linear_subspace_projection_PhD

灰色模型的MATLAB实现，用于预测模型，是数学建模中的常用模型。(GM(1,1)model matlab )

现有的几个网络拓扑随机发生器，其实很难生成理想的网络拓扑结构，其主要原因在于很难控制节点的疏密和间距。我们提出来的这个改进算法，在随机抛撒节点的时候使用了K均值聚类，由本算法作为网络拓扑发生器，网络节点分布均匀且疏密得当，边的分布也比较均衡(The few existing random network topology generator, is in fact very difficult to generate the desired network topology, the main reason it is difficult to control the node density and spacing. We put forward the improved algorithm, throw in random nodes when using the K-means clustering, by the algorithm as a network topology generator, network nodes and spacing evenly distributed properly, the edge of a more balanced distribution of)

removing after shocks in earthquake catalouge

存在麦克风增益相位误差时候的宽带波束形成器设计,采用最小二乘方法来实现。 (Phase error exists when the microphone gain broadband beamformer design, using the least squares method to implement.)

nous intéressons à la détection et à la localisation de défauts dans des systèmes dynamiques en combinant simultanément l’analyse en composantes principales et la transformée en ondelettes. Ce procédé d’analyse et de traitement, connu souvent sous le nom de MSPCA, permet à la fois l’extraction de la corrélation entre les différents capteurs (approche ACP) et le calcul de l’auto corrélation pour chaque capteur (approche ondelette).

交流电机调速系列教材之一交流电机调速理论(AC Motor Speed Control Series of motor control theory textbooks)

Mathematica教程，介绍如何启动Mathematica软件，如何输入并运行命令(Mathematica5 study)

说明：  线性判别分析（LDA）：将高维的样本投影到最佳鉴别矢量空间，来达到抽取分类信息和压缩特种空间维数的效果，投影后保证样本在新的子空间有最大的类间距离和最小的类内距离。(Linear discriminant analysis (LDA): projects high-dimensional samples into the best discriminant vector space to extract classification information and compress the dimension of special space. After projection, it ensures that the samples have the maximum inter class distance and the minimum intra class distance in the new subspace.)

基于MATLAB/SIMULINK模块，搭建了基于下垂控制策略的海洋波浪发电系统的仿真模型(Based on MATLAB/SIMULINK module, built a simulation model based control strategy drooping ocean wave power generation system)

very important to understand tomography of medical MRI images