Using SAS/IML : This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are

example we will measure a signal that is sparse in the time domain. We will use a random sensing matrix, and we will solve the recovery problem using the l1-Magic toolbox.

电网暂态分析是保证电网稳定运行的重要手段。随着电网广域测量系统(wide-area management system， WAMS)的发展，电网形成了具有时空特性的高维海量运行数据。传统的电网暂态分析采用物理模型，用严格的数学公式关联维度之间数据，这种模型不能充分利用海量电网运行数据，造成资源浪费。从数据驱动的角度，首先分析 WAMS 数据的应用情况，考虑电网运行数据特点建立数据模型。然后利用随机矩阵理论(random matrix theory， RMT) 建立平均谱半径(mean spect

基于随机矩阵理论的电网静态稳定态势评估方法-A Method for Power System Steady Stability Situation Assessment Based on Random Matrix Theory