Abaqus非线性教程

python的学习笔记 很赞的说，深入浅出，我爱python-learning python is Chan notes that in layman

微信小程序，将数据用柱状图，饼状图，折线图表示出来(WeChat small program, the data is represented by histogram, pie chart and polygon map.)

说明：  SLIC算法是simple linear iterative cluster的简称，该算法用来生成超像素（superpixel） 算法步骤： 已知一副图像大小M*N,可以从RGB空间转换为LAB空间，LAB颜色空间表现的颜色更全面 假如预定义参数K，K为预生成的超像素数量，即预计将M*N大小的图像(像素数目即为M*N)分隔为K个超像素块，每个超像素块范围大小包含[（M*N）/K]个像素 假设每个超像素区域长和宽都均匀分布的话，那么每个超像素块的长和宽均可定义为S，S=sqrt(M*N/K) 遍历操作，将每个像素块的中心点的坐标(x,y)及其lab的值保存起来，加入到事先定义好的集合中(Algorithmic steps: Given the size of an image M*N, it can be converted from RGB space to LAB space, and the color space of LAB is more comprehensive. If the predefined parameter K, K is the number of pre-generated super-pixels, i.e., the image of M*N size (the number of pixels is M*N) is expected to be divided into K super-pixel blocks, each of which contains [(M*N)/K] pixels. Assuming that the length and width of each super-pixel region are evenly distributed, the length and width of each super-pixel block can be defined as S, S = sqrt (M*N/K). The traversal operation saves the coordinates (x, y) and lab values of the central points of each pixel block and adds them to a pre-defined set.)

该文档详细介绍了supermap的一些基本知识，让用户能详细了解supermap。-The document detailed the SuperMap some basic knowledge, so that users can learn more about supermap.

三相异步电机经典的分步参数辨识MATLAB仿真模型，实现对定子电阻、转子电阻、漏感和互感的辨识。(The classical step-by-step parameter identification MATLAB simulation model of three-phase asynchronous motor can identify stator resistance, rotor resistance, leakage inductance and mutual inductance.)

说明：  修改了apache-ant里面部分的源码，可以在使用的时候更加好用(Modified Apache ant part of the source code, can be used more easily)

Markov方法的预测大全，很好应用的工具与方法；Markov method of forecasting Daquan, very good tools and methods of application(Markov method of forecasting Daquan, very good tools and methods of application)

在matlab中将upfc应用到实际的电力系统中(In matlab, upfc was applied to the actual power system)

说明：  使用卷积神经网络进行高光谱遥感数据分类，使用的数据源为Pavia University高光谱数据 文件夹log--日志文件夹，存放TensorBorad日志、网络参数文件、混淆矩阵图 文件夹Patch--存放数据处理的切片结果 文件夹PaviaU--高光谱数据下载存放位置 文件夹predicted--CNN对原始影像的分类结果 data.py--对原始高光谱影像进行数据处理，生成切片 net.py--神经网络模型 train.py--训练神经网络 utils.py--需要用到的函数 show.py--使用训练好的神经网络模型对原始数据进行分类(Using convolution neural network to classify hyperspectral remote sensing data, the data source is Pavia University hyperspectral data. Folder log -- Log folder, store TensorBorad logs, network parameter files, obfuscation matrix diagrams Folder Patch -- Stores slice results of data processing Folder PaviaU -- Place for Hyperspectral Data Download and Storage Folder predicted -- CNN classification results of original images Data.py -- Data processing of original hyperspectral images to generate slices Net.py--Neural Network Model Train.py--Training Neural Network Utils. py -- Functions to be used Show.py -- Classification of raw data using trained neural network model)

说明：  采用DAQ设计的多通道数据采集系统。该系统具有通道选择和显示的功能(A multi - channel data acquisition system designed by DAQ. The system has the function of channel selection and display)