A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were ...A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.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
由于电价政策复杂,执行环节多,监管难度大,电价执行错误现象时有发生,这不仅损害电力市场的公平性和效率,也影响电力企业的经济效益和用户的用电成本。提出了一种基于二次聚类的充电桩执行电价异常检测方法,首先进行电价执行异常分类及...由于电价政策复杂,执行环节多,监管难度大,电价执行错误现象时有发生,这不仅损害电力市场的公平性和效率,也影响电力企业的经济效益和用户的用电成本。提出了一种基于二次聚类的充电桩执行电价异常检测方法,首先进行电价执行异常分类及用电特征分析,其次通过K-means聚类算法剥离出电瓶车用户,进而在第二次聚类中采用含噪声应用的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法精确识别高价低接等更为复杂的违约情况。所提方法通过两次聚类分析,提高电价执行的准确性和效率,具有一定的理论意义和应用价值。展开更多
为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、...为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、方位和高度视觉测量模型与方法,实现船舶三维定位。利用融合视觉与AIS的船舶航行态势数据建立异常行为检测模型,自动识别、监测桥区水域危险船舶。试验结果表明:在单、多船的情况下视觉与AIS数据关联准确率分别达到98.45%、91.29%;能有效监测桥区船舶的运动状态。本研究可为保障船舶和桥梁的安全提供有效方法。展开更多
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.
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.
文摘由于电价政策复杂,执行环节多,监管难度大,电价执行错误现象时有发生,这不仅损害电力市场的公平性和效率,也影响电力企业的经济效益和用户的用电成本。提出了一种基于二次聚类的充电桩执行电价异常检测方法,首先进行电价执行异常分类及用电特征分析,其次通过K-means聚类算法剥离出电瓶车用户,进而在第二次聚类中采用含噪声应用的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法精确识别高价低接等更为复杂的违约情况。所提方法通过两次聚类分析,提高电价执行的准确性和效率,具有一定的理论意义和应用价值。
文摘为保障桥区通航安全,提出一种视觉与船舶自动识别系统(Automatic Identification System,AIS)融合的船舶自动监测方法。基于YOLOv5(You Only Look Once version 5)目标检测算法和Canny算法提取船舶图像轮廓信息,构建桥区水域目标距离、方位和高度视觉测量模型与方法,实现船舶三维定位。利用融合视觉与AIS的船舶航行态势数据建立异常行为检测模型,自动识别、监测桥区水域危险船舶。试验结果表明:在单、多船的情况下视觉与AIS数据关联准确率分别达到98.45%、91.29%;能有效监测桥区船舶的运动状态。本研究可为保障船舶和桥梁的安全提供有效方法。
