Probability Of Detection Values Research Articles

Avaliação de Indicadores Atmosféricos Utilizando o Modelo Numérico WRF em Eventos de Chuva na Cidade do Rio de Janeiro

The improvement in the forecast of extreme rainfall events contributes significantly the effectiveness of environmental monitoring performed routinely by operational centers. Therefore, in this study it was aimed to evaluate the performance of atmospheric indicators in rain events that occurred in the city of Rio de Janeiro, between 1997-2012, in order to identify which of them have higher reliability in identifying the favorable weather characteristics to the occurrence of severe rainfall. Using outputs from WRF model, thresholds were established for each of the atmospheric indicators analyzed in this paper, as well as the calculation of the probability of detection (POD) and false alarm ratio (FAR) of these indicators for days occurrence of severe rainfall and the days there was the presence of the same weather systems giving rise to severe rain events, but, however, these did not occur. With POD and RAF values obtained, we also sought to determine the most efficient indicators in the characterization of different atmospheric conditions between rainfall events analyzed. The results showed that the divergence of the wind in 300 hPa, specific humidity at 850 hPa, total energy of severe storm and precipitable water were the most efficient indicators in the identification of favorable atmospheric condition to the occurrence of severe rainfall in Rio de Janeiro the study period.

Sensitivity of WRF-EMS Model to Predict Rainfall Event on Wet and Dry Seasons Over West Sumatra

Sensitivity of WRF-EMS model using combination of convective and planetary boundary layer was analyzed using eight experiments that were conducted during wet and dry seasons over West Sumatra. Parameter used were forecast error, root mean square error (RMSE), threat score (TS), probability of detection (POD), false alarm ratio (FAR) for 24 and 48hours rainfall accumulation. WRF-EMS Model with combination of BMJ-ACM2 is the best interm schemes of the consistency of pattern and intensity of rainfall compared with TRMM for 24 and 48hours accumulation. Model verifications on Tabing station revealed that during wet season the values of RMSE were 0.64 and 0.38mm and TS values were 0.86 for 24hours and 0.73 for 48hours. Meanwhile, during dry season RMSE were 0.27 and 0.45 with TS values were 0.50 for both 24 and 48hours. POD and FAR values were 1.0 and 0.14 (24hours), 0.89 and 0.20 (48hours) during wet season. In addition, POD and FAR values were 0.67 and 0.34 (24hours), 0.75 and 0.40 (48hours) during dry seasons. Thus, we found that WRF-EMS model is more accurate to predict rainfall during the wet season than dry season over Tabing, West Sumatra.

Prediction of Frost Occurrences Using Statistical Modeling Approaches

We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.

GlutenTox® Pro Test for the Detection of Gluten in Select Foods and Surfaces.

The GlutenTox® Pro Test is an immunochromatographic test for the detection of gluten in foods and on surfaces with varying compositions and levels of processing, from raw foods/ingredients to final product testing. The Method Developer evaluation for the validation of the GlutenTox Pro Test Kit (Biomedal Diagnostics, Sevilla, Spain) for the detection of gluten in foods and on surfaces was conducted at Biomedal, S. L., Camas, Sevilla, Spain. The GlutenTox Pro test method was evaluated by testing the following: cross-reactivity, interference, specificity and sensitivity, robustness, stability, lot-to-lot variation, food matrix, and environmental surface. To evaluate the performance of the GlutenToxPro test for the detection of gluten, 10 matrixes were selected: rice flour, bread/biscuit, rolled oat, pâté, and yogurt (and a second bread matrix for incurred sampled testing) for the food matrix study and food-grade painted wood, plastic, rubber, sealed ceramic, and stainless steel for the environmental surface matrix study. For the food matrix study, 30 replicates were evaluated at six spiked levels of gluten (0, 3, 8, 15, 25, and 45 ppm) against four detection thresholds (5, 10, 20, and 40 ppm) for each food matrix. Additionally, 10 replicates were evaluated at a concentration of 10,000 ppm using all four detection thresholds only for rice flour matrix. Three replicates of each concentration level of gluten were analyzed using paired samples by the AOAC OMA 2012.01 reference method for each food matrix. For the environmental surface study, 30 replicates were evaluated at a low spike level of gluten (16 ng/16 cm2), five replicates at a high spike level of gluten (400 ng/16 cm2), and five replicates at an unspiked control level (0 ng/16 cm2) for each surface matrix. Upon completion of testing, the probability of detection values and confidence intervals were calculated and plotted versus the concentration level as determined by the reference method when applicable. An independent laboratory evaluation of the GlutenTox Pro Test Kit with rice flour and stainless steel environmental surface was conducted at Q Laboratories, Inc. (Cincinnati, OH). The GlutenTox Pro Test Kit demonstrated reliability as an effective rapid method for the detection of gluten in food matrixes (LOD 5 ppm gluten; threshold limits 5, 10, 20, and 40 ppm gluten) and on environmental surfaces (amount of detection 16 ng/16 cm2).

A Joint Multitarget Estimator for the Joint Target Detection and Tracking Filter

Abstract : This paper proposes a joint multitarget (JoM) estimator for the joint target detection and tracking (JoTT) filter. An efficient choice to the unknown JoM estimation constant (i.e., hypervolume around target state estimate) is proposed as a Pareto-optimal solution to a multi-objective nonlinear convex optimization problem. The multi-objective function is formulated as two convex objective functions in conflict. The first objective function is the information theoretic part of the problem and aims for entropy maximization, while the second one arises from the constraint in the definition of the JoM estimator and aims to improve the accuracy of the JoM estimates. The Pareto-optimal solution is obtained using the weighted sum method, where objective weights are determined as linear predictions from autoregressive models. In contrast to the marginal multitarget (MaM) estimator, the target-present decision from the JoM estimator depends on the spatial information as well as the cardinality information in the finite-set statistics (FISST) density. The simulation results demonstrate that the JoM estimator achieves better track management performance in terms of track confirmation latency and track maintenance than the MaM estimator for different values of detection probability. However, the proposed JoM estimator suffers from track termination latency more than the MaM estimator since the localization performance of the JoTT filter does deteriorate gradually after target termination.

Evaluation of 3M™ Molecular Detection Assay (MDA) Listeria monocytogenes for the Detection of Listeria monocytogenes in Selected Foods and Environmental Surfaces: Collaborative Study, First Action 2014.07.

The 3M™ Molecular Detection Assay (MDA) Listeria monocytogenes combines isothermal amplification and bioluminescence to detect Listeria monocytogenes with high specificity and efficiency in select foods and environmental samples. The 3M MDA Listeria monocytogenes method was evaluated using an unpaired study design in a multilaboratory collaborative study to the U.S. Department of Agriculture, Food Safety and Inspection Service Microbiology Laboratory Guidebook 8.09 (2011) Isolation and Identification of Listeria monocytogenes from Red Meat, Poultry, and Egg Products and Environmental Samples for deli turkey, and the AOAC Official Method of Analysis(SM) 993.12 Listeria monocytogenes in Milk and Dairy Products for full-fat (4% milk fat) cottage cheese following the current AOAC guidelines. A total of 16 laboratories located in the continental United States and Canada participated in this collaborative study. For deli turkey, 125 g test portions were evaluated using heat-stressed cells by each method. For full-fat cottage cheese, 25 g test portions were evaluated using nonheat-stressed cells. Each matrix had three inoculation levels: an uninoculated control level (0 CFU/test portion), and two levels artificially contaminated with L. monocytogenes, a low inoculum level (0.2-2 CFU/test portion) and a high inoculum level (2-5 CFU/test portion). In total, 1584 unpaired replicate samples were analyzed. Statistical analysis was conducted according to the probability of detection (POD) model. Results obtained for the low inoculum level full-fat cottage cheese test portions produced a difference in cross-laboratory POD (dLPOD) value of -0.08 with a 95% confidence interval (CI) of (-0.20, 0.05). For the low-level deli turkey test portions, a dLPOD value of -0.02 with a 95% CI of (-0.14, 0.11) was obtained.

Evaluation of 3M™ Molecular Detection Assay (MDA) Listeria for the Detection of Listeria species in Selected Foods and Environmental Surfaces: Collaborative Study, First Action 2014.06.

The 3M™ Molecular Detection Assay (MDA) Listeria is used with the 3M Molecular Detection System for the detection of Listeria species in food, food-related, and environmental samples after enrichment. The assay utilizes loop-mediated isothermal amplification to rapidly amplify Listeria target DNA with high specificity and sensitivity, combined with bioluminescence to detect the amplification. The 3M MDA Listeria method was evaluated using an unpaired study design in a multilaboratory collaborative study and compared to the AOAC Official Method of AnalysisSM (OMA) 993.12 Listeria monocytogenes in Milk and Dairy Products reference method for the detection of Listeria species in full-fat (4% milk fat) cottage cheese (25 g test portions). A total of 15 laboratories located in the continental United States and Canada participated. Each matrix had three inoculation levels: an uninoculated control level (0 CFU/test portion), and two levels artificially contaminated with Listeria monocytogenes, a low inoculum level (0.2-2 CFU/test portion) and a high inoculum level (2-5 CFU/test portion) using nonheat-stressed cells. In total, 792 unpaired replicate portions were analyzed. Statistical analysis was conducted according to the probability of detection (POD) model. Results obtained for the low inoculum level test portions produced a difference in cross-laboratory POD value of -0.07 with a 95% confidence interval of (-0.19, 0.06). No statistically significant differences were observed in the number of positive samples detected by the 3M MDA Listeria method versus the AOAC OMA method.

Evaluation of VIDAS Listeria monocytogenes Xpress (LMX) for the detection of Listeria monocytogenes in a variety of foods: First Action 2013.11.

The VIDAS Listeria monocytogenes Xpress (LMX) is an automated rapid screening enzyme immunoassay for the detection of Listeria monocytogenes in food products. The VIDAS LMX method was compared in a multi-laboratory collaborative study to AOAC Official Method 993.12 Listeria monocytogenes in Milk and Dairy Products reference method following current AOAC guidelines. A total of 14 laboratories participated, representing government and industry, throughout the United States. One matrix, queso fresco (soft Mexican cheese), was analyzed using two different test portion sizes, 25 and 125 g. Samples representing each portion size were artificially contaminated with L. monocytogenes at three levels: an uninoculated control level [0 colony forming units (CFU)/test portion], a low inoculum level (0.2-2 CFU/test portion), and a high inoculum level (2-5 CFU/test portion). For this evaluation, 1800 unpaired replicate test portions were analyzed by either the VIDAS LMX or AOAC 993.12. Each level was analyzed using the Probability of Detection (POD) statistical model. For the low-level inoculated test portions, difference in collaborator POD (dLPOD) values of 0.04, (-0.08, 0.15) and 0.01, (-0.10, 0.13), with 95% confidence intervals, were obtained, respectively, for 25 and 125 g test portions. The range of the confidence intervals for dLPOD values for both the 25 and 125 g test portions contain the point 0.0 indicating no statistically significant difference in the number of positive samples detected between the VIDAS LMX and the AOAC method. In addition to Oxford Agar (OXA), VIDAS LMX test portions were confirmed using Agar Listeria Ottavani and Agosti (ALOA), a proprietary chromogenic agar for the identification and differentiation of L. monocytogenes and Listeria species. No differences were observed between the two selective agars. The VIDAS LMX method, with the optional ALOA agar confirmation method, was adopted as Official First Action status for the detection of L. monocytogenes in a variety of foods.

Evaluation of VIDAS UP Listeria assay (LPT) for the detection of Listeria in a variety of foods and environmental surfaces: First Action 2013.10.

The VIDAS UP Listeria (LPT) is an automated rapid screening enzyme phage-ligand based assay for the detection of Listeria species in human food products and environmental samples. The VIDAS LPT method was compared in a multi-laboratory collaborative study to AOAC Official Method 993.12 Listeria monocytogenes in Milk and Dairy Products reference method following current AOAC guidelines. A total of 14 laboratories participated, representing government and industry, throughout the United States. One matrix, queso fresco (soft Mexican cheese), was analyzed using two different test portion sizes, 25 and 125 g. Samples representing each test portion size were artificially contaminated with Listeria species at three levels, an uninoculated control level [0 colony-forming units (CFU)/test portion], a low-inoculum level (0.2-2 CFU/test portion), and a high-inoculum level (2-5 CFU/test portion). For this evaluation, 1800 unpaired replicate test portions were analyzed by either the VIDAS LPT or AOAC 993.12. Each inoculation level was analyzed using the Probability of Detection (POD) statistical model. For the low-level inoculated test portions, difference in collaborator POD (dLPOD) values of 0.01, (-0.10, 0.13), with 95% confidence intervals, were obtained for both 25 and 125 g test portions. The range of the confidence intervals for dLPOD values for both the 25 and 125 g test portions contains the point 0.0 indicating no statistically significant difference in the number of positive samples detected between the VIDAS LPT and the AOAC methods. In addition to Oxford agar, VIDAS LPT test portions were confirmed using Agar Listeria Ottavani and Agosti (ALOA), a proprietary chromogenic agar for the identification and differentiation of L. monocytogenes and Listeria species. No differences were observed between the two selective agars. The VIDAS LPT method, with the optional ALOA agar confirmation method, was adopted as Official First Action status for the detection of Listeria species in a variety of foods and environmental samples.

Validation of the Meteosat Second Generation (MSG) fire monitoring product using ground observations over Turkey

Validation of the active fire monitoring (FIR) product from Spinning Enhanced Visible and Infrared Imager sensor of Meteosat Second Generation satellite is performed over Turkey. Ground truth data from the Ministry of Environment and Forestry for the years 2007, 2008, and 2009 is used by taking the active fire months of April–September into consideration. The contingency tables are obtained on a monthly basis and categorical statistics of probability of detection (POD) and false alarm rate (FAR) are derived. The results are introduced for each subclass, namely probable, possible, and combined cases. The highest POD amounts of 8.5, 1.93, and 9.4 % are obtained for the possible, probable, and combined cases, respectively, with the lowest FAR amounts of 70.9, 4.5, and 86.1 % for the same categories. FIR product validation with respect to predefined burnt area thresholds is introduced in the second part of the study in order to investigate burnt area effect in active fire detection. Increasing POD values with respect to burnt area amount is obtained with relatively higher values in probable than possible type. On the other hand, FAR variation is observed to be not correlated with the burnt area amount.

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.

Quantitative comparison of METEOSAT thunderstorm detection and nowcasting with in situ reports in the European Severe Weather Database (ESWD)

Severe thunderstorms constitute a major weather hazard in Europe, with an estimated total damage of €5–8 billion each year. Yet a pan-European database of severe weather reports in a homogeneous data format has become available only recently: the European Severe Weather Database (ESWD). We demonstrate the large potential of ESWD applications for storm detection and forecast evaluation purposes. The study of six warm season severe weather days in Europe from 2007 to 2008 revealed that up to 47% of the ESWD reports were located exactly within the polygons detected by the Cb-TRAM algorithm for three different stages of deep moist convection. The cool-season case study of extratropical cyclone “Emma” on 1 March 2008 showed that low-topped winter thunderstorms provide a challenge for satellite storm detection and nowcasting adapted to warm season storms with high, cold cloud tops. However, this case also demonstrated how ESWD reports alone can still be valuable to identify the hazardous regions along the cold front of the cyclone. The analysis of all warm season (JJA) severe weather days in Europe in 2008 corroborated these findings. There is good agreement between ESWD reports and Cb-TRAM detected thunderstorms, even though no exact correspondence between ESWD reports and Cb-TRAM cells is required (e.g., due to storm morphology). Correspondingly, a large portion of ESWD reports regarded as misses by our strict in/out-of-Cb-TRAM-polygon criterion were still located close to a Cb-TRAM cell. Quantitatively, only the probability of detection (POD) can be evaluated due to the different characteristics of the two data sources. For the JJA 2008 period, the POD for storm detection was 0.24 on average, with maximum values up to 0.58. The respective analysis for the 30 and 60 minute nowcasts yielded average POD values of 0.11 and 0.08, respectively, with maximum values of POD exceeding 0.4 on three different days for the 30 minute nowcast and on one day for the 60 minute nowcast.

Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Abstract Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

Review of models and simulators for NDT reliability (POD)

With the use of probabilistic and risk-based methods by industry it is often necessary to estimate and input values for the reliability of inspection methods typically in terms of probability of detection (POD). In many cases this has been based on expert judgement. In recent years a more structured approach has developed using multi-parameter POD models. POD models have been around since the early 1990s. Inspection reliability has featured recently in a number of important legal cases, which has focused attention on the factors affecting reliability and approaches used to estimate reliability in practical circumstances. This paper will review the development of models for POD in Europe and the USA in the last decade and the current state-of-the-art in inspection reliability modelling by ESR Technology and others. Examples will include models for composite inspection developed for the European Space Agency and a specific model-based and simulator 'POD Generator' approach developed in the offshore industry through the HOIS joint industry project. This allows the POD for corrosion mapping and radiography inspections to be estimated and parametric studies undertaken to estimate the reliability of commercial inspection eguipment in real situations. Other model-based approaches will be assessed. These include model-assisted POD approaches developed in the US aerospace industry, use of transfer functions to extend hit/miss and â versus a POD trial data to new applications, simulated POD trials, the addition of POD modules to established 3D and CAD inspection models and the use of NDT simulators. There are analogies now for POD modelling for NDT methods to the use of finite element analysis for stress analysis. In the early 1990s, FE analysis was treated with some suspicion and normal practice was to carry out expensive large-scale component tests. Nowadays, FE analysis is widely accepted and large-scale testing is carried out as the exception to validate the FE models. The use of model-assisted and model-derived methods to derive POD data has gained significant acceptance and arguably is becoming the preferred method to generate POD values, with POD trials providing model validation. Empirical methods developed in NNDTC to correct POD data for human and environmental effects are also discussed.

Assessing sounding-derived parameters as storm predictors in different latitudes

Many thermodynamic parameters and indices are currently being used as thunderstorm predictors because of their high correlations with the beginning and development of convection. Many of these indices have been developed for one specific area and their forecasting accuracy has generally been assessed in that zone and not in others. It is a highly intriguing question whether there are parameters or indices that may function adequately as thunderstorm predictors, as far as the Probability of Detection is concerned, irrespective of the latitude of the study zone. In order to approach this issue the present study focuses on data from 1692 sounding days in León (Spain), Zaragoza (Spain), Bordeaux (France) and Mendoza (Argentina). Specific discriminant models have already been developed for these areas. When comparing the results found by the different models constructed for each of the four study zones it may be noticed that there are no indices that function extremely well in all of the zones. Rather, a common ingredient pattern is observed for the beginning of convection): atmospheric instability and moist layers in the low atmosphere. It may also be concluded that sounding data alone are not enough to detect accurately the triggering mechanism, which is the third ingredient necessary for convection. The aim of this paper is to build a logistic equation integrating the four study zones. The stepwise method was employed with this purpose because it allows for the gradual inclusion of variables in the final equation according to their discriminating power. The results obtained suggest that Showalter Index and 850 hPa Dew Point Temperature are the variables that best characterize preconvective conditions irrespective of the geographic area considered. The values for POD (Probability of Detection) and FAR (False Alarm Ratio) are acceptable, but they are clearly lower than the ones obtained by each of the models in the study zone for which they were developed.

Predicting the probability of target detection in static infrared and visual scenes using the fuzzy logic approach

Abstract : The probability of detection (PD) of targets in static infrared and visually cluttered scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a robust method for the computation and prediction of the PD targets in cluttered scenes. The Mamdani/Assilian, and Sugeno Neurofuzzy-based models have been investigated. A large set of infrared (IR) imagery and a limited set of visual imagery has been used to model the relationships between several input parameters; the contrast, camouflage condition,range, aspect, width and experimental PD. The fuzzy and neuro-fuzzy models gave predicted PD values that had 0.98 correlation to the experimental PD's. The results obtained indicate the robustness of the fuzzy-based modeling techniques and the applicability of the FLA to those types of problems having to do with the modeling of human-in-the-loop target detection in any spectral regime.

Fuzzy logic approach for computing the probability of target detection in cluttered scenes

A tremendous number of fuzzy system-based applications are currently available. A new application of fuzzy systems theory is presented to compute the probability of target detection in cluttered in- frared images. Both the Mamdani and Assilian and Sugeno's fuzzy- based models have been investigated. A data set for one vehicle has been used to model the relationships between three input parameters, the Der clutter metric, the probability of edge (POE) metric, the Schmieder variance-based clutter metric, and the experimental probabil- ity of detection value. Both fuzzy models have given excellent predictive probability of detection values that are highly correlated to the observed probability of detection values. The preliminary results obtained suggest the adequacy of fuzzy-based modeling techniques to model the percep- tion of targets in infrared and visual scenes. © 1996 Society of Photo-Optical Instrumentation Engineers.

On the Effect of Integration in a Pulsed Radar, Randomly Modulated Carrier

The method of Marcum for finding the probability density function associated with a pulsed radar with integration and a square law detector is extended to include the case of a randomly modulated sinusoidal signal. Two mathematical procedures are developed, the well-known Edgeworth asymptotic series and a convergent series suggested by Varma. Sets of curves are drawn for probability of detection vs average signal-to-noise power ratio. Some previous work has shown very high values for probability of detection resulting if it is assumed necessary to detect only one out of many pulses. Such probability levels are reduced if a slowly varying random amplitude of signal is postulated.