shape-optimization

非常全的PPT元素，适合做各类报告，可以使你的ppt变得更加多元化。(PPT elements)

电力系统可靠性教材，电力系统可靠性评估分析(Power System Reliability Textbook, Power System Reliability Assessment and Analysis)

In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content-based searching. In this paper we survey some technical aspects of current content-based image retrieval systems.

汪利宏mcml使用说明文档，包括如何设置光束参数等(WANG Li-hong mcml the use of documentation, including how to set the beam parameters, etc.)

说明：  codesys是一款针对控制器的编程语言，操作简单，针对性强(CoDeSys is a programming language for controllers, which is easy to operate and has strong pertinence.)

飞利浦LPC2114中文手册，是学习ARM的好资料。(Philips LPC2114 English manual, is to learn good information on ARM.)

lcc书籍的中文翻译.详细介绍lcc的源代码,数据结构等.是用来分析lcc源代码的最佳工具.(LCC books in Chinese translation. Details LCC source code data structure. LCC is used to analyze source code for the best tools.)

说明：  中国科学院网络安全工程师培训教材，可以领略一下中国最高科技机构的怎么教学的。(Chinese Academy of Sciences Network Security Engineer training materials, you can enjoy some of China)

说明：  Python是一种计算机程序设计语言。是一种面向对象的动态类型语言，最初被设计用于编写自动化脚本(shell)，随着版本的不断更新和语言新功能的添加，越来越多被用于独立的、大型项目的开发。(Python is a computer programming language. As an object-oriented dynamic typed language, it was originally designed to write automated scripts (shells). With the updating of versions and the addition of new language functions, it is increasingly used in the development of independent, large-scale projects.)

关于关联向量机应用的最新文献！ 提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)