介绍了电网管理信息系统(Management Information System,MIS)、数据仓库和联机分析处理(on-line analytical processing,OLAP)服务,分析了基于数据仓库的MIS系统中OLAP设计的系统架构。系统基于Web Service和SQL Server 2000,数据仓库...介绍了电网管理信息系统(Management Information System,MIS)、数据仓库和联机分析处理(on-line analytical processing,OLAP)服务,分析了基于数据仓库的MIS系统中OLAP设计的系统架构。系统基于Web Service和SQL Server 2000,数据仓库中的数据经过分析服务器处理后提供OLAP服务,客户端上的数据透视表服务通过HTTP连接分析服务器获得数据并存储到高速缓存中进行分析。展开更多
To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performan...To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software.Significant research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology.Meanwhile,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors.Machine learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing data.In this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented.Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion identification.Finally,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed.These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.展开更多
文摘介绍了电网管理信息系统(Management Information System,MIS)、数据仓库和联机分析处理(on-line analytical processing,OLAP)服务,分析了基于数据仓库的MIS系统中OLAP设计的系统架构。系统基于Web Service和SQL Server 2000,数据仓库中的数据经过分析服务器处理后提供OLAP服务,客户端上的数据透视表服务通过HTTP连接分析服务器获得数据并存储到高速缓存中进行分析。
基金support from National Natural Science Foundation of China(Nos.62274140,61904141,52173234)the State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCMS-E-0422G03)the Shenzhen-Hong Kong-Macao Technology Research Program(Type C,202011033000145,SGDX2020110309300301).
文摘To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software.Significant research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology.Meanwhile,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors.Machine learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing data.In this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented.Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion identification.Finally,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed.These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
