Missed Alarm Rate Research Articles

Moving target detection based on multi-satellite joint passive microwave imaging

Using satellite formations to form a space-based passive interferometric imaging system can achieve high spatial resolution, but the sparse detection baseline will cause aliasing in the inversion image, affecting the detection of single-pixel point targets. Considering the slowly varying characteristics of the observation area, a target detection method based on image sequence is proposed. The background is estimated by multi-frame images, and the background of the inversion image is eliminated; energy is gathered based on sidelobe characteristics, the noise in the target area is estimated, and candidate targets are selected; the motion trajectory of the target is obtained by combining the temporal motion characteristics, and the detection of moving point targets is realized. Taking the formation system composed of 10 geostationary orbit satellites as an example, 300 simulation experiments are carried out for the detection of 50 ship point targets. The results show that the proposed method can realize point target detection in a large field of view, with an average false alarm rate 14.5%and an average missed alarm rate 19.5%.

Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack.

Monthly Maximum Magnitude Prediction in the North–South Seismic Belt of China Based on Deep Learning

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes.

Optimizing multivariate alarm systems: A study on joint false alarm rate, and joint missed alarm rate using linear programming technique

In modern complex industrial systems, multiple process variables interact with one another. The role of alarm systems in ensuring the safety of these systems is of utmost importance. Consequently, there is an increasing value placed on the assessment of the performance of multivariate alarm systems. As the dimensions of the system and the number of variables grow, designing optimal parameters for the multivariate alarm system using traditional approaches such as probability density function estimation becomes increasingly convoluted. In this paper, an approximate method is proposed for calculating two indices known as the Joint False Alarm Rate (JFAR) and Joint Missed Alarm Rate (JMAR), which are used to evaluate the performance of multivariate alarm systems. These indices are computed using the multivariate Markov chain method. The Markov chain is constructed by solving an optimal Linear Programming (LP) problem. Subsequently, joint indices are defined based on steady state estimations of a multivariate Markov chain. To validate the theoretical results obtained on the JFAR and JMAR and to demonstrate the proposed performance assessment and alarm system design procedures, numerical example and an industrial case study are provided.

Fault diagnosis method for redundancy sensors of electric actuator

In order to solve the singular fault modes of redundant position sensors in electric actuators, a fault diagnosis method based on Hall sensor analytical model is proposed on the basis of traditional redundancy comparison algorithm. Through the complete self-monitoring design of Hall sensor and the analytical model design, non-similar redundancy signals of sensors are formed. At the same time, a hypothesis testing method based on sliding data window is adopted, and by adjusting the width of the sliding window to meet the indicators of false alarm rate and missed alarm rate, a fault diagnosis algorithm for position sensors is formed as a whole. Finally, an algorithm validation was conducted for the 2∶2 singular fault mode of the position sensor. The results showed that the fault diagnosis algorithm based on the Hall sensor model can effectively solve the problems caused by 2∶2 singular faults, greatly improve the availability of sensor signals, and ensure the control accuracy and fault tolerance of the servo closed-loop.

Intelligent Monitoring Model for Lost Circulation Based on Unsupervised Time Series Autoencoder

Lost circulation, a common risk during the drilling process, significantly impacts drilling safety and efficiency. The presence of data noise and temporal evolution characteristics pose significant challenges to the accurate monitoring of lost circulation. Traditional supervised intelligent monitoring methods rely on large amounts of labeled data, which often do not consider temporal fluctuations in data, leading to insufficient accuracy and transferability. To address these issues, this paper proposes an unsupervised time series autoencoder (BiLSTM-AE) intelligent monitoring model for lost circulation, aiming to overcome the limitations of supervised algorithms. The BiLSTM-AE model employs BiLSTM for both the encoder and decoder, enabling it to comprehensively capture the temporal features and dynamic changes in the data. It learns the patterns of normal data sequences, thereby automatically identifying anomalous risk data points that deviate from the normal patterns during testing. Results show that the proposed model can efficiently identify and monitor lost circulation risks, achieving an accuracy of 92.51%, a missed alarm rate of 6.87%, and a false alarm rate of 7.71% on the test set. Compared to other models, the BiLSTM-AE model has higher accuracy and better timeliness, which is of great significance for improving drilling efficiency and ensuring drilling safety.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

A forward design approach for fault thresholds in redundant flight control systems

Modern fly-by-wire flight control systems use redundancy configurations to meet high safety requirements. The fault threshold value of the redundancy monitoring and voting algorithm affects the false alarm rate and missing alarm rate of the flight control system and the excessive false alarm rate and missing alarm rate reduce the reliability of the flight control system. Traditionally, the design of fault threshold is based on the empirical trial-and-error method, which suffers from the problem of unclear optimization direction. Forward design is needed to improve design efficiency and quality. In this paper, a forward design method for fault detection threshold is proposed to determine the upper limit value of the fault threshold by analyzing the effect of the voting signal change on the transient response of the aircraft when a channel fault occurs. The lower limit value of the fault threshold is determined by analyzing the channel history data. The effectiveness of the design method is demonstrated by digital simulation.

Data-Driven-Based Intelligent Alarm Method of Ultra-Supercritical Thermal Power Units

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms.

Construction of a Computer-Aided Dual-Parameter Landslide Prediction Method

This study focuses on the typical geological region of granitic weathered soil in the hilly areas of southern China, integrating computer-assisted statistical methods and intelligent computing techniques to analyze geological disaster data and hourly precipitation data from 2016 to 2022. The research indicates that in the study area, geological disasters triggered by precipitation generally occur within 48 hours following the precipitation event. For 90% of the events, the average rainfall intensity is concentrated between 1.2 to 27.7 mm/h, with 72% having an average rainfall intensity of less than 10 mm/h, and only 13% exceeding 20 mm/h. The influence of prior precipitation mainly depends on the cumulative effect of effective rainfall over the previous 11 days. Based on these findings, this paper constructs a meteorological risk prediction model for geological disasters using two parameters: the average rainfall intensity (I) and the duration of rainfall (D). After comparing the prediction results with 62 test samples from 2022, it is noted that the method's TS score is superior to other prediction methods, significantly reducing the false alarm and missed alarm rates for meteorological forecasts in the granitic weathered soil geological region. Moreover, the computer implementation of this model demonstrates its efficiency and reliability in handling large-scale geological data, providing strong computational support for geological disaster risk assessment.

Graph-based phishing detection: URLGBM model driven by machine learning

Phishing attacks are a form of social engineering that involves the transmission of deceptive communications imitating a trustworthy source to deceive users into revealing confidential information. Antiphishing systems have made significant strides in recent years, allowing internet users to secure confidential and private information against such attacks. In this study, we propose a URL graph representation based on a random walk algorithm, specifically PageRank, for weighting URL tokens. To create the graph, an imaginary walker visits the URL tokens one at a time and assigns a value to each token based on the probability of encountering the target URL during the walk. We studied different random walk (rw) variations and their effects on the URL string. The BM25 algorithm was employed to produce a sparse matrix for the classification task from the token scores obtained. Experiments conducted with logistic regression revealed that the proposed model achieved an accuracy of 98.98%, a false alarm rate of 1.72%, and a missing alarm rate of 0.302%. The model also attained a 97.17% accuracy on a benchmark dataset.

A Long-and Short-term Hot User Identification Method Based on Local Outlier Density

Power systems have many users; however, there are relatively few users focused on high-quality power supplies (namely, hot users), such as external query users, electricity theft, and information fraud from billions of users. Starting from power load data, this paper postulates that abnormal electricity load users are essential components of hot users. According to the characteristics of regional power loads, this paper first builds a cross-judgment method for abnormal loads to improve the identification accuracy, including spatial and temporal abnormal load detection methods. As the spatial method, the abnormal load detection based on the local outlier factor (LOF) algorithm detects different users from similar users using several constructed dimensional features. As the temporal method, dynamic abnormal load detection based on the short-term sequential (SS) algorithm is designed to improve the LOF missing alarm rate and computing efficiency with multi-dimensional features. This also accomplished abnormal load detection from historical dimensions. A long- and short-term hot user detection method is designed based on the outlier density in response to the insufficient support of single-point abnormal loads to judge hot users. Finally, experiments on the user load data in a certain region within a year test the abnormal detection method for long- and short-term hot user detection based on local outlier density (LOD). The experimental results indicate that this method can detect abnormal loads and search hot users. The long- and short-term hot user detection method based on the LOD can meet practical needs when only using user load data and has good generalization abilities.

Variational autoencoder-assisted unsupervised hardware fingerprint authentication in a fiber network.

Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters. The feasibility of the proposed scheme is experimentally verified by the successful classification of eight optical transmitters after a 20 km standard single-mode fiber (SSMF) transmission, to distinguish efficiently the rogue from legal devices. A recognition accuracy of 99% and a miss alarm rate of 0% are achieved even under the interference of multiple rogue devices. Moreover, the proposed scheme is verified to have a comparable performance with the results obtained from supervised learning.

Interactive design of intelligent wearable products based on network communication technology

At this stage, network communication technology is increasingly mature, and intelligent wearable products are also widely used in human daily life. Wearable products are popular with users because of their numerous types, complete functions and convenient services. Wearable products integrate interaction technology, and users can interact with products. However, how to improve the user’s interaction experience and reduce the user’s cognitive burden on the interaction interface is an urgent problem in the current product interaction design. Therefore, based on the analysis of the types and related technologies of wearable products, this paper made a specific analysis of the interaction design of wearable products, and established an interaction design model. At the same time, the wearable fall detection system was also tested by machine learning algorithm. The experimental results showed that the average test result of the algorithm in this paper was 87.39%, while the average test result of the traditional algorithm was 83.79%. In terms of the missed alarm rate of fall detection, the average test result of this algorithm was 6.4%, while the average test result of the traditional algorithm was 12.33%. In terms of fall detection sensitivity, the average test result of this algorithm was 92.50%, while the average test result of the traditional algorithm was 88.24%. Compared with traditional algorithms, this method performs better, with lower missed detection rate and higher sensitivity. Innovative combination of machine learning algorithm, through three-dimensional coordinate system, differentiation and vector sum formula, improves the accuracy and reliability of fall detection. In conclusion, the algorithm in this paper can effectively optimize the relevant performance of the system, thus improving the accuracy of the system’s fall detection.

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

EMDet: An entropy blending and multi-link parallel feature enhancement detection model for gas pipeline weak leakage detection

To improve the detection accuracy of gas pipeline leakage detection (PLD) especially when the leakage is weak, we propose a hybrid deep learning model, named EMDet in this paper, which incorporates two proposed algorithms called entropy blending (EB) and multi-link parallel feature enhancement (MPFE). Firstly, the EB algorithm is used to determine the optimal number of decomposition layer Lo and the optimal intrinsic mode function Io for variational mode decomposition (VMD), elevating the leakage-related information in Io. Then, three coarse-grained scales have been applied to extract effective and robust features, where the coarse-grained scales are calculated based on the sizes required by the MPFE algorithm. Moreover, the MPFE algorithm is proposed to enhance the representation of features, thereby improving the detection accuracy of PLD. After that, the performance of the proposed EMDet has been evaluated on the negative pressure wave (NPW) data collected from realistic urban environments. Compared with the state-of-the-art PLD models, the performance of EMDet not only reaches the total accuracy of 98.67%, the F1-score of 98.65%, and the fault detection rate of 98.68%, but also reduces the false alarm rate to 0.66% and the missing alarm rate to 1.32%. Finally, the robustness results demonstrate the superior performance and broad application prospects of EMDet for PLD.

Privacy and security trade‐off in cyber‐physical systems: An information theory‐based framework

AbstractThis article investigates the trade‐off between privacy and security in cyber‐physical systems, with the goal of designing a privacy‐preserving mechanism based on information theory. Considering the unreliability of the communication channel, we assume that the private data is vulnerable to eavesdropping and bias injection attacks. To maintain privacy, the system is equipped with a privacy‐preserving mechanism achieved by injecting Gaussian‐type privacy noise into transmitted data, which inevitably leads to degraded detecting performance. Therefore, we investigate the trade‐off between privacy level and detection performance, where the privacy level and the detection performance are measured by mutual information and Kullback–Leibler divergence, respectively. Then, the optimal privacy noise is obtained by solving a convex optimization problem for maximizing the privacy degree and constraining a bound on detection performance degradation. Furthermore, to optimize the detection performance, another convex optimization problem is proposed to minimize both the false alarm rate and the missed alarm rate while guaranteeing a level of the privacy. Finally, a numerical example of the vehicle tracking problem is adopted to illustrate the effectiveness of the designed framework.

A Robust Position Estimation Method in the Integrated Navigation System via Factor Graph

Achieving higher accuracy and robustness stands as the central objective in the navigation field. In complex urban environments, the integrity of GNSS faces huge challenges and the performance of integrated navigation systems can be significantly affected. As the proportion of faulty measurements rises, it can result in both missed alarms and false positives. In this paper, a robust method based on factor graph is proposed to improve the performance of integrated navigation systems. We propose a detection method based on multi-conditional analysis to determine whether GNSS is anomalous or not. Moreover, the optimal weight of GNSS measurement is estimated under anomalous conditions to mitigate the impact of GNSS outliers. The proposed method is evaluated through real-world road tests, and the results show the positioning accuracy of the proposed method is improved by more than 60% and the missed alarm rate is reduced by 80% compared with the traditional algorithms.

Unsupervised Anomaly Detection of Intermittent Demand for Spare Parts Based on Dual-Tailed Probability

The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not easy to represent the demand pattern of such sequences. Meanwhile, there are some aspects like manual report errors, environmental interference, sudden project changes, etc., that bring large and unexpected fluctuations to SPD sequences, i.e., anomalous demands. The inventory decision made based on the SPD sequences with anomalous demands is not trusted by enterprise engineers. For such SPD data, there are two great concerns, i.e., false alarms in which sparse demands are recognized to be anomalous and missing alarms in which the anomalous demands are categorized as normal due to their adjacent demands having extreme values. To address these concerns, a new unsupervised anomaly-detection method for intermittent time series is proposed based on a dual-tailed probability. First, the multi-way delay embedding transform (MDT) was applied on the raw SPD sequences to obtain higher-order tensors. Through Tucker tensor decomposition, the disturbance of extreme demands can be effectively reduced. For the reconstructed SPD sequences, then, the tail probability at each time point, as well as the empirical cumulative distribution function were calculated based on the probability of the demand occurrence. Second, to lessen the disturbance of sparse demand, the non-zero demand sequence was distilled from the raw SPD sequence, with the tail probability at each time point being calculated. Finally, the obtained dual-tailed probabilities were fused to determine the anomalous degree of each demand. The proposed method was validated on the two actual SPD datasets, which were collected from a large engineering manufacturing enterprise and a large vehicle manufacturing enterprise in China, respectively. The results demonstrated that the proposed method can effectively lower the false alarm rate and missing alarm rate with no supervised information provided. The detection results were trustworthy enough and, more importantly, computationally inexpensive, showing significant applicability to large-scale after-sales parts management.

Detecting Unauthorized Movement of Radioactive Material Packages in Transport with an Adam-Optimized BP Neural Network Model

The rapid expansion of nuclear technology across various sectors due to global economic growth has led to a substantial rise in the transportation of radioactive materials. The International Atomic Energy Agency (IAEA) estimates that approximately 20 million shipments of radioactive materials occur annually. In this context, ensuring the safety and security of radioactive material transportation is of significant importance. IAEA’s “Security of Radioactive Materials in Transport” (Nuclear Security Series No. 9-G) mandates that an effective transport security system should provide immediate detection of any unauthorized removal of the packages. In the present study, an innovative Adam-optimized BP neural network model is developed for detecting unauthorized movements of radioactive material packages. To analyze the performance of the proposed algorithm, numerous experiments were conducted. The results demonstrate that the proposed method achieves a 99.17% accuracy rate in detecting unauthorized movements of radioactive materials, with a missed alarm rate of 0.72% and a false alarm rate of 0.1%. This method also enables real-time detection of unauthorized removal of radioactive materials and effectively enhances the security of radioactive materials during transport to reduce the risks of theft, loss, diversion, or sabotage.