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 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
Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinemat...Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer pereeptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally, avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.展开更多
In order to discover the causes of the abnormal noise of shock absorbers, it is necessary to identify the operating characteristics of the shock absorbers. A micro-process model for operation of the hydraulic shock ab...In order to discover the causes of the abnormal noise of shock absorbers, it is necessary to identify the operating characteristics of the shock absorbers. A micro-process model for operation of the hydraulic shock absorber was presented. A novel concept, which describes the process of hydraulic shock absorber by dividing it into smaller steps, was proposed. The dynamic model and the differential equations were established. The results of numerical simulation agree well with data obtained from the vibrostand test, indicating that the collision between the piston and the oil, the alternation of static friction and sliding friction acted between the piston and the cylinder, and the adherence between valve plate and piston result in impact on the piston head near the top dead center and the bottom dead center. Ultimately, the impact excites the high-frequency vibration of the piston structure, which can generate the abnormal noise in the hydraulic shock absorber after its transfer. And the maximum vibration acceleration on the piston head and the abnormal noise increase with the increase of the gap between the oil and piston rod head, the maximum static friction force and the adhering function, respectively.展开更多
Based on the comprehensive research on core samples,well testing data and fluid parameters of the reservoirs,the depositional architecture of the abnormal low pressure compartment and fluid characteristics of Huatugou...Based on the comprehensive research on core samples,well testing data and fluid parameters of the reservoirs,the depositional architecture of the abnormal low pressure compartment and fluid characteristics of Huatugou oilfield of Qaidam basin were reported,and the correlation between the compartment and hydrocarbon accumulation was revealed. The result indicates that the reservoirs are located展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.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
Projectile size effect is of great importance since the scaling researches are extensively applied to concrete penetration investigations. This paper numerically deals with the projectile size effect on penetration re...Projectile size effect is of great importance since the scaling researches are extensively applied to concrete penetration investigations. This paper numerically deals with the projectile size effect on penetration resistance via the recently developed Lattice Discrete Particles Model(LDPM) which is featured with mesoscale constitutive laws governing the interaction between adjacent particles to account for cohesive fracture, strain hardening in compression and compaction due to pore collapse. Simulations of two different penetration tests are carried to shed some light on the size effect issue. The penetration numerical model is validated by matching the projectile deceleration curve of and predicting the depth of penetration(DOP). By constant velocity penetration simulations, the target resistance is found to be dependent on the projectile size. By best fitting numerical results of constant velocity penetration, a size effect law for target resistance is proposed and validated against literature data. Moreover, the size effect is numerically obtained in the projectile with longer extended nose part meanwhile the shorter extended nose is found to improve the DOP since the projectile nose is sharpened.展开更多
In order to improve insect-resistance of cottonand cultivate new cotton varieties,tissue cultureand plant regeneration of cotton(Gossypiumhirsutum L.)were studied with Xinluzao 4,Xi550,Jizi 492,Hengwu 89-30,Han 93-2 a...In order to improve insect-resistance of cottonand cultivate new cotton varieties,tissue cultureand plant regeneration of cotton(Gossypiumhirsutum L.)were studied with Xinluzao 4,Xi550,Jizi 492,Hengwu 89-30,Han 93-2 and Jizi123.A system of cotton tissue culture for展开更多
A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,t...A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.展开更多
The micro-nuclei and abnormal nuclei rates of peacock fish are tested,genetic toxic effects of rare-earth waste and its products(cement,plastic) on peacock in water are investigated.Results show that the leachate of r...The micro-nuclei and abnormal nuclei rates of peacock fish are tested,genetic toxic effects of rare-earth waste and its products(cement,plastic) on peacock in water are investigated.Results show that the leachate of rare-earth waste can lead to micronuclei and abnormal nuclei rates of peacock fish an obvious increase(P<0.01). Products made of the waste cause the micronuclei rate to be increased because of its low radio active action,but the change in abnormal nuclei rate can't reach a remarkable level.It shows that rare-earth waste has a certain effect of causing mutation on aquatic organism.Harmfulness of products made from this waste is decreased largely,and resources can be effectively saved.展开更多
In an abnormal high-temperature fire environment,a structure with mechanical-thermal weak-link can be used to predict the permanent failure before the failure of some strong-links,such as explosive initiator,thus to p...In an abnormal high-temperature fire environment,a structure with mechanical-thermal weak-link can be used to predict the permanent failure before the failure of some strong-links,such as explosive initiator,thus to protect the latter. An effective design can be defined as that the weak-link fails before the failure of the strong-link,and the system is safe; while an unsuccessful design means that the weak-link fails after the failure of the strong-link,and the system loses in safety. The probability of safety failure exists due to the uncertain failure temperatures of the weak-link and strong-link. In order to obtain the probability of safety failure,a statistical method was used to deal with the uncertainty of the failure temperatures. The integral method and stochastic simulation method were used in calculations. Finally,a sample was given to verify the consistence of the results given by two methods.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.
基金Project (60234030) supported by the National Natural Science Foundation of China
文摘Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer pereeptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally, avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.
基金Project(200244) supported by the Visiting Scholar Foundation of the State Key Laboratory of Mechanical Transmission, Chongqing University
文摘In order to discover the causes of the abnormal noise of shock absorbers, it is necessary to identify the operating characteristics of the shock absorbers. A micro-process model for operation of the hydraulic shock absorber was presented. A novel concept, which describes the process of hydraulic shock absorber by dividing it into smaller steps, was proposed. The dynamic model and the differential equations were established. The results of numerical simulation agree well with data obtained from the vibrostand test, indicating that the collision between the piston and the oil, the alternation of static friction and sliding friction acted between the piston and the cylinder, and the adherence between valve plate and piston result in impact on the piston head near the top dead center and the bottom dead center. Ultimately, the impact excites the high-frequency vibration of the piston structure, which can generate the abnormal noise in the hydraulic shock absorber after its transfer. And the maximum vibration acceleration on the piston head and the abnormal noise increase with the increase of the gap between the oil and piston rod head, the maximum static friction force and the adhering function, respectively.
文摘Based on the comprehensive research on core samples,well testing data and fluid parameters of the reservoirs,the depositional architecture of the abnormal low pressure compartment and fluid characteristics of Huatugou oilfield of Qaidam basin were reported,and the correlation between the compartment and hydrocarbon accumulation was revealed. The result indicates that the reservoirs are located
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
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.
基金supported by the Natural Science Foundation of Jiangsu Province (No. BK20170824)the Fundamental Research Funds for the Central Universities (No. 30917011343)
文摘Projectile size effect is of great importance since the scaling researches are extensively applied to concrete penetration investigations. This paper numerically deals with the projectile size effect on penetration resistance via the recently developed Lattice Discrete Particles Model(LDPM) which is featured with mesoscale constitutive laws governing the interaction between adjacent particles to account for cohesive fracture, strain hardening in compression and compaction due to pore collapse. Simulations of two different penetration tests are carried to shed some light on the size effect issue. The penetration numerical model is validated by matching the projectile deceleration curve of and predicting the depth of penetration(DOP). By constant velocity penetration simulations, the target resistance is found to be dependent on the projectile size. By best fitting numerical results of constant velocity penetration, a size effect law for target resistance is proposed and validated against literature data. Moreover, the size effect is numerically obtained in the projectile with longer extended nose part meanwhile the shorter extended nose is found to improve the DOP since the projectile nose is sharpened.
文摘In order to improve insect-resistance of cottonand cultivate new cotton varieties,tissue cultureand plant regeneration of cotton(Gossypiumhirsutum L.)were studied with Xinluzao 4,Xi550,Jizi 492,Hengwu 89-30,Han 93-2 and Jizi123.A system of cotton tissue culture for
基金Sponsored by the National Natural Science Foundation of China (60773129)the Excellent Youth Science and Technology Foundation of Anhui Province of China ( 08040106808)
文摘A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.
文摘The micro-nuclei and abnormal nuclei rates of peacock fish are tested,genetic toxic effects of rare-earth waste and its products(cement,plastic) on peacock in water are investigated.Results show that the leachate of rare-earth waste can lead to micronuclei and abnormal nuclei rates of peacock fish an obvious increase(P<0.01). Products made of the waste cause the micronuclei rate to be increased because of its low radio active action,but the change in abnormal nuclei rate can't reach a remarkable level.It shows that rare-earth waste has a certain effect of causing mutation on aquatic organism.Harmfulness of products made from this waste is decreased largely,and resources can be effectively saved.
基金Sponsored by Science and technology development fund of China academy of engineering physics( 2011A0203010)
文摘In an abnormal high-temperature fire environment,a structure with mechanical-thermal weak-link can be used to predict the permanent failure before the failure of some strong-links,such as explosive initiator,thus to protect the latter. An effective design can be defined as that the weak-link fails before the failure of the strong-link,and the system is safe; while an unsuccessful design means that the weak-link fails after the failure of the strong-link,and the system loses in safety. The probability of safety failure exists due to the uncertain failure temperatures of the weak-link and strong-link. In order to obtain the probability of safety failure,a statistical method was used to deal with the uncertainty of the failure temperatures. The integral method and stochastic simulation method were used in calculations. Finally,a sample was given to verify the consistence of the results given by two methods.
