dd-160128fc-2a.pdf.tar

说明：  图像处理，压缩感知中ROMP的代码实现。(Image processing, compression-aware in the ROMP of code to achieve.)

Fuzzy logic notes in pdf

说明：  A document file talking bout smart grid

混合动力汽车（HEV）是在电动汽车发展过程中出现的一种低油耗、低排放、续驶历程长、价格适中，兼具纯电动汽车和传统燃油汽车优点的新型汽车，由于在技术、经济和环境等方面的综合优势，HEV 被认为是目前最切实可行的清洁汽车方案。本文以混合动力轿车项目及其整车控制系统子课题为背景，主要研究HEV 的控制策略及其在实车上的应用。包括用matlab对汽车及其控制系统建模优化。(Hybrid electric vehicle (HEV) is in the process of development of electric vehicles the emergence of a low fuel consumption, low emission, long-driving course, affordable, both pure electric vehicles and conventional fuel vehicles the benefits of new cars, due to technical, economic and environmental aspects of the comprehensive advantages, HEV is considered to be the most viable clean vehicle program. In this paper, hybrid cars and the vehicle control system sub-project issues as background, the main research HEV control strategy and its application in the real car. Including the use of matlab modeling of the vehicle and its control system optimization.)

说明：  超紧耦合是 GPS/INS 组合导航系统的最新研究方向，它采用 INS 测得的载体动态信息辅助 GPS 接收机跟踪环 路，消除卫星信号中由于载体与卫星之间相对运动所产生的频率偏移，提高接收机在高动态环境下载波跟踪性能，同时还 可以压缩带宽，有效增强接收机抗干扰性能。介绍 GPS/INS 超紧耦合技术，给出 INS 辅助 GPS 载波跟踪环路结构图和相应 的数学模型，分析 INS 辅助 GPS 跟踪环路在动态环境下的动态残留及其对环路相位误差的影响，并进行了模型仿真，最后 对仿真结果进行了验证分析。仿真结果显示，辅助动态残留及其导致的环路相位误差的大小与加加速度、环路辅助时间间 隔成正比。 (The ultra-tightly coupled GPS/INS integrated navigation system latest research direction, it adopted the INS measured the carrier dynamic information auxiliary GPS receiver tracking loop to eliminate the satellite signal frequency due to the relative motion between the carrier and the satellite offset improve the receiver in high dynamic environment download wave tracking performance, but also can compress bandwidth, effectively enhance the anti-jamming performance of the receiver. Introduced GPS/INS ultra-tight coupling technology gives INS assisted GPS carrier tracking loop diagram and the corresponding mathematical model to analyze the impact of the INS assisted GPS tracking loop dynamic residues in a dynamic environment and the loop phase error, and model simulation, and analysis of simulation results verified. Simulation results show that the the residual secondary dynamic its resulting size of the error loop phase and jerk loop secondary is proportional to the time interval.)

本PPT着重研究了当前手游行业推广渠道的各种方式以及每种方式的利弊(The PPT focuses on the current hand travel industry marketing channels as well as the various ways the pros and cons of each approach)

most important paper in energy detection and alla paper refer to it

说明：  4kw便携式蓄电池的研究，对便携式的开发具有很好的指导意义，欢迎下载交流。(4kw portable battery research on the development of portable has a good guide, welcome to download the exchange.)

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 )

This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.