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
The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0....The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0.5 to 100 mm/min.Based on the stress−strain curves and the dynamic material model,the hot processing map was established,which demonstrates that the power dissipation factor(η)is the most sensitive to strain rate at 400℃via absorption of dislocations.At 400℃,sample at 0.5 mm/min possessesηof 0.89 because of its lower kernel average misorientation(KAM)value of 0.51,while sample at 100 mm/min possessesηof 0.46 with a higher KAM value of 1.147.In addition,the flow stress presents a slight decrease of 25.94 MPa at 10 mm/min compared to that at 100 mm/min and 100℃.The reasons are twofold:a special~34°texture component during 100℃-100 mm/min favoring the activation of basal slip,and dynamic recrystallization(DRX)also providing softening effect to some extent by absorbing dislocations.Difference in activation of basal slip among twin laminas during 100℃-100 mm/min results in deformation inhomogeneity within the grains,which generates stress that helps matrix grains tilt to a direction favorable to basal slip,forming the special~34°texture component.展开更多
A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of...A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.展开更多
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Project(52005362) supported by the National Natural Science Foundation of ChinaProjects(202303021221005,202303021211045) supported by the Natural Science Foundation of Shanxi Province,China+1 种基金Project(202402003) supported by the Patent Commercialization Program of Shanxi Province,ChinaProject supported by the Key Research and Development Plan of Xinzhou City,China。
文摘The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0.5 to 100 mm/min.Based on the stress−strain curves and the dynamic material model,the hot processing map was established,which demonstrates that the power dissipation factor(η)is the most sensitive to strain rate at 400℃via absorption of dislocations.At 400℃,sample at 0.5 mm/min possessesηof 0.89 because of its lower kernel average misorientation(KAM)value of 0.51,while sample at 100 mm/min possessesηof 0.46 with a higher KAM value of 1.147.In addition,the flow stress presents a slight decrease of 25.94 MPa at 10 mm/min compared to that at 100 mm/min and 100℃.The reasons are twofold:a special~34°texture component during 100℃-100 mm/min favoring the activation of basal slip,and dynamic recrystallization(DRX)also providing softening effect to some extent by absorbing dislocations.Difference in activation of basal slip among twin laminas during 100℃-100 mm/min results in deformation inhomogeneity within the grains,which generates stress that helps matrix grains tilt to a direction favorable to basal slip,forming the special~34°texture component.
基金supported by the National Natural Science Foundation of China(61806221).
文摘A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.
