信息门户的建设过程中需要容易实施且灵活高效的整合模式.为克服传统单点登录机制无法满足在动态松耦合环境下实现快速整合的缺陷,结合面向服务架构思想,提出一种轻量级门户单点登录服务机制(LSSO-Service,Lightweight Single Sign-on S...信息门户的建设过程中需要容易实施且灵活高效的整合模式.为克服传统单点登录机制无法满足在动态松耦合环境下实现快速整合的缺陷,结合面向服务架构思想,提出一种轻量级门户单点登录服务机制(LSSO-Service,Lightweight Single Sign-on Service),可为门户整合提供结构简单、完善通用、松散耦合、快速机动的单点登录服务.LSSO-Service基于高于对象层的分布式服务集成模式进行功能划分,可实现采用不同技术的应用系统在门户中的快速动态整合.阐述了LSSO-Service的设计思想和工作原理,并通过在国内某大型水利信息门户中的应用实践,说明该研究结果对于门户建设具有较高的理论意义和参考价值.展开更多
The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-...The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.展开更多
文摘信息门户的建设过程中需要容易实施且灵活高效的整合模式.为克服传统单点登录机制无法满足在动态松耦合环境下实现快速整合的缺陷,结合面向服务架构思想,提出一种轻量级门户单点登录服务机制(LSSO-Service,Lightweight Single Sign-on Service),可为门户整合提供结构简单、完善通用、松散耦合、快速机动的单点登录服务.LSSO-Service基于高于对象层的分布式服务集成模式进行功能划分,可实现采用不同技术的应用系统在门户中的快速动态整合.阐述了LSSO-Service的设计思想和工作原理,并通过在国内某大型水利信息门户中的应用实践,说明该研究结果对于门户建设具有较高的理论意义和参考价值.
基金National Key R&D Program of China(No.2020YFB1707700)。
文摘The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.
