optimal-design

Compaq Visual Fortran Error Messages，对于利用CVF编程的programmer是本很好的对照手册。(Compaq Visual Fortran Error Messages, programming, programmer for the use of CVF is a very good control of the manual.)

说明：  谐波傅里叶分解，旨在把复杂多变的曲线方程分解成各次规律的正弦波(Harmonic Fourier Decomposition)

读取RENIX文件（GPS导航电文），进行卫星轨道坐标解算，并将结果生成TXT文件输出。(The program can read standard RENIX file(Satellite navigation message file of GPS). and calculate the cordinate of Satellite (X,Y,Z). it will give an TXT format result.)

说明：  Kim 的非线性有限元分析经典教材，教材很经典，对于静态有限元分析讲的很透彻，书中附有matlab代码(Kim's nonlinear finite element analysis classic textbook, the textbook is very classic, for static finite element analysis is very thorough, the book with matlab code)

转子动力学研究中完整的支承松动数值仿真程序(Rotor dynamics study supports the complete relaxation of numerical simulation program)

运用物理光学法和矩量法计算带罩天线的远场辐射(The use of physical optics method and moment method with cover antenna far-field radiation )

计算材料学中用相场法模拟凝固相变以及晶粒长大的开源性程序(Computational Materials Science solidification using phase-field simulation of grain growth phase transitions and open-source program)

在visual c++6.0的环境下，使用C++语言编写的最小二乘法程序。(In visual c++6.0 environment, using C++ language least squares procedure.)

关于一的微分方程数值解,一维波动方程初边值问题Crank-Nicholson格式,及追赶法(On one of the numerical solution of differential equations, one-dimensional wave equation Crank-Nicholson format, and to catch up with the Law)

Matlab codes for Robust PCA multivariate control chart(Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (MCD). In MCD technique, finding a subset containing half of the data such that its covariance matrix has the lowest determinant, then using this subset to calculate the robust mean and the robust covariance matrix (Hubert, Rousseeuw, & Branden, 2005) Step2 Standardize data using robust mean and robust standard deviation from Step1. Apply PCA analysis, calculate the principalcomponent score matrix Y=ZA, where Z is the robust standardized data matrix, and A is p*p matrix of eigenvectors (also called principalcomponents))