motor (SRM), Permanent Magnet Synchronous Machine (PMSM) etc. by capturing the low frequency magnetic field radiated around these machines. The basic design and specification of the proposed antenna array for low frequency components is portrayed first. The design of the antenna is adjustable to fit for an extensive variety of segments. Subsequent to distinguishing the design and specifications of the antenna, the ideal area of the most delicate stray field has been identified for healthy current streaming around the machineries. Following this, short circuit representing faulty situation has been introduced and compared with the healthy cases. Precision has been found recognizing the faults using this one generic model of Antenna and the results are presented for three different machines i.e. IM, SRM and PMSM. Finite element method has been used to design the antenna and detect the optimum location and faults in the machines. Finally, a 3D Printer is proposed to be employed to build the antenna as per the details tended to in this paper contingent upon the power segments.

Comparison of multispectral remote-sensing techniques for monitoring subsurface drain conditions. [Imperial Valley, California

NASA Technical Reports Server (NTRS)

Goettelman, R. C.; Grass, L. B.; Millard, J. P.; Nixon, P. R.

1983-01-01

The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.

Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different...

Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

PubMed

Lin, Deborah S; Taylor, Peter; Tibbett, Mark

2014-10-01

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

A NATIONAL PROGRAM FOR MONITORING STREAM CONDITION IN THE WESTERN UNITED STATES

EPA Science Inventory

The U.S. Environmental Protection Agency recently initiated a four-year survey of streams in the Western United States as a component of the Environmental Monitoring and Assessment Program (EMAP). EMAP is developing indicators to monitor and assess the condition of ecological...

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

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

Kumar, Rajeeva; Kumar, Aditya; Dai, Dan

2012-12-31

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developedmore » will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to

Condition monitoring of turning process using infrared thermography technique - An experimental approach

NASA Astrophysics Data System (ADS)

Prasad, Balla Srinivasa; Prabha, K. Aruna; Kumar, P. V. S. Ganesh

2017-03-01

In metal cutting machining, major factors that affect the cutting tool life are machine tool vibrations, tool tip/chip temperature and surface roughness along with machining parameters like cutting speed, feed rate, depth of cut, tool geometry, etc., so it becomes important for the manufacturing industry to find the suitable levels of process parameters for obtaining maintaining tool life. Heat generation in cutting was always a main topic to be studied in machining. Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for development of the effective monitoring of tool condition monitoring systems with improved accuracy. From a process improvement point of view, it is definitely more advantageous to proactively monitor quality directly in the process instead of the product, so that the consequences of a defective part can be minimized or even eliminated. In the present work, a real time process monitoring method is explored using multiple sensors. It focuses on the development of a test bed for monitoring the tool condition in turning of AISI 316L steel by using both coated and uncoated carbide inserts. Proposed tool condition monitoring (TCM) is evaluated in the high speed turning using multiple sensors such as Laser Doppler vibrometer and infrared thermography technique. The results indicate the feasibility of using the dominant frequency of the vibration signals for the monitoring of high speed turning operations along with temperatures gradient. A possible correlation is identified in both regular and irregular cutting tool wear. While cutting speed and feed rate proved to be influential parameter on the depicted temperatures and depth of cut to be less influential. Generally, it is observed that lower heat and temperatures are generated when coated inserts are employed. It is found that cutting temperatures are gradually increased as edge wear and deformation developed.

System and method for statistically monitoring and analyzing sensed conditions

DOEpatents

Pebay, Philippe P [Livermore, CA; Brandt, James M [Dublin, CA; Gentile, Ann C [Dublin, CA; Marzouk, Youssef M [Oakland, CA; Hale, Darrian J [San Jose, CA; Thompson, David C [Livermore, CA

2011-01-04

A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

System and method for statistically monitoring and analyzing sensed conditions

DOEpatents

Pebay, Philippe P [Livermore, CA; Brandt, James M [Dublin, CA; Gentile, Ann C [Dublin, CA; Marzouk, Youssef M [Oakland, CA; Hale, Darrian J [San Jose, CA; Thompson, David C [Livermore, CA

2011-01-25

A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

System and method for statistically monitoring and analyzing sensed conditions

DOEpatents

Pebay, Philippe P [Livermore, CA; Brandt, James M. , Gentile; Ann C. , Marzouk; Youssef M. , Hale; Darrian J. , Thompson; David, C [Livermore, CA

2010-07-13

A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops.

PubMed

Zhang, Cunji; Yao, Xifan; Zhang, Jianming

2015-12-03

Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi(®) Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops.

Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

PubMed Central

Zhang, Cunji; Yao, Xifan; Zhang, Jianming

2015-01-01

Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops. PMID:26633418

A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring

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

Liao, T. W.; Ting, C.F.; Qu, Jun

2007-01-01

Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish differentmore » states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the decomposition level, and the GA parameters are properly selected.« less

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

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

Sheng, S.

2012-07-01

The Gearbox Reliability Collaborative (GRC) at the National Wind Technology Center (NWTC) tested two identical gearboxes. One was tested on the NWTCs 2.5 MW dynamometer and the other was field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not severe, the test gearbox was removed from the field and retested in the NWTCs dynamometer before it was disassembled. During the dynamometer retest, some vibration data along with testing condition information were collected. These datamore » enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project was to evaluate different vibration analysis algorithms used in wind turbine condition monitoring (CM) and find out whether the typical practices are effective. With involvement of both academic researchers and industrial partners, the project sets an example on providing cutting edge research results back to industry.« less

Monitoring Forest Condition in Europe: Impacts of Nitrogen and Sulfur Depositions on Forest Ecosystems

Treesearch

M. Lorenz; G. Becher; V. Mues; E. Ulrich

2006-01-01

Forest condition in Europe has been monitored over 19 years jointly by the United Nations Economic Commission for Europe (UNECE) and the European Union (EU). Large-scale variations of forest condition over space and time in relation to natural and anthropogenic factors are assessed on about 6,000 plots systematically spread across Europe. This large-scale monitoring...

Evaluation of Diesel Exhaust Continuous Monitors in Controlled Environmental Conditions

PubMed Central

Yu, Chang Ho; Patton, Allison P.; Zhang, Andrew; Fanac, Zhi-Hua (Tina); Weisel, Clifford P.; Lioy, Paul J.

2015-01-01

Diesel exhaust (DE) contains a variety of toxic air pollutants, including diesel particulate matter (DPM) and gaseous contaminants (e.g., carbon monoxide (CO)). DPM is dominated by fine (PM2.5) and ultrafine particles (UFP), and can be representatively determined by its thermal-optical refractory as elemental carbon (EC) or light-absorbing characteristics as black carbon (BC). The currently accepted reference method for sampling and analysis of occupational exposure to DPM is the National Institute for Occupational Safety and Health (NIOSH) Method 5040. However, this method cannot provide in-situ short-term measurements of DPM. Thus, real-time monitors are gaining attention to better examine DE exposures in occupational settings. However, real-time monitors are subject to changing environmental conditions. Field measurements have reported interferences in optical sensors and subsequent real-time readings, under conditions of high humidity and abrupt temperature changes. To begin dealing with these issues, we completed a controlled study to evaluate five real-time monitors: Airtec real-time DPM/EC Monitor, TSI SidePak Personal Aerosol Monitor AM510 (PM2.5), TSI Condensation Particle Counter 3007, microAeth AE51 BC Aethalometer, and Langan T15n CO Measurer. Tests were conducted under different temperatures (55, 70, and 80 °F), relative humidity (10, 40, and 80%), and DPM concentrations (50 and 200 µg/m3) in a controlled exposure facility. The 2-hour averaged EC measurements from the Airtec instrument showed relatively good agreement with NIOSH Method 5040 (R2=0.84; slope=1.17±0.06; N=27) and reported ~17% higher EC concentrations than the NIOSH reference method. Temperature, relative humidity, and DPM levels did not significantly affect relative differences in 2-hour averaged EC concentrations obtained by the Airtec instrument versus the NIOSH method (p<0.05). Multiple linear regression analyses, based on 1-min averaged data, suggested combined effects of up to 5

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

PubMed

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants

An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2014-01-01

This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

A suite of optical fibre sensors for structural condition monitoring

NASA Astrophysics Data System (ADS)

Sun, T.; Grattan, K. T. V.; Carlton, J.

2015-05-01

This paper is to review the research activities at City University London in the development of a range of fibre Bragg grating (FBG)-based sensors, including strain, temperature, relative humidity, vibration and acoustic sensors, with an aim to meet the increasing demands from industry for structural condition monitoring. As a result, arrays of optical fibre sensors have been instrumented into various types of structures, including concrete, limestone, marine propellers, pantograph and electrical motors, allowing for both static and dynamic monitoring and thus enhanced structural reliability and integrity.

BIRD COMMUNITIES AND HABITAT AS ECOLOGICAL INDICATORS OF FOREST CONDITION IN REGIONAL MONITORING

EPA Science Inventory

Ecological indicators for long-term monitoring programs are needed to detect and assess changing environmental conditions, We developed and tested community-level environmental indicators for monitoring forest bird populations and associated habitat. We surveyed 197 sampling plo...

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

PubMed

Del Giudice, G; Padulano, R; Siciliano, D

2016-01-01

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring †

PubMed Central

Mao, Yingchi; Qi, Hai; Ping, Ping; Li, Xiaofang

2017-01-01

Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED) was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability. PMID:29207535

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28

On-line condition monitoring applications in nuclear power plants

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

Hastiemian, H. M.; Feltus, M. A.

2006-07-01

Existing signals from process instruments in nuclear power plants can be sampled while the plant is operating and analyzed to verify the static and dynamic performance of process sensors, identify process-to-sensor problems, detect instrument anomalies such as venturi fouling, measure the vibration of the reactor vessel and its internals, or detect thermal hydraulic anomalies within the reactor coolant system. These applications are important in nuclear plants to satisfy a variety of objectives such as: 1) meeting the plant technical specification requirements; 2) complying with regulatory regulations; 3) guarding against equipment and process degradation; 4) providing a means for incipient failuremore » detection and predictive maintenance; or 5) identifying the root cause of anomalies in equipment and plant processes. The technologies that are used to achieve these objectives are collectively referred to as 'on-line condition monitoring.' This paper presents a review of key elements of these technologies, provides examples of their use in nuclear power plants, and illustrates how they can be integrated into an on-line condition monitoring system for nuclear power plants. (authors)« less

Diagnosis of abnormal patterns in multivariate microclimate monitoring: a case study of an open-air archaeological site in Pompeii (Italy).

PubMed

Merello, Paloma; García-Diego, Fernando-Juan; Zarzo, Manuel

2014-08-01

Chemometrics has been applied successfully since the 1990s for the multivariate statistical control of industrial processes. A new area of interest for these tools is the microclimatic monitoring of cultural heritage. Sensors record climatic parameters over time and statistical data analysis is performed to obtain valuable information for preventive conservation. A case study of an open-air archaeological site is presented here. A set of 26 temperature and relative humidity data-loggers was installed in four rooms of Ariadne's house (Pompeii). If climatic values are recorded versus time at different positions, the resulting data structure is equivalent to records of physical parameters registered at several points of a continuous chemical process. However, there is an important difference in this case: continuous processes are controlled to reach a steady state, whilst open-air sites undergo tremendous fluctuations. Although data from continuous processes are usually column-centred prior to applying principal components analysis, it turned out that another pre-treatment (row-centred data) was more convenient for the interpretation of components and to identify abnormal patterns. The detection of typical trajectories was more straightforward by dividing the whole monitored period into several sub-periods, because the marked climatic fluctuations throughout the year affect the correlation structures. The proposed statistical methodology is of interest for the microclimatic monitoring of cultural heritage, particularly in the case of open-air or semi-confined archaeological sites. Copyright © 2014 Elsevier B.V. All rights reserved.

Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

PubMed

Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

2015-06-01

Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Research on Land Ecological Condition Investigation and Monitoring Technology

NASA Astrophysics Data System (ADS)

Lv, Chunyan; Guo, Xudong; Chen, Yuqi

2017-04-01

The ecological status of land reflects the relationship between land use and environmental factors. At present, land ecological situation in China is worrying. According to the second national land survey data, there are about 149 million acres of arable land located in forests and grasslands area in Northeast and Northwest of China, Within the limits of the highest flood level, at steep slope above 25 degrees; about 50 million acres of arable land has been in heavy pollution; grassland degradation is still serious. Protected natural forests accounted for only 6% of the land area, and forest quality is low. Overall, the ecological problem has been eased, but the local ecological destruction intensified, natural ecosystem in degradation. It is urgent to find out the situation of land ecology in the whole country and key regions as soon as possible. The government attaches great importance to ecological environment investigation and monitoring. Various industries and departments from different angles carry out related work, most of it about a single ecological problem, the lack of a comprehensive surveying and assessment of land ecological status of the region. This paper established the monitoring index system of land ecological condition, including Land use type area and distribution, quality of cultivated land, vegetation status and ecological service, arable land potential and risk, a total of 21 indicators. Based on the second national land use survey data, annual land use change data and high resolution remote sensing data, using the methods of sample monitoring, field investigation and statistical analysis to obtain the information of each index, this paper established the land ecological condition investigation and monitoring technology and method system. It has been improved, through the application to Beijing-Tianjin-Hebei Urban Agglomeration, the northern agro-pastoral ecological fragile zone, and 6 counties (cities).

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

PubMed

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2017-08-01

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

Predictive modeling in Clostridium acetobutylicum fermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

NASA Astrophysics Data System (ADS)

Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.

2016-05-01

The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Reality Monitoring and Metamemory in Adults with Autism Spectrum Conditions

ERIC Educational Resources Information Center

Cooper, Rose A.; Plaisted-Grant, Kate C.; Baron-Cohen, Simon; Simons, Jon S.

2016-01-01

Studies of reality monitoring (RM) often implicate medial prefrontal cortex (mPFC) in distinguishing internal and external information, a region linked to autism-related deficits in social and self-referential information processing, executive function, and memory. This study used two RM conditions (self-other; perceived-imagined) to investigate…

New strategy to identify radicals in a time evolving EPR data set by multivariate curve resolution-alternating least squares.

PubMed

Fadel, Maya Abou; de Juan, Anna; Vezin, Hervé; Duponchel, Ludovic

2016-12-01

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra. Copyright © 2016 Elsevier B.V. All rights reserved.

A Computer Interview for Multivariate Monitoring of Psychiatric Outcome.

ERIC Educational Resources Information Center

Stevenson, John F.; And Others

Application of computer technology to psychiatric outcome measurement offers the promise of coping with increasing demands for extensive patient interviews repeated longitudinally. Described is the development of a cost-effective multi-dimensional tracking device to monitor psychiatric functioning, building on a previous local computer interview…

Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors.

PubMed

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression.

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu

Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

NASA Astrophysics Data System (ADS)

Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

2012-07-01

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

Smartphone Ownership and Interest in Mobile Applications to Monitor Symptoms of Mental Health Conditions

PubMed Central

Friedman, Rohn; Keshavan, Matcheri

2014-01-01

Background Patient retrospective recollection is a mainstay of assessing symptoms in mental health and psychiatry. However, evidence suggests that these retrospective recollections may not be as accurate as data collection though the experience sampling method (ESM), which captures patient data in “real time” and “real life.” However, the difficulties in practical implementation of ESM data collection have limited its impact in psychiatry and mental health. Smartphones with the capability to run mobile applications may offer a novel method of collecting ESM data that may represent a practical and feasible tool for mental health and psychiatry. Objective This paper aims to provide data on psychiatric patients’ prevalence of smartphone ownership, patterns of use, and interest in utilizing mobile applications to monitor their mental health conditions. Methods One hundred psychiatric outpatients at a large urban teaching hospital completed a paper-and-pencil survey regarding smartphone ownership, use, and interest in utilizing mobile applications to monitor their mental health condition. Results Ninety-seven percent of patients reported owning a phone and 72% reported that their phone was a smartphone. Patients in all age groups indicated greater than 50% interest in using a mobile application on a daily basis to monitor their mental health condition. Conclusions Smartphone and mobile applications represent a practical opportunity to explore new modalities of monitoring, treatment, and research of psychiatric and mental health conditions. PMID:25098314

Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions.

PubMed

Torous, John; Friedman, Rohn; Keshavan, Matcheri

2014-01-21

Patient retrospective recollection is a mainstay of assessing symptoms in mental health and psychiatry. However, evidence suggests that these retrospective recollections may not be as accurate as data collection though the experience sampling method (ESM), which captures patient data in "real time" and "real life." However, the difficulties in practical implementation of ESM data collection have limited its impact in psychiatry and mental health. Smartphones with the capability to run mobile applications may offer a novel method of collecting ESM data that may represent a practical and feasible tool for mental health and psychiatry. This paper aims to provide data on psychiatric patients' prevalence of smartphone ownership, patterns of use, and interest in utilizing mobile applications to monitor their mental health conditions. One hundred psychiatric outpatients at a large urban teaching hospital completed a paper-and-pencil survey regarding smartphone ownership, use, and interest in utilizing mobile applications to monitor their mental health condition. Ninety-seven percent of patients reported owning a phone and 72% reported that their phone was a smartphone. Patients in all age groups indicated greater than 50% interest in using a mobile application on a daily basis to monitor their mental health condition. Smartphone and mobile applications represent a practical opportunity to explore new modalities of monitoring, treatment, and research of psychiatric and mental health conditions.

Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy.

PubMed

Neal, Sharon L

2018-01-01

The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process

Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions.

PubMed

Yue Deng; Cheng Chen; Tsow, Francis; Xiaojun Xian; Forzani, Erica

2016-08-01

Volatile organic compounds (VOC) are organic chemicals that have high vapor pressure at regular conditions. Some VOC could be dangerous to human health, therefore it is important to determine real-time indoor and outdoor personal exposures to VOC. To achieve this goal, our group has developed a wearable gas monitor with a complete sensor fabrication and calibration protocol for free-living conditions. Correction factors for calibrating the sensors, including sensitivity, aging effect, and temperature effect are implemented into a Quick Response Code (QR code), so that the pre-calibrated quartz tuning fork (QTF) sensor can be used with the wearable monitor under free-living conditions.

Integrated Multivariate Health Monitoring System for Helicopters Main Rotor Drives: Development and Validation with In-Service Data

DTIC Science & Technology

2014-10-02

potential advantages of using multi- variate classification/discrimination/ anomaly detection meth- ods on real world accelerometric condition monitoring ...case of false anomaly reports. A possible explanation of this phenomenon could be given 8 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT...of those helicopters. 1. Anomaly detection by means of a self-learning Shewhart control chart. A problem highlighted by the experts of Agusta- Westland

78 FR 73112 - Monitoring System Conditions-Transmission Operations Reliability Standards; Interconnection...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-05

..., RM13-14-000 and RM13-15-000] Monitoring System Conditions--Transmission Operations Reliability...) 502-6817, [email protected] . Robert T. Stroh (Legal Information), Office of the General... Reliability Standards ``address the important reliability goal of ensuring that the transmission system is...

Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors

PubMed Central

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression. PMID:27907077

Monitoring growth condition of spring maize in Northeast China using a process-based model

NASA Astrophysics Data System (ADS)

Wang, Peijuan; Zhou, Yuyu; Huo, Zhiguo; Han, Lijuan; Qiu, Jianxiu; Tan, Yanjng; Liu, Dan

2018-04-01

Early and accurate assessment of the growth condition of spring maize, a major crop in China, is important for the national food security. This study used a process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model, driven by satellite-derived leaf area index and ground-based meteorological observations, to simulate net primary productivity (NPP) of spring maize in Northeast China from the first ten-day (FTD) of May to the second ten-day (STD) of August during 2001-2014. The growth condition of spring maize in 2014 in Northeast China was monitored and evaluated spatially and temporally by comparison with 5- and 13-year averages, as well as 2009 and 2013. Results showed that NPP simulated by the RS-P-YEC model, with consideration of multi-scattered radiation inside the crop canopy, could reveal the growth condition of spring maize more reasonably than the Boreal Ecosystem Productivity Simulator. Moreover, NPP outperformed other commonly used vegetation indices (e.g., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) for monitoring and evaluating the growth condition of spring maize. Compared with the 5- and 13-year averages, the growth condition of spring maize in 2014 was worse before the STD of June and after the FTD of August, and it was better from the third ten-day (TTD) of June to the TTD of July across Northeast China. Spatially, regions with slightly worse and worse growth conditions in the STD of August 2014 were concentrated mainly in central Northeast China, and they accounted for about half of the production area of spring maize in Northeast China. This study confirms that NPP is a good indicator for monitoring and evaluating growth condition because of its capacity to reflect the physiological characteristics of crops. Meanwhile, the RS-P-YEC model, driven by remote sensing and ground-based meteorological data, is effective for monitoring crop growth condition over large areas in a near real

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

PubMed Central

2012-01-01

Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Remote monitoring as a tool in condition assessment of a highway bridge

NASA Astrophysics Data System (ADS)

Tantele, Elia A.; Votsis, Renos A.; Onoufriou, Toula; Milis, Marios; Kareklas, George

2016-08-01

The deterioration of civil infrastructure and their subsequent maintenance is a significant problem for the responsible managing authorities. The ideal scenario is to detect deterioration and/or structural problems at early stages so that the maintenance cost is kept low and the safety of the infrastructure remains undisputed. The current inspection regimes implemented mostly via visual inspection are planned at specific intervals but are not always executed on time due to shortcomings in expert personnel and finance. However the introduction of technological advances in the assessment of infrastructures provides the tools to alleviate this problem. This study describes the assessment of a highway RC bridge's structural condition using remote structural health monitoring. A monitoring plan is implemented focusing on strain measurements; as strain is a parameter influenced by the environmental conditions supplementary data are provided from temperature and wind sensors. The data are acquired using wired sensors (deployed at specific locations) which are connected to a wireless sensor unit installed at the bridge. This WSN application enables the transmission of the raw data from the field to the office for processing and evaluation. The processed data are then used to assess the condition of the bridge. This case study, which is part of an undergoing RPF research project, illustrates that remote monitoring can alleviate the problem of missing structural inspections. Additionally, shows its potential to be the main part of a fully automated smart procedure of obtaining structural data, processed them and trigger an alarm when certain undesirable conditions are met.

Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

PubMed

Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

2017-08-07

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

Introducing passive acoustic filter in acoustic based condition monitoring: Motor bike piston-bore fault identification

NASA Astrophysics Data System (ADS)

Jena, D. P.; Panigrahi, S. N.

2016-03-01

Requirement of designing a sophisticated digital band-pass filter in acoustic based condition monitoring has been eliminated by introducing a passive acoustic filter in the present work. So far, no one has attempted to explore the possibility of implementing passive acoustic filters in acoustic based condition monitoring as a pre-conditioner. In order to enhance the acoustic based condition monitoring, a passive acoustic band-pass filter has been designed and deployed. Towards achieving an efficient band-pass acoustic filter, a generalized design methodology has been proposed to design and optimize the desired acoustic filter using multiple filter components in series. An appropriate objective function has been identified for genetic algorithm (GA) based optimization technique with multiple design constraints. In addition, the sturdiness of the proposed method has been demonstrated in designing a band-pass filter by using an n-branch Quincke tube, a high pass filter and multiple Helmholtz resonators. The performance of the designed acoustic band-pass filter has been shown by investigating the piston-bore defect of a motor-bike using engine noise signature. On the introducing a passive acoustic filter in acoustic based condition monitoring reveals the enhancement in machine learning based fault identification practice significantly. This is also a first attempt of its own kind.

Monitoring vegetation conditions from LANDSAT for use in range management

NASA Technical Reports Server (NTRS)

Haas, R. H.; Deering, D. W.; Rouse, J. W., Jr.; Schell, J. A.

1975-01-01

A summary of the LANDSAT Great Plains Corridor projects and the principal results are presented. Emphasis is given to the use of satellite acquired phenological data for range management and agri-business activities. A convenient method of reducing LANDSAT MSS data to provide quantitative estimates of green biomass on rangelands in the Great Plains is explained. Suggestions for the use of this approach for evaluating range feed conditions are presented. A LANDSAT Follow-on project has been initiated which will employ the green biomass estimation method in a quasi-operational monitoring of range readiness and range feed conditions on a regional scale.

SSME Condition Monitoring Using Neural Networks and Plume Spectral Signatures

NASA Technical Reports Server (NTRS)

Hopkins, Randall; Benzing, Daniel

1996-01-01

For a variety of reasons, condition monitoring of the Space Shuttle Main Engine (SSME) has become an important concern for both ground tests and in-flight operation. The complexities of the SSME suggest that active, real-time condition monitoring should be performed to avoid large-scale or catastrophic failure of the engine. In 1986, the SSME became the subject of a plume emission spectroscopy project at NASA's Marshall Space Flight Center (MSFC). Since then, plume emission spectroscopy has recorded many nominal tests and the qualitative spectral features of the SSME plume are now well established. Significant discoveries made with both wide-band and narrow-band plume emission spectroscopy systems led MSFC to develop the Optical Plume Anomaly Detection (OPAD) system. The OPAD system is designed to provide condition monitoring of the SSME during ground-level testing. The operational health of the engine is achieved through the acquisition of spectrally resolved plume emissions and the subsequent identification of abnormal emission levels in the plume indicative of engine erosion or component failure. Eventually, OPAD, or a derivative of the technology, could find its way on to an actual space vehicle and provide in-flight engine condition monitoring. This technology step, however, will require miniaturized hardware capable of processing plume spectral data in real-time. An objective of OPAD condition monitoring is to determine how much of an element is present in the SSME plume. The basic premise is that by knowing the element and its concentration, this could be related back to the health of components within the engine. For example, an abnormal amount of silver in the plume might signify increased wear or deterioration of a particular bearing in the engine. Once an anomaly is identified, the engine could be shut down before catastrophic failure occurs. Currently, element concentrations in the plume are determined iteratively with the help of a non-linear computer

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

NASA Astrophysics Data System (ADS)

Delvecchio, S.; Bonfiglio, P.; Pompoli, F.

2018-01-01

This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) under both test bench and vehicle operating conditions. Over recent years, several authors have summarized what is known in critical reviews mainly focused on reciprocating machines in general or on specific signal processing techniques: no attempts to deal with IC engine condition monitoring have been made. This paper first gives a brief summary of the generation of sound and vibration in ICEs in order to place further discussion on fault vibro-acoustic diagnosis in context. An overview of the monitoring and diagnostic techniques described in literature using both vibration and acoustic signals is also provided. Different faulty conditions are described which affect combustion, mechanics and the aerodynamics of ICEs. The importance of measuring acoustic signals, as opposed to vibration signals, is due since the former seem to be more suitable for implementation on on-board monitoring systems in view of their non-intrusive behaviour, capability in simultaneously capturing signatures from several mechanical components and because of the possibility of detecting faults affecting airborne transmission paths. In view of the recent needs of the industry to (-) optimize component structural durability adopting long-life cycles, (-) verify the engine final status at the end of the assembly line and (-) reduce the maintenance costs monitoring the ICE life during vehicle operations, monitoring and diagnosing system requests are continuously growing up. The present review can be considered a useful guideline for test engineers in understanding which types of fault can be diagnosed by using vibro-acoustic signals in sufficient time in both test bench and operating conditions and which transducer and signal processing technique (of which the essential background theory is here reported) could be

Monitoring of Double Stud Wall Moisture Conditions in the Northeast

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

Ueno, K.

2015-03-01

Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double stud walls have a higher risk of interior-sourced condensation moisture damage, when compared with high-R approaches using exterior insulating sheathing.; Moisture conditions in double stud walls were monitored in Zone 5A (Massachusetts); three double stud assemblies were compared.

Monitoring of Double-Stud Wall Moisture Conditions in the Northeast

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

Ueno, K.

2015-03-01

Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double-stud walls have a higher risk of interior-sourced condensation moisture damage when compared with high-R approaches using exterior insulating sheathing. Moisture conditions in double-stud walls were monitored in Zone 5A (Massachusetts); three double-stud assemblies were compared.

Why is it important to monitor social conditions in wilderness?

Treesearch

Alan E. Watson

1990-01-01

“Social conditions in wilderness” refers to all aspects of human use of the wilderness that pose the possibility of impact to the resource and visitor experiences. The reasons for monitoring (1) use levels and use trends (including characteristics of use and users) and (2) the quality of the recreation experiences provided (ability to provide naturalness, privacy, and...

Wireless acceleration sensor of moving elements for condition monitoring of mechanisms

NASA Astrophysics Data System (ADS)

Sinitsin, Vladimir V.; Shestakov, Aleksandr L.

2017-09-01

Comprehensive analysis of the angular and linear accelerations of moving elements (shafts, gears) allows an increase in the quality of the condition monitoring of mechanisms. However, existing tools and methods measure either linear or angular acceleration with postprocessing. This paper suggests a new construction design of an angular acceleration sensor for moving elements. The sensor is mounted on a moving element and, among other things, the data transfer and electric power supply are carried out wirelessly. In addition, the authors introduce a method for processing the received information which makes it possible to divide the measured acceleration into the angular and linear components. The design has been validated by the results of laboratory tests of an experimental model of the sensor. The study has shown that this method provides a definite separation of the measured acceleration into linear and angular components, even in noise. This research contributes an advance in the range of methods and tools for condition monitoring of mechanisms.

Development of an In-Situ Decommissioning Sensor Network Test Bed for Structural Condition Monitoring - 12156

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

Zeigler, Kristine E.; Ferguson, Blythe A.

2012-07-01

The Savannah River National Laboratory (SRNL) has established an In Situ Decommissioning (ISD) Sensor Network Test Bed, a unique, small scale, configurable environment, for the assessment of prospective sensors on actual ISD system material, at minimal cost. The Department of Energy (DOE) is presently implementing permanent entombment of contaminated, large nuclear structures via ISD. The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. Validation of ISD system performance models and verification of actual system conditions can be achieved through the development a system of sensors to monitor the materials andmore » condition of the structure. The ISD Sensor Network Test Bed has been designed and deployed to addresses the DOE-Environmental Management Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building at the Savannah River Site. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of groutable thermistors for temperature and moisture monitoring, strain gauges for crack growth monitoring, tilt-meters for settlement monitoring, and a communication system for data collection. Baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment. The Sensor Network Test Bed at SRNL uses COTS sensors on concrete blocks from the outer wall of the P Reactor Building to measure conditions expected to occur in ISD structures. Knowledge and lessons learned gained from installation, testing, and monitoring of the equipment will be applied to sensor installation in a meso-scale test bed at FIU and in future ISD structures. The initial data collected from the

Testing & Evaluation of Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions

DOT National Transportation Integrated Search

2012-01-01

This report summarizes activities in support of the DOT contract on Testing & Evaluating Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions & Improve Visual Inspection Procedures. The work of this project was performed by Dr...

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Development of a real time monitor and multivariate method for long term diagnostics of atmospheric pressure dielectric barrier discharges: application to He, He/N2, and He/O2 discharges.

PubMed

O'Connor, N; Milosavljević, V; Daniels, S

2011-08-01

In this paper we present the development and application of a real time atmospheric pressure discharge monitoring diagnostic. The software based diagnostic is designed to extract latent electrical and optical information associated with the operation of an atmospheric pressure dielectric barrier discharge (APDBD) over long time scales. Given that little is known about long term temporal effects in such discharges, the diagnostic methodology is applied to the monitoring of an APDBD in helium and helium with both 0.1% nitrogen and 0.1% oxygen gas admixtures over periods of tens of minutes. Given the large datasets associated with the experiments, it is shown that this process is much expedited through the novel application of multivariate correlations between the electrical and optical parameters of the corresponding chemistries which, in turn, facilitates comparisons between each individual chemistry also. The results of these studies show that the electrical and optical parameters of the discharge in helium and upon the addition of gas admixtures evolve over time scales far longer than the gas residence time and have been compared to current modelling works. It is envisaged that the diagnostic together with the application of multivariate correlations will be applied to rapid system identification and prototyping in both experimental and industrial APDBD systems in the future.

GEOGRAPHIC-SPECIFIC WATER QUALITY CRITERIA DEVELOPMENT WITH MONITORING DATA USING CONDITIONAL PROBABILITIES - A PROPOSED APPROACH

EPA Science Inventory

A conditional probability approach using monitoring data to develop geographic-specific water quality criteria for protection of aquatic life is presented. Typical methods to develop criteria using existing monitoring data are limited by two issues: (1) how to extrapolate to an...

Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring

PubMed Central

Sun, Jiedi; Yu, Yang; Wen, Jiangtao

2017-01-01

Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN. The compressed data acquisition is realized by projection transformation and can greatly reduce the data volume, which needs the nodes to process and transmit. The reconstruction of original signals is achieved in the host computer by complicated algorithms. The bearing vibration signals not only exhibit the sparsity property, but also have specific structures. This paper introduced the block sparse Bayesian learning (BSBL) algorithm which works by utilizing the block property and inherent structures of signals to reconstruct CS sparsity coefficients of transform domains and further recover the original signals. By using the BSBL, CS reconstruction can be improved remarkably. Experiments and analyses showed that BSBL method has good performance and is suitable for practical bearing-condition monitoring. PMID:28635623

Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Monitoring Global Crop Condition Indicators Using a Web-Based Visualization Tool

Treesearch

Bob Tetrault; Bob Baldwin

2006-01-01

Global crop condition information for major agricultural regions in the world can be monitored using the web-based application called Crop Explorer. With this application, U.S. and international producers, traders, researchers, and the public can access remote sensing information used by agricultural economists and scientists who predict crop production worldwide. For...

Monitoring network-design influence on assessment of ecological condition in wadeable streams

EPA Science Inventory

We investigated outcomes of three monitoring networks for assessing ecological character and condition of wadeable streams in the Waikato region, New Zealand. Sites were selected 1) based on a professional judgment network, 2) within categories of stream and watershed characteris...

Fast Multivariate Search on Large Aviation Datasets

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

Advanced multivariable control of a turboexpander plant

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

Altena, D.; Howard, M.; Bullin, K.

1998-12-31

This paper describes an application of advanced multivariable control on a natural gas plant and compares its performance to the previous conventional feed-back control. This control algorithm utilizes simple models from existing plant data and/or plant tests to hold the process at the desired operating point in the presence of disturbances and changes in operating conditions. The control software is able to accomplish this due to effective handling of process variable interaction, constraint avoidance and feed-forward of measured disturbances. The economic benefit of improved control lies in operating closer to the process constraints while avoiding significant violations. The South Texasmore » facility where this controller was implemented experienced reduced variability in process conditions which increased liquids recovery because the plant was able to operate much closer to the customer specified impurity constraint. An additional benefit of this implementation of multivariable control is the ability to set performance criteria beyond simple setpoints, including process variable constraints, relative variable merit and optimizing use of manipulated variables. The paper also details the control scheme applied to the complex turboexpander process and some of the safety features included to improve reliability.« less

Remote sensing of Northern mines: supporting operation and environmental monitoring in cold conditions

NASA Astrophysics Data System (ADS)

Tuomela, Anne; Davids, Corine; Knutsson, Sven; Knutsson, Roger; Rauhala, Anssi; Rossi, Pekka M.; Rouyet, Line

2017-04-01

Northern areas of Finland, Sweden and Norway have mineral-rich deposits. There are several active mines in the area but also closed ones and deposits with plans for future mining. With increasing demand for environmental protection in the sensitive Northern conditions, there is a need for more comprehensive monitoring of the mining environment. In our study, we aim to develop new opportunities to use remote sensing data from satellites and unmanned aerial vehicles (UAVs) in improving mining safety and monitoring, for example in the case of mine waste storage facilities. Remote sensing methods have evolved fast, and could in many cases enable precise, reliable, and cost-efficient data collection over large areas. The study has focused on four mining areas in Northern Fennoscandia. Freely available medium-resolution (e.g. Sentinel-1), commercial high-resolution (e.g. TerraSAR-X) and Synthetic Aperture Radar (SAR) data has been collected during 2015-2016 to study how satellite remote sensing could be used e.g. for displacement monitoring using SAR Interferometry (InSAR). Furthermore, UAVs have been utilized in similar data collection in a local scale, and also in collection of thermal infrared data for hydrological monitoring of the areas. The development and efficient use of the methods in mining areas requires experts from several fields. In addition, the Northern conditions with four distinct seasons bring their own challenges for the efficient use of remote sensing, and further complicate their integration as standardised monitoring methods for mine environments. Based on the initial results, remote sensing could especially enhance the monitoring of large-scale structures in mine areas such as tailings impoundments.

Monitoring psychosocial stress at work: development of the Psychosocial Working Conditions Questionnaire.

PubMed

Widerszal-Bazyl, M; Cieślak, R

2000-01-01

Many studies on the impact of psychosocial working conditions on health prove that psychosocial stress at work is an important risk factor endangering workers' health. Thus it should be constantly monitored like other work hazards. The paper presents a newly developed instrument for stress monitoring called the Psychosocial Working Conditions Questionnaire (PWC). Its structure is based on Robert Karasek's model of job stress (Karasek, 1979; Karasek & Theorell, 1990). It consists of 3 main scales Job Demands, Job Control, Social Support and 2 additional scales adapted from the Occupational Stress Questionnaire (Elo, Leppanen, Lindstrom, & Ropponen, 1992), Well-Being and Desired Changes. The study of 8 occupational groups (bank and insurance specialists, middle medical personnel, construction workers, shop assistants, government and self-government administration officers, computer scientists, public transport drivers, teachers, N = 3,669) indicates that PWC has satisfactory psychometrics parameters. Norms for the 8 groups were developed.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Monitoring Polaris and Seeing Conditions at PARI

NASA Astrophysics Data System (ADS)

Crawford, April

2016-01-01

Pisgah Astronomical Research Institute (PARI) was originally built by NASA to track and collect data from satellites. The location in the Pisgah National Forest was chosen due to the excellent ability of the surrounding mountains to block radio interference and light pollution. The PARI observatory has been monitoring Polaris for over 10 years and has amassed a large collection of images of the star and those surrounding it. While several telescopes have been used throughout the project, we are currently using a Omni XLT Series Celestron and an SBIG ST-8300M CCD camera with a 0.70 arcsecond/pixel ratio. The software is run on Windows, however, we will be making a switch to Linux and implementing a new program to control the camera. The new images, once converted to a usable format (ST10 to FITS), can be automatically fed into an in-house Java program to track the variability of the star and simultaneously determine the seeing conditions experienced on the campus. Since we have several years worth of data, the program will also be used to provide a history of variability and seeing conditions. We ultimately hope to be able to track the possible changes in variability of Polaris, as it's current location on the HR diagram is being studied. The data could also prove valuable for our on-site scientists and many visiting students to study on campus. We are also developing a relative scale for our seeing conditions, accompanied by FWHM measurements in arcseconds that will can be compared to those of surrounding observatories in mountainous areas.

Multicenter Study on Incubation Conditions for Environmental Monitoring and Aseptic Process Simulation.

PubMed

Guinet, Roland; Berthoumieu, Nicole; Dutot, Philippe; Triquet, Julien; Ratajczak, Medhi; Thibaudon, Michel; Bechaud, Philippe; Arliaud, Christophe; Miclet, Edith; Giordano, Florine; Larcon, Marjorie; Arthaud, Catherine

Environmental monitoring and aseptic process simulations represent an integral part of the microbiological quality control system of sterile pharmaceutical products manufacturing operations. However, guidance documents and manufacturers practices differ regarding recommendations for incubation time and incubation temperature, and, consequently, the environmental monitoring and aseptic process simulation incubation strategy should be supported by validation data. To avoid any bias coming from in vitro studies or from single-site manufacturing in situ studies, we performed a collaborative study at four manufacturing sites with four samples at each location. The environmental monitoring study was performed with tryptic soy agar settle plates and contact plates, and the aseptic process simulation study was performed with tryptic soy broth and thioglycolate broth. The highest recovery rate was obtained with settle plates (97.7%) followed by contact plates (65.4%) and was less than 20% for liquid media (tryptic soy broth 19% and thioglycolate broth 17%). Gram-positive cocci and non-spore-forming Gram-positive rods were largely predominant with more than 95% of growth and recovered best at 32.5 °C. The highest recovery of molds was obtained at 22.5 °C alone or as the first incubation temperature. Strict anaerobes were not recovered. At the end of the five days of incubation no significant statistical difference was obtained between the four conditions. Based on these data a single incubation temperature at 32.5 °C could be recommended for these four manufacturing sites for both environmental monitoring and aseptic process simulation, and a second plate could be used, periodically incubated at 22.5 °C. Similar studies should be considered for all manufacturing facilities in order to determine the optimal incubation temperature regime for both viable environmental monitoring and aseptic process simulation. Microbiological environmental monitoring and aseptic process

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

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

Mobed, Parham; Pednekar, Pratik; Bhattacharyya, Debangsu

Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desiredmore » for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.« less

Incorporating Equipment Condition Assessment in Risk Monitors for Advanced Small Modular Reactors

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

Coble, Jamie B.; Coles, Garill A.; Meyer, Ryan M.

2013-10-01

Advanced small modular reactors (aSMRs) can complement the current fleet of large light-water reactors in the USA for baseload and peak demand power production and process heat applications (e.g., water desalination, shale oil extraction, hydrogen production). The day-to-day costs of aSMRs are expected to be dominated by operations and maintenance (O&M); however, the effect of diverse operating missions and unit modularity on O&M is not fully understood. These costs could potentially be reduced by optimized scheduling, with risk-informed scheduling of maintenance, repair, and replacement of equipment. Currently, most nuclear power plants have a “living” probabilistic risk assessment (PRA), which reflectsmore » the as-operated, as-modified plant and combine event probabilities with population-based probability of failure (POF) for key components. “Risk monitors” extend the PRA by incorporating the actual and dynamic plant configuration (equipment availability, operating regime, environmental conditions, etc.) into risk assessment. In fact, PRAs are more integrated into plant management in today’s nuclear power plants than at any other time in the history of nuclear power. However, population-based POF curves are still used to populate fault trees; this approach neglects the time-varying condition of equipment that is relied on during standard and non-standard configurations. Equipment condition monitoring techniques can be used to estimate the component POF. Incorporating this unit-specific estimate of POF in the risk monitor can provide a more accurate estimate of risk in different operating and maintenance configurations. This enhanced risk assessment will be especially important for aSMRs that have advanced component designs, which don’t have an available operating history to draw from, and often use passive design features, which present challenges to PRA. This paper presents the requirements and technical gaps for developing a framework to

Construction Condition and Damage Monitoring of Post-Tensioned PSC Girders Using Embedded Sensors.

PubMed

Shin, Kyung-Joon; Lee, Seong-Cheol; Kim, Yun Yong; Kim, Jae-Min; Park, Seunghee; Lee, Hwanwoo

2017-08-10

The potential for monitoring the construction of post-tensioned concrete beams and detecting damage to the beams under loading conditions was investigated through an experimental program. First, embedded sensors were investigated that could measure pre-stress from the fabrication process to a failure condition. Four types of sensors were installed on a steel frame, and the applicability and the accuracy of these sensors were tested while pre-stress was applied to a tendon in the steel frame. As a result, a tri-sensor loading plate and a Fiber Bragg Grating (FBG) sensor were selected as possible candidates. With those sensors, two pre-stressed concrete flexural beams were fabricated and tested. The pre-stress of the tendons was monitored during the construction and loading processes. Through the test, it was proven that the variation in thepre-stress had been successfully monitored throughout the construction process. The losses of pre-stress that occurred during a jacking and storage process, even those which occurred inside the concrete, were measured successfully. The results of the loading test showed that tendon stress and strain within the pure span significantly increased, while the stress in areas near the anchors was almost constant. These results prove that FBG sensors installed in a middle section can be used to monitor the strain within, and the damage to pre-stressed concrete beams.

Construction Condition and Damage Monitoring of Post-Tensioned PSC Girders Using Embedded Sensors

PubMed Central

Shin, Kyung-Joon; Lee, Seong-Cheol; Kim, Yun Yong; Kim, Jae-Min; Park, Seunghee; Lee, Hwanwoo

2017-01-01

The potential for monitoring the construction of post-tensioned concrete beams and detecting damage to the beams under loading conditions was investigated through an experimental program. First, embedded sensors were investigated that could measure pre-stress from the fabrication process to a failure condition. Four types of sensors were installed on a steel frame, and the applicability and the accuracy of these sensors were tested while pre-stress was applied to a tendon in the steel frame. As a result, a tri-sensor loading plate and a Fiber Bragg Grating (FBG) sensor were selected as possible candidates. With those sensors, two pre-stressed concrete flexural beams were fabricated and tested. The pre-stress of the tendons was monitored during the construction and loading processes. Through the test, it was proven that the variation in thepre-stress had been successfully monitored throughout the construction process. The losses of pre-stress that occurred during a jacking and storage process, even those which occurred inside the concrete, were measured successfully. The results of the loading test showed that tendon stress and strain within the pure span significantly increased, while the stress in areas near the anchors was almost constant. These results prove that FBG sensors installed in a middle section can be used to monitor the strain within, and the damage to pre-stressed concrete beams. PMID:28796156

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

NASA Astrophysics Data System (ADS)

Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

2018-02-01

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

Experts' perceptions on the entrepreneurial framework conditions

NASA Astrophysics Data System (ADS)

Correia, Aldina; e Silva, Eliana Costa; Lopes, I. Cristina; Braga, Alexandra; Braga, Vitor

2017-11-01

The Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship. This database includes information of more than 100 countries concerning several aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, in two main sources of primary data: the Adult Population Survey and the National Expert Survey. In the present work the National Expert Survey datasets for 2011, 2012 and 2013 are analyzed with the purpose of studying the effects of different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions (EFCs). The results of the multivariate analysis of variance for the 2013 data show significant differences of the entrepreneurship experts when compared the 2011 and 2012 surveys. For the 2013 data entrepreneur experts are less favorable then most of the other experts to the EFCs.

Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

PubMed

Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

2016-10-01

Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

VegScape: U.S. Crop Condition Monitoring Service

NASA Astrophysics Data System (ADS)

mueller, R.; Yang, Z.; Di, L.

2013-12-01

Since 1995, the US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has provided qualitative biweekly vegetation condition indices to USDA policymakers and the public on a weekly basis during the growing season. Vegetation indices have proven useful for assessing crop condition and identifying the areal extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. With growing emphasis on more extreme weather events and food security issues rising to the forefront of national interest, a new vegetation condition monitoring system was developed. The new vegetation condition portal named VegScape was initiated at the start of the 2013 growing season. VegScape delivers web mapping service based interactive vegetation indices. Users can use an interactive map to explore, query and disseminate current crop conditions. Vegetation indices like Normal Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and mean, median, and ratio comparisons to prior years can be constructed for analytical purposes and on-demand crop statistics. The NASA MODIS satellite with 250 meter (15 acres) resolution and thirteen years of data history provides improved spatial and temporal resolutions and delivers improved detailed timely (i.e., daily) crop specific condition and dynamics. VegScape thus provides supplemental information to support NASS' weekly crop reports. VegScape delivers an agricultural cultivated crop mask and the most recent Cropland Data Layer (CDL) product to exploit the agricultural domain and visualize prior years' planted crops. Additionally, the data can be directly exported to Google Earth for web mashups or delivered via web mapping services for uses in other applications. VegScape supports the ethos of data democracy by providing free and open access to digital geospatial data layers using open geospatial standards, thereby supporting transparent and collaborative government

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I + II + III supernatant in human albumin separation

NASA Astrophysics Data System (ADS)

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-01

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I + II + III (FI + II + III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (Rp2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501 g/L, 0.465 g/L and 5.57 for TP, and 0.969, 0.530 g/L, 0.341 g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI + II + III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I+II+III supernatant in human albumin separation.

PubMed

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-15

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I+II+III (FI+II+III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (R p 2 ), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501g/L, 0.465g/L and 5.57 for TP, and 0.969, 0.530g/L, 0.341g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI+II+III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS. Copyright © 2016 Elsevier B.V. All rights reserved.

New methods for the condition monitoring of level crossings

NASA Astrophysics Data System (ADS)

García Márquez, Fausto Pedro; Pedregal, Diego J.; Roberts, Clive

2015-04-01

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a 'normal' or 'expected' shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque-Bera and Ljung-Box have been considered for testing the model.

Real time video analysis to monitor neonatal medical condition

NASA Astrophysics Data System (ADS)

Shirvaikar, Mukul; Paydarfar, David; Indic, Premananda

2017-05-01

One in eight live births in the United States is premature and these infants have complications leading to life threatening events such as apnea (pauses in breathing), bradycardia (slowness of heart) and hypoxia (oxygen desaturation). Infant movement pattern has been hypothesized as an important predictive marker for these life threatening events. Thus estimation of movement along with behavioral states, as a precursor of life threatening events, can be useful for risk stratification of infants as well as for effective management of disease state. However, more important and challenging is the determination of the behavioral state of the infant. This information includes important cues such as sleep position and the status of the eyes, which are important markers for neonatal neurodevelopment state. This paper explores the feasibility of using real time video analysis to monitor the condition of premature infants. The image of the infant can be segmented into regions to localize and focus on specific areas of interest. Analysis of the segmented regions can be performed to identify different parts of the body including the face, arms, legs and torso. This is necessary due to real-time processing speed considerations. Such a monitoring system would be of great benefit as an aide to medical staff in neonatal hospital settings requiring constant surveillance. Any such system would have to satisfy extremely stringent reliability and accuracy requirements, before it can be deployed in a hospital care unit, due to obvious reasons. The effect of lighting conditions and interference will have to be mitigated to achieve such performance.

Remote monitoring of parental incubation conditions in the greater sandhill crane

USGS Publications Warehouse

Gee, G.F.; Hatfield, J.; Howey, P.J.

1995-01-01

To monitor incubation conditions in nests of greater sandhill cranes, a radiotransmitting egg was built using six temperature sensors, a position sensor, and a light sensor. Sensor readings were received, along with time of observations, and stored in a computer. The egg was used to monitor incubation in nests of six pairs of cranes during 1987 and 1988. Ambient temperature was also measured. Analysis of covariance (ANCOVA) was used to relate highest egg temperature, core egg temperature, and lowest egg temperature to ambient temperature, time since the egg was last turned, and time since the beginning of incubation. Ambient temperature had the greatest effect on egg temperature (P 0.0001), followed by the time since the beginning of incubation and time since the egg was last turned. Pair effect, the class variable in the ANCOVA. was also very significant (P < 0.0001). A nine-term Fourier series was used to estimate the average core egg temperature versus time of day and was found to fit the data well (r2 = 0.94). The Fourier series will be used to run a mechanical incubator to simulate natural incubation conditions for cranes.

Estimating the decomposition of predictive information in multivariate systems

NASA Astrophysics Data System (ADS)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2006-02-01

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

Monitoring Conditions Leading to SCC/Corrosion of Carbon Steel in Fuel Grade Ethanol

DOT National Transportation Integrated Search

2011-02-11

This is the draft final report of the project on field monitoring of conditions that lead to SCC in ethanol tanks and piping. The other two aspects of the consolidated program, ethanol batching and blending effects (WP#325) and source effects (WP#323...

[Bioimpedance means of skin condition monitoring during therapeutic and cosmetic procedures].

PubMed

Alekseenko, V A; Kus'min, A A; Filist, S A

2008-01-01

Engineering and technological problems of bioimpedance skin surface mapping are considered. A typical design of a device based on a PIC 16F microcontroller is suggested. It includes a keyboard, LCD indicator, probing current generator with programmed frequency tuning, and units for probing current monitoring and bioimpedance measurement. The electrode matrix of the device is constructed using nanotechnology. A microcontroller-controlled multiplexor provides scanning of interelectrode impedance, which makes it possible to obtain the impedance image of the skin surface under the electrode matrix. The microcontroller controls the probing signal generator frequency and allows layer-by-layer images of skin under the electrode matrix to be obtained. This makes it possible to use reconstruction tomography methods for analysis and monitoring of the skin condition during therapeutic and cosmetic procedures.

Testing ZigBee Motes for Monitoring Refrigerated Vegetable Transportation under Real Conditions

PubMed Central

Ruiz-Garcia, Luis; Barreiro, Pilar; Robla, Jose Ignacio; Lunadei, Loredana

2010-01-01

Quality control and monitoring of perishable goods during transportation and delivery services is an increasing concern for producers, suppliers, transport decision makers and consumers. The major challenge is to ensure a continuous ‘cold chain’ from producer to consumer in order to guaranty prime condition of goods. In this framework, the suitability of ZigBee protocol for monitoring refrigerated transportation has been proposed by several authors. However, up to date there was not any experimental work performed under real conditions. Thus, the main objective of our experiment was to test wireless sensor motes based in the ZigBee/IEEE 802.15.4 protocol during a real shipment. The experiment was conducted in a refrigerated truck traveling through two countries (Spain and France) which means a journey of 1,051 kilometers. The paper illustrates the great potential of this type of motes, providing information about several parameters such as temperature, relative humidity, door openings and truck stops. Psychrometric charts have also been developed for improving the knowledge about water loss and condensation on the product during shipments. PMID:22399917

Testing ZigBee motes for monitoring refrigerated vegetable transportation under real conditions.

PubMed

Ruiz-Garcia, Luis; Barreiro, Pilar; Robla, Jose Ignacio; Lunadei, Loredana

2010-01-01

Quality control and monitoring of perishable goods during transportation and delivery services is an increasing concern for producers, suppliers, transport decision makers and consumers. The major challenge is to ensure a continuous 'cold chain' from producer to consumer in order to guaranty prime condition of goods. In this framework, the suitability of ZigBee protocol for monitoring refrigerated transportation has been proposed by several authors. However, up to date there was not any experimental work performed under real conditions. Thus, the main objective of our experiment was to test wireless sensor motes based in the ZigBee/IEEE 802.15.4 protocol during a real shipment. The experiment was conducted in a refrigerated truck traveling through two countries (Spain and France) which means a journey of 1,051 kilometers. The paper illustrates the great potential of this type of motes, providing information about several parameters such as temperature, relative humidity, door openings and truck stops. Psychrometric charts have also been developed for improving the knowledge about water loss and condensation on the product during shipments.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Breath acetone to monitor life style interventions in field conditions: an exploratory study.

PubMed

Samudrala, Devasena; Lammers, Gerwen; Mandon, Julien; Blanchet, Lionel; Schreuder, Tim H A; Hopman, Maria T; Harren, Frans J M; Tappy, Luc; Cristescu, Simona M

2014-04-01

To assess whether breath acetone concentration can be used to monitor the effects of a prolonged physical activity on whole body lipolysis and hepatic ketogenesis in field conditions. Twenty-three non-diabetic, 11 type 1 diabetic, and 17 type 2 diabetic subjects provided breath and blood samples for this study. Samples were collected during the International Four Days Marches, in the Netherlands. For each participant, breath acetone concentration was measured using proton transfer reaction ion trap mass spectrometry, before and after a 30-50 km walk on four consecutive days. Blood non-esterified free fatty acid (NEFA), beta-hydroxybutyrate (BOHB), and glucose concentrations were measured after walking. Breath acetone concentration was significantly higher after than before walking, and was positively correlated with blood NEFA and BOHB concentrations. The effect of walking on breath acetone concentration was repeatedly observed on all four consecutive days. Breath acetone concentrations were higher in type 1 diabetic subjects and lower in type 2 diabetic subjects than in control subjects. Breath acetone can be used to monitor hepatic ketogenesis during walking under field conditions. It may, therefore, provide real-time information on fat burning, which may be of use for monitoring the lifestyle interventions. Copyright © 2014 The Obesity Society.

[A portable impedance meter for monitoring liquid compartments of human body under space flight conditions].

PubMed

Noskov, V B; Nikolaev, D V; Tuĭkin, S A; Kozharinov, V I; Grachev, V A

2007-01-01

A portable two-frequency tetrapolar impedance meter was developed to study the state of liquid compartments of human body under zero-gravity conditions. The portable impedance meter makes it possible to monitor the hydration state of human body under conditions of long-term space flight on board international space station.

Diagonal dominance for the multivariable Nyquist array using function minimization

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1977-01-01

A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.

Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions.

PubMed

Thap, Tharoeun; Yoon, Kwon-Ha; Lee, Jinseok

2016-04-15

We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Drought: A comprehensive R package for drought monitoring, prediction and analysis

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang

2015-04-01

Drought may impose serious challenges to human societies and ecosystems. Due to complicated causing effects and wide impacts, a universally accepted definition of drought does not exist. The drought indicator is commonly used to characterize drought properties such as duration or severity. Various drought indicators have been developed in the past few decades for the monitoring of a certain aspect of drought condition along with the development of multivariate drought indices for drought characterizations from multiple sources or hydro-climatic variables. Reliable drought prediction with suitable drought indicators is critical to the drought preparedness plan to reduce potential drought impacts. In addition, drought analysis to quantify the risk of drought properties would provide useful information for operation drought managements. The drought monitoring, prediction and risk analysis are important components in drought modeling and assessments. In this study, a comprehensive R package "drought" is developed to aid the drought monitoring, prediction and risk analysis (available from R-Forge and CRAN soon). The computation of a suite of univariate and multivariate drought indices that integrate drought information from various sources such as precipitation, temperature, soil moisture, and runoff is available in the drought monitoring component in the package. The drought prediction/forecasting component consists of statistical drought predictions to enhance the drought early warning for decision makings. Analysis of drought properties such as duration and severity is also provided in this package for drought risk assessments. Based on this package, a drought monitoring and prediction/forecasting system is under development as a decision supporting tool. The package will be provided freely to the public to aid the drought modeling and assessment for researchers and practitioners.

Innovative monitoring campaign of the environmental conditions of the Stibbert museum in Florence

NASA Astrophysics Data System (ADS)

Angelini, E.; Civita, F.; Corbellini, S.; Fulginiti, D.; Giovagnoli, A.; Grassini, S.; Parvis, M.

2016-02-01

Conservation of ancient metallic artefact displayed inside museums is a complex problem due to the large number of constraints mainly related to the artefacts fruition by people. The development of a simple procedure for monitoring the artefact conservation state promptly highlighting risky conditions without impacting on the normal museum operations could be of interest in the cultural heritage world. This paper describes the interesting results obtained by using a highly sensitive and innovative methodology for evaluating the safety level of the museum indoor areas, and more specifically of the interior of the showcases, with respect to the metallic artefacts. The methodology is based on the use of an innovative smart sensors network and of copper reference samples. The smart sensors network was employed for the continuous monitoring of temperature and relative humidity close to the artefacts, i.e. inside the display showcases. The reference specimens were Cu coated with a 100 nm Cu nanostructured layer put for 1 year in the exhibition rooms inside and outside the showcases and characterised by means of normal imaging, colorimetric and FESEM techniques at regular intervals. The results of the monitoring activity evidenced the higher reactivity to the environmental aggressivity of the nanocoated copper specimen with respect to bulk artefacts and therefore the possibility to use them as alerts to possible corrosion phenomena that may occur to the real artefacts. A proper temperature and relative humidity monitoring inside the showcases and close to each group of artefacts is a powerful though economic and non-invasive way to highlight most of the possible critical display conditions.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Monitoring trail conditions: New methodological considerations

USGS Publications Warehouse

Marion, Jeffrey L.; Leung, Yu-Fai; Nepal, Sanjay K.

2006-01-01

The U.S. National Park Service (NPS) accommodates nearly 300 million visitors per year, visitation that has the potential to produce negative effects on fragile natural and cultural resources. The policy guidance from the NPS Management Policies recognizes the legitimacy of providing opportunities for public enjoyment of parks while acknowledging the need for managers to “seek ways to avoid, or to minimize to the greatest degree practicable, adverse impacts on park resources and values” (NPS 2001). Thus, relative to visitor use, park managers must evaluate the types and extents of resource impacts associated with recreational activities, and determine to what extent they are unacceptable and constitute impairment. Visitor impact monitoring programs can assist managers in making objective evaluations of impact acceptability and impairment and in selecting effective impact management practices by providing quantitative documentation of the types and extent of recreationrelated impacts on natural resources. Monitoring programs are explicitly authorized in Section 4.1 of the Management Policies: Natural systems in the national park system, and the human influences upon them, will be monitored to detect change. The Service will use the results of monitoring and research to understand the detected change and to develop appropriate management actions.

Thick-film acoustic emission sensors for use in structurally integrated condition-monitoring applications.

PubMed

Pickwell, Andrew J; Dorey, Robert A; Mba, David

2011-09-01

Monitoring the condition of complex engineering structures is an important aspect of modern engineering, eliminating unnecessary work and enabling planned maintenance, preventing failure. Acoustic emissions (AE) testing is one method of implementing continuous nondestructive structural health monitoring. A novel thick-film (17.6 μm) AE sensor is presented. Lead zirconate titanate thick films were fabricated using a powder/sol composite ink deposition technique and mechanically patterned to form a discrete thick-film piezoelectric AE sensor. The thick-film sensor was benchmarked against a commercial AE device and was found to exhibit comparable responses to simulated acoustic emissions.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Multivariate multiscale entropy of financial markets

NASA Astrophysics Data System (ADS)

Lu, Yunfan; Wang, Jun

2017-11-01

In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.

Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

PubMed

Catelani, Tiago A; Santos, João Rodrigo; Páscoa, Ricardo N M J; Pezza, Leonardo; Pezza, Helena R; Lopes, João A

2018-03-01

This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T 2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

PubMed Central

Levman, Jacob; Takahashi, Emi

2016-01-01

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Distributed acoustic fibre optic sensors for condition monitoring of pipelines

NASA Astrophysics Data System (ADS)

Hussels, Maria-Teresa; Chruscicki, Sebastian; Habib, Abdelkarim; Krebber, Katerina

2016-05-01

Industrial piping systems are particularly relevant to public safety and the continuous availability of infrastructure. However, condition monitoring systems based on many discrete sensors are generally not well-suited for widespread piping systems due to considerable installation effort, while use of distributed fibre-optic sensors would reduce this effort to a minimum. Specifically distributed acoustic sensing (DAS) is employed for detection of third-party threats and leaks in oil and gas pipelines in recent years and can in principle also be applied to industrial plants. Further possible detection routes amenable by DAS that could identify damage prior to emission of medium are subject of a current project at BAM, which aims at qualifying distributed fibre optic methods such as DAS as a means for spatially continuous monitoring of industrial piping systems. Here, first tests on a short pipe are presented, where optical fibres were applied directly to the surface. An artificial signal was used to define suitable parameters of the measurement system and compare different ways of applying the sensor.

Application of TRIZ approach to machine vibration condition monitoring problems

NASA Astrophysics Data System (ADS)

Cempel, Czesław

2013-12-01

Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

Embedded Strain Gauges for Condition Monitoring of Silicone Gaskets

PubMed Central

Schotzko, Timo; Lang, Walter

2014-01-01

A miniaturized strain gauge with a thickness of 5 µm is molded into a silicone O-ring. This is a first step toward embedding sensors in gaskets for structural health monitoring. The signal of the integrated sensor exhibits a linear correlation with the contact pressure of the O-ring. This affords the opportunity to monitor the gasket condition during installation. Thus, damages caused by faulty assembly can be detected instantly, and early failures, with their associated consequences, can be prevented. Through the embedded strain gauge, the contact pressure applied to the gasket can be directly measured. Excessive pressure and incorrect positioning of the gasket can cause structural damage to the material of the gasket, which can lead to an early outage. A platinum strain gauge is fabricated on a thin polyimide layer and is contacted through gold connections. The measured resistance pressure response exhibits hysteresis for the first few strain cycles, followed by a linear behavior. The short-term impact of the embedded sensor on the stability of the gasket is investigated. Pull-tests with O-rings and test specimens have indicated that the integration of the miniaturized sensors has no negative impact on the stability in the short term. PMID:25014099

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

NASA Astrophysics Data System (ADS)

Li, Lin; Tao, Jian-Feng; Yu, Hai-Dong; Huang, Yi-Xiang; Liu, Cheng-Liang

2017-11-01

The gripper cylinder that provides braced force for Tunnel Boring Machine (TBM) might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition (EMD) method is established for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiffness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the characteristic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylinder based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

Fast computation of the multivariable stability margin for real interrelated uncertain parameters

NASA Technical Reports Server (NTRS)

Sideris, Athanasios; Sanchez Pena, Ricardo S.

1988-01-01

A novel algorithm for computing the multivariable stability margin for checking the robust stability of feedback systems with real parametric uncertainty is proposed. This method eliminates the need for the frequency search involved in another given algorithm by reducing it to checking a finite number of conditions. These conditions have a special structure, which allows a significant improvement on the speed of computations.

Information support of monitoring of technical condition of buildings in construction risk area

NASA Astrophysics Data System (ADS)

Skachkova, M. E.; Lepihina, O. Y.; Ignatova, V. V.

2018-05-01

The paper presents the results of the research devoted to the development of a model of information support of monitoring buildings technical condition; these buildings are located in the construction risk area. As a result of the visual and instrumental survey, as well as the analysis of existing approaches and techniques, attributive and cartographic databases have been created. These databases allow monitoring defects and damages of buildings located in a 30-meter risk area from the object under construction. The classification of structures and defects of these buildings under survey is presented. The functional capabilities of the developed model and the field of it practical applications are determined.

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

Aspects of structural health and condition monitoring of offshore wind turbines

PubMed Central

Antoniadou, I.; Dervilis, N.; Papatheou, E.; Maguire, A. E.; Worden, K.

2015-01-01

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector. PMID:25583864

Aspects of structural health and condition monitoring of offshore wind turbines.

PubMed

Antoniadou, I; Dervilis, N; Papatheou, E; Maguire, A E; Worden, K

2015-02-28

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring

PubMed Central

Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K. K.

2018-01-01

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), and oxidants (Ox) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO2 and ozone on a newly introduced oxidant sensor. PMID:29360749

Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring.

PubMed

Wei, Peng; Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K K

2018-01-23

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), and oxidants (O x ) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO₂ and ozone on a newly introduced oxidant sensor.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

PubMed

Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

2018-02-09

The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Availability analysis of mechanical systems with condition-based maintenance using semi-Markov and evaluation of optimal condition monitoring interval

NASA Astrophysics Data System (ADS)

Kumar, Girish; Jain, Vipul; Gandhi, O. P.

2018-03-01

Maintenance helps to extend equipment life by improving its condition and avoiding catastrophic failures. Appropriate model or mechanism is, thus, needed to quantify system availability vis-a-vis a given maintenance strategy, which will assist in decision-making for optimal utilization of maintenance resources. This paper deals with semi-Markov process (SMP) modeling for steady state availability analysis of mechanical systems that follow condition-based maintenance (CBM) and evaluation of optimal condition monitoring interval. The developed SMP model is solved using two-stage analytical approach for steady-state availability analysis of the system. Also, CBM interval is decided for maximizing system availability using Genetic Algorithm approach. The main contribution of the paper is in the form of a predictive tool for system availability that will help in deciding the optimum CBM policy. The proposed methodology is demonstrated for a centrifugal pump.

An approach to effectiveness monitoring of floodplain channel aquatic habitat: channel condition assessment.

Treesearch

Richard D. Woodsmith; James R. Noel; Michael L. Dilger

2005-01-01

The condition of aquatic habitat and the health of species dependent on that habitat are issues of significant concern to land management agencies, other organizations, and the public at large in southeastern Alaska, as well as along much of the Pacific coastal region of North America. We develop and test a set of effectiveness monitoring procedures for measuring...

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Multivariate stochastic simulation with subjective multivariate normal distributions

Treesearch

P. J. Ince; J. Buongiorno

1991-01-01

In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

What are we monitoring and why? Using geomorphic principles to frame eco-hydrological assessments of river condition.

PubMed

Brierley, Gary; Reid, Helen; Fryirs, Kirstie; Trahan, Nadine

2010-04-01

Monitoring and assessment are integral components in adaptive management programmes that strive to improve the condition of river systems. Unfortunately, these procedures are generally applied with an emphasis upon biotic attributes and water quality, with limited regard for the geomorphic structure, function and evolutionary trajectory of a river system. Geomorphic principles convey an understanding of the landscape context within which ecohydrologic processes interact. Collectively, geo-eco-hydrologic understanding presents a coherent biophysical template that can be used to frame spatially and temporally rigorous approaches to monitoring that respect the inherent diversity, variability and complexity of any given river system. This understanding aids the development of management programmes that 'work with nature.' Unless an integrative perspective is used to monitor river condition, conservation and rehabilitation plans are unlikely to reach their true potential. (c) 2010 Elsevier B.V. All rights reserved.

Multivariable bio-inspired photonic sensors for non-condensable gases

NASA Astrophysics Data System (ADS)

Potyrailo, Radislav A.; Karker, Nicholas; Carpenter, Michael A.; Minnick, Andrew

2018-02-01

Existing gas sensors often lose their measurement accuracy in practical field applications. To mitigate this significant problem, here, we report a demonstration of fabricated multivariable photonic sensors inspired by a known nanostructure of Morpho butterfly scales for detection of exemplary non-condensable gases such as H2, CO, and CO2. We fabricated bio-inspired nanostructures using conventional photolithography and chemical etching and detected individual gases that were difficult or unrealistic to detect using natural Morpho nanostructures. Such bio-inspired gas sensors are the critical step in the development of new sensors with improved accuracy for diverse operational scenarios. While this report is our initial demonstration of responses of fabricated multivariable sensors to individual gases in pristine laboratory conditions, it is a significant milestone in understanding the next steps toward field tests and practical applications of these sensors.

Accuracy of fundus autofluorescence imaging for the diagnosis and monitoring of retinal conditions: a systematic review.

PubMed

Frampton, Geoff K; Kalita, Neelam; Payne, Liz; Colquitt, Jill; Loveman, Emma

2016-04-01

Natural fluorescence in the eye may be increased or decreased by diseases that affect the retina. Imaging methods based on confocal scanning laser ophthalmoscopy (cSLO) can detect this 'fundus autofluorescence' (FAF) by illuminating the retina using a specific light 'excitation wavelength'. FAF imaging could assist the diagnosis or monitoring of retinal conditions. However, the accuracy of the method for diagnosis or monitoring is unclear. To conduct a systematic review to determine the accuracy of FAF imaging using cSLO for the diagnosis or monitoring of retinal conditions, including monitoring of response to therapy. Electronic bibliographic databases; scrutiny of reference lists of included studies and relevant systematic reviews; and searches of internet pages of relevant organisations, meetings and trial registries. Databases included MEDLINE, EMBASE, The Cochrane Library, Web of Science and the Medion database of diagnostic accuracy studies. Searches covered 1990 to November 2014 and were limited to the English language. References were screened for relevance using prespecified inclusion criteria to capture a broad range of retinal conditions. Two reviewers assessed titles and abstracts independently. Full-text versions of relevant records were retrieved and screened by one reviewer and checked by a second. Data were extracted and critically appraised using the Quality Assessment of Diagnostic Accuracy Studies criteria (QUADAS) for assessing risk of bias in test accuracy studies by one reviewer and checked by a second. At all stages any reviewer disagreement was resolved through discussion or arbitration by a third reviewer. Eight primary research studies have investigated the diagnostic accuracy of FAF imaging in retinal conditions: choroidal neovascularisation (one study), reticular pseudodrusen (three studies), cystoid macular oedema (two studies) and diabetic macular oedema (two studies). Sensitivity of FAF imaging using an excitation wavelength of 488�

77 FR 24228 - Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-23

... Used in Nuclear Power Plants AGENCY: Nuclear Regulatory Commission. ACTION: Regulatory guide; issuance... guide, (RG) 1.218, ``Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants... of electric cables for nuclear power plants. RG 1.218 is not intended to be prescriptive, instead it...

Novel texture-based descriptors for tool wear condition monitoring

NASA Astrophysics Data System (ADS)

Antić, Aco; Popović, Branislav; Krstanović, Lidija; Obradović, Ratko; Milošević, Mijodrag

2018-01-01

All state-of-the-art tool condition monitoring systems (TCM) in the tool wear recognition task, especially those that use vibration sensors, heavily depend on the choice of descriptors containing information about the tool wear state which are extracted from the particular sensor signals. All other post-processing techniques do not manage to increase the recognition precision if those descriptors are not discriminative enough. In this work, we propose a tool wear monitoring strategy which relies on the novel texture based descriptors. We consider the module of the Short Term Discrete Fourier Transform (STDFT) spectra obtained from the particular vibration sensors signal utterance as the 2D textured image. This is done by identifying the time scale of STDFT as the first dimension, and the frequency scale as the second dimension of the particular textured image. The obtained textured image is then divided into particular 2D texture patches, covering a part of the frequency range of interest. After applying the appropriate filter bank, 2D textons are extracted for each predefined frequency band. By averaging in time, we extract from the textons for each band of interest the information regarding the Probability Density Function (PDF) in the form of lower order moments, thus obtaining robust tool wear state descriptors. We validate the proposed features by the experiments conducted on the real TCM system, obtaining the high recognition accuracy.

Monitoring the Condition of Education.

ERIC Educational Resources Information Center

Buccino, Alphonse

Five categories of data collection are recommended for monitoring the quality of education: (1) outcomes, based on an input-output model, including data from student testing and credentials and degrees; (2) participation--who is served by education; (3) resources available to education; (4) long-term impact of education on work, income,…

Physical working conditions as covered in European monitoring questionnaires.

PubMed

Tynes, Tore; Aagestad, Cecilie; Thorsen, Sannie Vester; Andersen, Lars Louis; Perkio-Makela, Merja; García, Francisco Javier Pinilla; Blanco, Luz Galiana; Vermeylen, Greet; Parent-Thirion, Agnes; Hooftman, Wendela; Houtman, Irene; Liebers, Falk; Burr, Hermann; Formazin, Maren

2017-06-05

The prevalence of workers with demanding physical working conditions in the European work force remains high, and occupational physical exposures are considered important risk factors for musculoskeletal disorders (MSD), a major burden for both workers and society. Exposures to physical workloads are therefore part of the European nationwide surveys to monitor working conditions and health. An interesting question is to what extent the same domains, dimensions and items referring to the physical workloads are covered in the surveys. The purpose of this paper is to determine 1) which domains and dimensions of the physical workloads are monitored in surveys at the national level and the EU level and 2) the degree of European consensus among these surveys regarding coverage of individual domains and dimensions. Items on physical workloads used in one European wide/Spanish and five other European nationwide work environment surveys were classified into the domains and dimensions they cover, using a taxonomy agreed upon among all participating partners. The taxonomy reveals that there is a modest overlap between the domains covered in the surveys, but when considering dimensions, the results indicate a lower agreement. The phrasing of items and answering categories differs between the surveys. Among the domains, the three domains covered by all surveys are "lifting, holding & carrying of loads/pushing & pulling of loads", "awkward body postures" and "vibrations". The three domains covered less well, that is only by three surveys or less, are "physical work effort", "working sitting", and "mixed exposure". This is the fırst thorough overview to evaluate the coverage of domains and dimensions of self-reported physical workloads in a selection of European nationwide surveys. We hope the overview will provide input to the revisions and updates of the individual countries' surveys in order to enhance coverage of relevant domains and dimensions in all surveys and to increase

Condition Monitoring for Helicopter Data. Appendix A

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2000-01-01

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wind Turbine Drivetrain Condition Monitoring During GRC Phase 1 and Phase 2 Testing

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

Sheng, S.; Link, H.; LaCava, W.

2011-10-01

This report will present the wind turbine drivetrain condition monitoring (CM) research conducted under the phase 1 and phase 2 Gearbox Reliability Collaborative (GRC) tests. The rationale and approach for this drivetrain CM research, investigated CM systems, test configuration and results, and a discussion on challenges in wind turbine drivetrain CM and future research and development areas, will be presented.

Monitoring Streambed Scour/Deposition Under Nonideal Temperature Signal and Flood Conditions

NASA Astrophysics Data System (ADS)

DeWeese, Timothy; Tonina, Daniele; Luce, Charles

2017-12-01

Streambed erosion and deposition are fundamental geomorphic processes in riverbeds, and monitoring their evolution is important for ecological system management and in-stream infrastructure stability. Previous research showed proof of concept that analysis of paired temperature signals of stream and pore waters can simultaneously provide monitoring scour and deposition, stream sediment thermal regime, and seepage velocity information. However, it did not address challenges often associated with natural systems, including nonideal temperature variations (low-amplitude, nonsinusoidal signal, and vertical thermal gradients) and natural flooding conditions on monitoring scour and deposition processes over time. Here we addressed this knowledge gap by testing the proposed thermal scour-deposition chain (TSDC) methodology, with laboratory experiments to test the impact of nonideal temperature signals under a range of seepage velocities and with a field application during a pulse flood. Both analyses showed excellent match between surveyed and temperature-derived bed elevation changes even under very low temperature signal amplitudes (less than 1°C), nonideal signal shape (sawtooth shape), and strong and changing vertical thermal gradients (4°C/m). Root-mean-square errors on predicting the change in streambed elevations were comparable with the median grain size of the streambed sediment. Future research should focus on improved techniques for temperature signal phase and amplitude extractions, as well as TSDC applications over long periods spanning entire hydrographs.

Automated System Of Monitoring Of The Physical Condition Of The Staff Of The Enterprise

NASA Astrophysics Data System (ADS)

Pilipenko, A.

2017-01-01

In the work the author solves an important applied problem of increasing of safety of engineering procedures and production using technologies of monitoring of a condition of employees. The author offers a work algorithm, structural and basic electric schemes of system of collection of data of employee’s condition of the enterprise and some parameters of the surrounding environment. In the article the author offers an approach to increasing of efficiency of acceptance of management decisions at the enterprise at the expense of the prompt analysis of information about employee’s condition and productivity of his work and also about various parameters influencing these factors.

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

Utilization of wireless structural health monitoring as decision making tools for a condition and reliability-based assessment of railroad bridges

NASA Astrophysics Data System (ADS)

Flanigan, Katherine A.; Johnson, Nephi R.; Hou, Rui; Ettouney, Mohammed; Lynch, Jerome P.

2017-04-01

The ability to quantitatively assess the condition of railroad bridges facilitates objective evaluation of their robustness in the face of hazard events. Of particular importance is the need to assess the condition of railroad bridges in networks that are exposed to multiple hazards. Data collected from structural health monitoring (SHM) can be used to better maintain a structure by prompting preventative (rather than reactive) maintenance strategies and supplying quantitative information to aid in recovery. To that end, a wireless monitoring system is validated and installed on the Harahan Bridge which is a hundred-year-old long-span railroad truss bridge that crosses the Mississippi River near Memphis, TN. This bridge is exposed to multiple hazards including scour, vehicle/barge impact, seismic activity, and aging. The instrumented sensing system targets non-redundant structural components and areas of the truss and floor system that bridge managers are most concerned about based on previous inspections and structural analysis. This paper details the monitoring system and the analytical method for the assessment of bridge condition based on automated data-driven analyses. Two primary objectives of monitoring the system performance are discussed: 1) monitoring fatigue accumulation in critical tensile truss elements; and 2) monitoring the reliability index values associated with sub-system limit states of these members. Moreover, since the reliability index is a scalar indicator of the safety of components, quantifiable condition assessment can be used as an objective metric so that bridge owners can make informed damage mitigation strategies and optimize resource management on single bridge or network levels.

Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State

USGS Publications Warehouse

Wilmoth, Siri K.; Irvine, Kathryn M.; Larson, Chad

2015-01-01

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions.

PubMed

Qiu, Lei; Yuan, Shenfang; Mei, Hanfei; Fang, Fang

2016-02-26

Structural Health Monitoring (SHM) technology is considered to be a key technology to reduce the maintenance cost and meanwhile ensure the operational safety of aircraft structures. It has gradually developed from theoretic and fundamental research to real-world engineering applications in recent decades. The problem of reliable damage monitoring under time-varying conditions is a main issue for the aerospace engineering applications of SHM technology. Among the existing SHM methods, Guided Wave (GW) and piezoelectric sensor-based SHM technique is a promising method due to its high damage sensitivity and long monitoring range. Nevertheless the reliability problem should be addressed. Several methods including environmental parameter compensation, baseline signal dependency reduction and data normalization, have been well studied but limitations remain. This paper proposes a damage propagation monitoring method based on an improved Gaussian Mixture Model (GMM). It can be used on-line without any structural mechanical model and a priori knowledge of damage and time-varying conditions. With this method, a baseline GMM is constructed first based on the GW features obtained under time-varying conditions when the structure under monitoring is in the healthy state. When a new GW feature is obtained during the on-line damage monitoring process, the GMM can be updated by an adaptive migration mechanism including dynamic learning and Gaussian components split-merge. The mixture probability distribution structure of the GMM and the number of Gaussian components can be optimized adaptively. Then an on-line GMM can be obtained. Finally, a best match based Kullback-Leibler (KL) divergence is studied to measure the migration degree between the baseline GMM and the on-line GMM to reveal the weak cumulative changes of the damage propagation mixed in the time-varying influence. A wing spar of an aircraft is used to validate the proposed method. The results indicate that the crack

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Design and realization of high voltage disconnector condition monitoring system

NASA Astrophysics Data System (ADS)

Shi, Jinrui; Xu, Tianyang; Yang, Shuixian; Li, Buoyang

2017-08-01

The operation status of the high voltage disconnector directly affects the safe and stable operation of the power system. This article uses the wireless frequency hopping communication technology of the communication module to achieve the temperature acquisition of the switch contacts and high voltage bus, to introduce the current value of the loop in ECS, and judge the operation status of the disconnector by considering the ambient temperature, calculating the temperature rise; And through the acquisition of the current of drive motor in the process of switch closing and opening, and fault diagnosis of the disconnector by analyzing the change rule of the drive motor current, the condition monitoring of the high voltage disconnector is realized.

An online condition monitoring system implemented an internet connectivity and FTP for low speed slew bearing

NASA Astrophysics Data System (ADS)

Caesarendra, W.; Kosasih, B.; Tjahjowidodo, T.; Ariyanto, M.; Daryl, LWQ; Pamungkas, D.

2018-04-01

Rapid and reliable information in slew bearing maintenance is not trivial issue. This paper presents the online monitoring system to assist maintenance engineer in order to monitor the bearing condition of low speed slew bearing in sheet metal company. The system is able to pass the vibration information from the place where the bearing and accelerometer sensors are attached to the data center; and from the data center it can be access by opening the online monitoring website from any place and by any person. The online monitoring system is built using some programming languages such as C language, MATLAB, PHP, HTML and CSS. Generally, the flow process is start with the automatic vibration data acquisition; then features are calculated from the acquired vibration data. These features are then sent to the data center; and form the data center, the vibration features can be seen through the online monitoring website. This online monitoring system has been successfully applied in School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong.

A modern diagnostic approach for automobile systems condition monitoring

NASA Astrophysics Data System (ADS)

Selig, M.; Shi, Z.; Ball, A.; Schmidt, K.

2012-05-01

An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

Monitoring and Assessment of Military Installation Land Condition under Training Disturbance Using Remote Sensing

NASA Astrophysics Data System (ADS)

Rijal, Santosh

Various military training activities are conducted in more than 11.3 million hectares of land (> 5,500 training sites) in the United States (U.S.). These training activities directly and indirectly degrade the land. Land degradation can impede continuous military training. In order to sustain long term training missions and Army combat readiness, the environmental conditions of the military installations need to be carefully monitored and assessed. Furthermore, the National Environmental Policy Act of 1969 (NEPA) and the U.S. Army Regulation 200-2 require the DoD to minimize the environmental impacts of training and document the environmental consequences of their actions. To achieve these objectives, the Department of Army initiated an Integrated Training Area Management (ITAM) program to manage training lands through assessing their environmental requirements and establishing policies and procedures to achieve optimum, sustainable use of training lands. One of the programs under ITAM, Range and Training Land Assessment (RTLA) was established to collect field-based data for monitoring installation's environmental condition. Due to high cost and inefficiencies involved in the collection of field data, the RTLA program was stopped in several military installations. Therefore, there has been a strong need to develop an efficient and low cost remote sensing based methodology for assessing and monitoring land conditions of military installations. It is also important to make a long-term assessment of installation land condition for understanding cumulative impacts of continuous military training activities. Additionally, it is unclear that compared to non-military land condition, to what extent military training activities have led to the degradation of land condition for military installations. The first paper of this dissertation developed a soil erosion relevant and image derived cover factor (ICF) based on linear spectral mixture (LSM) analysis to assess and

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Native crustacean species as a bioindicator of freshwater ecosystem pollution: A multivariate and integrative study of multi-biomarker response in active river monitoring.

PubMed

Bertrand, Lidwina; Monferrán, Magdalena Victoria; Mouneyrac, Catherine; Amé, María Valeria

2018-05-04

The aim of this study was to evaluate the ability of Palaemonetes argentinus to evidence the environmental degradation due to pollutants mixture in a freshwater aquatic ecosystem. For this purpose, an active monitoring (96 h exposure) was carried out in seven sites along the Ctalamochita River basin (Córdoba, Argentina), as a case of study. Our results evidenced sewage discharges impact in the water quality index, as well as metal pollution in water (Ag, Al, B, Pb, Hg) and sediments (Hg) with a potential effect on aquatic biota. The accumulation of total metals measured in exposed P. argentinus showed significant correlation with metals in water. Also, metallothioneins in cephalothorax showed significant changes along the basin, correlating with soluble concentrations of Cr, Zn, Cd, Hg, and V measured in shrimp tissues, which would be reflecting their bioavailability in the environment. In addition, the increase in antioxidant and detoxifying enzymes suggests the occurrence of oxidative stress in exposed shrimps. The integrative biomarker response index (IBR) pointed out the effect of metals on P. argentinus but also the occurrence of others pollutants. Finally, a high consensus was observed for water, sediments, and shrimps through the multivariate analysis (90%), indicating that P. argentinus can reflect changes in the abiotic matrixes. Moreover, studied sites were grouped according to their environmental quality. The use of active biomonitoring and the integration of biological responses through an IBR confirm that native biota could be a useful monitoring tool for bioavailable pollutants in aquatic ecosystems constituting a highly valuable approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Informal and formal trail monitoring protocols and baseline conditions: Acadia National Park

USGS Publications Warehouse

Marion, Jeffrey L.; Wimpey, Jeremy F.; Park, L.

2011-01-01

At Acadia National Park, changing visitor use levels and patterns have contributed to an increasing degree of visitor use impacts to natural and cultural resources. To better understand the extent and severity of these resource impacts and identify effective management techniques, the park sponsored this research to develop monitoring protocols, collect baseline data, and identify suggestions for management strategies. Formal and informal trails were surveyed and their resource conditions were assessed and characterized to support park planning and management decision-making.

Reproducibility of NMR Analysis of Urine Samples: Impact of Sample Preparation, Storage Conditions, and Animal Health Status

PubMed Central

Schreier, Christina; Kremer, Werner; Huber, Fritz; Neumann, Sindy; Pagel, Philipp; Lienemann, Kai; Pestel, Sabine

2013-01-01

Introduction. Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining 1H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. Methods. We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after 1H NMR spectroscopy. Results. We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at −20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. Conclusion. Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions. PMID:23865070

Reproducibility of NMR analysis of urine samples: impact of sample preparation, storage conditions, and animal health status.

PubMed

Schreier, Christina; Kremer, Werner; Huber, Fritz; Neumann, Sindy; Pagel, Philipp; Lienemann, Kai; Pestel, Sabine

2013-01-01

Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining (1)H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after (1)H NMR spectroscopy. We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at -20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions.

Adaptive responses of the cardiovascular system to prolonged spaceflight conditions: assessment with Holter monitoring

NASA Technical Reports Server (NTRS)

Baevsky, R. M.; Bennett, B. S.; Bungo, M. W.; Charles, J. B.; Goldberger, A. L.; Nikulina, G. A.

1997-01-01

This article presents selected findings obtained with Holter monitoring from two crew members of the expedition, performed during a 175-day space mission on board orbital space station "MIR." Using mathematical processing of daily cardiointervals files, 5-minute sections of records were analyzed consecutively. Then, the average daily values of indices, the average-per-every-eight-hours values (morning, evening, night) and mean values per hour were computed. The results of analysis showed that prolonged exposure of man to microgravity conditions leads to important functional alteration in human neuroautonomic regulatory mechanisms. Both crew members had significant increase of heart rate, the rise of stress index, the decrease in power of the spectrum in the range of respiratory sinus arrhythmia. These marked signs of activation of the sympathetic section of the vegetative nervous system showed individual variations. The analysis of the daily collection of cardiointervals with Holter monitoring allows us to understand and forecast the functional feasibilities of the human organism under a variety of stress conditions associated with acute and chronic microgravity exposure.

Implementation of an Integrated, Portable Transformer Condition Monitoring Instrument in the Classroom and On-Site

ERIC Educational Resources Information Center

Chatterjee, B.; Dey, D.; Chakravorti, S.

2010-01-01

The development of integrated, portable, transformer condition monitoring (TCM) equipment for classroom demonstrations as well as for student exercises conducted in the field is discussed. Demonstrations include experimentation with real-world transformers to illustrate concepts such as polarization and depolarization current through oil-paper…

Modeling hurricane evacuation traffic : a mobile real-time traffic counter for monitoring hurricane evacuation traffic conditions.

DOT National Transportation Integrated Search

2006-04-01

In this research report, an investigation was conducted to identify a suitable traffic monitoring device for collecting traffic data during actual emergency evacuation conditions that may result from hurricanes in Louisiana. The study reviewed thorou...

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Multivariate thermo-hygrometric characterisation of the archaeological site of Plaza de l'Almoina (Valencia, Spain) for preventive conservation.

PubMed

Fernández-Navajas, Angel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-07-29

Preventive conservation requires monitoring and control of the parameters involved in the deterioration process, mainly temperature and relative humidity. It is important to characterise an archaeological site prior to carrying out comparative studies in the future for preventive conservation, either by regular studies to verify whether the conditions are constant, or occasional ones when the boundary conditions are altered. There are numerous covered archaeological sites, but few preventive conservation works that give special attention to the type of cover installed. In particular, there is no background of microclimatic studies in sites that are in the ground and, as in the Plaza de l'Almoina (Valencia, Spain), are buried and partially covered by a transparent roof. A large effect of the transparent cover was found by the sensors located below this area, with substantial increases in temperature and a decrease in the relative humidity during the day. Surrounding zones also have values above the recommended temperature values. On the other hand, the influence of a buried water drainage line near the site is notable, causing an increase in relative humidity levels in the surrounding areas. Multivariate statistical analyses enabled us to characterise the microclimate of the archaeological site, allowing future testing to determine whether the conservation conditions have been altered.

Multivariate Thermo-Hygrometric Characterisation of the Archaeological Site of Plaza de l’Almoina (Valencia, Spain) for Preventive Conservation

PubMed Central

Fernández-Navajas, Ángel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-01-01

Preventive conservation requires monitoring and control of the parameters involved in the deterioration process, mainly temperature and relative humidity. It is important to characterise an archaeological site prior to carrying out comparative studies in the future for preventive conservation, either by regular studies to verify whether the conditions are constant, or occasional ones when the boundary conditions are altered. There are numerous covered archaeological sites, but few preventive conservation works that give special attention to the type of cover installed. In particular, there is no background of microclimatic studies in sites that are in the ground and, as in the Plaza de l’Almoina (Valencia, Spain), are buried and partially covered by a transparent roof. A large effect of the transparent cover was found by the sensors located below this area, with substantial increases in temperature and a decrease in the relative humidity during the day. Surrounding zones also have values above the recommended temperature values. On the other hand, the influence of a buried water drainage line near the site is notable, causing an increase in relative humidity levels in the surrounding areas. Multivariate statistical analyses enabled us to characterise the microclimate of the archaeological site, allowing future testing to determine whether the conservation conditions have been altered. PMID:23899937

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Conductive ink print on PA66 gear for manufacturing condition monitoring sensors

NASA Astrophysics Data System (ADS)

Futagawa, Shintaro; Iba, Daisuke; Kamimoto, Takahiro; Nakamura, Morimasa; Miura, Nanako; Iizuka, Takashi; Masuda, Arata; Sone, Akira; Moriwaki, Ichiro

2018-03-01

Failures detection of rotating machine elements, such as gears, is an important issue. The purpose of this study was to try to solve this issue by printing conductive ink on gears to manufacture condition-monitoring sensors. In this work, three types of crack detection sensor were designed and the sprayed conductive ink was directly sintered on polyimide (PI) - coated polyamide (PA) 66 gears by laser. The result showed that it was possible to produce narrow circuit lines of the conductive ink including Ag by laser sintering technique and the complex shape sensors on the lateral side of the PA66 gears, module 1.0 mm and tooth number 48. A preliminary operation test was carried out for investigation of the function of the sensors. As a result of the test, the sensors printed in this work should be effective for detecting cracks at tooth root of the gears and will allow for the development of better equipment and detection techniques for health monitoring of gears.

Condition monitoring of 3G cellular networks through competitive neural models.

PubMed

Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

2005-09-01

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

MONITORING STREAM CONDITION IN THE WESTERN UNITED STATES

EPA Science Inventory

The U.S. Environmental Protection Agency Environmental Monitoring and Assessment Program (EMAP) is a national research program to develop the tools necessary to monitor and assess the- status and trends of ecological resources. EMAP's goal is to develop the scientific underst...

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

Trial by fire: a multivariate examination of the relation between job tenure and work injuries.

PubMed

Breslin, F C; Smith, P

2006-01-01

This study examined the relation between months on the job and lost-time claim rates, with a particular focus on age related differences. Workers' compensation records and labour force survey data were used to compute claim rates per 1000 full time equivalents. To adjust for potential confounding, multivariate analyses included age, sex, occupation, and industry, as well job tenure as predictors of claim rates. At any age, the claim rates decline as time on the job increases. For example, workers in the first month on the job were over four times more likely to have a lost-time claim than workers with over one year in their current job. The job tenure injury associations were stronger among males, the goods industry, manual occupations, and older adult workers. The present results suggest that all worker subgroups examined show increased risk when new on the job. Recommendations for improving this situation include earlier training, starting workers in low hazard conditions, reducing job turnover rates in firms, and improved monitoring of hazard exposures that new workers encounter.

Multivariate normative comparisons using an aggregated database

PubMed Central

Murre, Jaap M. J.; Huizenga, Hilde M.

2017-01-01

In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

Online Condition Monitoring of a Rail Fastening System on High-Speed Railways Based on Wavelet Packet Analysis

PubMed Central

Wei, Jiahong; Liu, Chong; Ren, Tongqun; Liu, Haixia; Zhou, Wenjing

2017-01-01

The rail fastening system is an important part of a high-speed railway track. It is always critical to the operational safety and comfort of railway vehicles. Therefore, the condition detection of the rail fastening system, looseness or absence, is an important task in railway maintenance. However, the vision-based method cannot identify the severity of rail fastener looseness. In this paper, the condition of rail fastening system is monitored based on an automatic and remote-sensing measurement system. Meanwhile, wavelet packet analysis is used to analyze the acceleration signals, based on which two damage indices are developed to locate the damage position and evaluate the severity of rail fasteners looseness, respectively. To verify the effectiveness of the proposed method, an experiment is performed on a high-speed railway experimental platform. The experimental results show that the proposed method is effective to assess the condition of the rail fastening system. The monitoring system significantly reduces the inspection time and increases the efficiency of maintenance management. PMID:28208732

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging

NASA Astrophysics Data System (ADS)

Bravo-Imaz, Inaki; Davari Ardakani, Hossein; Liu, Zongchang; García-Arribas, Alfredo; Arnaiz, Aitor; Lee, Jay

2017-09-01

This paper focuses on analyzing motor current signature for fault diagnosis of gearboxes operating under transient speed regimes. Two different strategies are evaluated, extensively tested and compared to analyze the motor current signature in order to implement a condition monitoring system for gearboxes in industrial machinery. A specially designed test bench is used, thoroughly monitored to fully characterize the experiments, in which gears in different health status are tested. The measured signals are analyzed using discrete wavelet decomposition, in different decomposition levels using a range of mother wavelets. Moreover, a dual-level time synchronous averaging analysis is performed on the same signal to compare the performance of the two methods. From both analyses, the relevant features of the signals are extracted and cataloged using a self-organizing map, which allows for an easy detection and classification of the diverse health states of the gears. The results demonstrate the effectiveness of both methods for diagnosing gearbox faults. A slightly better performance was observed for dual-level time synchronous averaging method. Based on the obtained results, the proposed methods can used as effective and reliable condition monitoring procedures for gearbox condition monitoring using only motor current signature.

Environmental, political, and economic determinants of water quality monitoring in Europe

NASA Astrophysics Data System (ADS)

Beck, Lucas; Bernauer, Thomas; Kalbhenn, Anna

2010-11-01

Effective monitoring is essential for effective pollution control in national and international water systems. To what extent are countries' monitoring choices driven by environmental criteria, as they should be? And to what extent are they also influenced by other factors, such as political and economic conditions? To address these questions, we describe and explain the evolution of one of the most important international environmental monitoring networks in Europe, the one for water quality, in the time period 1965-2004. We develop a geographic information system that contains information on the location of several thousand active monitoring stations in Europe. Using multivariate statistics, we then examine whether and to what extent the spatial and temporal clustering of monitoring intensity is driven by environmental, political, and economic factors. The results show that monitoring intensity is higher in river basins exposed to greater environmental pressure. However, political and economic factors also play a strong role in monitoring decisions: democracy, income, and peer pressure are conducive to monitoring intensity, and monitoring intensity generally increases over time. Moreover, even though monitoring is more intense in international upstream-downstream settings, we observe only a weak bias toward more monitoring downstream of international borders. In contrast, negative effects of European Union (EU) membership and runup to the EU's Water Framework Directive are potential reasons for concern. Our results strongly suggest that international coordination and standardization of water quality monitoring should be intensified. It will be interesting to apply our analytical approach also to other national and international monitoring networks, for instance, the U.S. National Water-Quality Assessment Program or the European Monitoring and Evaluation Program for air pollution.

Cross-country transferability of multi-variable damage models

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

Plant Condition Remote Monitoring Technique

NASA Technical Reports Server (NTRS)

Fotedar, L. K.; Krishen, K.

1996-01-01

This paper summarizes the results of a radiation transfer study conducted on houseplants using controlled environmental conditions. These conditions included: (1) air and soil temperature; (2) incident and reflected radiation; and (3) soil moisture. The reflectance, transmittance, and emittance measurements were conducted in six spectral bands: microwave, red, yellow, green, violet and infrared, over a period of three years. Measurements were taken on both healthy and diseased plants. The data was collected on plants under various conditions which included: variation in plant bio-mass, diurnal variation, changes in plant pathological conditions (including changes in water content), different plant types, various disease types, and incident light wavelength or color. Analysis of this data was performed to yield an algorithm for plant disease from the remotely sensed data.

Kinetic approach for the enzymatic determination of levodopa and carbidopa assisted by multivariate curve resolution-alternating least squares.

PubMed

Grünhut, Marcos; Garrido, Mariano; Centurión, Maria E; Fernández Band, Beatriz S

2010-07-12

A combination of kinetic spectroscopic monitoring and multivariate curve resolution-alternating least squares (MCR-ALS) was proposed for the enzymatic determination of levodopa (LVD) and carbidopa (CBD) in pharmaceuticals. The enzymatic reaction process was carried out in a reverse stopped-flow injection system and monitored by UV-vis spectroscopy. The spectra (292-600 nm) were recorded throughout the reaction and were analyzed by multivariate curve resolution-alternating least squares. A small calibration matrix containing nine mixtures was used in the model construction. Additionally, to evaluate the prediction ability of the model, a set with six validation mixtures was used. The lack of fit obtained was 4.3%, the explained variance 99.8% and the overall prediction error 5.5%. Tablets of commercial samples were analyzed and the results were validated by pharmacopeia method (high performance liquid chromatography). No significant differences were found (alpha=0.05) between the reference values and the ones obtained with the proposed method. It is important to note that a unique chemometric model made it possible to determine both analytes simultaneously. Copyright 2010 Elsevier B.V. All rights reserved.

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multivariable control altitude demonstration on the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.

1979-01-01

The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.

PREFACE: 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2012)

NASA Astrophysics Data System (ADS)

Ball, Andrew; Mishra, Rakesh; Gu, Fengshou; Rao, Raj B. K. N.

2012-05-01

The proactive multidisciplinary conceptual philosophy of Condition Monitoring and Diagnostic Engineering Management (COMADEM) was conceived and has been nurtured, developed and sustained since 1988. Since then, it is gratifying to note that the condition monitoring, diagnostic and prognostic community worldwide (representing industrialists, academics, research and development organizations, professional/private establishments and many hardware/software vending organizations) has warmly welcomed and supported this venture. As is evidenced, many have reaped (and are reaping) the benefits of COMADEM interdiscipline through continuous knowledge discovery, generation and dissemination. We are now proud to celebrate the 25th Annual Event (Silver Jubilee) in Huddersfield, the most beautiful part of the United Kingdom. The theme of this Congress is 'Sustained Prosperity through Proactive Monitoring, Diagnosis, Prognosis and Management'. This proceedings is enriched by contributions from many keynote experts representing many industry and academic establishments worldwide. Authors from more than 30 different countries have pooled their rich multidisciplinary up-to-date knowledge, in order to share their invaluable experience with the COMADEM community. In this proceedings, the readers will find more than 120 refereed papers encompassing a number of topical areas of interest relating to the theme of the congress. The proceedings of COMADEM 2012 will appear in the Open Access Journal of Physics: Conference Series (JPCS), which is part of the IOP Conference Series. All papers published in the IOP Conference Series are fully citable and upon publication will be free to download. We would like to express our deep gratitude to all the keynote speakers, authors, referees, exhibitors, Technical Co-Sponsoring Organizations, Gold Sponsors, IOP Publishers, COMADEM 2012 organizing committee members, delegates and many others on whom the success of this prestigious event depends

Multi-Fibre Optode Microsensors: affordable designs for monitoring oxygen in soils under varying environmental conditions

NASA Astrophysics Data System (ADS)

Rezanezhad, F.; Milojevic, T.; Parsons, C. T.; Smeaton, C. M.; Van Cappellen, P.

2017-12-01

Molecular oxygen (O2) measurements in field and laboratory soil and sediment systems provide useful insight into the biogeochemical functioning of natural environments. However, monitoring soil and sediment O2 is often challenging due to high costs, analyte consumption, and limited customizability and durability of existing O2 sensors. To meet this challenge, an in-house luminescence-based Multi Fibre Optode (MuFO) microsensor system was developed to monitor O2 levels under changing moisture and temperature regimes. The design is simplified by the use of a basic DSLR camera, LED light and fibre optic cables. The technique is based on O2 quenching the luminescent light intensity emitted from a luminophore (platinum(II) meso-tetra(pentafluorophenyl)porphyrin, PtTFPP) that is dip-coated onto the tips of the fibre optic cables, where increasing O2 corresponds to decreasing light intensity, based on the classic Stern-Volmer relationship. High-resolution digital images of the sensor-emitted light are then converted into % O2 saturation. The method was successfully tested in two artificial soil (20% peat, 80% sand) column experiments designed to simulate freeze-thaw cycles (temperature cycling from -10°C to 25°C) and water table fluctuations under controlled conditions. Depth distributions of O2 levels were monitored without interruption for multiple freeze-thaw and water table cycles. No degradation of optode performance or O2 signals were observed for the duration of the column experiments, which supports the long-term deployment of the microsensors for continuous O2 monitoring in field and laboratory settings. The technical specifications of the system are fair, with a detection limit of 0.2% O2 saturation. The main advantages of the MuFO system over commercial applications are the comparatively low cost ($1,800 USD; about ¼ the cost of commercial versions) and ease of customizability. The system has been further developed for near real-time monitoring in the field

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

[Fundamentals of socio-hygienic monitoring of environmental conditions for students of higher education schools].

PubMed

Blinova, E G; Kuchma, V R

2012-01-01

Socioeconomic transformations and the poor environment of an industrial megalopolis negatively affected quality of life and morbidity rates in students (n = 2160). Academic intensity contributed to an increase in overall morbidity and morbidity from nervous system involvement. The regional sociohygienic monitoring of high-school training conditions within the framework of the surveillance system substantiates programs to prevent worse health and life quality in high school students.

Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition

USGS Publications Warehouse

Brown, Jesslyn; Howard, Daniel M.; Wylie, Bruce K.; Friesz, Aaron M.; Ji, Lei; Gacke, Carolyn

2015-01-01

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Assessment of benthic changes during 20 years of monitoring the Mexican Salina Cruz Bay.

PubMed

González-Macías, C; Schifter, I; Lluch-Cota, D B; Méndez-Rodríguez, L; Hernández-Vázquez, S

2009-02-01

In this work a non-parametric multivariate analysis was used to assess the impact of metals and organic compounds in the macro infaunal component of the mollusks benthic community using surface sediment data from several monitoring programs collected over 20 years in Salina Cruz Bay, Mexico. The data for benthic mollusks community characteristics (richness, abundance and diversity) were linked to multivariate environmental patterns, using the Alternating Conditional Expectations method to correlate the biological measurements of the mollusk community with the physicochemical properties of water and sediments. Mollusks community variation is related to environmental characteristics as well as lead content. Surface deposit feeders are increasing their relative density, while subsurface deposit feeders are decreasing with respect to time, these last are expected to be more related with sediment and more affected then by its quality. However gastropods with predatory carnivore as well as chemosymbiotic deposit feeder bivalves have maintained their relative densities along time.

Multisensor Capacitance Probes for Simultaneously Monitoring Rice Field Soil-Water- Crop-Ambient Conditions.

PubMed

Brinkhoff, James; Hornbuckle, John; Dowling, Thomas

2017-12-26

Multisensor capacitance probes (MCPs) have traditionally been used for soil moisture monitoring and irrigation scheduling. This paper presents a new application of these probes, namely the simultaneous monitoring of ponded water level, soil moisture, and temperature profile, conditions which are particularly important for rice crops in temperate growing regions and for rice grown with prolonged periods of drying. WiFi-based loggers are used to concurrently collect the data from the MCPs and ultrasonic distance sensors (giving an independent reading of water depth). Models are fit to MCP water depth vs volumetric water content (VWC) characteristics from laboratory measurements, variability from probe-to-probe is assessed, and the methodology is verified using measurements from a rice field throughout a growing season. The root-mean-squared error of the water depth calculated from MCP VWC over the rice growing season was 6.6 mm. MCPs are used to simultaneously monitor ponded water depth, soil moisture content when ponded water is drained, and temperatures in root, water, crop and ambient zones. The insulation effect of ponded water against cold-temperature effects is demonstrated with low and high water levels. The developed approach offers advantages in gaining the full soil-plant-atmosphere continuum in a single robust sensor.

Density measurements as a condition monitoring approach for following the aging of nuclear power plant cable materials

NASA Astrophysics Data System (ADS)

Gillen, K. T.; Celina, M.; Clough, R. L.

1999-10-01

Monitoring changes in material density has been suggested as a potentially useful condition monitoring (CM) method for following the aging of cable jacket and insulation materials in nuclear power plants. In this study, we compare density measurements and ultimate tensile elongation results versus aging time for most of the important generic types of commercial nuclear power plant cable materials. Aging conditions, which include thermal-only, as well as combined radiation plus thermal, were chosen such that potentially anomalous effects caused by diffusion-limited oxidation (DLO) are unimportant. The results show that easily measurable density increases occur in most important cable materials. For some materials and environments, the density change occurs at a fairly constant rate throughout the mechanical property lifetime. For cases involving so-called induction-time behavior, density increases are slow to moderate until after the induction time, at which point they begin to increase dramatically. In other instances, density increases rapidly at first, then slows down. The results offer strong evidence that density measurements, which reflect property changes under both radiation and thermal conditions, could represent a very useful CM approach.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Evaluation of drinking quality of groundwater through multivariate techniques in urban area.

PubMed

Das, Madhumita; Kumar, A; Mohapatra, M; Muduli, S D

2010-07-01

Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Clinical outcome of continuous facial nerve monitoring during primary parotidectomy.

PubMed

Terrell, J E; Kileny, P R; Yian, C; Esclamado, R M; Bradford, C R; Pillsbury, M S; Wolf, G T

1997-10-01

To assess whether continuous facial nerve monitoring during parotidectomy is associated with a lower incidence of facial nerve paresis or paralysis compared with parotidectomy without monitoring and to assess the cost of such monitoring. A retrospective analysis of outcomes for patients who underwent parotidectomy with or without continuous facial nerve monitoring. University medical center. Fifty-six patients undergoing parotidectomy in whom continuous electromyographic monitoring was used and 61 patients in whom it was not used. (1) The incidence of early and persistent facial nerve paresis or paralysis and (2) the cost associated with facial nerve monitoring. Early, unintentional facial weakness was significantly lower in the group monitored by electromyograpy (43.6%) than in the unmonitored group (62.3%) (P=.04). In the subgroup of patients without comorbid conditions or surgeries, early weakness in the monitored group (33.3%) remained statistically lower than the rate of early weakness in the unmonitored group (57.5%) (P=.03). There was no statistical difference in the final facial nerve function or incidence of permanent nerve injury between the groups or subgroups. After multivariate analysis, nonmonitored status (odds ratio [OR], 3.22), advancing age (OR, 1.47 per 10 years), and longer operative times (OR, 1.3 per hour) were the only significant independent predictive variables significantly associated with early postoperative facial weakness. The incremental cost of facial nerve monitoring was $379. The results suggest that continuous electromyographic monitoring of facial muscle during primary parotidectomy reduces the incidence of short-term postoperative facial paresis. Advantages and disadvantages of this technique need to be considered together with the additional costs in deciding whether routine use of continuous monitoring is a useful, cost-effective adjunct to parotid surgery.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions

NASA Astrophysics Data System (ADS)

Strączkiewicz, M.; Barszcz, T.; Jabłoński, A.

2015-07-01

All commonly used condition monitoring systems (CMS) enable defining alarm thresholds that enhance efficient surveillance and maintenance of dynamic state of machinery. The thresholds are imposed on the measured values such as vibration-based indicators, temperature, pressure, etc. For complex machinery such as wind turbine (WT) the total number of thresholds might be counted in hundreds multiplied by the number of operational states. All the parameters vary not only due to possible machinery malfunctions, but also due to changes in operating conditions and these changes are typically much stronger than the former ones. Very often, such a behavior may lead to hundreds of false alarms. Therefore, authors propose a novel approach based on parameterized description of the threshold violation. For this purpose the novelty and severity factors are introduced. The first parameter refers to the time of violation occurrence while the second one describes the impact of the indicator-increase to the entire machine. Such approach increases reliability of the CMS by providing the operator with the most useful information of the system events. The idea of the procedure is presented on a simulated data similar to those from a wind turbine.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Developing RCM Strategy for Hydrogen Fuel Cells Utilizing On Line E-Condition Monitoring

NASA Astrophysics Data System (ADS)

Baglee, D.; Knowles, M. J.

2012-05-01

Fuel cell vehicles are considered to be a viable solution to problems such as carbon emissions and fuel shortages for road transport. Proton Exchange Membrane (PEM) Fuel Cells are mainly used in this purpose because they can run at low temperatures and have a simple structure. Yet high maintenance costs and the inherent dangers of maintaining equipment using hydrogen are two main issues which need to be addressed. The development of appropriate and efficient strategies is currently lacking with regard to fuel cell maintenance. A Reliability Centered Maintenance (RCM) approach offers considerable benefit to the management of fuel cell maintenance since it includes an identification and consideration of the impact of critical components. Technological developments in e-maintenance systems, radio-frequency identification (RFID) and personal digital assistants (PDAs) have proven to satisfy the increasing demand for improved reliability, efficiency and safety. RFID technology is used to store and remotely retrieve electronic maintenance data in order to provide instant access to up-to-date, accurate and detailed information. The aim is to support fuel cell maintenance decisions by developing and applying a blend of leading-edge communications and sensor technology including RFID. The purpose of this paper is to review and present the state of the art in fuel cell condition monitoring and maintenance utilizing RCM and RFID technologies. Using an RCM analysis critical components and fault modes are identified. RFID tags are used to store the critical information, possible faults and their cause and effect. The relationship between causes, faults, symptoms and long term implications of fault conditions are summarized. Finally conclusions are drawn regarding suggested maintenance strategies and the optimal structure for an integrated, cost effective condition monitoring and maintenance management system.

Unsupervised classification of multivariate geostatistical data: Two algorithms

NASA Astrophysics Data System (ADS)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

An Improved Method to Control the Critical Parameters of a Multivariable Control System

NASA Astrophysics Data System (ADS)

Subha Hency Jims, P.; Dharmalingam, S.; Wessley, G. Jims John

2017-10-01

The role of control systems is to cope with the process deficiencies and the undesirable effect of the external disturbances. Most of the multivariable processes are highly iterative and complex in nature. Aircraft systems, Modern Power Plants, Refineries, Robotic systems are few such complex systems that involve numerous critical parameters that need to be monitored and controlled. Control of these important parameters is not only tedious and cumbersome but also is crucial from environmental, safety and quality perspective. In this paper, one such multivariable system, namely, a utility boiler has been considered. A modern power plant is a complex arrangement of pipework and machineries with numerous interacting control loops and support systems. In this paper, the calculation of controller parameters based on classical tuning concepts has been presented. The controller parameters thus obtained and employed has controlled the critical parameters of a boiler during fuel switching disturbances. The proposed method can be applied to control the critical parameters like elevator, aileron, rudder, elevator trim rudder and aileron trim, flap control systems of aircraft systems.

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

PubMed

Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

2015-01-01

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Monitoring the mechanical behaviour of electrically conductive polymer nanocomposites under ramp and creep conditions.

PubMed

Pedrazzoli, D; Dorigato, A; Pegoretti, A

2012-05-01

Various amounts of carbon black (CB) and carbon nanofibres (CNF) were dispersed in an epoxy resin to prepare nanocomposites whose mechanical behaviour, under ramp and creep conditions, was monitored by electrical measurements. The electrical resistivity of the epoxy resin was dramatically reduced by both nanofillers after the percolation threshold (1 wt% for CB and 0.5 wt% for CNF), reaching values in the range of 10(3)-10(4) omega . cm for filler loadings higher than 2 wt%. Due to the synergistic effects between the nanofillers, an epoxy system containing a total nanofiller amount of 2 wt%, with a relative CB/CNF ratio of 90/10 was selected for the specific applications. A direct correlation between the tensile strain and the increase of the electrical resistance was observed over the whole experimental range, and also the final failure of the samples was clearly detected. Creep tests confirmed the possibility to monitor the various deformational stages under constant loads, with a strong dependency from the temperature and the applied stress. The obtained results are encouraging for a possible application of nanomodified epoxy resin as a matrix for the preparation of structural composites with sensing (i.e., damage-monitoring) capabilities.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Real-Time Condition Monitoring and Fault Diagnosis of Gear Train Systems Using Instantaneous Angular Speed (IAS) Analysis

NASA Astrophysics Data System (ADS)

Sait, Abdulrahman S.

This dissertation presents a reliable technique for monitoring the condition of rotating machinery by applying instantaneous angular speed (IAS) analysis. A new analysis of the effects of changes in the orientation of the line of action and the pressure angle of the resultant force acting on gear tooth profile of spur gear under different levels of tooth damage is utilized. The analysis and experimental work discussed in this dissertation provide a clear understating of the effects of damage on the IAS by analyzing the digital signals output of rotary incremental optical encoder. A comprehensive literature review of state of the knowledge in condition monitoring and fault diagnostics of rotating machinery, including gearbox system is presented. Progress and new developments over the past 30 years in failure detection techniques of rotating machinery including engines, bearings and gearboxes are thoroughly reviewed. This work is limited to the analysis of a gear train system with gear tooth surface faults utilizing angular motion analysis technique. Angular motion data were acquired using an incremental optical encoder. Results are compared to a vibration-based technique. The vibration data were acquired using an accelerometer. The signals were obtained and analyzed in the phase domains using signal averaging to determine the existence and position of faults on the gear train system. Forces between the mating teeth surfaces are analyzed and simulated to validate the influence of the presence of damage on the pressure angle and the IAS. National Instruments hardware is used and NI LabVIEW software code is developed for real-time, online condition monitoring systems and fault detection techniques. The sensitivity of optical encoders to gear fault detection techniques is experimentally investigated by applying IAS analysis under different gear damage levels and different operating conditions. A reliable methodology is developed for selecting appropriate testing

Long-term seafloor monitoring at an open ocean aquaculture site in the western Gulf of Maine, USA: development of an adaptive protocol.

PubMed

Grizzle, R E; Ward, L G; Fredriksson, D W; Irish, J D; Langan, R; Heinig, C S; Greene, J K; Abeels, H A; Peter, C R; Eberhardt, A L

2014-11-15

The seafloor at an open ocean finfish aquaculture facility in the western Gulf of Maine, USA was monitored from 1999 to 2008 by sampling sites inside a predicted impact area modeled by oceanographic conditions and fecal and food settling characteristics, and nearby reference sites. Univariate and multivariate analyses of benthic community measures from box core samples indicated minimal or no significant differences between impact and reference areas. These findings resulted in development of an adaptive monitoring protocol involving initial low-cost methods that required more intensive and costly efforts only when negative impacts were initially indicated. The continued growth of marine aquaculture is dependent on further development of farming methods that minimize negative environmental impacts, as well as effective monitoring protocols. Adaptive monitoring protocols, such as the one described herein, coupled with mathematical modeling approaches, have the potential to provide effective protection of the environment while minimize monitoring effort and costs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adaptation to high throughput batch chromatography enhances multivariate screening.

PubMed

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Submerged Medium Voltage Cable Systems at Nuclear Power Plants. A Review of Research Efforts Relevant to Aging Mechanisms and Condition Monitoring

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

Brown, Jason; Bernstein, Robert; White, II, Gregory Von

academic and industrial literature was performed to identify : 1) findings regarding the degradation mechanisms of submerged cabling and 2) condition monitoring methods that may prove useful in predict ing the remaining lifetime of submerged medium voltage p ower cables . The re search was conducted by a multi - disciplinary team , and s ources includ ed official NRC reports, n ational l aboratory reports , IEEE standards, conference and journal proceedings , magazine articles , PhD dissertations , and discussions with experts . The purpose of this work was to establish the current state - of - the - art in material degradation modeling and cable condition monitoring techniques and to identify research gaps . Subsequently, future areas of focus are recommended to address these research gaps and thus strengthen the efficacy of the NRC's developing cable condition monitoring program . Results of this literature review and details of the test ing recommendations are presented in this report . FOREWORD To ensure the safe, re liable, and cost - effective long - term operation of nuclear power plants, many systems, structures, and components must be continuously evaluated. The Nuclear Regulatory Commission (NRC) has identified that cables in submerged environments are of concern, particularly as plants are seeking license renewal. To date, there is a lack of consensus on aging and degradation mechanisms even though the area of submerged cables has been extensively studied. Consequently, the ability to make lifetime predictions for submerged cable does not yet exist. The NRC has engaged Sandia National Laboratories (SNL) to lead a coordinated effort to help elucidate the aging and degradation of cables in submerged environments by collaborating with cable manufacturers, utilities, universities, and other government agencies. A team of SNL experts was assembled from the laboratories including electrical condition monitoring, mat erial science, polymer degradation, plasma physics

Monitoring of the infrastructure and services used to handle and automatically produce Alignment and Calibration conditions at CMS

NASA Astrophysics Data System (ADS)

Sipos, Roland; Govi, Giacomo; Franzoni, Giovanni; Di Guida, Salvatore; Pfeiffer, Andreas

2017-10-01

The CMS experiment at CERN LHC has a dedicated infrastructure to handle the alignment and calibration data. This infrastructure is composed of several services, which take on various data management tasks required for the consumption of the non-event data (also called as condition data) in the experiment activities. The criticality of these tasks imposes tights requirements for the availability and the reliability of the services executing them. In this scope, a comprehensive monitoring and alarm generating system has been developed. The system has been implemented based on the Nagios open source industry standard for monitoring and alerting services, and monitors the database back-end, the hosting nodes and key heart-beat functionalities for all the services involved. This paper describes the design, implementation and operational experience with the monitoring system developed and deployed at CMS in 2016.

Invisible work of using and monitoring knowledge by parents (end-users) of children with chronic conditions.

PubMed

Lagosky, Stephanie; Bartlett, Doreen; Shaw, Lynn

2016-01-01

Parents who care for young children with chronic conditions are knowledge users. Their efforts, time, and energy to source, consider and monitor information add to the 'invisible' work of parents in making decisions about care, school transitions, and interventions. Little is known or understood about the work of parents as knowledge users. To understand the knowledge use patterns and how these patterns may be monitored in parents caring for their young children with cerebral palsy (CP). An embedded case study methodology was used. In-depth qualitative interviews and visual mapping were employed to collect and analyze data based on the experiences of three mothers of young children with CP. Knowledge use in parents caring for their young children with CP is multi-factorial, complex and temporal. Findings resulted in a provisional model elaborating on the ways knowledge is used by parents and how it may be monitored. The visual mapping of pathways and actions of parents as end users makes the processes of knowledge use more visible and open to be valued as well as appreciated by others. The provisional model has implications for knowledge mobilization as a strategy in childhood rehabilitation and the facilitation of knowledge use in the lives of families with children with chronic health conditions.

Pump Coupling & Motor bearing damage detection using Condition Monitoring at DTPS

NASA Astrophysics Data System (ADS)

Bari, H. M.; Deshpande, A. A.; Jalkote, P. S.; Patil, S. S.

2012-05-01

This paper shares a success story out of the implementation of Co-ordinated Condition Monitoring techniques at DTPS, wherein imminent Mis-alignment of HT auxiliary BFP - 1B and Motor bearing failure of ID FAN - 1B was diagnosed. On 30/12/2010, Booster Pump DE horizontal reading increased from 4.8 to 5.1 and then upto 5.9 mm/sec. It was suspected that Booster pump was mis-aligned with Motor. To confirm misalignment, Phase Analysis was also done which showed that Coupling phase difference was 180 Degrees. Vibration & Phase Analysis helped in diagnosing the exact root cause of abnormity in advance, saving plant from huge losses which could have caused total cost of £ 104,071. On 06/01/2011, ID fan 1B Motor NDE & DE horizontal vibration readings deviated from 0.5 to 0.8 and 0.6 to 0.8 mm/sec (RMS) respectively. Noise level increased from 99.1 to 101.9 db. It was suspected that Motor bearings had loosened over the shaft. Meanwhile, after opening of Motor, Inner race of NDE side was found cracked and loosened over the shaft. Vibration Analysis & Noise Monitoring helped in diagnosing the exact root cause of abnormity in advance, saving plant from huge losses which could have caused total cost of £ 308,857.

Structural Health Monitoring of Composite Plates Under Ambient and Cryogenic Conditions

NASA Technical Reports Server (NTRS)

Engberg, Robert C.

2005-01-01

Methods for structural health monitoring are now 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. Different types of excitation and response signals and different sensors are employed in composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency and high frequency chirps 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 and operating environment. These results are furthermore compared with previous work using externally mounted sensors. More work is needed to accurately account for changes in temperature seen in these environments and be statistically significant. Sensor development and placement strategy are other areas of further work to make structural health monitoring more robust. Results from this and other work might 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 2nd generation reusable launch vehicle using composites as a structural element.

Some Recent Developments on Complex Multivariate Distributions

ERIC Educational Resources Information Center

Krishnaiah, P. R.

1976-01-01

In this paper, the author gives a review of the literature on complex multivariate distributions. Some new results on these distributions are also given. Finally, the author discusses the applications of the complex multivariate distributions in the area of the inference on multiple time series. (Author)

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Usefulness of LANDSAT data for monitoring plant development and range conditions in California's annual grassland

NASA Technical Reports Server (NTRS)

Carneggie, D. M.; Degloria, S. D.; Colwell, R. N.

1975-01-01

A network of sampling sites throughout the annual grassland region of California was established to correlate plant growth stages and forage production to climatic and other environmental factors. Plant growth and range conditions were further related to geographic location and seasonal variations. A sequence of LANDSAT data was obtained covering critical periods in the growth cycle. This was analyzed by both photointerpretation and computer aided techniques. Image characteristics and spectral reflectance data were then related to forage production, range condition, range site and changing growth conditions. It was determined that repeat sequences with LANDSAT color composite images do provide a means for monitoring changes in range condition. Spectral radiance data obtained from magnetic tape can be used to determine quantitatively the critical stages in the forage growth cycle. A computer ratioing technique provided a sensitive indicator of changes in growth stages and an indication of the relative differences in forage production between range sites.

A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Litt, Jonathan S.

2010-01-01

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

Development of a Decision Support System for Monitoring, Reporting, Forecasting Ecological Conditions of the Appalachian Trail

Treesearch

Y. Wang; R. Nemani; F. Dieffenbach; K. Stolte; G. Holcomb

2010-01-01

This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decision-making on management of the A.T. by providing a coherent framework for data integration,...

Hierarchy of temporal responses of multivariate self-excited epidemic processes

NASA Astrophysics Data System (ADS)

Saichev, Alexander; Maillart, Thomas; Sornette, Didier

2013-04-01

Many natural and social systems are characterized by bursty dynamics, for which past events trigger future activity. These systems can be modelled by so-called self-excited Hawkes conditional Poisson processes. It is generally assumed that all events have similar triggering abilities. However, some systems exhibit heterogeneity and clusters with possibly different intra- and inter-triggering, which can be accounted for by generalization into the "multivariate" self-excited Hawkes conditional Poisson processes. We develop the general formalism of the multivariate moment generating function for the cumulative number of first-generation and of all generation events triggered by a given mother event (the "shock") as a function of the current time t. This corresponds to studying the response function of the process. A variety of different systems have been analyzed. In particular, for systems in which triggering between events of different types proceeds through a one-dimension directed or symmetric chain of influence in type space, we report a novel hierarchy of intermediate asymptotic power law decays ˜ 1/ t 1-( m+1) θ of the rate of triggered events as a function of the distance m of the events to the initial shock in the type space, where 0 < θ < 1 for the relevant long-memory processes characterizing many natural and social systems. The richness of the generated time dynamics comes from the cascades of intermediate events of possibly different kinds, unfolding via random changes of types genealogy.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

Rack protection monitor

DOEpatents

Orr, Stanley G.

2000-01-01

A hardwired, fail-safe rack protection monitor utilizes electromechanical relays to respond to the detection by condition sensors of abnormal or alarm conditions (such as smoke, temperature, wind or water) that might adversely affect or damage equipment being protected. When the monitor is reset, the monitor is in a detection mode with first and second alarm relay coils energized. If one of the condition sensors detects an abnormal condition, the first alarm relay coil will be de-energized, but the second alarm relay coil will remain energized. This results in both a visual and an audible alarm being activated. If a second alarm condition is detected by another one of the condition sensors while the first condition sensor is still detecting the first alarm condition, both the first alarm relay coil and the second alarm relay coil will be de-energized. With both the first and second alarm relay coils de-energized, both a visual and an audible alarm will be activated. In addition, power to the protected equipment will be terminated and an alarm signal will be transmitted to an alarm central control. The monitor can be housed in a separate enclosure so as to provide an interface between a power supply for the protected equipment and the protected equipment.

Multivariate Cryptography Based on Clipped Hopfield Neural Network.

PubMed

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2018-02-01

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

Remote sensing of vegetation pattern and condition to monitor changes in Everglades biogeochemistry

USGS Publications Warehouse

Jones, John W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management.

System monitoring feedback in cinemas and harvesting energy of the air conditioning condenser

NASA Astrophysics Data System (ADS)

Pop, P. P.; Pop-Vadean, A.; Barz, C.; Latinovic, T.; Chiver, O.

2017-05-01

Our article monitors the degree of emotional involvement of the audience in the action film in theaters by measuring the concentration of CO2. The software performs data processing obtained dispersion sensors and displays data during the film. The software will also trigger the start of the air conditioning condenser where we can get harvesting energy by installing a piezoelectric device. Useful energy can be recovered from various waste produced in cinema. The time lag between actions and changes in environmental systems determines that decisions made now will affect subsequent generations and the future of our environment.

F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

1977-01-01

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Multivariate Analysis To Quantify Species in the Presence of Direct Interferents: Micro-Raman Analysis of HNO 3 in Microfluidic Devices

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

Lines, Amanda M.; Nelson, Gilbert L.; Casella, Amanda J.

Microfluidic devices are a growing field with significant potential for application to small scale processing of solutions. Much like large scale processing, fast, reliable, and cost effective means of monitoring the streams during processing are needed. Here we apply a novel Micro-Raman probe to the on-line monitoring of streams within a microfluidic device. For either macro or micro scale process monitoring via spectroscopic response, there is the danger of interfering or confounded bands obfuscating results. By utilizing chemometric analysis, a form of multivariate analysis, species can be accurately quantified in solution despite the presence of overlapping or confounded spectroscopic bands.more » This is demonstrated on solutions of HNO 3 and NaNO 3 within micro-flow and microfluidic devices.« less

A Multivariate Investigation of Employee Absenteeism.

DTIC Science & Technology

1980-05-01

A MULTIVARIATE INVESTIGATION OF EMPLOYEE ABSENTEEISM.(U) MAY 80 J R TERBORG, G A OAVIS, F J SMITH N00014-78"C-0756 UNCLASSIFIED TR-80-5 NL inuuununn...COMPLEX ORGANIZATIONS PROGRAM IN INDUSTRIAL ORGANIZATIONAL PSYCHOLOG C, DEPARTMENT OF PSYCHOLOGY a- UNIVERSITY OF HOUSTON C HOUSTON, TEXAS T7004 C...a-o I *I-- . ’ 4 , ... ,.I .,.- .S 7Jn .jA A Multivariate Investigation of Employee Absenteeism James R. Terborg & Gregory A. Davis University of

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

PubMed Central

Montzka, Carsten; Pauwels, Valentijn R. N.; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-01-01

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

Multivariate and multiscale data assimilation in terrestrial systems: a review.

PubMed

Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-11-26

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

Monitoring the Restored Mangrove Condition at Perancak Estuary, Jembrana, Bali, Indonesia from 2001 to 2015

NASA Astrophysics Data System (ADS)

Ruslisan, R.; Kamal, M.; Sidik, F.

2018-02-01

Mangrove is unique vegetation that lives in tidal areas around the tropical and subtropical coasts. It has important physical, biological, and chemical roles for balancing the ecosystem, as well as serving as carbon pool. Therefore, monitoring the mangrove condition is very important step prior to any management and conservation actions in this area. This study aims to map and monitor the condition of restored mangroves in Perancak Estuary, Jembrana, Bali, Indonesia from 2001 to 2015. We used IKONOS-2, WorldView-2 and WorldView-3 image data to map the extent and canopy cover density of mangroves using visual delineation and semi-empirical modelling through Enhanced Vegetation Index (EVI) as a proxy. The results show that there was a significant increase in mangrove extent from 78.08 hectares in 2001 to 122.54 hectares in 2015. In term of mangrove canopy density, the percentage of high and very-high canopy density classes has increased from 32% in 2001 to 57% in 2015. On the other hand, there were slight changes in low and medium canopy density classes during the observation period. Overall, the result figures from both area extent and canopy density indicates the successful implementation of mangrove restoration effort in Perancak Estuary during the last 14 years.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

NASA Technical Reports Server (NTRS)

Soeder, J. F.

1983-01-01

As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

Quality of life in relation to upper and lower respiratory conditions among retired 9/11-exposed firefighters with pulmonary disability.

PubMed

Berninger, Amy; Webber, Mayris P; Weakley, Jessica; Gustave, Jackson; Zeig-Owens, Rachel; Lee, Roy; Al-Othman, Fairouz; Cohen, Hillel W; Kelly, Kerry; Prezant, David J

2010-12-01

To examine health-related quality of life (HRQoL) and World Trade Center (WTC) cough syndrome conditions in male firefighters who retired due to a 9/11-related pulmonary disability. From 3/1/2008 to 1/31/2009, we contacted 275 disability-retired firefighters and compared their HRQoL and current aerodigestive conditions to those from WTC-exposed non-disabled retired and active firefighters. Relationships between HRQoL and explanatory variable(s) were examined using multivariable linear regression models. Mean physical component summary (PCS) scores were lowest in disabled retirees compared with non-disabled retirees and actives: 36.4 (9.6), 49.4 (8.7), and 53.1 (5.1), respectively (P < 0.0001). Mean mental component summary (MCS) scores were closer: 44.5 (11.9), 48.1 (8.5), and 48.7 (7.4), respectively (P < 0.0001). In multivariable models, after adjustment for many factors, PCS scores were not associated with early WTC arrival, but were inversely associated with disability retirement and all WTC cough syndrome conditions. MCS scores were inversely associated with early WTC arrival and most WTC cough syndrome conditions, but were not associated with disability retirement. WTC cough syndrome conditions predict lower HRQoL scores even 8 years after exposure, independent of retirement status. These data suggest that monitoring physical conditions of individuals with occupational exposures might help identify those at risk for impaired HRQoL.

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study.

PubMed

Ferguson, Ty; Rowlands, Alex V; Olds, Tim; Maher, Carol

2015-03-27

Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson's correlation. All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

Monitoring and modeling conditions for regional shallow landslide initiation in the San Francisco Bay area, California

NASA Astrophysics Data System (ADS)

Collins, B. D.; Stock, J. D.; Godt, J. W.

2012-12-01

Intense winter storms in the San Francisco Bay area (SFBA) of California often trigger widespread landsliding, including debris flows that originate as shallow (<3 m) landslides. The strongest storms result in the loss of lives and millions of dollars in damage. Whereas precipitation-based rainfall intensity-duration landslide initiation thresholds are available for the SFBA, antecedent soil moisture conditions also play a major role in determining the likelihood for landslide generation from a given storm. Previous research has demonstrated that antecedent triggering conditions can be obtained using pre-storm precipitation thresholds (e.g., 250-400 mm of seasonal pre-storm rainfall). However, these types of thresholds do not account for the often cyclic pattern of wetting and drying that can occur early in the winter storm season (i.e. October - December), and which may skew the applicability of precipitation-only based thresholds. To account for these cyclic and constantly evolving soil moisture conditions, we have pursued methods to measure soil moisture directly and integrate these measurements into predictive analyses. During the past three years, the USGS installed a series of four subsurface hydrology monitoring stations in shallow landslide-prone locations of the SFBA to establish a soil-moisture-based antecedent threshold. In addition to soil moisture sensors, the monitoring stations are each equipped with piezometers to record positive pore water pressure that is likely required for shallow landslide initiation and a rain gauge to compare storm intensities with existing precipitation-based thresholds. Each monitoring station is located on a natural, grassy hillslope typically composed of silty sands, underlain by sandstone, sloping at approximately 30°, and with a depth to bedrock of approximately 1 meter - conditions typical of debris flow generation in the SFBA. Our observations reveal that various locations respond differently to seasonal

Fast and Flexible Multivariate Time Series Subsequence Search

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Oza, Nikunj C.; Zhu, Qiang; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which often contain several gigabytes of data. Surprisingly, research on MTS search is very limited. Most of the existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two algorithms to solve this problem (1) a List Based Search (LBS) algorithm which uses sorted lists for indexing, and (2) a R*-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences. Both algorithms guarantee that all matching patterns within the specified thresholds will be returned (no false dismissals). The very few false alarms can be removed by a post-processing step. Since our framework is also capable of Univariate Time-Series (UTS) subsequence search, we first demonstrate the efficiency of our algorithms on several UTS datasets previously used in the literature. We follow this up with experiments using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>99%) thus needing actual disk access for only less than 1% of the observations. To the best of our knowledge, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of

Scattering amplitudes from multivariate polynomial division

NASA Astrophysics Data System (ADS)

Mastrolia, Pierpaolo; Mirabella, Edoardo; Ossola, Giovanni; Peraro, Tiziano

2012-11-01

We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Gröbner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.

Monitoring the condition of natural resources in US national parks.

PubMed

Fancy, S G; Gross, J E; Carter, S L

2009-04-01

The National Park Service has developed a long-term ecological monitoring program for 32 ecoregional networks containing more than 270 parks with significant natural resources. The monitoring program assists park managers in developing a broad-based understanding of the status and trends of park resources as a basis for making decisions and working with other agencies and the public for the long-term protection of park ecosystems. We found that the basic steps involved in planning and designing a long-term ecological monitoring program were the same for a range of ecological systems including coral reefs, deserts, arctic tundra, prairie grasslands, caves, and tropical rainforests. These steps involve (1) clearly defining goals and objectives, (2) compiling and summarizing existing information, (3) developing conceptual models, (4) prioritizing and selecting indicators, (5) developing an overall sampling design, (6) developing monitoring protocols, and (7) establishing data management, analysis, and reporting procedures. The broad-based, scientifically sound information obtained through this systems-based monitoring program will have multiple applications for management decision-making, research, education, and promoting public understanding of park resources. When combined with an effective education program, monitoring results can contribute not only to park issues, but also to larger quality-of-life issues that affect surrounding communities and can contribute significantly to the environmental health of the nation.

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

PubMed Central

Fan, Wei; Tsui, Kwok-Leung; Lin, Jianhui

2018-01-01

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions. PMID:29495446

On a Family of Multivariate Modified Humbert Polynomials

PubMed Central

Aktaş, Rabia; Erkuş-Duman, Esra

2013-01-01

This paper attempts to present a multivariable extension of generalized Humbert polynomials. The results obtained here include various families of multilinear and multilateral generating functions, miscellaneous properties, and also some special cases for these multivariable polynomials. PMID:23935411

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Compact Multi-Gas Monitor for Life Support Systems Control in Space: Evaluation Under Realistic Environmental Conditions

NASA Technical Reports Server (NTRS)

Alonso, Jesus Delgado; Phillips, Straun; Chullen, Cinda; Mendoza, Edgar

2014-01-01

Advanced space life support systems require lightweight, low-power, durable sensors for monitoring critical gas components. A luminescence-based optical flow-through cell to monitor carbon dioxide, oxygen, and humidity has been developed and was demonstrated using bench-top instrumentation under environmental conditions relevant to portable life support systems, including initially pure oxygen atmosphere, temperature range from 50 F to 150 F, and humidity from dry to 100% RH and under conditions of water condensation. This paper presents the most recent progress in the development of this sensor technology. Trace gas contaminants in a space suit, originating from hardware and material off-gassing and crew member metabolism, are from many chemical families. The result is a gas mix much more complex than the pure oxygen fed into the space suit, and this complexity may interfere with gas sensor readings. This paper presents an evaluation of optical sensor performance when exposed to the most significant trace gases reported to be found in space suits. A study of the calibration stability of the sensors is also presented. For that purpose, a profile of temperature, pressure, humidity, and gas composition for the duration of an EVA has been defined, and the performance of sensors operated repeatedly under those conditions has been studied. Finally, this paper presents the first compact readout unit for these optical sensors, designed for the volume, power, and weight restrictions of a PLSS.

Predictive monitoring and diagnosis of periodic air pollution in a subway station.

PubMed

Kim, YongSu; Kim, MinJung; Lim, JungJin; Kim, Jeong Tai; Yoo, ChangKyoo

2010-11-15

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models--global and seasonal--were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources. Copyright © 2010 Elsevier B.V. All rights reserved.

Remote sensing of vegetation pattern and condition to monitor changes in everglades biogeochemistry

USGS Publications Warehouse

Jones, J.W.

2011-01-01

Ground-based studies of biogeochemistry and vegetation patterning yield process understanding, but the amount of information gained by ground-based studies can be greatly enhanced by efficient, synoptic, and temporally resolute monitoring afforded by remote sensing. The variety of presently available Everglades vegetation maps reflects both the wide range of application requirements and the need to balance cost and capability. More effort needs to be applied to documenting and understanding vegetation distribution and condition as indicators of biogeochemistry and contamination. Ground-based and remote sensing studies should be modified to maximize their synergy and utility for adaptive management. Copyright ?? 2011 Taylor & Francis Group, LLC.

Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

ERIC Educational Resources Information Center

Ruscio, John; Kaczetow, Walter

2008-01-01

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

Chemiluminescence-based multivariate sensing of local equivalence ratios in premixed atmospheric methane-air flames

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

Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.

Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using themore » leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.« less

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has

Identification of Critical Operation Conditions of Industrial Gearboxes by 24/7 Monitoring of Oil Quality, Oil Aging, and Additive Consumption

NASA Astrophysics Data System (ADS)

Mauntz, M.; Peuser, J.

2017-05-01

The demand for wind energy grows at exponential rates. At the same time improving reliability, reduced operation and maintenance costs are the key priorities in wind tur-bine maintenance strategies [1]. This paper provides information about a novel online oil condition monitoring system to give a solution to the mentioned priorities. The presented sensor system enables damage prevention of the wind turbine gear-box by an advanced warning time of critical operation conditions and an enhanced oil exchange interval realized by a precise measurement of the electrical conductivity, the relative permittivity and the oil temperature. A new parameter, the WearSens® Index (WSi) is introduced. The mathematical model of the WSi combines all measured values and its gradients in one single parameter for a comprehensive monitoring to prevent wind turbines from damage. Furthermore, the WSi enables a long-term prognosis on the next oil change by 24/7 server data logging. Corrective procedures and/or maintenance can be carried out before actual damage occurs. First WSi results of an onshore wind turbine installation compared to traditional vibration monitoring are shown.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

Long-term monitoring of the Sedlec Ossuary - Analysis of hygrothermal conditions

NASA Astrophysics Data System (ADS)

Pavlík, Zbyšek; Balík, Lukáš; Maděra, Jiří; Černý, Robert

2016-07-01

The Sedlec Ossuary is one of the twelve UNESCO World Heritage Sites in the Czech Republic. Although the ossuary is listed among the most visited Czech tourist attractions, its technical state is almost critical and a radical renovation is necessary. On this account, hygrothermal performance of the ossuary is experimentally researched in the presented paper in order to get information on moisture sources and to get necessary data for optimized design of renovation treatments and reconstruction solutions that will allow preserve the historical significance of this attractive heritage site. Within the performed experimental analysis, the interior and exterior climatic conditions are monitored over an almost three year period together with relative humidity and temperature profiles measured in the most damage parts of the ossuary chapel. On the basis of measured data, the long-term hygrothermal state of the ossuary building is accessed and the periods of possible surface condensation are identified.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.

PubMed

Rathore, Anurag S; Kumar Singh, Sumit; Pathak, Mili; Read, Erik K; Brorson, Kurt A; Agarabi, Cyrus D; Khan, Mansoor

2015-01-01

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers.

Determining the cost of implementing and operating a remote patient monitoring programme for the elderly with chronic conditions: A systematic review of economic evaluations.

PubMed

Peretz, Daniel; Arnaert, Antonia; Ponzoni, Norma N

2018-01-01

Introduction Remote patient monitoring (RPM) in conjunction with home nursing visits is becoming increasingly popular for the follow-up of patients with chronic conditions and evidence exists that it improves patients' health outcomes. Current cost data is reported inconsistently and often gathered from studies of poor methodological quality, making it difficult for decision-makers who consider implementing this service in their organizations. This study reviewed the cost of RPM programmes targeting elderly patients with chronic conditions. Methods After evaluation against the inclusion and exclusion criteria and appraisal against two criteria which are important for economic evaluations, data from selected studies were extracted and grouped into meaningful cost categories, then adjusted to reflect November 2015 US dollars. Results In the 13 selected studies, the newly-created cost category 'Combined intervention cost' (reflecting equipment purchasing, servicing and monitoring cost) for the various RPM programmes ranged from US$275-US$7963 per patient per year. The three main findings are: (a) RPM programme costs have decreased since 2004 due to cheaper technology; (b) monitoring a single vital sign is likely to be less costly than monitoring multiple vital signs; and (c) programmes targeting hypertension or congestive heart failure are less costly than those targeting respiratory diseases or multiple conditions. Conclusions This review recommends that future studies present their cost data with more granularity, that grouping of costs should be minimized and that any assumptions, such as amortization, should be made explicit. In addition, studies should compare programmes with similar characteristics in terms of type of conditions, number of vital signs monitored, etc. for more generalizable results.

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

Tool Condition Monitoring in Micro-End Milling using wavelets

NASA Astrophysics Data System (ADS)

Dubey, N. K.; Roushan, A.; Rao, U. S.; Sandeep, K.; Patra, K.

2018-04-01

In this work, Tool Condition Monitoring (TCM) strategy is developed for micro-end milling of titanium alloy and mild steel work-pieces. Full immersion slot milling experiments are conducted using a solid tungsten carbide end mill for more than 1900 s to have reasonable amount of tool wear. During the micro-end milling process, cutting force and vibration signals are acquired using Kistler piezo-electric 3-component force dynamometer (9256C2) and accelerometer (NI cDAQ-9188) respectively. The force components and the vibration signals are processed using Discrete Wavelet Transformation (DWT) in both time and frequency window. 5-level wavelet packet decomposition using Db-8 wavelet is carried out and the detailed coefficients D1 to D5 for each of the signals are obtained. The results of the wavelet transformation are correlated with the tool wear. In case of vibration signals, de-noising is done for higher frequency components (D1) and force signals were de-noised for lower frequency components (D5). Increasing value of MAD (Mean Absolute Deviation) of the detail coefficients for successive channels depicted tool wear. The predictions of the tool wear are confirmed from the actual wear observed in the SEM of the worn tool.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

PubMed Central

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-01-01

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

PubMed

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-04-29

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

Remote Monitor Alarm System

NASA Technical Reports Server (NTRS)

Stute, Robert A. (Inventor); Galloway, F. Houston (Inventor); Medelius, Pedro J. (Inventor); Swindle, Robert W. (Inventor); Bierman, Tracy A. (Inventor)

1996-01-01

A remote monitor alarm system monitors discrete alarm and analog power supply voltage conditions at remotely located communications terminal equipment. A central monitoring unit (CMU) is connected via serial data links to each of a plurality of remote terminal units (RTUS) that monitor the alarm and power supply conditions of the remote terminal equipment. Each RTU can monitor and store condition information of both discrete alarm points and analog power supply voltage points in its associated communications terminal equipment. The stored alarm information is periodically transmitted to the CMU in response to sequential polling of the RTUS. The number of monitored alarm inputs and permissible voltage ranges for the analog inputs can be remotely configured at the CMU and downloaded into programmable memory at each RTU. The CMU includes a video display, a hard disk memory, a line printer and an audio alarm for communicating and storing the alarm information received from each RTU.

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Kidney function monitoring and nonvitamin K oral anticoagulant dosage in atrial fibrillation.

PubMed

Andreu Cayuelas, Jose Manuel; Caro Martínez, Cesar; Flores Blanco, Pedro Jose; Elvira Ruiz, Gines; Albendin Iglesias, Helena; Cerezo Manchado, Juan Jose; Bailen Lorenzo, Jose Luis; Januzzi, James L; García Alberola, Arcadio; Manzano-Fernández, Sergio

2018-06-01

Clinical practice guidelines recommend regular kidney function monitoring in atrial fibrillation patients on nonvitamin K oral anticoagulants (NOAC); however, information regarding compliance with these recommendations in daily life conditions is scarce. We sought to determine the compliance with kidney function monitoring recommendations in nonvalvular atrial fibrillation (NVAF) patients starting NOAC and its implication on the appropriateness of NOAC dosage. This study involves the retrospective analysis of a multicentre registry including consecutive NVAF patients who started NOAC (n = 692). Drug dosage changes and serum creatinine determinations were recorded during 1-year follow-up. European Heart Rhythm Association criteria were used to define the appropriateness of kidney function monitoring as well as adequate NOAC dosage. During the follow-up (334 ± 89 days), the compliance with kidney function monitoring recommendations was 61% (n = 425). After multivariate adjustment, age (OR × year: 0.92 (CI 95%: 0.89-0.95) P < .001), creatinine clearance (OR × mL/min: 1.02 (CI 95%: 1.01-1.03) P < .001) and adequate NOAC dosage at baseline (OR: 1.54 (CI 95%: 1.06-2.23), P = .024) were independent predictors of appropriate kidney function monitoring. Compliance with kidney function monitoring recommendations was independently associated with change to appropriate NOAC dose after 1 year (OR: 2.80 (CI 95%: 1.01-7.80), P = .049). Noncompliance with kidney function monitoring recommendations is common in NVAF patients starting NOAC, especially in elderly patients with kidney dysfunction. Compliance with kidney function monitoring recommendations was associated with adequate NOAC dosage at 1-year follow-up. Further studies are warranted to evaluate the implication of kidney function monitoring on prognosis. © 2018 Stichting European Society for Clinical Investigation Journal Foundation.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

A systematic review of the routine monitoring of growth in children of primary school age to identify growth-related conditions.

PubMed

Fayter, D; Nixon, J; Hartley, S; Rithalia, A; Butler, G; Rudolf, M; Glasziou, P; Bland, M; Stirk, L; Westwood, M

2007-06-01

To clarify the role of growth monitoring in primary school children, including obesity, and to examine issues that might impact on the effectiveness and cost-effectiveness of such programmes. Electronic databases were searched up to July 2005. Experts in the field were also consulted. Data extraction and quality assessment were performed on studies meeting the review's inclusion criteria. The performance of growth monitoring to detect disorders of stature and obesity was evaluated against National Screening Committee (NSC) criteria. In the 31 studies that were included in the review, there were no controlled trials of the impact of growth monitoring and no studies of the diagnostic accuracy of different methods for growth monitoring. Analysis of the studies that presented a 'diagnostic yield' of growth monitoring suggested that one-off screening might identify between 1:545 and 1:1793 new cases of potentially treatable conditions. Economic modelling suggested that growth monitoring is associated with health improvements [incremental cost per quality-adjusted life-year (QALY) of 9500 pounds] and indicated that monitoring was cost-effective 100% of the time over the given probability distributions for a willingness to pay threshold of 30,000 pounds per QALY. Studies of obesity focused on the performance of body mass index against measures of body fat. A number of issues relating to human resources required for growth monitoring were identified, but data on attitudes to growth monitoring were extremely sparse. Preliminary findings from economic modelling suggested that primary prevention may be the most cost-effective approach to obesity management, but the model incorporated a great deal of uncertainty. This review has indicated the potential utility and cost-effectiveness of growth monitoring in terms of increased detection of stature-related disorders. It has also pointed strongly to the need for further research. Growth monitoring does not currently meet all NSC

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

What`s normal?: Body condition in Great Lakes herring gulls

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

Hebert, C.E.; Shutt, J.L.

1994-12-31

The Canadian Wildlife Service`s herring gull (Larus argentatus) surveillance program has demonstrated the usefulness of this species as a monitor of spatial and temporal trends in contaminant levels. However, the effects of environmental contaminants on gulls are difficult to distinguish from the effects of other anthropogenic stressors such as the introduction of exotic species, overfishing and habitat loss. To understand the relative importance of these factors in regulating the success of individual gulls and, hence, gull populations, the authors must first have a better understanding of what constitutes a ``normal`` bird. Improving the ability to differentiate between normal and abnormalmore » birds is crucial in any health assessment of Great Lakes gulls. Body condition has been shown to be an important measure of a bird`s ability to provide energy for egg production, migration etc. Numerous approaches have been used to assess condition, most of which required that the bird be sacrificed. In this study, the authors describe a nonlethal technique to quantify body condition in herring gulls. Multivariate statistics are used to quantify body size, relate body size to total mass and from that, determine relative body condition. Initially, body condition is assessed in gulls from a reference colony where reproductive success is normal and anthropogenic influences are limited. This reference population is then used as a baseline against which other gull populations are compared.« less

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of Unmanned Aircraft System (UAS) to Monitor Forest Health Conditions in Alaska

NASA Astrophysics Data System (ADS)

Webley, P. W.; Hatfield, M. C.; Heutte, T. M.; Winton, L. M.

2017-12-01

US Forest Service Alaska Region Forest Health Protection (FHP) and University of Alaska Fairbanks (UAF), Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) are evaluating the capability of Unmanned Aerial Systems (UAS, "drone" informally) to monitor forest health conditions in Alaska's Interior Region. On July 17-20 2017, FHP and ACUASI deployed two different UAS at permanent forest inventory plots managed by the UAF programs Bonanza Creek Long Term Ecological Research (LTER) and Cooperative Alaska Forest Inventory (CAFI). The purpose of the mission was to explore capabilities of UAS for evaluating aspen tree mortality at inaccessible locations and at a scale and precision not generally achievable with currently used ground- or air-based methods. Drawing from experience gained during the initial 2016 campaign, this year emphasized the efficient use of UAS to accomplish practical field research in a variety of realistic situations. The vehicles selected for this years' effort included the DJI Matrice quadcopter with the Zenmuse-X3 camera to quickly capture initial video of the site and tree conditions; followed by the ING Responder (single rotor electric helicopter based on the Gaui X7 airframe) outfitted with a Nikon D810 camera to collect high-resolution stills suitable for construction of orthomosaic models. A total of 12 flights were conducted over the campaign, with two full days dedicated to the Delta Junction Gerstle River Intermediate (GRI) sites and the remaining day at the Bonanza Creek site. In addition to demonstrating the ability of UAS to operate safely and effectively in various canopy conditions, the effort also validated the ability of teams to deliver UAS and scientific payloads into challenging terrain using all-terrain vehicles (ATV) and foot traffic. Analysis of data from the campaign is underway. Because the permanent plots have been recently evaluated it is known that nearly all aspen mortality is caused by an aggressive canker

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Chronic Conditions Among Children Investigated by Child Welfare: A National Sample

PubMed Central

Hurlburt, Michael S.; Heneghan, Amy M.; Zhang, Jinjin; Rolls-Reutz, Jennifer; Silver, Ellen J.; Fisher, Emily; Landsverk, John; Horwitz, Sarah McCue

2013-01-01

OBJECTIVE: To assess the presence of chronic health conditions (CHCs) among a nationally representative sample of children investigated by child welfare agencies. METHODS: The study included 5872 children, aged 0 to 17.5 years, whose families were investigated for maltreatment between February 2008 and April 2009. Using data from the second National Survey of Child and Adolescent Well-Being, we examined the proportion of children who had CHC. We developed 2 categorical and 2 noncategorical measures of CHC from the available data and analyzed them by using bivariate and multivariable analyses. RESULTS: Depending on the measure used, 30.6% to 49.0% of all children investigated were reported by their caregivers to have a CHC. Furthermore, the children identified by using diverse methods were not entirely overlapping. In the multivariable analyses, children with poorer health were more likely to be male, older, and receiving special educational services but not more likely to be in out-of-home placements. CONCLUSIONS: The finding that a much higher proportion of these children have CHC than in the general population underscores the substantial health problems of children investigated by child welfare agencies and the need to monitor their health carefully, regardless of their placement postinvestigation. PMID:23420907

The multivariate egg: quantifying within- and among-clutch correlations between maternally derived yolk immunoglobulins and yolk androgens using multivariate mixed models.

PubMed

Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara

2014-03-01

Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

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

Wang, Feng, E-mail: fwang@unu.edu; Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft; Huisman, Jaco

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lackmore » of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

Validation of the concentration profiles obtained from the near infrared/multivariate curve resolution monitoring of reactions of epoxy resins using high performance liquid chromatography as a reference method.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2007-03-07

This paper reports the validation of the results obtained by combining near infrared spectroscopy and multivariate curve resolution-alternating least squares (MCR-ALS) and using high performance liquid chromatography as a reference method, for the model reaction of phenylglycidylether (PGE) and aniline. The results are obtained as concentration profiles over the reaction time. The trueness of the proposed method has been evaluated in terms of lack of bias. The joint test for the intercept and the slope showed that there were no significant differences between the profiles calculated spectroscopically and the ones obtained experimentally by means of the chromatographic reference method at an overall level of confidence of 5%. The uncertainty of the results was estimated by using information derived from the process of assessment of trueness. Such operational aspects as the cost and availability of instrumentation and the length and cost of the analysis were evaluated. The method proposed is a good way of monitoring the reactions of epoxy resins, and it adequately shows how the species concentration varies over time.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

The evolution of multivariate maternal effects.

PubMed

Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

2014-04-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

The Evolution of Multivariate Maternal Effects

PubMed Central

Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart

2014-01-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346

Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

NASA Astrophysics Data System (ADS)

Aghakouchak, Amir; Tourian, Mohammad J.

2015-04-01

Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Application of infrared microspectroscopy and multivariate analysis for monitoring the effect of adjunct cultures during Swiss cheese ripening.

PubMed

Chen, G; Kocaoglu-Vurma, N A; Harper, W J; Rodriguez-Saona, L E

2009-08-01

Improved cheese flavor has been attributed to the addition of adjunct cultures, which provide certain key enzymes for proteolysis and affect the dynamics of starter and nonstarter cultures. Infrared microspectroscopy provides unique fingerprint-like spectra for cheese samples and allows for rapid monitoring of cheese composition during ripening. The objective was to use infrared microspectroscopy and multivariate analysis to evaluate the effect of adjunct cultures on Swiss cheeses during ripening. Swiss cheeses, manufactured using a commercial starter culture combination and 1 of 3 adjunct Lactobacillus spp., were evaluated at d 1, 6, 30, 60, and 90 of ripening. Cheese samples (approximately 20 g) were powdered with liquid nitrogen and homogenized using water and organic solvents, and the water-soluble components were separated. A 3-microL aliquot of the extract was applied onto a reflective microscope slide, vacuum-dried, and analyzed by infrared microspectroscopy. The infrared spectra (900 to 1,800 cm(-1)) produced specific absorption profiles that allowed for discrimination among different cheese samples. Cheeses manufactured with adjunct cultures showed more uniform and consistent spectral profiles, leading to the formation of tight clusters by pattern-recognition analysis (soft independent modeling of class analogy) as compared with cheeses with no adjuncts, which exhibited more spectral variability among replicated samples. In addition, the soft independent modeling of class analogy discriminating power indicated that cheeses were differentiated predominantly based on the band at 1,122 cm(-1), which was associated with S-O vibrations. The greatest changes in the chemical profile of each cheese occurred between d 6 and 30 of warm-room ripening. The band at 1,412 cm(-1), which was associated with acidic AA, had the greatest contribution to differentiation, indicating substantial changes in levels of proteolysis during warm-room ripening in addition to propionic

Structural health monitoring methodology for aircraft condition-based maintenance

NASA Astrophysics Data System (ADS)

Saniger, Jordi; Reithler, Livier; Guedra-Degeorges, Didier; Takeda, Nobuo; Dupuis, Jean Pierre

2001-06-01

Reducing maintenance costs while keeping a constant level of safety is a major issue for Air Forces and airlines. The long term perspective is to implement condition based maintenance to guarantee a constant safety level while decreasing maintenance costs. On this purpose, the development of a generalized Structural Health Monitoring System (SHMS) is needed. The objective of such a system is to localize the damages and to assess their severity, with enough accuracy to allow low cost corrective actions. The present paper describes a SHMS based on acoustic emission technology. This choice was driven by its reliability and wide use in the aerospace industry. The described SHMS uses a new learning methodology which relies on the generation of artificial acoustic emission events on the structure and an acoustic emission sensor network. The calibrated acoustic emission events picked up by the sensors constitute the knowledge set that the system relies on. With this methodology, the anisotropy of composite structures is taken into account, thus avoiding the major cause of errors of classical localization methods. Moreover, it is adaptive to different structures as it does not rely on any particular model but on measured data. The acquired data is processed and the event's location and corrected amplitude are computed. The methodology has been demonstrated and experimental tests on elementary samples presented a degree of accuracy of 1cm.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

On the reliability of Shewhart-type control charts for multivariate process variability

NASA Astrophysics Data System (ADS)

Djauhari, Maman A.; Salleh, Rohayu Mohd; Zolkeply, Zunnaaim; Li, Lee Siaw

2017-05-01

We show that in the current practice of multivariate process variability monitoring, the reliability of Shewhart-type control charts cannot be measured except when the sub-group size n tends to infinity. However, the requirement of large n is meaningless not only in manufacturing industry where n is small but also in service industry where n is moderate. In this paper, we introduce a new definition of control limits in the two most appreciated control charts in the literature, i.e., the improved generalized variance chart (IGV-chart) and vector variance chart (VV-chart). With the new definition of control limits, the reliability of the control charts can be determined. Some important properties of new control limits will be derived and the computational technique of probability of false alarm will be delivered.

A novel multivariate STeady-state index during general ANesthesia (STAN).

PubMed

Castro, Ana; de Almeida, Fernando Gomes; Amorim, Pedro; Nunes, Catarina S

2017-08-01

The assessment of the adequacy of general anesthesia for surgery, namely the nociception/anti-nociception balance, has received wide attention from the scientific community. Monitoring systems based on the frontal EEG/EMG, or autonomic state reactions (e.g. heart rate and blood pressure) have been developed aiming to objectively assess this balance. In this study a new multivariate indicator of patients' steady-state during anesthesia (STAN) is proposed, based on wavelet analysis of signals linked to noxious activation. A clinical protocol was designed to analyze precise noxious stimuli (laryngoscopy/intubation, tetanic, and incision), under three different analgesic doses; patients were randomized to receive either remifentanil 2.0, 3.0 or 4.0 ng/ml. ECG, PPG, BP, BIS, EMG and [Formula: see text] were continuously recorded. ECG, PPG and BP were processed to extract beat-to-beat information, and [Formula: see text] curve used to estimate the respiration rate. A combined steady-state index based on wavelet analysis of these variables, was applied and compared between the three study groups and stimuli (Wilcoxon signed ranks, Kruskal-Wallis and Mann-Whitney tests). Following institutional approval and signing the informed consent thirty four patients were enrolled in this study (3 excluded due to signal loss during data collection). The BIS index of the EEG, frontal EMG, heart rate, BP, and PPG wave amplitude changed in response to different noxious stimuli. Laryngoscopy/intubation was the stimulus with the more pronounced response [Formula: see text]. These variables were used in the construction of the combined index STAN; STAN responded adequately to noxious stimuli, with a more pronounced response to laryngoscopy/intubation (18.5-43.1 %, [Formula: see text]), and the attenuation provided by the analgesic, detecting steady-state periods in the different physiological signals analyzed (approximately 50 % of the total study time). A new multivariate approach for

Impact of ACA Health Reforms for People With Mental Health Conditions.

PubMed

Thomas, Kathleen C; Shartzer, Adele; Kurth, Noelle K; Hall, Jean P

2018-02-01

This brief report explores the impact of health reform for people with mental illness. The Health Reform Monitoring Survey was used to examine health insurance, access to care, and employment for 1,550 people with mental health conditions pre- and postimplementation of the Affordable Care Act (ACA) and by state Medicaid expansion status. Multivariate logistic regressions with predictive margins were used. Post-ACA reforms, people with mental health conditions were less likely to be uninsured (5% versus 13%; t=-6.89, df=50, p<.001) and to report unmet need due to cost of mental health care (17% versus 21%; t=-3.16, df=50, p=.002) and any health services (46% versus 51%; t=-3.71, df=50, p<.001), and they were more likely to report a usual source of care (82% versus 76%; t=3.11, df=50, p=.002). These effects were experienced in both Medicaid expansion and nonexpansion states. Findings underscore the importance of ACA improvements in the quality of health insurance coverage.

Impact of different environmental conditions on lithium-ion batteries performance through the thermal monitoring with fiber sensors

NASA Astrophysics Data System (ADS)

Nascimento, Micael; Ferreira, Marta S.; Pinto, João. L.

2017-08-01

In this work, an optical fiber sensing network has been developed to assess the impact of different environmental conditions on lithium batteries performance through the real time thermal monitoring. The battery is submitted to constant current charge and different discharge C-rates, under normal and abusive operating conditions. The results show that for the discharge C-rate of 5.77C, the LiB under cold and dry climates had 32.5% and 27.2% lower temperature variations, when compared with temperate climates, respectively. The higher temperature shift detected in the temperate climate was related to the battery better performance regarding discharge capacity and power capabilities.

Monitoring of conditions inside gas aggregation cluster source during production of Ti/TiOx nanoparticles

NASA Astrophysics Data System (ADS)

Kousal, J.; Kolpaková, A.; Shelemin, A.; Kudrna, P.; Tichý, M.; Kylián, O.; Hanuš, J.; Choukourov, A.; Biederman, H.

2017-10-01

Gas aggregation sources are nowadays rather widely used in the research community for producing nanoparticles. However, the direct diagnostics of conditions inside the source are relatively scarce. In this work, we focused on monitoring the plasma parameters and the composition of the gas during the production of the TiOx nanoparticles. We studied the role of oxygen in the aggregation process and the influence of the presence of the particles on the plasma. The construction of the source allowed us to make a 2D map of the plasma parameters inside the source.

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

Impact of storage conditions on the urinary metabolomics fingerprint.

PubMed

Laparre, Jérôme; Kaabia, Zied; Mooney, Mark; Buckley, Tom; Sherry, Mark; Le Bizec, Bruno; Dervilly-Pinel, Gaud

2017-01-25

Urine stability during storage is essential in metabolomics to avoid misleading conclusions or erroneous interpretations. Facing the lack of comprehensive studies on urine metabolome stability, the present work performed a follow-up of potential modifications in urinary chemical profile using LC-HRMS on the basis of two parameters: the storage temperature (+4 °C, -20 °C, -80 °C and freeze-dried stored at -80 °C) and the storage duration (5-144 days). Both HILIC and RP chromatographies have been implemented in order to globally monitor the urinary metabolome. Using an original data processing associated to univariate and multivariate data analysis, our study confirms that chemical profiles of urine samples stored at +4 °C are very rapidly modified, as observed for instance for compounds such as:N-acetyl Glycine, Adenosine, 4-Amino benzoic acid, N-Amino diglycine, creatine, glucuronic acid, 3-hydroxy-benzoic acid, pyridoxal, l-pyroglutamic acid, shikimic acid, succinic acid, thymidine, trigonelline and valeryl-carnitine, while it also demonstrates that urine samples stored at -20 °C exhibit a global stability over a long period with no major modifications compared to -80 °C condition. This study is the first to investigate long term stability of urine samples and report potential modifications in the urinary metabolome, using both targeted approach monitoring individually a large number (n > 200) of urinary metabolites and an untargeted strategy enabling assessing for global impact of storage conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Low cost digester monitoring under realistic conditions: Rural use of biogas and digestate quality.

PubMed

Castro, L; Escalante, H; Jaimes-Estévez, J; Díaz, L J; Vecino, K; Rojas, G; Mantilla, L

2017-09-01

The purpose of this work was to assess the behaviour of anaerobic digestion of cattle manure in a rural digester under realistic conditions, and estimate the quality and properties of the digestate. The data obtained during monitoring indicated that the digester operation was stable without risk of inhibition. It produced an average of 0.85Nm 3 biogas/d at 65.6% methane, providing an energy savings of 76%. In addition, the digestate contained high nutrient concentrations, which is an important feature of fertilizers. However, this method requires post-treatment due to the presence of pathogens. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the use of multi-agent systems for the monitoring of industrial systems

NASA Astrophysics Data System (ADS)

Rezki, Nafissa; Kazar, Okba; Mouss, Leila Hayet; Kahloul, Laid; Rezki, Djamil

2016-03-01

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Continuous glucose monitoring--a study of the Enlite sensor during hypo- and hyperbaric conditions.

PubMed

Adolfsson, Peter; Örnhagen, Hans; Eriksson, Bengt M; Cooper, Ken; Jendle, Johan

2012-06-01

The performance and accuracy of the Enlite(™) (Medtronic, Inc., Northridge, CA) sensor may be affected by microbubble formation at the electrode surface during hypo- and hyperbaric conditions. The effects of acute pressure changes and of prewetting of sensors were investigated. On Day 1, 24 sensors were inserted on the right side of the abdomen and back in one healthy individual; 12 were prewetted with saline solution, and 12 were inserted dry. On Day 2, this procedure was repeated on the left side. All sensors were attached to an iPro continuous glucose monitoring (CGM) recorder. Hypobaric and hyperbaric tests were conducted in a pressure chamber, with each test lasting 105 min. Plasma glucose values were obtained at 5-min intervals with a HemoCue(®) (Ängelholm, Sweden) model 201 glucose analyzer for comparison with sensor glucose values. Ninety percent of the CGM systems operated during the tests. The mean absolute relative difference was lower during hyperbaric than hypobaric conditions (6.7% vs. 14.9%, P<0.001). Sensor sensitivity was slightly decreased (P<0.05) during hypobaric but not during hyperbaric conditions. Clarke Error Grid Analysis showed that 100% of the values were found in the A+B region. No differences were found between prewetted and dry sensors. The Enlite sensor performed adequately during acute pressure changes and was more accurate during hyperbaric than hypobaric conditions. Prewetting the sensors did not improve accuracy. Further studies on type 1 diabetes subjects are needed under various pressure conditions.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis.

PubMed

Ebrahimi, Milad; Gerber, Erin L; Rockaway, Thomas D

2017-05-15

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

A multivariate ecogeographic analysis of macaque craniodental variation.

PubMed

Grunstra, Nicole D S; Mitteroecker, Philipp; Foley, Robert A

2018-06-01

To infer the ecogeographic conditions that underlie the evolutionary diversification of macaques, we investigated the within- and between-species relationships of craniodental dimensions, geography, and environment in extant macaque species. We studied evolutionary processes by contrasting macroevolutionary patterns, phylogeny, and within-species associations. Sixty-three linear measurements of the permanent dentition and skull along with data about climate, ecology (environment), and spatial geography were collected for 711 specimens of 12 macaque species and analyzed by a multivariate approach. Phylogenetic two-block partial least squares was used to identify patterns of covariance between craniodental and environmental variation. Phylogenetic reduced rank regression was employed to analyze spatial clines in morphological variation. Between-species associations consisted of two distinct multivariate patterns. The first represents overall craniodental size and is negatively associated with temperature and habitat, but positively with latitude. The second pattern shows an antero-posterior tooth size contrast related to diet, rainfall, and habitat productivity. After controlling for phylogeny, however, the latter dimension was diminished. Within-species analyses neither revealed significant association between morphology, environment, and geography, nor evidence of isolation by distance. We found evidence for environmental adaptation in macaque body and craniodental size, primarily driven by selection for thermoregulation. This pattern cannot be explained by the within-species pattern, indicating an evolved genetic basis for the between-species relationship. The dietary signal in relative tooth size, by contrast, can largely be explained by phylogeny. This cautions against adaptive interpretations of phenotype-environment associations when phylogeny is not explicitly modelled. © 2018 Wiley Periodicals, Inc.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.