example4

工作是棋盘图像二值化和棋子识别。针对棋盘全局二值化存在的问题,提出了基于相邻像素灰度差阈值的棋盘图像二值化方法。(The work is to binarize the chessboard image and recognize the chessboard. Aiming at the problem of global binarization of chessboard, a method of binarization of chessboard image based on gray difference threshold of adjacent pixels is proposed.)

IP地址监测和端口扫描-monitoring IP address and port scanning

ad hoc网络mac层协议修改和仿真源程序(ad hoc network of mac layer protocol source code modification and simulation)

Q-learning 算法实现AGV的最优路径规划，实测效果非常好，对于研究深度学习和强化学习的同学很有帮助！(The Q-learning algorithm realizes the optimal path planning of AGV, and the measured results are very good. It is very helpful for students who are studying deep learning and reinforcement learning.)

根据k-shell算法,对网络进行划分,得到每一层的子网(k-shell )

能够建造一个简单无线网络并进行仿真 需要在NS2中实现(Able to build a simple wireless network and the need to achieve simulation in NS2)

捕获数学函数异常-capture mathematical function abnormalities

此代码使用 C 到套接字的一部分，你可以节省大量的时间阅读我的代码，用容易理解的评论后的程序

多智能体系统简单的一致性仿真，图形实现出来。(Multi-agent system simple consistency simulation graphics to achieve them.)

说明：  针对短期风电功率预测，提出一种基于经验小波变换预处理的核极限学习机组合预测方法。首先采用 EWT 对风电场实测风速数据进行自适应分解并提取具有傅立叶紧支撑的模态信号分量，针对每个分量分别构建 KELM 预测模型，最后对各个预测模型的输出进行叠加得到风速预测值并根据风电场风功特性曲线可得对应风电功率预测值。(Aiming at short-term wind power prediction, a kernel-based learning machine combination prediction method based on empirical wavelet transform preprocessing is proposed. Firstly, the EWT is used to adaptively decompose the measured wind speed data of the wind farm and extract the modal signal components with Fourier tight support. The KELM prediction model is constructed for each component. Finally, the output of each prediction model is superimposed to obtain the wind speed prediction value. The wind power characteristic curve of the wind farm can be obtained corresponding to the predicted wind power.)