光伏

C est le code source en langage C du jeu du morpion. Une "intelligence artificiel" est programmée.

交流白色LED灯驱动电路的浅析，测试表明已达到预期的效果(The exchange of the white LED lamp driver circuit of the analysis, testing has indicated that the desired results)

说明：  以悬臂梁为例，用有限元法编程生成质量矩阵和刚度矩阵（采用平面梁单元，单元数在10到30之间）。 使用matlab的用于广义特征值问题分析的函数（eig(A,B,'chol')，该命令使用乔莱斯基分解的方法，计算对称矩阵A和对称正定矩阵B的广义特征值问题。A对应刚度矩阵，B对应质量矩阵）计算梁的前三阶固有频率和振型，计算得到梁的前三阶固有频率和振型，并画出振型图。(Taking cantilever beam as an example, the mass matrix and stiffness matrix are programmed by finite element method (using planar beam elements, with units ranging from 10 to 30). Using Matlab's function (eig (A, B,'chol') for the generalized eigenvalue problem analysis, the command uses the Jo Lessky decomposition method to calculate the generalized eigenvalue problem of the symmetric matrix A and the symmetric positive definite matrix B. A corresponds to the stiffness matrix and B corresponding to the mass matrix. The first three natural frequencies and modes of the beam are calculated, and the first three natural frequencies and modes of the beams are calculated, and the vibration pattern is drawn.)

说明：  copula函数的应用代码。Copula函数描述的是变量间的相关性，需要安装一个Patton_copula_toolbox工具箱。(The application code of Copula function. Copula function describes the correlation between variables, and requires a Patton to be installed_ copula_ Toolbox.)

超市购物系统 输入数量 价格 折扣 就会给出您应该付多少钱(Supermarket shopping system, enter quantity, price discount will give you how much you should pay)

说明：  瑞萨R5F1000FG 串口，LCD显示(Risa R5F1000FG Serial Port, LCD Display)

JSON2 JS http://www.JSON.org/json2.js 2009-06-29 Public Domain. NO WARRANTY EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK. See http://www.JSON.org/js.html -JSON2 for javascript http://www.JSON.org/json2.js 2009-06-29 Public Domain.

基于多基站的无源定位中的FDOA方法的定位精度GDOP分析(FDOA GDOP)

说明：  学习ofdm很实用的书籍，还有相应的硬件实现(Very useful books to learn OFDM)

K-means算法是基于划分的思想，因此算法易于理解且实现方法简单易行，但需要人工选择初始的聚类数目即算法是带参数的。类的数目确定往往非常复杂和具有不确定性，因此需要专业的知识和行业经验才能较好的确定。而且因为初始聚类中心的选择是随机的，因此会造成部分初始聚类中心相似或者处于数据边缘，造成算法的迭代次数明显增加，甚至会因为个别数据而造成聚类失败的现象。(K-means algorithm is based on the idea of partitioning, so the algorithm is easy to understand and the implementation method is simple and feasible, but it requires manual selection of the initial number of clusters, that is, the algorithm is with parameters. The number of classes is often very complex and uncertain, so professional knowledge and industry experience are needed to better determine. Moreover, because the selection of initial clustering centers is random, some initial clustering centers will be similar or at the edge of data, resulting in a significant increase in the number of iterations of the algorithm, and even the phenomenon of clustering failure due to individual data.)