As the cash register system gradually prevailed in shopping malls, detecting the abnormal status of the cash register system has gradually become a hotspot issue. This paper analyzes the transaction data of a shopping...As the cash register system gradually prevailed in shopping malls, detecting the abnormal status of the cash register system has gradually become a hotspot issue. This paper analyzes the transaction data of a shopping mall. When calculating the degree of data difference, the coefficient of variation is used as the attribute weight;the weighted Euclidean distance is used to calculate the degree of difference;and k-means clustering is used to classify different time periods. It applies the LOF algorithm to detect the outlier degree of transaction data at each time period, sets the initial threshold to detect outliers, deletes the outliers, and then performs SAX detection on the data set. If it does not pass the test, then it will gradually expand the outlying domain and repeat the above process to optimize the outlier threshold to improve the sensitivity of detection algorithm and reduce false positives.展开更多
An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a...An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.展开更多
To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions...To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.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
文摘As the cash register system gradually prevailed in shopping malls, detecting the abnormal status of the cash register system has gradually become a hotspot issue. This paper analyzes the transaction data of a shopping mall. When calculating the degree of data difference, the coefficient of variation is used as the attribute weight;the weighted Euclidean distance is used to calculate the degree of difference;and k-means clustering is used to classify different time periods. It applies the LOF algorithm to detect the outlier degree of transaction data at each time period, sets the initial threshold to detect outliers, deletes the outliers, and then performs SAX detection on the data set. If it does not pass the test, then it will gradually expand the outlying domain and repeat the above process to optimize the outlier threshold to improve the sensitivity of detection algorithm and reduce false positives.
文摘An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.
基金funding from the EU Smarter project(PEOPLE-2013-IAPP-610675)
文摘To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.
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。
