在水利水电工程领域,应用信息技术较普及的是计算机辅助设计技术,然而这些研究和应用仅仅表现的是图形本身的几何属性,并没有将一些关键的物理属性、拓扑等信息融入到图形之中,忽略了图形和信息的融合。本文基于BIM理论,提出水利水电工...在水利水电工程领域,应用信息技术较普及的是计算机辅助设计技术,然而这些研究和应用仅仅表现的是图形本身的几何属性,并没有将一些关键的物理属性、拓扑等信息融入到图形之中,忽略了图形和信息的融合。本文基于BIM理论,提出水利水电工程的图形信息模型(HPIM,Hydropwer Project Information Modeling)的总体框架,通过CAD中的三维几何造型引擎(ACIS)和拓扑运算用基本图元构造出工程几何形态,并将图形运算与图元扩展数据贮存相结合,实现图形与信息的融合。构造出的图形信息模型为水利水电工程不同阶段的应用提供简洁的共享的方法。展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
文摘在水利水电工程领域,应用信息技术较普及的是计算机辅助设计技术,然而这些研究和应用仅仅表现的是图形本身的几何属性,并没有将一些关键的物理属性、拓扑等信息融入到图形之中,忽略了图形和信息的融合。本文基于BIM理论,提出水利水电工程的图形信息模型(HPIM,Hydropwer Project Information Modeling)的总体框架,通过CAD中的三维几何造型引擎(ACIS)和拓扑运算用基本图元构造出工程几何形态,并将图形运算与图元扩展数据贮存相结合,实现图形与信息的融合。构造出的图形信息模型为水利水电工程不同阶段的应用提供简洁的共享的方法。
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
