SSC

说明：  自由振动，受迫振动，Newmark状态空间纽马克法，Wilson威尔逊法。(Free vibration, forced vibration, Newmark state space Newmark method, Wilson Wilson method.)

this is my cv for example

基于pscad平台搭建的风机模型，可以用于次同步振荡或者是电力系统稳定性的研究(Wind turbine model based on PSCAD platform can be used to study subsynchronous oscillation or power system stability)

说明：  可以将CTD的压力、温度和电导率转换为深度、盐度、声速和密度(Convert CTD raw data to Depth, salinity, sound velocity and density.)

智力游戏源码,用VC编写,可以用于台机机与PC上的游戏开发-Intellectual game source, use VC to prepare, can be used in Taiwan

一份串口通信的好资料,对初学串口者很有用滴-A good serial communication data of the serial are very useful beginner drops

这是关于迁移学习的一个slides，介绍了当前迁移学习的一些进展和前沿工作。(This is a slides about migration learning. It introduces some progress and frontier work of current transfer learning.)

固高科技 GT400 系列控制软件开发 插补练习中双轴同时运动 立即插补和缓冲区插补(GT400 series of solid high-tech exercise control software interpolation axis interpolation and also sports an immediate buffer interpolation)

说明：  实现搜索功能 在百度百科中自由发挥 是随意搜索 不对的另找(Implementing Search Function)

说明：  用N来表示训练用例（样本）的个数，M表示特征数目。 输入特征数目m，用于确定决策树上一个节点的决策结果；其中m应远小于M。 从N个训练用例（样本）中以有放回抽样的方式，取样N次，形成一个训练集（即bootstrap取样），并用未抽到的用例（样本）作预测，评估其误差。 对于每一个节点，随机选择m个特征，决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征，计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝，这有可能在建完一棵正常树状分类器后会被采用）。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)