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
In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationshi...In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.展开更多
处于改建阶段的智能变电站采样模式复杂,继电保护装置难以发现采样回路轻微异常,导致回路隐患暴露时间严重滞后。针对上述问题,分析改建时期智能变电站的采样模式和二次设备配置情况,提出基于同源录波数据比对的继电保护采样回路异常检...处于改建阶段的智能变电站采样模式复杂,继电保护装置难以发现采样回路轻微异常,导致回路隐患暴露时间严重滞后。针对上述问题,分析改建时期智能变电站的采样模式和二次设备配置情况,提出基于同源录波数据比对的继电保护采样回路异常检测方法。首先,利用双向编码器表征(bidirectional encoder representations from transformers,BERT)语言模型与余弦相似度算法,实现同源录波数据的通道匹配。然后,利用重采样技术和曼哈顿距离完成波形的采样频率统一与时域对齐。最后,基于动态时间规整(dynamic time warping,DTW)算法提出改进算法,并结合采样点偏移量共同设置采样回路的异常判据。算例分析表明,该方法可以完成录波数据的同源通道匹配,实现波形的一致性对齐,并且相比于传统DTW算法,改进DTW算法对异常状态识别的灵敏性和准确性更高。根据异常判据能够有效检测继电保护采样回路的异常状态,确保了智能变电站的安全可靠运行。展开更多
图像异常检测旨在识别并定位图像中的异常区域,针对现有算法中不同层次特征信息利用不充分的问题,提出了基于多层次特征融合网络的图像异常检测算法。通过使用融合了异常先验知识的伪异常数据生成算法,对训练集进行了异常数据扩充,将异...图像异常检测旨在识别并定位图像中的异常区域,针对现有算法中不同层次特征信息利用不充分的问题,提出了基于多层次特征融合网络的图像异常检测算法。通过使用融合了异常先验知识的伪异常数据生成算法,对训练集进行了异常数据扩充,将异常检测任务转化为监督学习任务;构建了多层次特征融合网络,将神经网络中不同层次特征进行融合,丰富了特征中的低层纹理信息和高层语义信息,使得用于异常检测的特征更具区分性;训练时,设计了分数约束损失和一致性约束损失,并结合特征约束损失对整个网络模型进行训练。实验结果表明,MVTec数据集上图像级检测接收机工作特性曲线下面积(area under the receiver operating characteristic, AUROC)平均值为98.7%,像素级定位AUROC平均值为97.9%,每区域重叠率平均值为94.2%,均高于现有的异常检测算法。展开更多
近年来,黑客攻击、网络钓鱼、勒索病毒等事件频发,使得区块链交易异常检测成为当前研究的热点之一。XGBoost作为一种基于梯度提升框架的机器学习算法,可灵活地处理数据特征,不仅关注模型的预测误差,还考虑了每个样本的相对重要性。在前...近年来,黑客攻击、网络钓鱼、勒索病毒等事件频发,使得区块链交易异常检测成为当前研究的热点之一。XGBoost作为一种基于梯度提升框架的机器学习算法,可灵活地处理数据特征,不仅关注模型的预测误差,还考虑了每个样本的相对重要性。在前人区块链交易异常检测研究的基础上,将XGBoost与随机森林算法相结合,提出了基于XGBoost和随机森林的区块链异常交易检测模型(Blockchain Anomalous Transaction Detection Based on XGBoost and Random Forests,BATD_XRF)。模型分为数据特征处理、特征选择、分类检测3个模块,选取Elliptic Data Set、Bitcoin Alpha Dataset和Bitcoin OTC Trust Network Dataset数据集,使用主成分分析方法对数据进行二分类。特征选择过程中通过Gini Mean Decrease计算平均递减量,进一步确定特征的相对重要性。分类检测过程中提出网络搜索参数调优算法,并进行10倍交叉验证。最终通过与其他模型的对比实验,证明了所提模型在区块链交易异常检测中的准确性更高,训练时间更短。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.
文摘In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.
文摘处于改建阶段的智能变电站采样模式复杂,继电保护装置难以发现采样回路轻微异常,导致回路隐患暴露时间严重滞后。针对上述问题,分析改建时期智能变电站的采样模式和二次设备配置情况,提出基于同源录波数据比对的继电保护采样回路异常检测方法。首先,利用双向编码器表征(bidirectional encoder representations from transformers,BERT)语言模型与余弦相似度算法,实现同源录波数据的通道匹配。然后,利用重采样技术和曼哈顿距离完成波形的采样频率统一与时域对齐。最后,基于动态时间规整(dynamic time warping,DTW)算法提出改进算法,并结合采样点偏移量共同设置采样回路的异常判据。算例分析表明,该方法可以完成录波数据的同源通道匹配,实现波形的一致性对齐,并且相比于传统DTW算法,改进DTW算法对异常状态识别的灵敏性和准确性更高。根据异常判据能够有效检测继电保护采样回路的异常状态,确保了智能变电站的安全可靠运行。
文摘图像异常检测旨在识别并定位图像中的异常区域,针对现有算法中不同层次特征信息利用不充分的问题,提出了基于多层次特征融合网络的图像异常检测算法。通过使用融合了异常先验知识的伪异常数据生成算法,对训练集进行了异常数据扩充,将异常检测任务转化为监督学习任务;构建了多层次特征融合网络,将神经网络中不同层次特征进行融合,丰富了特征中的低层纹理信息和高层语义信息,使得用于异常检测的特征更具区分性;训练时,设计了分数约束损失和一致性约束损失,并结合特征约束损失对整个网络模型进行训练。实验结果表明,MVTec数据集上图像级检测接收机工作特性曲线下面积(area under the receiver operating characteristic, AUROC)平均值为98.7%,像素级定位AUROC平均值为97.9%,每区域重叠率平均值为94.2%,均高于现有的异常检测算法。
文摘近年来,黑客攻击、网络钓鱼、勒索病毒等事件频发,使得区块链交易异常检测成为当前研究的热点之一。XGBoost作为一种基于梯度提升框架的机器学习算法,可灵活地处理数据特征,不仅关注模型的预测误差,还考虑了每个样本的相对重要性。在前人区块链交易异常检测研究的基础上,将XGBoost与随机森林算法相结合,提出了基于XGBoost和随机森林的区块链异常交易检测模型(Blockchain Anomalous Transaction Detection Based on XGBoost and Random Forests,BATD_XRF)。模型分为数据特征处理、特征选择、分类检测3个模块,选取Elliptic Data Set、Bitcoin Alpha Dataset和Bitcoin OTC Trust Network Dataset数据集,使用主成分分析方法对数据进行二分类。特征选择过程中通过Gini Mean Decrease计算平均递减量,进一步确定特征的相对重要性。分类检测过程中提出网络搜索参数调优算法,并进行10倍交叉验证。最终通过与其他模型的对比实验,证明了所提模型在区块链交易异常检测中的准确性更高,训练时间更短。
