High

半二次优化算法，研究。软阈值公式推导。最早的一篇论文。非常有参考价值。(Half-quadratic optimization algorithms, research. Soft threshold formula derivation. The earliest paper. Very valuable reference.)

斯坦福大学计算机系Andrew N.G教授于2011年CVPR发表文章Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis，文件是实现的code，很有用哦(Andrew NG, Professor of the Department of Computer Science at Stanford University published in 2011 CVPR article Learning hierarchical spatio-temporal features for action recognition with independent Sbspaces are analysis, file implementation code very useful oh)

工程领域中的格子波尔兹曼方法，很经典的书籍，内含较多的代码(Engineering fields Lattice Boltzmann Method, classic books, containing more code)

求解变分不等式的两种外梯度算法对比。包括一种修正的外梯度法及其优化方法。(Solving Variational Inequality gradient algorithm for comparison of two things. Including an amendment to the outer gradient method and its optimization.)

memetic algorthium in the distributed generation enivorment

DataDemon-时频分析解决方案 随着科技迅速发展，讯号分析处理者面临的是日趋庞大的计算数据量，和越来越复杂的仿真模式。如何缩短开发时间、节省研究经费、又能实现高效能、高弹性的运算平台供后续研究使用便越来越重要。 DynaDx提出DataDemon-时频分析解决方案，以组件组合的设计概念，免除繁杂程序写作方式，提供讯号处理的可视化控制组件，让处理讯号分析的过程就像拼组模型一般，使用者可依据自己的构想去组合出更多元之讯号分析呈现。不再需要从头撰写繁杂的程序代码便能使用各种讯号处理的算法，也不必将时间花在除错、评估程序运行效能，而能专注于研究目标的分析及后续应用的开发上。 (DataDemon-time-frequency analysis solutions With the rapid development of science and technology, signal analysis and processing is increasingly faced by the huge amount of data calculation, and increasingly complex simulation models. How to reduce development time, save research funding, but also to achieve high performance, flexible computing platform for subsequent research use have become increasingly important. DynaDx DataDemon-time frequency analysis proposed solutions to the design concept of component combinations, eliminating the complicated procedures written in a way to provide a visual signal processing control unit, so that the signal processing analysis process as spelled group of models in general, users can be based on their own idea to mix the signal out more element analysis presented. No longer need to write complicated code from scratch, will be able to use a variety of signal processing algorithms, nor do you have time to spend debugging, performance evaluation pro)

Web数据挖掘(世界著名计算机教材精选),作者BingLiu刘冰 刘兵(BingLiu)，伊利诺伊大学芝加哥分校(UIC)教授，他在爱丁堡大学获得人工智能博士学位。刘兵教授是Web挖掘研究领域的国际知名专家，在Web内容挖掘、互联网观点挖掘、数据挖掘等领域有非常高的造诣，他先后在国际著名学术期刊与重要国际学术会议(如KDD、WWW、AAAI、SIGIR、ICML、TKDE等)上发布关于数据挖掘、Web挖掘和文本挖掘论文一百多篇。刘兵教授担任过多个国际期刊的编辑，也是多个国际学术会议(如WWW、KDD与AAAI等)的程序委员会委员。 本书旨在阐述web数据挖掘的概念及其核心算法，使读者获得相对完整的关于web数据挖掘的算法和技术知识。本书不仅介绍了搜索、页面爬取和资源探索以及链接分析等传统的Web挖掘主题，而且还介绍了结构化数据的抽取、信息整合、观点挖掘和Web使用挖掘等内容，这些内容在已有书籍中没有提及过，但它们在Web数据挖掘中却占有非常重要的地位。全书分为两大部分：第一部分包括第2章到第5章，介绍数据挖掘的基础；第二部分包括第6章到第12章，介绍Web相关的挖掘任务。 (Web data mining (world-renowned computer materials selection), the authors BingLiu Liu Bing Liu Bing (BingLiu), University of Illinois at Chicago (UIC) professor, Ph.D. in Artificial Intelligence at Edinburgh University. Professor Liu Bing is the field of Web mining research internationally renowned expert in Web content mining, Internet, view mining, data mining and other areas have very high attainments, he worked in internationally renowned academic journals and major international conferences (such as KDD, WWW, AAAI, SIGIR, ICML, TKDE, etc.) on the release of data mining, Web mining and text mining papers more than a hundred articles. Professor Liu Bing served as editor of several international journals, but also a number of international conferences (such as WWW, KDD and AAAI, etc.) of the Program Committee. This book seeks to explain the concept and web data mining of the core algorithm, so that readers gain a relatively complete information on web data mining algorithms )

Lbm用于模拟顶盖驱动流 强制对流 碰撞迁移过程(LBM for lid driven flow)

针对传统煤矿节能减排优化模型选取的目标函数比较单一的问题,构建了涵盖经济效益、能源消耗、污染物排放量等目标函数的煤矿节能减排多目标优化模型,并应用基于改进的蝙蝠算法寻找3个目标函数之间的优化解,实现了经济效益最大化、能源消耗最低化、污染物排放量最少化的优化结果。仿真结果表明,相比于PSO-E、NSGA-II算法,改进的蝙蝠算法能够在较短的迭代步数内获取较高的个体适应度,且能够实现较佳的多目标优化结果,符合节能规划的目标需求。(Aiming at the problem that the objective function of the traditional coal mine energy saving and emission reduction optimization model is relatively simple, the multi-objective optimization model of coal mine energy saving and emission reduction is constructed, which covers economic benefit, energy consumption and pollutant emission. And the optimized solution is used to find the 3 target functions based on the improved bat algorithm, and the economy is realized. The optimization results of maximizing efficiency, minimizing energy consumption and minimizing pollutant emissions are achieved. The simulation results show that, compared with the PSO-E and NSGA-II algorithms, the improved bat algorithm can obtain higher individual fitness within a shorter iteration step, and can achieve better multi-objective optimization results, which is in line with the target demand of energy saving planning.)

基于稀疏分解的宽带信号DOA估计方法，使用了基于贝叶斯的方法具有良好的估计精度和分辨率(Wideband signal sparse decomposition DOA estimation method based on the use of a method based on Bayesian estimation has good accuracy and resolution)