chuankou

超精美的网页TAB标签导航特效，TAB选项卡特效，网页选项卡，基于Prototype插件，还带有动画效果，简直就是完美了，同时还使用了moo.fx.pack.js，以及prototype.lite.js插件，选项卡被点击后，流畅的动画效果也是值得关注的一个亮点。

说明：  Petri网中XML根结点为net,其属性有id、type，其子结点有四个，分别为token，place，transition，arc。根据传入矩阵生成Petri网(In Petri nets, the root node of XML is net, and its attributes are ID and type. There are four sub-nodes: token, place, transition and arc. Generating Petri Nets from Input Matrix)

说明：  Example DokuSolver Demo

Delphi自动查找DSN读取SQL并打开数据表，程序基于原生Delphi实现，无需使用第三方控件，在Delphi7环境下直接编译即可，运行效果如测试截图所示。

说明：  用于计算面齿轮传动的误差分析，同时能够计算齿轮的啮合刚度，分析出传动精度(It can be used to calculate the error analysis of face gear transmission and the meshing stiffness of gears, and to analyze the transmission accuracy.)

实现科学计算器，为广大学生阶层提供技术支持，为下一步设计打下坚实基础(Realizing Scientific Calculator)

说明：  本文主要将蚁群算法用于解决连续函数优化的问题(This paper will be used for ant colony algorithm to solve the problem of continuous function optimization)

信息化是将来管理系统改进的主要方向 虽然电子化档案是重要的发展方向，但是实物档案也是重要的资产，特别是涉及保密内容时，两者同样重要 采用自动识别手段，提高操作效率是发展方向 实现档案在位监管，提高资产保全率是重要方向 减少实物档案的遗失等问题发生 从而避免财产损失，杜绝涉密事件的发生(Informatization is the main direction of the improvement of management system in the future. Although electronic archives are an important development direction, physical archives are also important assets, especially when it comes to confidentiality. Using automatic identification method to improve operational efficiency is the direction of development. It is an important direction to realize the supervision of archives in place and improve the asset preservation rate. Reduce the loss of physical files and other problems. So as to avoid property losses and eliminate secret incidents.)

一本很好的关于数控方面的PPT，她详细的说明了数控的历史和它的使用，并附带了很多有用的案例-A very good on the NC side of the PPT, her detailed description of the CNC

Hamming 神经网络从功能上来看是最小Hamming 距离分类器．利用它能够完成不完整输入信息与所存储模式的最小汉明距离分类． 　　 Hamming 网络是一个双层神经网络，第一层网（即匹配子网络）是用来计算输入模式与该网络已经学习过的各样本之间的匹配测度．第二层网（即竞争子网络）接收从匹配子网络送来的未知模式与已存各样本的匹配测度，然后经过多次迭代运算就可以求得与输入模式相匹配的样本．-Hamming neural network from the functional point of view is the smallest Hamming distance classifier. Use it to complete incomplete input information storage model with the smallest Hamming distance classifier. Hamming network is a double neural network, the first network layer (ie matching sub-network) is used to calculate the input model and the network has been studied by the match between the sample test degrees. The second layer network (that is, competitive-network) from the receiver-matching network sent an unknown model has been deposited with the samples matching measure, Then after many iterative computation can be obtained with the input mode matching sample.