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Congress on Evolutionary Computation

作者： Min Zhang Wenjian Luo Xingxin Pei Xufa Wang Nature Inspired Computing and Application Laboratory Department of Computer Science and Technology University of Science and Technology China Anhui Key Laboratory of software in Computing Communication University of Science and Technology China University of Science and Technology of China Hefei Anhui CN Nature Inspired Computing and Application Laboratory Department of Computer Science and Technology of China University of Science and Technology China

A novel self-adaption strategy for the parameter epsiv in epsiv-MOEA is proposed in this paper based on the analyses of the relationship between the value of epsiv and the maximum number of non-dominated solutions. Then this novel strategy is applied in epsiv-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter s, epsiv-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper.

关键词： Evolutionary computation

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From Wrapping to Knowledge: Domain Ontology Learning from Deep Web

From Wrapping to Knowledge: Domain Ontology Learning from De...

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International Symposiums on Information Processing (ISIP)

作者： Zhiming Cui Pengpeng Zhao Wei Fang Chao Lin The Institute of Intelligent Information Processing and Application China Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781424430536;9780769531519

The next generation of the Web, called Semantic Web, has to improve the Web with semantic page annotations to enable knowledge-level querying and searches. However, manual construction of these ontologies is a time consuming and difficult task. In this paper, we describe an automatic extraction method that learns domain ontologies for semantic web from deep web. Our approach first learns a base ontology from deep web query interfaces, then grows the current ontology by probing the sources and discovering additional concepts and instances from the result pages. We have evaluated our approach in several real-world domains. Preliminary results indicate that the proposed extraction method discovers concepts and instances with high accuracy.

关键词： Wrapping Ontologies Semantic Web Information processing Databases Knowledge engineering Chaos application software Laboratories Machine learning

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Ontology-Based Deep Web Synchronous-Annotation

Ontology-Based Deep Web Synchronous-Annotation

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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： PengYu Hu Wei Fang ZhiMing Cui Jiangsu Provincial Key Laboratory of Computer Information Processing Technology The Institute of Intelligent Information Processing and Application Soochow University Suzhou China

An increasing number of databases have become Web accessible through HTML form-based search interfaces, which is so-called deep Web. For full utilization of deep Web resources and improving Web intelligence, which is essential for many applications such as deep Web data collection and comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present a synchronous-annotation approach that introduce domain ontology as a global schema ordered by Web databases to the annotation process. We combine ontology, interface schema and result schema and adopt the strategy of query ontology instance to implement annotation. In order to verify the effectiveness of the method proposed in this paper, we test on a number of different areas of Web databases. The experimental results indicate that the proposed approach is more effective than existing approaches.

关键词： Ontologies Databases Vocabulary Data mining Books Music Motion pictures

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SDWS: Semantic Search for Deep Web Data

SDWS: Semantic Search for Deep Web Data

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International Symposiums on Information Processing (ISIP)

作者： Wei Fang Zhiming Cui Pengyu Hu Li Huang Pengpeng Zhao Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou Jiangsu China Institute of Intelligence Information Processing and Application China

ISBN: (纸本)9781424430536;9780769531519

A lot of high quality and wealthy data are hidden in backend database and search engines can not index this page, which is called Deep Web. It is mostly accessible through query interfaces. SDWS, a semantic search engine for Deep Web is presented. We are studying and implementing semantic Web technology to the each process of Deep Web information integrated, and expertise in Deep Web discovering, annotating query results and integrating information. The novel approach promise better access to Deep Web.

关键词： Ontologies Search engines Semantic Web Information processing Portals Deductive databases application software Laboratories Indexes Context-aware services

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STUDY ON THE CONSTRUCTION OF DOMAIN TEXT CLASSIFICATION MODEL WITH THE HELP OF DOMAIN KNOWLEDGE

STUDY ON THE CONSTRUCTION OF DOMAIN TEXT CLASSIFICATION MODE...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： JIAN-YI GUO ZHENG-TAO YU LU HAN ZHI-KUN ZHANG XIANG-YAN MENG CUN-LI MAO The School of Information Engineering and Automation Kunming University of Science and Technology The School of Information Engineering and Automation Kunming University of Science and Technology The Institute of Intelligent Information Processing Computer Technology Application Key Laboratory

Traditional text classification model uses statistical methods to obtain features. But in the aspect of discrimination domain and non-domain text category, domain knowledge relations haven't been taken account of in these methods. A domain text classification model was presented in this paper. This model used the support vector machine learning algorithm, gained domain classification feature words through statistic and union domain words, structured domain classification feature space. With the help of domain knowledge relations, computed relevance between domain concepts, got domain classification feature weight. Finally domain text classification was realized. An experiment in the Yunnan tourism domain was carried on to confirm that domain knowledge relations have a good influence on the domain text classification. The classification accuracy rate has been increased 0.04 than improved TFIDF method.

关键词： Tezt classification Feature selection Domain knowledge Relations

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SHORT-TERM POWER LOAD FORECASTING WITH LEAST SQUARES SUPPORT VECTOR MACHINES AND WAVELET TRANSFORM

SHORT-TERM POWER LOAD FORECASTING WITH LEAST SQUARES SUPPORT...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： QI-SONG CHEN XIAO-WEI CHEN XIN ZHANG SHI-HUAN XIONG School of Computer Science and Technology Guizhou University Guiyang 550025 China GuiZhou Key Laboratory for Photoelectric Technology and Application Guiyang 550025 China Mathematics and Computer Science Department of Guizhou Education College Guiyang 550003 China

Based on least squares support vector machines (LS-SVM) and Wavelet Transform theory, a novel approach for short-term power load forecasting is presented. The historical time series is decomposed by wavelet, so the approximate part and several detail parts are obtained. Then the results of Wavelet Transform are predicted by a separate LS-SVM predictor. The new forecast model combines the advantage of WT with LS-SVM. Compared with other predictors, this forecast model has greater generalizing ability and higher accuracy.

关键词： Wavelet Transform least squares support vector machines short-term power load forecasting

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Universally composable Secure RFID communication protocol in supply chains

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Jisuanji Xuebao/Chinese Journal of computers 2008年第10期31卷 1754-1767页

作者： Zhang, Fan Sun, Xuan Ma, Jian-Feng Cao, Chun-Jie Zhu, Jian-Ming Key Laboratory of Computer Networks and Information Security Xidian University Xi'an 710071 China Institute of Beijing Computer Technology and Application Second Academy China Aerospace Science and Industry Corporation Beijing 100854 China Huhhot Railway Bureau Huhhot 010020 China School of Information Central University of Finance Economics Beijing 100081 China

Supply chain management is the major field for RFID application. However, little work has been conducted to address the security issues in this context. Existing RFID solutions cannot be applied directly in this field because of a set of special RFID security requirements to be addressed for supply chain management. The major contribution of this paper is to identify the unique set of security requirements for secure RFID communications in supply chains, propose a universally composable model that satisfies the security requirements, and design a lightweight protocol that realizes the universally composable model. This paper further defines the security requirement of unlinkability, and classifies the typical RFID protocols according to the security requirements.

关键词： Supply chain management

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Design and Implementation of Embedded Multimedia Surveillance System

Design and Implementation of Embedded Multimedia Surveillanc...

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International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Yan Liu Ren-Fa Li Cheng Xu Fei Yu School of Computer and Communication Hunan University Changsha China Guangdong Province Key Laboratory of Electronic Commerce Market Application Technology Guangdong University of Business Studies Guangzhou China Provincial Key Laboratory of Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China

Due to the thrive of networking multimedia, artificial intelligence and embedded system, video surveillance system is evolved from computer-based to embedded-based. This paper presents an intelligent multimedia surveillance system based on embedded video server. Design and implementation issues of this surveillance system are discussed including system architecture, hardware design and software framework. Moreover, practical applications show that our framework can suitable for commercial applications.

关键词： Multimedia systems Artificial intelligence application software Embedded system Video surveillance computer networks Embedded computing Intelligent systems Network servers computer architecture

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A Robust Double Watermarking 3D Mesh Model Based On Feature

A Robust Double Watermarking 3D Mesh Model Based On Feature

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Xiaoqing Feng Li li Zhigen Pan Shusen Sun Daxing Zhang Institute of Computer Application Techniques College of Information Zhejiang University of Finance and Economics Hangzhou China State Key Laboratory of CAD&CG University of Zhejiang Hangzhou China Institute of Graphics and Image Hangzhou Dianzi University China School of Informatics and Electronics Zhejiang University of Science and Technology Hangzhou China

A new robust watermarking 3D mesh model algorithm is presented in this paper. We embed two different watermarks into a 3D mesh. One of the two watermarks is based on feature information and another is embedded by Nielson norm. The two watermarks do not disturb each other. Since the algorithm is blind, it does not need the original model during extraction. The algorithm is applied to many mesh models. These experiments show that the watermarking scheme not only can keep original mesh feature information but also can resist some attacks, such as affine transformation, noising and cropping.

关键词： Watermarking Mathematical model Three dimensional displays Computational modeling Robustness Solid modeling Feature extraction

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A Real-time Relaxed Garbage Collection in DSM

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电子学报(英文版) 2007年第1期16卷 7-12页

作者： WU Junmin SONG Zhenhua Anhui Province Co-Key Laboratory of High Performance Computing and Application Department of Computer Science ＆ Technology University of Science ＆ Technology of China Hefei 230027 China

Garbage collection is a memory management mechanism for automatically reclaiming garbage objects in memory. It can effectively relieve the programmers' burden and optimize the design of programs. Currently many garbage collection methods for distributed systems have been put forth as the distributed systems are more and more popular. Relaxed consistency model is a very important kind of memory consistency model in distributed system, but it is inefficiently to perform garbage collection in the Distributed shared memory system (DSM) on relaxed memory model with traditional methods. This article proposes a trace-based garbage collection algorithm for DSM system based on relaxed memory model. Our algorithm notes the references among active objects of every node in DSM system, which makes the application process and the collection process able to execute concurrently. And it makes use of the relaxed consistency model's features to reduce communication cost and response delay. Furthermore, the use of two tables insures the algorithm correct and effective. This algorithm displays its advantages of real-time and incremental, and it is proved to be correct and effective.

关键词：垃圾回收算法分布式共享存储系统实时弱一致性模型 DSM

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