zig

这是一个边缘检测程序,提取最大连通区域的边缘(This is an edge detection program to extract the edge of the largest connected region)

输入年月日显示星期 输入年月日显示星期(输入年月日显示星期)

MVDR自适应波束形成的LMS算法使用，LMS算法迭代次数对波束形成结果的影响(MVDR adaptive beamforming using the LMS algorithm, LMS algorithm is the number of iterations on the results of beam-forming)

说明：  A simple analytical model that predicts DSM spurious tones with arbitrary stimulus

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

对局部放电信号去噪的性能进行评价，RMSE值越接近于1，去噪效果越好(The performance of partial discharge signal denoising is evaluated, and the closer the RMSE value is to 1, the better the denoising effect is)

对一电力网络实现顺流跟踪，计算分流比例因子，深度优先搜索供电路径，计算发电机在负荷中的分配比例等。(Downstream of a power network tracking, triage scale factor calculation, depth-first search powered path, calculating the distribution of generators in the load ratio.)

独立向量分析（IVA）是对独立成分分析（ICA）算法的一种扩展，将ICA中的单变量成分扩展为多维变量成分，可有效避免卷积盲源分离过程中的排序模糊性问题。(Independent vector analysis (IVA), a multivariate extension of independent component analysis, tackles the convolutedly mixed blind source separation (BSS) problem in a way to avoid the permutation problem by employing a multivariate source prior of the short-time Fourier transform (STFT) components. )

说明：  基于MATLAB psat的双机六节点系统(IEEE6_bus,can run well)

simulink的入门教程，老师上课讲的课件，较易懂，适用于初学者(simulink introductory tutorial, the teacher talked about class courseware, more easy to understand for beginners)