rayleigh

本教程面向的是那些从未使用过LabWindows/CVI的读者，但是假设读者以有了C语言的基础并且熟悉Windows 2000/9x/NT操作系统。在每一章节的学习中，作者都是通过一个具体的实例让读者迅速的掌握本章的知识点；而不是长篇大论，述及边枝细叶，反而使读者望而却步，只见树木，不见森林。(This tutorial looks for readers who have never used LabWindows/CVI, but assumes that the reader has the basics of the C language and is familiar with the Windows 2000/9x/NT operating system. In each chapter of the study, the author is through a concrete instance allows readers to quickly grasp the knowledge of this chapter; instead of the side branches and leaves a long and minute statement, instead, make the reader prohibitive, cannot see the wood for the trees.)

USB 协议基础及ch375 usb芯片-USB protocol basis and ch375 usb chip

java编程,学习简单的flink，了解其基本语法，flink的应用场景。(Java programming, learn simple flink, understand its basic grammar, Flink application scenarios.)

matlab code for CST about homework

UDDS工况下的电流数据，可以带入MATLAB中进行仿真。(Current data under UDDS conditions can be brought into MATLAB for simulation.)

说明：  流数据聚类算法 基于密度的Denstream算法python源代码(Denstream steam data clustering method)

手机游戏连连看的主要代码，手机游戏连连看的主要代码-Mobile Games Lianliankan the main code, mobile game Lianliankan major code

产生一组均值为1，方差为4的正态分布随机序列（1000个样本），估计该序列的均值与方差(A set of normal distribution random sequences (1000 samples) with mean value of 1 and variance of 4 is generated, and the mean and variance of the sequence are estimated)

说明：  该文基于模型预测控制理论， 提出一种考虑多时空尺度协调的风电集群有功分层预测控制方法。 该方法将风电集群在空间尺度上分为三层控制层，将超短期风电功率预测值在时间尺度上逐层细化，并对各层滚动优化环节进行了优化建模。 仿真分析中通过与常用有功分配方法对比， 结果表明文中所提方法在保证系统安全运行和满足集群计划的同时，能够提高风电消纳能力，合理利用风能资源和分配集群内风电功率。(Based on the model predictive control theory, this paper proposes a wind power cluster active layered predictive control method that considers multi-temporal scale coordination. The method divides the wind power cluster into three layers on the spatial scale, and predicts the ultra-short-term wind power prediction value on a time scale, and optimizes the rolling optimization of each layer. Compared with the commonly used active power allocation method in the simulation analysis, the results show that the proposed method can improve the wind power consumption capacity, rationally utilize the wind energy resources and allocate the wind power in the cluster while ensuring the safe operation of the system and meeting the cluster plan.)

2018年IEEE进化计算大会（CEC）提出的全局优化问题的新的启发式算法。 土狼优化算法（COA）是由Juliano Pierezan和Leandro dos Santos Coelho（2018）提出的用于全局优化的自然启发的元启发式算法，包含完整matlab代码。不同于灰狼算法，COA算法并没有等级制度。(In 2018, IEEE conference on evolutionary computation (CEC) puts forward a new heuristic algorithm of global optimization problems. Hyenas optimization algorithm (COA) is by Juliano Pierezan and Leandro DOS Santos Coelho, (2018) proposed a heuristic algorithm for global optimization of nature inspired, contains the complete matlab code. Of COA is different from the grey Wolf is not hierarchical)