Code

Matlab环境下基于OPC技术实现动态矩阵控制 Matlab环境下基于OPC技术实现动态矩阵控制(Matlab environment based on OPC technology to achieve dynamic matrix control Matlab environment OPC technology based on Dynamic Matrix Control)

这个是空间谱估计中阵元互耦校正算法，自己写的。(This is the spatial spectrum estimation Mutual Coupling correction algorithm, wrote it myself.)

This is a code for calculating PSNR of two images.

mean-shift algorithm matlab code

Denmark Technical University的大牛Hansen，第二版风力机空气动力学。(Denmark Technical University' s Daniel Hansen, the second edition of wind turbine aerodynamics.)

matlab编写的航迹控制程序，对于初学者能够起到重要启发作用(The matlab track written control procedures, can play an important inspiration for beginners)

computer architecture and parallel processing

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

已知串联机构各个构件的运动，求取其运动所需的力，和其运动学逆解计算(Serial mechanism known to all members of the movement, the force required to strike the motion, and the calculation of inverse kinematics)

This is the function programmes of fan in an system noise removal