This paper studies the relationship between water level step-variation anomalies and regional seismic activity.The train of thinking is as follows:First,a series of water level step-variation anomalies are regarded as...This paper studies the relationship between water level step-variation anomalies and regional seismic activity.The train of thinking is as follows:First,a series of water level step-variation anomalies are regarded as sequential step-variation anomalies; next,these sequential anomalies are divided into several sub-sequential anomalies according to the temporal density of step-variations in different segments of the sequence; then the generation and evolution processes of various sub-sequential anomalies are analyzed to find their relation with regional moderate-strong earthquake activities,and finally the various sub-sequential anomalies are synthesized as sequential anomalies so as to analyze their relation with the tendency of seismic activities.By the above method,this paper has analyzed the relationship between a series of water level step-variation anomalies at the Wanquan well since 1981 and several regional moderate-strong earthquake activities.According to the monthly frequency,amplitude and展开更多
Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical c...Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical circulations including the Asian monsoon as well as the convective activity are studied. In order to study the physical process of the air-sea interaction related to STH anomaly, the correlation of STH with SST at various sea areas, lagged and simultaneous, has been calculated. Comparing the difference of OLR, wind fields, vertical circulations and SST anomalies in the strong and weak STH, we investigate the characteristics of global circulations and the SST distributions related to the anomalous STH at the western Pacific both in winter and summer. Much attention has been paid to the study of the air-sea interaction and the relationship between the East Asian monsoon and the STH in the western Pacific. A special vertical circulation, related to the STH anomalies is found, which connects the monsoon current to the west and the vertical flow influenced by the SST anomaly in the tropical eastern Pacific.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies a...Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.展开更多
As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and...As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.展开更多
To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military ...To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause o...In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.展开更多
Introduction:The occurrence of congenital anomalies is one of the serious challenges in the world.Therefore,identifying related factors to reduce it is of particular importance.This study aimed to determine the incide...Introduction:The occurrence of congenital anomalies is one of the serious challenges in the world.Therefore,identifying related factors to reduce it is of particular importance.This study aimed to determine the incidence and factors related to congenital anomalies.Methods:An epidemiology study was conducted on 1567 infants and their parents in Kermanshah,Iran.The required information was extracted from the ffles of mothers in health centers.The data collection tool was a researcher-made checklist of 39 questions.The data was statistically analyzed with the STATA version 14 software.Result:The incidence of congenital anomalies was 2.9%(n=45).Brain anomalies(n=10)and pulmonary anomalies(n=8)were the most common congenital anomalies in newborns.The results showed that parents’age(p=0.001),place of residence(p=0.022),mother’s occupation(p=0.010),hemoglobin level(p=0.002),blood pressure disorders(p=0.001),bleeding during pregnancy(p=0.001),infection during pregnancy(p=0.001),multivitamins(p=0.002)and women’s previous birth records such as previous abnormal birth history(p=0.015),abortion history(p=0.001),stillbirth history(p=0.001),birth history of infant less than 2500 g(p=0.001)was found to have a statistically signiffcant relationship with congenital anomalies.Conclusion:The incidence of congenital anomalies was high in Kermanshah city.Considering the identiffcation of risk factors and preventive factors related to congenital anomalies,it is suggested that interventions be carried out in health centers to increase awareness among pregnant women to reduce the incidence of anomalies.展开更多
Anomaly detection is an important research area in a diverse range of real-world applications.Although many algorithms have been proposed to address anomaly detection for numerical datasets,categorical and mixed datas...Anomaly detection is an important research area in a diverse range of real-world applications.Although many algorithms have been proposed to address anomaly detection for numerical datasets,categorical and mixed datasets remain a significant challenge,primarily because a natural distance metric is lacking.Consequently,the methods proposed in the literature implement entirely different assumptions regarding the definition of cate-gorical anomalies.This paper presents a novel categorical anomaly detection approach,offering two key con-tributions to existing methods.First,a novel surprisal-based anomaly score is introduced,which provides a more accurate assessment of anomalies by considering the full distribution of categorical values.Second,the proposed method considers complex correlations in the data beyond the pairwise interactions of features.This study proposed and tested the novel categorical surprisal anomaly detection algorithm(CSAD)by comparing and evaluating it against six competitors.The experimental results indicate that CSAD produced the best overall performance,achieving the highest average ROC-AUC and PR-AUC values of 0.8 and 0.443,respectively.Furthermore,CSAD's execution time is satisfactory even when processing large,high-dimensional datasets.展开更多
The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,t...The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,this paper proposes a method based on Fuzzy Fusion of variablesand Discriminant mapping of features for Clustering(FFD-Clustering)to detect anomalies in originalmonitoring data from Aircraft Communication Addressing and Reporting System(ACARS).Firstly,associated variables are fuzzily grouped to extract the underlying distribution characteristics and trendsfrom the data.Secondly,a multi-layer contrastive denoising-based feature Fusion Encoding Network(FEN)is designed for each variable group,which can construct representative features for each variablegroup through eliminating strong noise and complex interrelations between variables.Thirdly,a featureDiscriminative Mapping Network(DMN)based on reconstruction difference re-clustering is designed,which can distinguish dissimilar feature vectors when mapping representative features to a unified fea-ture space.Finally,the K-means clustering is used to detect the abnormal feature vectors in the unifiedfeature space.Additionally,the algorithm is capable of reconstructing identified abnormal vectors,thereby locating the abnormal variable groups.The performance of this algorithm was tested ontwo public datasets and real original monitoring data from four aero-engines'ACARS,demonstratingits superiority and application potential in aero-engine anomaly detection.展开更多
Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,p...Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,poor temporal dependency handling,and suboptimal real-time performance,sometimes even neglecting the temporal relationships between data.To address these issues and improve anomaly detection performance by better capturing temporal dependencies,we propose an unsupervised time series anomaly detection method,VLT-Anomaly.First,we enhance the Variational Autoencoder(VAE)module by redesigning its network structure to better suit anomaly detection through data reconstruction.We introduce hyperparameters to control the weight of the Kullback-Leibler(KL)divergence term in the Evidence Lower Bound(ELBO),thereby improving the encoder module’s decoupling and expressive power in the latent space,which yields more effective latent representations of the data.Next,we incorporate transformer and Long Short-Term Memory(LSTM)modules to estimate the long-term dependencies of the latent representations,capturing both forward and backward temporal relationships and performing time series forecasting.Finally,we compute the reconstruction error by averaging the predicted results and decoder reconstruction and detect anomalies through grid search for optimal threshold values.Experimental results demonstrate that the proposed method performs superior anomaly detection on multiple public time series datasets,effectively extracting complex time-related features and enabling efficient computation and real-time anomaly detection.It improves detection accuracy and robustness while reducing false positives and false negatives.展开更多
BACKGROUND 2D-echocardiography(2DE)has been the primary imaging modality in children with Kawasaki disease(KD)to assess coronary arteries.AIM To report the presence and implications of incidental congenital coronary a...BACKGROUND 2D-echocardiography(2DE)has been the primary imaging modality in children with Kawasaki disease(KD)to assess coronary arteries.AIM To report the presence and implications of incidental congenital coronary artery anomalies that had been misinterpreted as coronary artery abnormalities(CAAs)on 2DE.METHODS Records of children diagnosed with KD,who underwent computed tomography coronary angiography(CTCA)at our center between 2013-2023 were reviewed.We identified 3 children with congenital coronary artery anomalies in this cohort on CTCA.Findings of CTCA and 2DE were compared in these 3 children.RESULTS Of the 241 patients with KD who underwent CTCA,3(1.24%)had congenital coronary artery anomalies on CTCA detected incidentally.In all 3 patients,baseline 2DE had identified CAAs.CTCA was then performed for detailed evaluation as per our unit protocol.One(11-year-boy)amongst the 3 patients had complete KD,while the other two(3.3-year-boy;4-month-girl)had incomplete KD.CTCA revealed separate origins of left anterior descending artery and left circumflex from left sinus[misinterpreted as dilated left main coronary artery(LCA)on 2DE],single coronary artery(interpreted as dilated LCA on 2DE)and dilated right coronary artery on 2DE in case of anomalous origin of LCA from the main pulmonary artery.The latter one was subsequently operated upon.CONCLUSION CTCA is essential for detailed assessment of coronary arteries in children with KD especially in cases where there is suspicion of congenital coronary artery anomalies.Relying solely on 2DE may not be sufficient in such cases,and findings from CTCA can significantly impact therapeutic decision-making.展开更多
Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly...Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly(SLA)in the China Seas and its adjacent waters.The validation between Haiyang-2(HY-2)measurement proceeded to be geophysical data records(GDR)and moored buoys indicate that HY-2 scatterometer-measured wind speed outperforms that derived from altimeter,with lower root-mean-squared error(RMSE)(1.87 m/s vs.2.03 m/s),smaller bias(−0.06 m/s vs.0.47 m/s),same correlation(COR)(0.84),and reduced scatter index(SI)(0.27 vs.0.29).Conversely,GDR product from HY-2 altimeter demonstrates reliable accuracy of significant wave height(SWH)(RMSE:0.37 m,bias:−0.03 m,COR:0.92,SI:0.30).Further time series analysis of HY-2 data reveals synchronized oscillations among SLA,wind speed and SWH with SLA strongly influencing wind speed under extreme conditions.Seasonal and regional disparities are evident:wind speed positively correlates with SLA in spring but shows a negative correlation in summer,while autumn and winter exhibit weak correlations.Periodic linkages between SWH and SLA are prominent in summer and autumn.In addition,the regional analysis shows that the Bohai Sea experiences declining autumn/winter wind speeds with higher SLA but without consistent SWH trends,while the Yellow Sea demonstrates summer covariation among wind speed,SWH and SLA.The East China Sea maintains synchronized SLA-wind speed-SWH relationship throughout spring,summer and winter,while the South China Sea shows alignment only in spring.The largest SLA,wind speed and SWH variations occur in the East China Sea and South China Sea,primarily driven by vigorous energy exchanges processes with the open ocean.These findings highlight distinct response mechanisms of regional marine dynamics to SLA,shaped by localized hydrological-climatic interactions.展开更多
Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled a...Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled and often consists of normal samples contaminated with a small proportion of unknown anomalies.To address this,this paper proposes a diffusion-based AD method,Anomaly Detection Denoising Diffusion Probabilistic Model(AD-DDPM)for robust AD.The method employs a U-attention-net to capture local and global features and introduces a filtered contrastive mechanism to mitigate the impact of contaminated training data.By leveraging the probabilistic nature of diffusion models,AD-DDPM effectively models normal data distributions,achieving superior AD even with polluted samples.Experimental validation on fault simulation datasets demonstrates the method’s exceptional performance,outperforming traditional machine learning and deep learning baselines.The proposed approach offers a promising solution for reliable health monitoring in industrial settings.展开更多
We introduce a pioneering anomaly detection framework within spatial crowdsourcing Internet of Drone Things(IoDT),specifically designed to improve bushfire management in Australia’s expanding urban areas.This framewo...We introduce a pioneering anomaly detection framework within spatial crowdsourcing Internet of Drone Things(IoDT),specifically designed to improve bushfire management in Australia’s expanding urban areas.This framework innovatively combines Graph Neural Networks(GNN)and advanced data fusion techniques to enhance IoDT capabilities.Through spatial crowdsourcing,drones collectively gather diverse,real-time data across multiple locations,creating a rich dataset for analysis.This method integrates spatial,temporal,and various data modalities,facilitating early bushfire detection by identifying subtle environmental and operational changes.Utilizing a complex GNN architecture,our model effectively processes the intricacies of spatially crowdsourced data,significantly increasing anomaly detection accuracy.It incorporates modules for temporal pattern recognition and spatial analysis of environmental impacts,leveraging multimodal data to detect a wide range of anomalies,from temperature shifts to humidity variations.Our approach has been empirically validated,achieving an F1 score of 0.885,highlighting its superior anomaly detection performance.This integration of spatial crowdsourcing with IoDT not only establishes a new standard for environmental monitoring but also contributes significantly to disaster management and urban sustainability.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
文摘This paper studies the relationship between water level step-variation anomalies and regional seismic activity.The train of thinking is as follows:First,a series of water level step-variation anomalies are regarded as sequential step-variation anomalies; next,these sequential anomalies are divided into several sub-sequential anomalies according to the temporal density of step-variations in different segments of the sequence; then the generation and evolution processes of various sub-sequential anomalies are analyzed to find their relation with regional moderate-strong earthquake activities,and finally the various sub-sequential anomalies are synthesized as sequential anomalies so as to analyze their relation with the tendency of seismic activities.By the above method,this paper has analyzed the relationship between a series of water level step-variation anomalies at the Wanquan well since 1981 and several regional moderate-strong earthquake activities.According to the monthly frequency,amplitude and
文摘Using the data of 500 hPa geopotential height from 1951 to 1995, SST roughly in the same period and OLR data from 1974 to 1994, the relation between the anomalies of subtropical high (STH for short) and the tropical circulations including the Asian monsoon as well as the convective activity are studied. In order to study the physical process of the air-sea interaction related to STH anomaly, the correlation of STH with SST at various sea areas, lagged and simultaneous, has been calculated. Comparing the difference of OLR, wind fields, vertical circulations and SST anomalies in the strong and weak STH, we investigate the characteristics of global circulations and the SST distributions related to the anomalous STH at the western Pacific both in winter and summer. Much attention has been paid to the study of the air-sea interaction and the relationship between the East Asian monsoon and the STH in the western Pacific. A special vertical circulation, related to the STH anomalies is found, which connects the monsoon current to the west and the vertical flow influenced by the SST anomaly in the tropical eastern Pacific.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金the support from the National Natural Science Foundation of China(Nos.52279103,52379103)the Natural Science Foundation of Shandong Province(No.ZR2023YQ049)。
文摘Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.
基金supported in part by the Guangxi Science and Technology Major Program under grant AA22068067the Guangxi Natural Science Foundation under grant 2023GXNSFAA026236 and 2024GXNSFDA010064the National Natural Science Foundation of China under project 62172119.
文摘As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.
基金supported by the National Major Science and Technology Project,China(No.J2019-Ⅳ-0007-0075)the Fundamental Research Funds for the Central Universities,China(No.JKF-20240036)。
文摘To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
文摘In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.
文摘Introduction:The occurrence of congenital anomalies is one of the serious challenges in the world.Therefore,identifying related factors to reduce it is of particular importance.This study aimed to determine the incidence and factors related to congenital anomalies.Methods:An epidemiology study was conducted on 1567 infants and their parents in Kermanshah,Iran.The required information was extracted from the ffles of mothers in health centers.The data collection tool was a researcher-made checklist of 39 questions.The data was statistically analyzed with the STATA version 14 software.Result:The incidence of congenital anomalies was 2.9%(n=45).Brain anomalies(n=10)and pulmonary anomalies(n=8)were the most common congenital anomalies in newborns.The results showed that parents’age(p=0.001),place of residence(p=0.022),mother’s occupation(p=0.010),hemoglobin level(p=0.002),blood pressure disorders(p=0.001),bleeding during pregnancy(p=0.001),infection during pregnancy(p=0.001),multivitamins(p=0.002)and women’s previous birth records such as previous abnormal birth history(p=0.015),abortion history(p=0.001),stillbirth history(p=0.001),birth history of infant less than 2500 g(p=0.001)was found to have a statistically signiffcant relationship with congenital anomalies.Conclusion:The incidence of congenital anomalies was high in Kermanshah city.Considering the identiffcation of risk factors and preventive factors related to congenital anomalies,it is suggested that interventions be carried out in health centers to increase awareness among pregnant women to reduce the incidence of anomalies.
文摘Anomaly detection is an important research area in a diverse range of real-world applications.Although many algorithms have been proposed to address anomaly detection for numerical datasets,categorical and mixed datasets remain a significant challenge,primarily because a natural distance metric is lacking.Consequently,the methods proposed in the literature implement entirely different assumptions regarding the definition of cate-gorical anomalies.This paper presents a novel categorical anomaly detection approach,offering two key con-tributions to existing methods.First,a novel surprisal-based anomaly score is introduced,which provides a more accurate assessment of anomalies by considering the full distribution of categorical values.Second,the proposed method considers complex correlations in the data beyond the pairwise interactions of features.This study proposed and tested the novel categorical surprisal anomaly detection algorithm(CSAD)by comparing and evaluating it against six competitors.The experimental results indicate that CSAD produced the best overall performance,achieving the highest average ROC-AUC and PR-AUC values of 0.8 and 0.443,respectively.Furthermore,CSAD's execution time is satisfactory even when processing large,high-dimensional datasets.
基金co-supported by the National Science and Technology Major Project,China(No.J2019-I-0001-0001)the National Natural Science Foundation of China(No.52105545)。
文摘The original monitoring data from aero-engines possess characteristics such as high dimen-sionality,strong noise,and imbalance,which present substantial challenges to traditional anomalydetection methods.In response,this paper proposes a method based on Fuzzy Fusion of variablesand Discriminant mapping of features for Clustering(FFD-Clustering)to detect anomalies in originalmonitoring data from Aircraft Communication Addressing and Reporting System(ACARS).Firstly,associated variables are fuzzily grouped to extract the underlying distribution characteristics and trendsfrom the data.Secondly,a multi-layer contrastive denoising-based feature Fusion Encoding Network(FEN)is designed for each variable group,which can construct representative features for each variablegroup through eliminating strong noise and complex interrelations between variables.Thirdly,a featureDiscriminative Mapping Network(DMN)based on reconstruction difference re-clustering is designed,which can distinguish dissimilar feature vectors when mapping representative features to a unified fea-ture space.Finally,the K-means clustering is used to detect the abnormal feature vectors in the unifiedfeature space.Additionally,the algorithm is capable of reconstructing identified abnormal vectors,thereby locating the abnormal variable groups.The performance of this algorithm was tested ontwo public datasets and real original monitoring data from four aero-engines'ACARS,demonstratingits superiority and application potential in aero-engine anomaly detection.
基金support from the Fundamental Research Funds for Central Public Welfare Research Institutes(SK202324)the Central Guidance on Local Science and Technology Development Fund of Hebei Province(236Z0104G)+1 种基金the National Natural Science Foundation of China(62476078)the Geological Survey Project of China Geological Survey(G202304-2).
文摘Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,poor temporal dependency handling,and suboptimal real-time performance,sometimes even neglecting the temporal relationships between data.To address these issues and improve anomaly detection performance by better capturing temporal dependencies,we propose an unsupervised time series anomaly detection method,VLT-Anomaly.First,we enhance the Variational Autoencoder(VAE)module by redesigning its network structure to better suit anomaly detection through data reconstruction.We introduce hyperparameters to control the weight of the Kullback-Leibler(KL)divergence term in the Evidence Lower Bound(ELBO),thereby improving the encoder module’s decoupling and expressive power in the latent space,which yields more effective latent representations of the data.Next,we incorporate transformer and Long Short-Term Memory(LSTM)modules to estimate the long-term dependencies of the latent representations,capturing both forward and backward temporal relationships and performing time series forecasting.Finally,we compute the reconstruction error by averaging the predicted results and decoder reconstruction and detect anomalies through grid search for optimal threshold values.Experimental results demonstrate that the proposed method performs superior anomaly detection on multiple public time series datasets,effectively extracting complex time-related features and enabling efficient computation and real-time anomaly detection.It improves detection accuracy and robustness while reducing false positives and false negatives.
文摘BACKGROUND 2D-echocardiography(2DE)has been the primary imaging modality in children with Kawasaki disease(KD)to assess coronary arteries.AIM To report the presence and implications of incidental congenital coronary artery anomalies that had been misinterpreted as coronary artery abnormalities(CAAs)on 2DE.METHODS Records of children diagnosed with KD,who underwent computed tomography coronary angiography(CTCA)at our center between 2013-2023 were reviewed.We identified 3 children with congenital coronary artery anomalies in this cohort on CTCA.Findings of CTCA and 2DE were compared in these 3 children.RESULTS Of the 241 patients with KD who underwent CTCA,3(1.24%)had congenital coronary artery anomalies on CTCA detected incidentally.In all 3 patients,baseline 2DE had identified CAAs.CTCA was then performed for detailed evaluation as per our unit protocol.One(11-year-boy)amongst the 3 patients had complete KD,while the other two(3.3-year-boy;4-month-girl)had incomplete KD.CTCA revealed separate origins of left anterior descending artery and left circumflex from left sinus[misinterpreted as dilated left main coronary artery(LCA)on 2DE],single coronary artery(interpreted as dilated LCA on 2DE)and dilated right coronary artery on 2DE in case of anomalous origin of LCA from the main pulmonary artery.The latter one was subsequently operated upon.CONCLUSION CTCA is essential for detailed assessment of coronary arteries in children with KD especially in cases where there is suspicion of congenital coronary artery anomalies.Relying solely on 2DE may not be sufficient in such cases,and findings from CTCA can significantly impact therapeutic decision-making.
基金The National Natural Science Foundation of China under contract No.42376174the Natural Science Foundation of Shanghai under contract No.23ZR1426900。
文摘Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly(SLA)in the China Seas and its adjacent waters.The validation between Haiyang-2(HY-2)measurement proceeded to be geophysical data records(GDR)and moored buoys indicate that HY-2 scatterometer-measured wind speed outperforms that derived from altimeter,with lower root-mean-squared error(RMSE)(1.87 m/s vs.2.03 m/s),smaller bias(−0.06 m/s vs.0.47 m/s),same correlation(COR)(0.84),and reduced scatter index(SI)(0.27 vs.0.29).Conversely,GDR product from HY-2 altimeter demonstrates reliable accuracy of significant wave height(SWH)(RMSE:0.37 m,bias:−0.03 m,COR:0.92,SI:0.30).Further time series analysis of HY-2 data reveals synchronized oscillations among SLA,wind speed and SWH with SLA strongly influencing wind speed under extreme conditions.Seasonal and regional disparities are evident:wind speed positively correlates with SLA in spring but shows a negative correlation in summer,while autumn and winter exhibit weak correlations.Periodic linkages between SWH and SLA are prominent in summer and autumn.In addition,the regional analysis shows that the Bohai Sea experiences declining autumn/winter wind speeds with higher SLA but without consistent SWH trends,while the Yellow Sea demonstrates summer covariation among wind speed,SWH and SLA.The East China Sea maintains synchronized SLA-wind speed-SWH relationship throughout spring,summer and winter,while the South China Sea shows alignment only in spring.The largest SLA,wind speed and SWH variations occur in the East China Sea and South China Sea,primarily driven by vigorous energy exchanges processes with the open ocean.These findings highlight distinct response mechanisms of regional marine dynamics to SLA,shaped by localized hydrological-climatic interactions.
基金supported by The National Natural Science Foundation of China under Grant(5247512)National Key Lab of Aerospace Power System and Plasma Technology Foundation(APSPT202304002).
文摘Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled and often consists of normal samples contaminated with a small proportion of unknown anomalies.To address this,this paper proposes a diffusion-based AD method,Anomaly Detection Denoising Diffusion Probabilistic Model(AD-DDPM)for robust AD.The method employs a U-attention-net to capture local and global features and introduces a filtered contrastive mechanism to mitigate the impact of contaminated training data.By leveraging the probabilistic nature of diffusion models,AD-DDPM effectively models normal data distributions,achieving superior AD even with polluted samples.Experimental validation on fault simulation datasets demonstrates the method’s exceptional performance,outperforming traditional machine learning and deep learning baselines.The proposed approach offers a promising solution for reliable health monitoring in industrial settings.
文摘We introduce a pioneering anomaly detection framework within spatial crowdsourcing Internet of Drone Things(IoDT),specifically designed to improve bushfire management in Australia’s expanding urban areas.This framework innovatively combines Graph Neural Networks(GNN)and advanced data fusion techniques to enhance IoDT capabilities.Through spatial crowdsourcing,drones collectively gather diverse,real-time data across multiple locations,creating a rich dataset for analysis.This method integrates spatial,temporal,and various data modalities,facilitating early bushfire detection by identifying subtle environmental and operational changes.Utilizing a complex GNN architecture,our model effectively processes the intricacies of spatially crowdsourced data,significantly increasing anomaly detection accuracy.It incorporates modules for temporal pattern recognition and spatial analysis of environmental impacts,leveraging multimodal data to detect a wide range of anomalies,from temperature shifts to humidity variations.Our approach has been empirically validated,achieving an F1 score of 0.885,highlighting its superior anomaly detection performance.This integration of spatial crowdsourcing with IoDT not only establishes a new standard for environmental monitoring but also contributes significantly to disaster management and urban sustainability.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
