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Fast volumetric non-destructive testing methods are needed, especially for quality control in manufacturing lines. Ultrasonic testing with full waveform evaluation is a promising method for this. However, changes in coupling conditions or environmental factors can significantly alter the ultrasound signal, sometimes more than actual defects. This study investigates the effect of various factors on the ultrasound signal based on a Monte Carlo study with wavefield simulations. The test specimens comprise aluminium plates with holes of varying sizes and positions. Using both experimental as well as simulated data, the performance of two commonly used comparison metrics, namely the ${R^2}$ R 2 score and the magnitude-squared coherence integral, for detecting defects in manufactured parts is evaluated. It was found that the magnitude-squared coherence integral is more robust against random influences than the ${R^2}$ R 2 score. Additionally, factors influencing the entire plate exhibit the most significant impact on the signals. The hole positions and dimensions change the signals and the value of the comparison metrics significantly and are difficult to distinguish by one metric. A deep learning model, however, is capable of performing this task and it outperforms the comparison metrics in defect detection. The performance of the approaches is assessed with probability of detection curves. [ABSTRACT FROM AUTHOR]

DTBird is an automated detection and audio deterrent system designed to discourage birds from approaching spinning wind turbines. With Golden Eagles (Aquila chrysaetos) the focal species of interest, we evaluated DTBird's performance at two commercial facilities, one situated in a desert landscape in California, USA (Manzana site), and the other in a temperate grassland/scrub landscape on a ridgeline above the Columbia River in Washington, USA (Goodnoe Hills site). To evaluate DTBird's detection and deterrent-triggering functions, we used fixed-wing unmanned aerial vehicles (UAVs) as eagle surrogates in experimental flight trials, involving planned transect arrays that supported evaluating DTBird responses within a 240-m-radius expected maximum, hemispheric detection envelope. We quantified the probability of detection and used logistic regression to evaluate the influence of several predictors. We also built a general linear mixed-effects model (GLMM) to evaluate the influence of several environmental covariates and flight metrics on DTBird detection and deterrent-triggering response distances. The estimated probability of detection was similar at the two sites (64–66%), increased from morning through afternoon (effects of sun positioning), and was highest when the target flew at moderate distances from the turbine through the midsection of the camera viewsheds. The GLMM analysis confirmed modest variation relating to the five distinct UAV models used, possibly mimicking variation that would apply to eagles of variable size and coloration. The analysis also demonstrated that response distances averaged marginally shorter at the Manzana site and increased (suggesting improved detectability) under uniform cloud cover and as the UAV travel rate and exposure of the UAV profile to the cameras increased. Our experience also emphasized that effective use of foam-bodied, fixed-wing UAVs as eagle surrogates at wind facilities can be strongly limited by complicated topography that restricts centralized flight operations; intervening obstacles such as overhead powerlines that restrict automated flight missions; and excessive wind, inclement weather, and rough landing conditions around many turbines that can easily lead to UAV crashes and damage. Using eagle-like UAVs with immobile wings as eagle surrogates also might have constrained the insights generated from the study. Nevertheless, the indicated relationships can help future system users understand the environmental conditions in which DTBird and similar automated systems are likely to perform best and other factors that can substantially influence the targeting accuracy of such systems. [ABSTRACT FROM AUTHOR]

This research explores the process of generating artificial training data for the detection and classification of defective areas in X-ray computed tomography (XCT) scans in the agricultural domain using AI techniques. It aims to determine the minimum detectability limit for such defects through analyses regarding the Probability of Detection based on analytic XCT simulations. For this purpose, the presented methodology introduces randomized shape variations in surface models used as descriptors for specimens in XCT simulations for generating virtual XCT data. Specifically, the agricultural sector is targeted in this work in terms of analyzing common degradation or defective areas in rice products. This is of special interest due to the huge biological genotypic and phenotypic variations occurring in nature. The proposed method is demonstrated on the application of analyzing rice grains for common defects (chalky and pore areas). [ABSTRACT FROM AUTHOR]

The growing significance of wind energy in supplying renewable electricity underlines the increasing importance of wind turbine efficiency. Hybrid towers, integrating steel and pre-stressed concrete in a stacked structure, address traditional limitations in nacelle height but face new vulnerabilities, exemplified by a collapse in September 2021. This highlights the crucial need for continuous monitoring, particularly of the tower structure's tendons. This study introduces acoustic emission monitoring as a novel approach for the early detection of wire breaks within the highly stressed tendons of hybrid towers. The investigations described focus on evaluating the suitability of this method for the specific use case and developing a generalized monitoring approach. Accordingly, background noise in an operating wind turbine tower was recorded and analyzed over a year-long operational period. Correlation analyses of these data unveiled intricate relationships between operational parameters and noise levels, with wind speed, rotor speed, and blade pitch angle exerting influence. Laboratory experiments were conducted on a full-scale specimen, and wire breaks were artificially provoked to characterize the damage signal and assess its attenuation in relevant structural components. The experimental results were integrated into a stochastic model to determine feasible sensor distances, aiming for a 90% probability of detection at a 95% confidence level. Low attenuation along the tendon was identified, enabling reliable detection over significant distances. Nevertheless, practical considerations suggest a focus on tendon anchorages, with the potential for grouped monitoring in specific areas to optimize sensor deployment. The study proposes a sensor network configuration to enhance the safety and reliability of wind turbine structures. [ABSTRACT FROM AUTHOR]

The performance of the Fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), the land component of ERA5 (ERA5-Land), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Global Precipitation Climatology Center (GPCC) was evaluated in characterizing various precipitation trend characteristics. The analyses were conducted using 817 rain gauges distributed across a diverse range of climatic conditions throughout Iran. GPCC exhibited superior performance compared to other alternatives in identifying trend directions, correctly recognizing annual, autumnal, wintertime, and springtime precipitation trends in 75.1%, 75.0%, 85.5%, and 61.7% of the studied areas, respectively. Moreover, GPCC outperformed the other options in detecting the significance level of trends. CHIRPS performed less reliably, detecting an overall false positive trend. CHIRPS exhibited an upward trend for heavy precipitation events, contradicting to observed decreasing trends. As for the slope quantity, all alternatives provided unsatisfactory estimates. This indicates that while GPCC can depict the climate change impacts, it failed to provide reliable estimates for the magnitude of changes. These findings underscore that determining the absolute value of a variable does not necessarily guarantee suitability for other analyses. The long-term datasets of GPCC, ERA5, and ERA5-Land were analyzed for 1972–2001 and 1988–2017, revealing that despite poorer performance in the earlier period, they accurately depicted the direction of change in most cases. The results suggest that gridded datasets can accurately portray the effects of climatic changes on precipitation under data limitation. [ABSTRACT FROM AUTHOR]

In this study, we propose a simple spectrum sensing method based on exploiting the properties of a group‐delay phaser. Following the theory that an additive white noise should have a flat spectrum over the band of interest, which is not the case for most data‐modulated signals, the spectrum shape of input waveforms has been the test variable. The latter enables a clear distinguishing method between a noise background and a communication signal of a transmission body operating over the desired band. The phaser scatters the frequency components of received signals in time space, allowing a time‐domain inspection of the corresponding spectral response. The accurate closed‐form analytical expression for the probability of detection in an additive white Gaussian noise (AWGN) channel has been derived, in addition to the false alarm probability. The probability of detection has been studied versus signal‐to‐noise ratio (SNR) in AWGN and multipath channels. As well, the receiver operating characteristic (ROC) curves have been plotted for different values of SNR. A good performance has been achieved by our scheme, which has recorded a detection probability of 0.86 for a false alarm probability of 0.1, and further shown a kind of robustness against noise uncertainties. Experimental works have eventually been conducted, and the empirical results also validate the effectiveness of the elaborated approach. An improvement of around 30% in the probability of detection has been realized over the energy‐based sensing technique, at low measures of false alarm probability. [ABSTRACT FROM AUTHOR]

In this paper, a novel and exact mathematical formulation of three mean level constant false alarm rate (CFAR) detectors has been developed considering a homogeneous gamma-distributed (GM) clutter. These detectors include the cell averaging (CA), the greatest-of (GO), and the smallest-of (SO)-CFAR detection schemes. To do so, we derived closed-form expressions for the probability density function (pdf), and the cumulative distribution function (cdf) of the sum of an exponential fluctuating target submerged in gamma-distributed sea clutter. Then, we developed exact and explicit expressions for the probability of false alarm (Pfa) and probability of detection (Pd) of the optimal fixed threshold detector and the three considered CFAR detectors. The correctness of our expressions is then examined and validated via numerical simulations. Assuming a homogeneous GM background, the detection performance of these three detectors is carried out, discussed, and compared with that of the optimal detector. Experimental results with Sentinel-1 synthetic aperture radar (SAR) data demonstrate the effectiveness of the proposed closed-form expressions and show that the considered CFAR schemes are efficient for ship detection in sea clutter backgrounds. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a notable technology for enhancing spectrum usage in wireless communication. On the other hand, cognitive radio (CR) networks are also renowned technology for increasing spectrum efficiency. However, fifth-generation wireless networks cannot provide a dynamic wireless environment. This barrier is overcome by sixth-generation (6G) wireless networks. In 6G, a dynamic wireless environment can be achieved by an intelligent reflecting surface (IRS). IRS is an eminent technology that enhances the overall quality of experience in wireless systems. This paper presents users' performance analysis in IRS-aided NOMA-based 6G CR networks to capitalize on these technologies. The most popular five machine learning (ML)-based classifiers have been considered to sense the feature of the spectrum and evaluate the performance of the IRS-aided NOMA-based 6G CR network for the probability of detection, throughput, and energy efficiency. The simulation results have been validated for the proposed network with and without ML-based classifiers. Further, the performance of the proposed network has been tested for the different ratios of sensing time to total time, probability of false alarms, and different signal sizes of the CR network. The time complexity of the proposed network has been evaluated and found that the network has satisfactory inference time. The simulation results also suggest that the proposed network may fulfill the spectrum, energy, and reliability requirements of the 6G wireless networks. [ABSTRACT FROM AUTHOR]

This paper presents an energy detection-based spectrum sensing approach implemented on the licensed Low Power Wide Area Network (LPWAN) i.e. Narrow Band-IoT communication model. Energy detection-based spectrum sensing depends upon the perceived signal strength of a primary user. Current work utilizes the computed Primary User's SNR present in NB-IoT network to evaluate the probability of detection at a given sampling time (NS), distance and defined probability of false alarm (PFA). Effect of multiple relays in spectrum detection performance is also investigated. Furthermore, for optimal energy detection relay and non-relay-based NB-IoT models are compared and analyzed using MATLAB. Performance evaluation of spectrum detection in presence of Noise uncertainty and Dynamic threshold for differing values of distance, sensing time (NS) and SNR in terms of Probability of False Alarm (PFA) and Probability of Detection (PD) has also been investigated. [ABSTRACT FROM AUTHOR]

The traditional probability of detection (POD) method, as described in the Department of Defense Handbook MIL-HDBK-1823A for nondestructive evaluation systems, does not take the time dependency of data collection into account. When applied to in situ sensors for the measurement of flaw sizes, such as fatigue-induced crack length and corrosion-induced mass loss, the validity and reliability of the traditional method is unknown. In this paper, the POD for in situ sensors and their associated reliability assessment for detectable flaw sizes are evaluated using a size-of-damage-at-detection (SODAD) method and a random parameter model (RPM). Although applicable to other sensors, this study is focused on long-period fiber gratings (LPFG) corrosion sensors with thin Fe–C coating. The SODAD method uses corrosion-induced mass losses when successfully detected from different sensors for the first time, while the RPM model considers the randomness and difference between mass loss datasets from different sensors. The Fe–C coated LPFG sensors were tested in 3.5 wt.% NaCl solution until the wavelength of transmission spectra did not change. The wavelength shift of 70% of the tested sensors ranged from 6 to 10 nm. Given a detection threshold of 2 nm in wavelength, the mass losses at 90% POD are 31.87%, 37.57%, and 34.00%, which are relatively consistent, and the upper-bound mass losses at 95% confidence level are 33.20%, 47.30%, and 40.83% from the traditional, SODAD, and RPM methods, respectively. In comparison with the SODAD method, the RPM method is more robust to any departure from model assumptions since significantly more data are used. For the 90% POD at 95% confidence level, the traditional method underestimated the mass loss by approximately 19%, which is unconservative in engineering applications. [ABSTRACT FROM AUTHOR]

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection. [ABSTRACT FROM AUTHOR]

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Lane changing behavior (LCB) prediction is a crucial functionality of advanced driver-assistance systems and autonomous vehicles. Predicting whether or not the driver of a considered ego vehicle is likely to change lanes in the near future plays an important role in improving road safety and traffic efficiency. Understanding the underlying intentions behind the driver’s behavior is an important factor for the effectiveness of assistance and monitoring systems. Machine learning (ML) algorithms have been broadly used to predict this behavior by analyzing datasets of traffic and driving data related to the considered ego vehicle. However, this technology has not yet been widely adopted in commercial products. Further improvements in these algorithms are necessary to enhance their robustness and reliability. In some domains, receiver operating characteristic and precision-recall curves are commonly used to evaluate ML algorithms, not considering the effects of process parameters in the evaluation, while it might be necessary to access the performance of these algorithms with respect to such parameters. This paper proposes the use of deep autoencoders to extract multi-level features from datasets, which can then be used to train an ensemble of classifiers. This allows for taking advantage of high feature-extraction capabilities of deep learning models and improving the final result using ensemble learning techniques. The concept of probability of detection is used in combination with the networks employed here to evaluate which classifiers can detect the correct LCB better in a statistical sense. Applications on data acquired from a driving simulator show that the proposed method can be adopted to improve the reliability of the classifiers, and ensemble ANNs perform best in predicting the upcoming human behavior in this dynamical context earlier than 3 s before the event itself.

Repeated inspections have been reported to improve the reliability of nondestructive inspection and can be evaluated by multiplying the likelihood function. However, repeated inspections conducted by a single inspector may not be independent, because the subsequent inspections may be influenced by previous inspection results. The probability of detection (POD) quantifies the sensitivity and reliability of an inspection system. In this study, eddy-current inspection data were used to assess the effect of repeated inspections on POD improvement. Specifically, repeated measures correlation (RMC) analysis was performed, which did not violate the assumption of independence to analyze intra-individual association, considering the nonindependence of repeated measures. Nonindependent repeated inspections performed using a combination of two datasets reduced the uncertainty in POD. Moreover, RMC yielded further improvements in POD and reduced the uncertainty. [ABSTRACT FROM AUTHOR]

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system. [ABSTRACT FROM AUTHOR]

Lane changing behavior (LCB) prediction is a crucial functionality of advanced driver-assistance systems and autonomous vehicles. Predicting whether or not the driver of a considered ego vehicle is likely to change lanes in the near future plays an important role in improving road safety and traffic efficiency. Understanding the underlying intentions behind the driver's behavior is an important factor for the effectiveness of assistance and monitoring systems. Machine learning (ML) algorithms have been broadly used to predict this behavior by analyzing datasets of traffic and driving data related to the considered ego vehicle. However, this technology has not yet been widely adopted in commercial products. Further improvements in these algorithms are necessary to enhance their robustness and reliability. In some domains, receiver operating characteristic and precision-recall curves are commonly used to evaluate ML algorithms, not considering the effects of process parameters in the evaluation, while it might be necessary to access the performance of these algorithms with respect to such parameters. This paper proposes the use of deep autoencoders to extract multi-level features from datasets, which can then be used to train an ensemble of classifiers. This allows for taking advantage of high feature-extraction capabilities of deep learning models and improving the final result using ensemble learning techniques. The concept of probability of detection is used in combination with the networks employed here to evaluate which classifiers can detect the correct LCB better in a statistical sense. Applications on data acquired from a driving simulator show that the proposed method can be adopted to improve the reliability of the classifiers, and ensemble ANNs perform best in predicting the upcoming human behavior in this dynamical context earlier than 3 s before the event itself. [ABSTRACT FROM AUTHOR]

Probability of detection (POD) is an acknowledged mean of evaluation for many investigations aiming at detecting some specific property of a subject of interest. For instance, it has had many applications for Non-Destructive Evaluation (NDE), aimed at identifying defects within structural architectures, and can easily be used for structural health monitoring (SHM) systems, meant as a compact and more integrated evolution of the former technology. In this paper, a probability of detection analysis is performed to estimate the reliability of an SHM system, applied to a wing box composite spar for bonding line quality assessment. Such a system is based on distributed fiber optics deployed on the reference component at specific locations for detecting strains; the attained data are then processed by a proprietary algorithm whose capability was already tested and reported in previous works, even at full-scale level. A finite element (FE) model, previously validated by experimental results, is used to simulate the presence of damage areas, whose effect is to modify strain transfer between adjacent parts. Numerical data are used to verify the capability of the SHM system in revealing the presence of the modeled physical discontinuities with respect to a specific set of loads, running along the beam up to cover its complete extension. The POD is then estimated through the analysis of the collected data sets, wide enough to assess the global SHM system performance. The results of this study eventually aim at improving the current strategies adopted for SHM for bonding analysis by identifying the intimate behavior of the system assessed at the date. The activities herein reported have been carried out within the RESUME project. [ABSTRACT FROM AUTHOR]

In nondestructive testing (NDT), ensuring defect detection, measurement accuracy, and reliability guarantees various components' structural integrity and safety. The Probability of Detection (POD) concept has emerged as a fundamental measure of the effectiveness of an inspection technique in identifying defects. Since NDT plays a crucial role in aerospace, manufacturing, and infrastructure industries, enhancing POD has become critical. POD refers to the likelihood that a flaw or defect of a certain size will be detected using the NDT technique. The "â versus a" and the "hit/miss" methods are particularly notable among the commonly employed POD estimation methods. The POD curve is determined based on crack size measurements in the "â versus a" approach, typically used in ultrasonic testing. On the other hand, the "hit/miss" method establishes the POD curve by analysing binary outcomes, where a "hit" signifies successful detection and a "miss" denotes detection failure. This review focuses on POD in the context of NDT, specifically in phased array ultrasonic corrosion mapping (PAUCM), to uncover current uncertainty parameters and explore an innovative avenue for enhancing POD assessment by incorporating the material surface temperature as an additional parameter. [ABSTRACT FROM AUTHOR]

The assessment of safety-critical components for fatigue applications is a key requirement for metal additive manufacturing (AM) applications. Material anomalies play a relevant role in determining the fatigue resistance properties of a component. X-ray computed tomography (CT) helps collect important information on these flaws, such as their size and position within a part.In this study, we discuss how to employ anomaly data detected on an AlSi10Mg bracket manufactured by laser-powder bed fusion to describe the prospective allowable life of a component under a given operating condition.A statistical analysis was conducted on the specimens and component to derive the correlation between different resolution scans and analyze the uncertainties of the micro-CT measurements. The full-scale non-destructive evaluation (NDE) can be constrained to large voxel sizes. Eventually, the authors proposed a fully probabilistic route for assessment instead of a simple deterministic assessment based on safety factors. This assessment enables designers to consider the uncertainties of the assessment (uncertainties of micro-CT detection and the model for fatigue strength).

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Abstract Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was

Abstract Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony. The two most parsimonious N‐mixture models (i.e. AICc) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators.

The growing significance of wind energy in supplying renewable electricity underlines the increasing importance of wind turbine efficiency. Hybrid towers, integrating steel and pre-stressed concrete in a stacked structure, address traditional limitations in nacelle height but face new vulnerabilities, exemplified by a collapse in September 2021. This highlights the crucial need for continuous monitoring, particularly of the tower structure’s tendons. This study introduces acoustic emission monitoring as a novel approach for the early detection of wire breaks within the highly stressed tendons of hybrid towers. The investigations described focus on evaluating the suitability of this method for the specific use case and developing a generalized monitoring approach. Accordingly, background noise in an operating wind turbine tower was recorded and analyzed over a year-long operational period. Correlation analyses of these data unveiled intricate relationships between operational parameters and noise levels, with wind speed, rotor speed, and blade pitch angle exerting influence. Laboratory experiments were conducted on a full-scale specimen, and wire breaks were artificially provoked to characterize the damage signal and assess its attenuation in relevant structural components. The experimental results were integrated into a stochastic model to determine feasible sensor distances, aiming for a 90% probability of detection at a 95% confidence level. Low attenuation along the tendon was identified, enabling reliable detection over significant distances. Nevertheless, practical considerations suggest a focus on tendon anchorages, with the potential for grouped monitoring in specific areas to optimize sensor deployment. The study proposes a sensor network configuration to enhance the safety and reliability of wind turbine structures.

Individual responses to changes in the environment by different species drive a better understanding of the dynamics of diversity and habitat filtering. Hypotheses based on functional and morphometric characteristics of the species are especially useful for accounting for different interpretations of biological responses.This study aimed to determine the responses of semiaquatic species of insects to habitat changes from the elaboration of hypotheses based on the importance of species traits facing environmental variations. We tested these hypotheses with maximum likelihood models to explain the occurrence of the species in streams of the central Amazon region.In a total of 17 collected Gerromorpha species, we used 14 to develop five predictive models of occurrence considering the bionomic characteristics of each species and their possible relationships with habitat changes in 33 streams in the central Amazon region. We used maximum likelihood models to assess the fit of the models to the observed occurrence, with environmental characteristics as covariates affecting the occurrence probability, and sampling effort affecting the detection probability.A total of five species exhibited changes in the probability of occurrence in streams related to an environmental condition (riparian forest disturbance, flow heterogeneity, and surface habitat heterogeneity). The probability of occurrence tended to reduce for four of the five species and increase for one with increase of environmental impacts. Furthermore, the sampling effort covariate did not affect the probability of detection in most species, indicating that the group showed a high detection.The use of functional characteristics of Gerromorpha species to develop ecological hypotheses proved relevant and indicated unexpected relationships, expanding our knowledge of this community of aquatic insects. Therefore, including the individual responses of organisms to environmental variations helps in studies on aquatic biodiversity, both for basic ecology and for conservation and bioassessment. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method's performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods. [ABSTRACT FROM AUTHOR]

Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size.We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony.The two most parsimonious N‐mixture models (i.e. AICc) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size.Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators. [ABSTRACT FROM AUTHOR]

This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is "model-assisted," as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is "predictive" in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization. [ABSTRACT FROM AUTHOR]

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection.

ABSTRACT: The early detection of major mastitis pathogens is crucial for the udder health management of dairy herds. Testing of pooled milk samples, either individual test-day cow samples (TDCS) or aseptically collected pre-milk quarter samples (PMQS) may provide an easy to use and cost-effective group level screening tool. Therefore, the aim of this study was (1) to evaluate the sensitivity (Se) and specificity (Sp) of 2 commercial multiplex real-time PCR test kits applied to pooled milk samples using a Bayesian latent class analysis and (2) to estimate the probability of detection in relation to the pool size and the number of cows positively tested by bacteriological culture (BC) within a pool. Pools of 10, 20 and 50 cows were assembled from 1,912 test-day samples and 7,336 PMQS collected from a total of 2,045 cows from 2 commercial dairy farms. Two commercial quantitative real-time PCR kits were applied to detect Staphylococcus aureus, Streptococcus agalactiae, and Streptococcus dysgalactiae in the pooled samples, and a BC was applied to PMQS yielding a cumulative pool result. A pool was considered BC-positive if it contained at least one BC-positive PMQS. Pathogens were more frequently detected in the PMQS pools than in the TDCS pools. Pools of 10 cows showed the highest probability of detection irrespective of sample type or type of PCR kit compared with larger pool sizes. Estimation with a Bayesian latent class analysis resulted in a median Se in PMQS pools of 10 cows for Staph. aureus of 63.3% for PCR kit I, 78.1% for PCR kit II, and 95.5% for BC; the Sp values were 97.0%, 97.6%, and 89.1%, respectively. The estimated median Se for Strep. species for PCR kits ranged between 77.5 and 85.6% and for BC between 73.7% and 79.2%; the median Sp values ranged between 93.6 and 99.2% for PCR kits, and between 96.9% and 97.4% for BC. In addition, the probability of detection increased with an increasing number of BC-positive cows per pool. To achieve a probability of detection of 90%, the estimated number of positive cows in PMQS pools of 10 cows for kit I was 4.1 for Staph. aureus, 1.5 for Strep. agalactiae, and 1.3 for Strep. dysgalactiae; for the equivalent TDCS pools and pathogens, 6.9, 1.9, and 2.0 positive cows were required, respectively. For Kit II and PMQS pools, the number of positive cows required was 2.8 for Staph. aureus, 1.4 for Strep. agalactiae, and 1.2 for Strep. dysgalactiae; for the equivalent TDCS pools and pathogens, 5.3, 1.8, and 2.0 positive cows were required, respectively. In conclusion, the type of samples used for pooling, the pool size and the number of infected cows per pool determine the probability of detecting an infection with major mastitis pathogens within a pool by PCR testing.

Visible particles are a critical quality attribute for parenteral products and must be monitored. A carefully designed, executed, and controlled drug product manufacturing process including a final 100 % visual inspection and appropriate end-product controls ensures that visible particles are consistently minimized and demonstrates that the injectable DP is practically free from visible particles. Visual inspection, albeit appearing as a simple analytical procedure, requires several technical and operational controls to ensure adequate performance. To gather new data on particle visibility and shed light on this decade-old challenge, a multi-company blinded visual inspection threshold study was conducted. A major goal of the study was visual assessment of several particle types of different sizes in small volume vials, as a challenging configuration for visual inspection, across 9 biopharmaceutical companies in order to determine the visibility limit. The study results provide key insights into limitations and challenges of visual inspection, namely, no universal visibility limit can be applied to all particle types as the detectability varies with particle type, number, and size. The study findings underscore the necessity of setting realistic expectations on size-based visibility limits in visual inspection, robust procedures for analyst training and qualification, and harmonization of guidelines globally. [ABSTRACT FROM AUTHOR]

This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity. [ABSTRACT FROM AUTHOR]

The dye penetrant inspection method used to reveal surface weld discontinuities is an important factor for quality verification in the manufacture of structural components; however, it is probable that certain size-dependent discontinuities may or may not be detected. Then, how reliable can it be? In this sense, the objective of the research has been to estimate the reliability of dye penetrant inspection to detect discontinuities in relation to their size. Six experimental tests were performed by three inspectors, with visible and fluorescent dye penetrants, on twenty welded joints of similar surface characteristics, containing 63 typical weld discontinuities arranged according to shape and size, whereas POD reliability quantitative estimates were developed by the hit-or-miss statistical method. For the test system, the fluorescent penetrants, due to their greater sensitivity compared to the visible ones, registered greater reliability in revealing smaller discontinuities. The POD estimators were a50 (1.469 mm < 1.978 mm), a90 (6.348 mm < 7.474 mm), a90/95 (14.58 mm < 15.77 mm). Fluorescent dyes allowed a higher rate and probability of detection; both factors showed a tendency to increase as the discontinuities size increased. [ABSTRACT FROM AUTHOR]

The integration of optical fibre communication with multiple input multiple output-nonorthogonal multiple access (MIMO-NOMA) waveforms in cognitive radio (CR) systems is examined in this study. The proposed system leverages the advantages of optical fibre, including high bandwidth and immunity to electromagnetic interference to facilitate the transmission and reception of MIMO-NOMA signals in a CR environment. Moreover, MIMO-NOMA signal was detected and analysed by the hybrid-discrete cosine transform-Welch (H-DCT-W) method. Based on the modes results, a detection probability greater than 0.96%, a false alarm probability equal to 0.06, and a global system error probability equal to 0.09% were obtained with a signal-to-noise ratio (SNR) less than 0 dB, while maintaining a simple level of complexity. The results obtained in this paper indicate the potential of the optical fibre-based MIMO-NOMA system based on H-DCT-W technology in CR networks. Therefore, its suitability for practical CR applications is demonstrated by the improvements obtained in false alarms, detection probability, and error rates at low levels of SNR. This study contributes to the development of efficient and reliable wireless communication systems by linking cooperation and synergy concerning MIMO-NOMA, optical fibres, as well as the proposed detection technique (H-DCT-W). [ABSTRACT FROM AUTHOR]

Introduction/purpose: Measuring target acquisition performance in imaging systems with human-in-the-loop plays an essential role in military applications. This paper presents an extended review on the application of image clutter metrics for target acquisition, with the aim of using objective measures to predict the detection probability, false alarm probability and mean search time of the target in the image. Methods: To determine the degree of clutter, simple features on the global (picture-wise) and local (target-wise) level were used as well as contrastbased clutter metrics, target size and metrics derived from image quality assessment measures. Along with the standard ones, the features derived from the distribution of mean subtracted contrast normalized coefficients were also used. To compare the results of the objective scores and the experimental results obtained on the publicly available Search_2 dataset, regression laws accepted in the literature were applied. Linear correlations and rank correlations were used as quantitative measures of agreement. Results: It is shown that the best agreement with target acquisition indicators is obtained by applying clutter metrics derived from image quality assessment measures. The correlation with the results of subjective tests is up to 90%, which indicates the need for further research. A special contribution of the paper is the analysis of the target acquisition prediction performance using simple features at the global and local level, where it is shown that the prediction performance can be improved by determining the features around the target. Furthermore, it was shown that the false alarm probability and the probability of detection can be predicted based on the mean target search time in the image with a probability higher than 90%. Conclusion: In addition to obtaining a high degree of agreement between the objective metrics of clutter and the results of subjective tests (up to 90%), there is a need to improve the existing and develop new metrics as well as to conduct new subjective tests.

This paper describes, for the first time, the application of an Elastodynamic Boundary Element Method (BEM) in Laplace Domain for the Structural Health Monitoring (SHM) of poly-crystalline materials. The study focuses on Ultrasonic Guided Wave (UGW) propagation and investigates the wave–material interactions at micro-scale. The study aims to investigate the interaction of UGWs with assessing micro-structural features such as grain size, morphology, degradation, and flaws. Numerical simulations of the most common micro-structural features demonstrate the accuracy and validity of the proposed method. Particular attention is paid to the study of porosity and its influence on material macro-properties. Different crystal morphologies such as cubic, rhombic, and truncated octahedral are considered. The detection of voids based on the changes in the amplitude and Time of Arrival (ToA) of the backscattered signal is investigated. The study also considers inter-granular cracks, which cause laceration, and examines flaw position/orientation, length, and distance from a specific reference. Furthermore, a framework is proposed for generating Probability of Detection (PoD) curves using numerical simulations. Experimental tests in pristine conditions are shown to be in good agreement with the numerical simulations in terms of ToA, signal amplitude, and wave velocity. The numerical simulations provide insights into wave propagation and wave–material interactions, including different types of defects at the micro-scale. Overall, the BEM and UGW methods are shown to be effective tools for better understanding micro-structural features and their influence on the macro-structural properties of poly-crystalline materials. [ABSTRACT FROM AUTHOR]

In this article, we use the saddlepoint approximation method to compute the cumulative distribution function of non-central complex Wilks statistic. This approximation does not require conditions on the dimension, degrees of freedom, and non-centrality matrix. We also compare our results with known approximations to show the accuracy of our approximation. For applications, we compute the power functions of the Wilks statistic in the complex multivariate analysis of variance and the probability of detection of the Wilks detector in signal detection in noise model. [ABSTRACT FROM AUTHOR]