2DPhC_PWEBandstructure

有限元弧长法控制计算的原理，中文英文的都有，并介绍了具体程序的流程图(Finite element calculation of arc-length method control principle, Chinese English have, and describes specific procedures flowchart)

基于Matlab的RCS(雷达散射截面）计算案例。 利用时域有限差分法(FDTD)的计算电磁学手段，给出了较为准确的结果。(An exact example to calculate the RCS based on Matlab. Using the FDTD method, it indicates that the simulation is quite acurate.)

利用扩展卡尔曼算法( EKF)对高动态直接扩频信号的载波相位和频率进行了估计,并利用载波辅助技术测量载波相位的变化值来 校正伪码延时环,减小多普勒频移对伪码延时的影响,得到了精确的延时估计值,提 高了伪码延时锁定环的动态跟踪性能.(The PN delay loop is adjusted through measuring the varied value of carrier phase with carrier aiding technology. The effect of Doppler shift on PN code delay is reduced through carrier aid- ing technology. The precise delay estimation is obtained. The dynamic tracking performance of the PN delay loop is improved.)

点云曲面建模，基于距离函数，运用移动最小二乘法，拟合曲面，提取等值面，用于散乱点云的快速三角网格构建(Point cloud surface modeling, based on the distance function, the use of moving least squares method, fitting surface, isosurface extraction for fast scattered point cloud build triangular mesh)

多点高斯平稳随机过程模拟，运用了快速傅里叶变换进行模拟。(Multi-Gaussian stationary random process simulation, using a fast Fourier transform simulation.)

berkeley加州大学SAP系列软件之SolidSAP，由Edward L.Wilson教授开发的三维结构源程序，功能强大，代码清晰，含学习手册。(SolidSAP, a three-dimensional structure source program developed by Professor Edward L. Wilson, is a software of the SAP series at the University of California, Berkeley. It has powerful functions, clear code and learning manual.)

fluent软件UDF，用于焊接过程模拟，是高斯分布的面热源。(weld heart source)

用基于拉格朗日函数Hesse矩阵的SQP方法求解约束优化问题(Based on Lagrangian with SQP Hesse matrix method for solving constrained optimization problems)

一个计算结构波浪力的程序，小直径桩柱结构，中等直径桩柱结构，多桩结构(A program for calculating the wave force of the structure, a small-diameter pile structure, a medium-diameter pile structure, a multi-pile structure)

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))