Imageprocessing

电网三相短路电流仿真，可以自己设定哪个短路(Grid three-phase short-circuit current simulation can be set for which short-circuit)

计算大气折射修正，利用分段模型，采用matlab软件编程计算(matlab caculate the refraction of atmoshpere)

this is hopfield method in neuralNetwork

电压空间矢量ＳＶＰＷＭ仿真，希望对大家有帮助(Space voltage vector SVPWM simulation, we hope to help)

自适应波束形成技术程序代码（有注解） 包括1.均匀线阵方向图2.波束宽度与波达方向及阵元数的关系当阵元间距时，会出现栅瓣，导致空间模糊 类似于时域滤波，天线方向图是最优权的傅立叶变换5.最大信噪比准则方向图和功率谱6.ASC旁瓣相消----MSE准则7.线性约束最小方差(LCMV)准则(Adaptive beamforming technology code (Annotated))

matlab 一些文件，运用mabtf可以有很强的功能(Matlab some documents used mabtf can have a very strong function)

This report will provide some insight on the new incompressible CFD solver that will be available in version 980 of LS-DYNA.

完整的惯性领域解算数学模型及解算算法，可以进行理论仿真及实验数据验证。(Complete inertial field solver solver algorithms and mathematical models can be theoretical simulation and experimental data validation.)

设置GUI界面，计算闭合导线平差，获得平差结果并绘制图形(Set up the GUI interface, calculate the adjustment of the closed traverse, obtain the adjustment result and draw the graph)

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)