False Alarm Research Articles

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

Pt/Zn-TCPP Nanozyme-Based Flexible Immunoassay for Dual-Mode Pressure-Temperature Monitoring of Low-Abundance Proteins.

Pressure and temperature, as common physical parameters, are important for monitoring human health. In contrast, single-mode monitoring is prone to causing experimental errors. Herein, we innovatively designed a dual-mode flexible sensing platform based on a platinum/zinc-meso-tetrakis(4-carboxyphenyl)porphyrin (Pt/Zn-TCPP) nanozyme for the quantitative monitoring of carcinoembryonic antigen (CEA) in biological fluids with pressure and temperature readouts. The Pt/Zn-TCPP nanozyme with catalytic and photothermal efficiencies was synthesized by means of integrating photosensitizers into porous materials. The flexible sensing system after the antigen-antibody reaction recognized the pressure using a flexible skin-like pressure sensor with a digital multimeter readout, whereas the temperature was acquired via the photoheat conversion system of the Pt/Zn-TCPP nanozyme under 808 nm near-infrared (NIR) irradiation using a portable NIR imaging camera on a smartphone. Meanwhile, the dual-mode flexible sensing system was carried out on a homemade three-dimensional (3D)-printed device. Results revealed that the developed dual-mode immunosensing platform could exhibit good pressure and temperature responses within the dynamic range of 0.5-100 ng mL-1 CEA with the detection limits of 0.24 and 0.13 ng mL-1, respectively. In addition, the pressure and temperature were sensed simultaneously without crosstalk interference. Importantly, the dual-mode flexible immunosensing system can effectively avoid false alarms during the measurement, thus providing great potential for simple and low-cost development for point-of-care testing.

Factors influencing subseasonal predictability of northern Eurasian cold spells

AbstractCold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

Building consumption anomaly detection: A comparative study of two probabilistic approaches

This paper investigates the performance of two probabilistic approaches, Ensemble batch Prediction Intervals (EnbPI) a conformal prediction approach and XGBoost Location, Scale and Shape (XGBoostLSS), in predicting building energy consumption and in detecting systemic anomalies with proposed alarm matrix. The research questions focus on the effectiveness of these models in providing both point and probabilistic predictions and their utility in identifying collective anomalies. Both models showed good point and distribution prediction performance. For example, the observed point prediction had CV-RMSE in the range of 9 to 17%, outperforming recommendations from ASHRAE. Furthermore, a post-processing stage, the alarm matrix, effectively flags collective, repetitive anomalies, thus shifting focus from conventional point anomalies. The EnbPI-based method yields higher recall rates, with a trade-off of having more false alarms, while XGBoostLSS-based method excels in precision, minimizing overlooked alarms. Moreover, a robustness analysis was carried out to evaluate how these models performed when faced with training datasets containing anomalies. The robustness analysis revealed that the EnbPI-based method was more prone to overfitting, meaning its performance actually improved when the training data included some noise. On the other hand, the XGBoostLSS-based method was more stable, performing well with low levels of noise with performance drop when the noise level was high. While the findings contribute significantly to building energy consumption prediction and anomaly detection, future research could address performance in dynamic environments for the methodology and explore continual learning strategies.

Automotive Parts Defect Detection Based on YOLOv7

Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection algorithms have been proven to be ineffective, which often leads to missing detection or wrong identification. To address these issues, this paper introduces a defect detection algorithm based on YOLOv7. To enhance the detection of small objects and streamline the model, we incorporate the ECA attention mechanism into the network structure’s backbone. Considering the small sizes of defect targets on automotive parts and the complexity of their backgrounds, we redesign the neck portion of the model. This redesign includes the integration of the BiFPN feature fusion module to enhance feature fusion, with the aim of minimizing missed detections and false alarms. Additionally, we employ the Alpha-IoU loss function in the prediction phase to enhance the model’s accuracy, which is crucial for reducing false detection. The IoU loss function also boosts the model’s efficiency at converging. The evaluation of this model utilized the Northeastern University steel dataset and a proprietary dataset and demonstrated that the average accuracy (mAP) of the MBEA-YOLOv7 detection network was 76.2% and 94.1%, respectively. These figures represent improvements of 5.7% and 4.7% over the original YOLOv7 network. Moreover, the detection speed for individual images ranges between 1–2 ms. This enhancement in detection accuracy for small targets does not compromise detection speed, fulfilling the requirements for real-time, dynamic inspection of defects.

Phantom perception as a Bayesian Inference problem - a pilot study.

Tinnitus is the perception of a sound in the absence of an external source. Although the role of the auditory system is well investigates, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see if the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioural and electrophysiological responses to the learning and test phases in (i) healthy young adults and (ii) people with continuous subjective tinnitus and matched controls. We observed that in both parts of the study, the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. In patients with tinnitus, we observe no significant difference in behavioural or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms going together with increased evoked activity during the learning and test phases in the visual paradigm. This impresses the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the current study also presents preliminary data supporting evidence that tinnitus patients maybe building stronger perceptual models.

Improving performance of defect detection by setting skewed tolerance and joint tolerances in crimp force monitor

AbstractA Crimp Force Monitor (CFM) detects defects by whether the crimp force curve of the force sensor exceeds the set tolerance, with the goal of low defect miss rate and false alarm rate. In this study, typical defects made by hand are crimped with an automatic crimping machine together with good products to obtain continuous data of the crimping force curves and to process the data. For the peak and area deviations of the crimp force curve, the good products show skew‐normal distribution, and the defective samples show outliers and different deviation intensities in the positive and negative directions. Furthermore, by manually intervening in the batch production of multiple groups of defects and comparing the defect miss rate and false alarm rate of CFM with different tolerance settings, the peak skewed tolerance, joint loose tolerance and joint skewed tolerance tree settings can meet a low defect miss rate and low false alarm rate at the same time. The group test results verify the skew‐normal distribution of crimping force curve deviation rate and the characteristics of deviation intensities, providing reference for the tolerance setting of CFM in automated terminal crimping production.

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process.

Bayesian Control charts are emerging as the most efficient statistical tools for monitoring manufacturing processes and providing effective control over process variability. The Bayesian approach is particularly suitable for addressing parametric uncertainty in the manufacturing industry. In this study, we determine the monitoring threshold for the shape parameter of the Inverse Gaussian distribution (IGD) and design different exponentially-weighted-moving-average (EWMA) control charts based on different loss functions (LFs). The impact of hyperparameters is investigated on Bayes estimates (BEs) and posterior risks (PRs). The performance measures such as average run length (ARL), standard deviation of run length (SDRL), and median of run length (MRL) are employed to evaluate the suggested approach. The designed Bayesian charts are evaluated for different settings of smoothing constant of the EWMA chart, different sample sizes, and pre-specified false alarm rates. The simulative study demonstrates the effectiveness of the suggested Bayesian method-based EWMA charts as compared to the conventional classical setup-based EWMA charts. The proposed techniques of EWMA charts are highly efficient in detecting shifts in the shape parameter and outperform their classical counterpart in detecting faults quickly. The proposed technique is also applied to real-data case studies from the aerospace manufacturing industry. The quality characteristic of interest was selected as the monthly industrial production index of aircraft from January 1980 to December 2022. The real-data-based findings also validate the conclusions based on the simulative results.

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Guided wave propagation (GWP) is commonly employed for the design of SHM systems. However, GWP is sensitive to variations in the material properties, often leading to false alarms. To address this issue, uncertainty quantification (UQ) is employed to improve the reliability of the predictions. Computational structural mechanics is a useful tool for the simulation of GWP. Even so, the application of UQ methods requires numerous solutions, while large-scale, transient GWP simulations increase the computational cost. In this paper, we propose a machine learning (ML)-based reduced order model (ROM), annotated as BO-ML-ROM, to decrease the computational time of the GWP simulation. The ROM is integrated with a Bayesian optimization (BO) framework to adaptively sample the data for the ROM training. The results showed that BO outperforms one-shot sampling methods, both in terms of accuracy and speed. The developed ROM, which is orders of magnitude faster than the original solver, is then applied for forward uncertainty quantification of the GWP in an aluminum plate and a composite panel under varying material properties. The UQ analysis reveals the higher impact of the uncertainties in the wave reflections. Furthermore, to compute the influence of the material properties on the predictions, a sensitivity analysis (SA) is performed using the variance-based Sobol’ indices and the Shapley additive explanations, where it is shown that the uncertainties in the elastic properties have a varying influence across time. The predicted results reveal the efficiency of BO-ML-ROM for the simulation of the GWP and demonstrate its utility for UQ and SA.

Matched Stochastic Resonance Enhanced Underwater Passive Sonar Detection under Non-Gaussian Impulsive Background Noise.

Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with α=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to α=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.

Automated Stand-Alone Bot for Intruder Detection

The Automated Standalone Bot for Intruder Detection System represents a cutting-edge approach to bolstering security measures through the deployment of an intelligent surveillance system. By harnessing sophisticated computer vision techniques, this system is capable of vigilantly monitoring its surroundings and swiftly identifying potential security threats with remarkable accuracy. Through continuous real-time analysis of video feeds and the application of advanced image processing algorithms, the bot can promptly detect any unauthorized individuals or suspicious activities within its designated area of coverage. Upon detecting a potential intruder, the system initiates an immediate response protocol, which may include activating alarms, notifying security personnel, or implementing deterrent measures to deter any further intrusion attempts. What sets this system apart is its ability to continually refine its intruder detection capabilities over time, leveraging machine learning algorithms to adapt to evolving threats and minimize false positives. Moreover, the system demonstrates remarkable discernment by distinguishing between human subjects, animals, and other objects, thereby reducing the occurrence of false alarms and enhancing its overall reliability. This comprehensive approach not only fortifies security measures but also instills a greater sense of confidence in the system's ability to safeguard the premises effectively. Key Words: Automated Standalone Bot, Intruder Detection System, Security Measures, Intelligent System, Computer Vision Techniques, Surveillance, Real-time Analysis, Video Feeds, Image Processing Algorithms, Swift Detection, Unauthorized Individuals, Suspicious Activities, Response Protocol, Alarms, Notification, Deterrent Measures, Machine Learning Algorithms, Adaptation, False Positives, Reliability, Human Subjects, Animals, False Alarms, Premises Security

A silver lining to a busted forecast? Building relationships after the storm through humanising messages

AbstractGrounded in the quiet weather communication typology, we conducted two between‐subjects experiments comparing humanising to organisational voice messages in predicting disaster organisation‐public relationships, publics' message passing intentions, and publics' community resilience perceptions in the U.S. tornado context. Study 1 examines these relationships in the missed event context, where a tornado was not forecasted, but occurred. Study 2 examines these relationships in the false alarm context, where a tornado was forecasted, but did not occur. Results show differing processes across the two studies, with Study 1 results showing direct message effects, but no indirect effects. Study 2 results show indirect effects of the experimental condition on the outcomes via perceptions of conversational human voice. The discussion extends the quiet weather communication typology by theorising how context influences message strategy effectiveness.

Social identity threat attenuates own-race bias in face recognition under the “Asian-Caucasian” context

People typically remember faces of their own race better than the faces of other races, which is known as the own-race bias. Social-cognitive theories on the own-race bias debate if motivation plays a role in racial face recognition under the “Asian-Caucasian” context. We investigated the effect of social-identity-threat based motivation on own-race bias. In Experiment 1, Chinese participants were primed with a negative image of own-race by associating Chinese (i.e., own-race) faces with negative personality traits. Results showed that such social identity threat marginally affect the own-race bias in face recognition sensitivity (Experiment 1a), and it did not influence participants' implicit positive attitudes toward their ethnicity (Experiment 1b). In Experiments 2, 3 and 4, the social identity threat was manipulated by reading an article before the face recognition task. Experiment 2 found that the social identity threat significantly affected the own-race bias in face recognition false alarm. Although we did not find the modulation effect of group on ORB with ANOVA in Experiments 3 and 4, we observed the significant indirect effect of group on ORB through the mediator of generalized threat. Together, the current study provides evidence of social motivation in the own-race bias in the “Asian-Caucasian” setting.

Early fault detection for rolling bearings: A meta‐learning approach

AbstractEarly fault detection (EFD) of rolling bearings aims at detecting the early symptoms of faults by monitoring small deviations of health states. Accurate EFD enables predictive maintenance and contributes to the stability of mechanical systems. In recent years, machine learning based methods have shown impressive performance on EFD. Most of the current machine learning‐based methods assume the availability for a large amount of data. However, in practice, the authors may only have a very limited amount of training data, which makes it hard to learn a reliable machine learning model. To address this concern, in this work, the authors propose to tackle EFD via meta learning. Specifically, the authors first formulate EFD as a few‐shot learning problem and then propose to tackle this problem with a metric‐based meta learning method. Furthermore, ensemble learning is further leveraged to improve the detection robustness. For the proposed method, the distribution difference from the working conditions and the bearings are considered. The experimental results on two bearing datasets show that the proposed method can achieve better EFD performance, that is, detecting incipient faults earlier while bringing in lower false alarms, compared with several frequently used EFD methods.

Disruption detection for a cognitive digital supply chain twin using hybrid deep learning

Recent disruptive events, such as COVID-19 and Russia–Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue.

A novel intrusion detection system based on a hybrid quantum support vector machine and improved Grey Wolf optimizer

AbstractThe Internet of Things (IoT) has grown significantly in recent years, allowing devices with sensors to share data via the internet. Despite the growing popularity of IoT devices, they remain vulnerable to cyber-attacks. To address this issue, researchers have proposed the Hybrid Intrusion Detection System (HIDS) as a way to enhance the security of IoT. This paper presents a novel intrusion detection model, namely QSVM-IGWO, for improving the detection capabilities and reducing false positive alarms of HIDS. This model aims to improve the performance of the Quantum Support Vector Machine (QSVM) by incorporating parameters from the Improved Grey Wolf Optimizer (IGWO) algorithm. IGWO is introduced under the hypothesis that the social hierarchy observed in grey wolves enhances the searching procedure and overcomes the limitations of GWO. In addition, the QSVM model is employed for binary classification by selecting the kernel function to obtain an optimal solution. Experimental results show promising performance of QSVM-IGWO in terms of accuracy, Recall, Precision, F1 score, and ROC curve, when compared with recent detection models.

False alarm suppressing for passive underwater acoustic target detecting with computer visual techniques

Given the complexity of marine environments, the detection of underwater acoustic targets frequently relies on manual visual interpretation of the imagery displayed on monitoring screens, which limits the application of related technologies on unmanned platforms. To replace human visual observation, understanding, and reasoning within underwater unmanned equipment, this study explores an intelligent detection method for Bearing Time Records based on computer vision techniques, drawing inspiration from human visual perception in detecting targets on BTR images. Firstly, an unsupervised learning method is employed to extract the bases representing various signal patterns from BTR images, with clear physical meanings. Subsequently, we utilize both algorithm-driven and data-driven methods for automated base classification, and both methods achieve a remarkable 100% accuracy rate in automatic base classification. In comparison to human visual detection, this automated approach exhibits a false alarm rate of less than 3%.

Infrared small target detection algorithm based on filter kernel combination optimization learning method

Infrared small target detection in complex backgrounds remains a big challenge due to its small size and weak energy, which makes it easy to be submerged in complex backgrounds. Common filtering algorithms are difficult to apply to various complex scenes and generate many false alarms and poor robustness. Therefore, this paper proposes an infrared small target detection algorithm based on filter kernel combination optimization learning method. For the sample image block use typical filter kernels and weight coefficients to calculate the combination of filtering results, and construct the loss function of the results and the true value. The optimal weight coefficients are learned using an optimization method, which effectively achieves the fusion of multiple features, then we build a sample library containing comprehensive priori information. In the implementation, an online inference method based on similarity measure is proposed to match test images with sample library image blocks to obtain optimal combination weights. By utilizing the comprehensive priori information to enhance the adaptive ability of the algorithm for various complex backgrounds. Finally, get the position of the infrared small target. The experimental results show that our algorithm has excellent detection performance for infrared small targets with complex background and low signal to clutter ratio, and the AUC value is 0.9980, which is more robust than other comparative algorithms.