laser-propagation

Simple multiobjective optimization

多核程序设计比较好的资料。Windows的代码资料，比较全面实用可靠(multiple core program design)

改进型pso ,可以在matlab7.0环境下运行,其他版本的还未运行过,不能保证(Improved pso, can run matlab7.0 environment, other versions have not yet run, does not guarantee)

计算电磁学，本程序用于电场积分方程求解圆柱散射。(Electric field integral equation (EFIE) for 2-D cylinder scattering. )

A {@code CharacterCodingException} is thrown when an encoding or decoding error occurs.

混沌matlab工具箱免费拨接版本，无限次试用(Chaos the matlab toolbox free dial-up version, unlimited trial)

matlab prectise material

All codes in Matlab.

The model(DFIG) provided is as simple as is appropriate for bulk power system dynamic studies. It is valuable to put the model limitations in the context of what analysis is required

为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识 别。对同一负荷下的已知故障信号进行变分模态分解，利用 奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法 应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value decomposition was proposed for fault diagnosis using standard fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were decomposed by VMD, and the modes characteristics were further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the test samples were clustered by a Hamming nearness approach, and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under variable loads. By comparing with a method based on EMD, this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)