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113th Annual ASEE Conference and Exposition, 2006

作者： Eschenbach, Elizabeth Johnson, James H. Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States Board of Scientific Counselors Executive Committee U.S. Environmental Protection Agency Office of Research and Development United States EPA's Science Advisory Board Department of Energy-sponsored HBCU/MI Environmental Technology Consortium Board Environmental Studies and Toxicology and the Engineering Council of the American Society for Engineering Education United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute ot Technology U.S. Air Forcer United States Army's Aberdeen Proving Ground NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Civil Engineering Washington University St. Louis United States Environmental Engineering Science Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States U.S. EPA Office of Research and Development United States National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University of Illinois United States

The Collaborative Large-scale engineering Analysis Network for Environmental Research (CLEANER) Project Office has been established with funding from the National science Foundation (NSF) to the University of Illinois Urbana-Champaign (UIUC) and a coalition of 11 other institutions. The project office is coordinating the creation of a strategic plan consisting of research, cyberinfrastructure, and education plans that set forth a roadmap for the collaborative engineering analysis network for the study of environmental problems. This report will be completed by July 2007 and requires input from K-12 educators, administrators and other stakeholders in K-12 education. This paper describes the CLEANER project, provides an example of the CLEANER Education Committee's vision for the K-12 area and lists contacts for further information. The intention of this paper is to elicit input from the K-12 engineering education community, including K-12 educators, on how the CLEANER Education Plan could better meet the future needs of K-12 students and educators © American Society for engineering Education, 2006.

关键词： engineering education

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Simplifying trigonmetric expressions automatically by computer

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Jisuanji Xuebao/Chinese Journal of computers 2006年第10期29卷 1869-1875页

作者： Fu, Hong-Guang Zhong, Xiu-Qin Zeng, Zhen-Bing Chengdu Institute of Computer Applications Chinese Acad. of Sci. Chengdu 610041 China Software Institute East China Normal University Shanghai 200062 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China

Automated simplification of trigonometric expressions is an important problem that hasn't been completely solved by current computer algebraic systems. Even if the symbolic computation softwares like Maple, Mathematica and Derive, etc., they are still not applicable enough to the problem. This paper presents a number of unique prescriptions for the ordering of some trigonometric rules, which have been derived by observing how human experts follow their intuitive rules. The authors have implemented the procedure in Lisp because of its suitability for formula manipulations and rule-based reasoning systems. Consequently, it can simplify many trigonometric expressions that are even difficult to do by hand, simultaneously with readable process, and it achieves much better results for many hard problems than Maple and Mathematica do.

关键词： Algebra

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Genetic K-Medoids Spatial Clustering with Obstacles Constraints

Genetic K-Medoids Spatial Clustering with Obstacles Constrai...

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International IEEE Conference on Intelligent Systems, IS

作者： Xueping Zhang Jiayao Wang Fang Wu Zhongshan Fan Wenbo Xu Institute of Surveying and Mapping People''s Liberation Army Information and Engineering University Zhengzhou Henan China Geomatics and Applications Laboratory Liaoning Technical University Fuxin Liaoning China College of Computer Science and Engineering Henan University of Technology Zhengzhou Henan China Henan Academy of Traffic Science and Technology Zhengzhou Henan China School of Information Technology Southern Yangtze University Wuxi Jiangsu China

Spatial clustering is an important research topic in spatial data mining (SDM). It is not only an important effective method but also a prelude of other task for SDM. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. So, many methods have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on genetic algorithms (GAs) and K-Medoids, called GKSCOC, which aims to cluster spatial data with obstacles constraints. It can not only give attention to higher local constringency speed and stronger global optimum search, but also consider the obstacles constraints and make the results of spatial clustering more practice. Its performance has compared to GAs, K-Medoids; and the results on real datasets show that it is better than standard GAs and K-Medoids. The drawback of this method is a comparatively slower speed in spatial clustering

关键词： Clustering algorithms Data mining Clustering methods Genetic algorithms Partitioning algorithms Genetic engineering Application software Laboratories Geographic Information Systems Biomedical imaging

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An efficient fuzzy based neuro - Genetic algorithm for stock market prediction

An efficient fuzzy based neuro - Genetic algorithm for stock...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Srinivasa, K.G. Venugopal, K.R. Patnaik, L.M. Department of Computer Science and Engineering College of Engineering University Visvesvaraya Bangalore - 560001 India Microprocessor Applications Laboratory Indian Institute of Science Bangalore - 560012 India

ISBN: (纸本)0972741216

Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data and are immune to noise in the input. In this paper, we propose a fuzzy based neuro-genetic algorithm - Fuzzy based Evolutionary Approach to Self Organizing Map(FEASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. The algorithm was tested on real stock market data of companies like Intel, General Motors, Infosys, Wipro, Microsoft, IBM, etc. The algorithm consistently outperformed regression model, backpropagation algorithm and Kohonen network in predicting the stock market values. Copyright © IICAI 2005.

关键词： Genetic algorithms

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A self-adaptive migration model genetic algorithms for data mining applications

A self-adaptive migration model genetic algorithms for data ...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

ISBN: (纸本)0972741216

Data mining involves the process of extracting nontrivial knowledge or hidden patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously per-forming a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabtese database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others. Copyright © IICAI 2005.

关键词： Genetic algorithms

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GaXsearch: An XML information retrieval mechanism using genetic algorithms

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18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence

作者： Srinivasa, K.G. Sharath, S. Venugopal, K.R. Patnaik, Lalit M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore - 560001 India Infosys Technologies Bangalore India Microprocessor Applications Laboratory Indian Institute of Science India

ISBN: (纸本)3540304622

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient to retrieve relevant XML documents. In this paper, Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed GaXsearch outperforms XSEarch [1] and XRank [2] with respect to accuracy and query execution time. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Information retrieval

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Selective dissemination of XML documents using GAs and SVM

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International Conference on Computational Intelligence and Security, CIS 2005

ISBN: (纸本)3540308180

XML has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Self Adaptive Migration Model Genetic Algorithm (SAMGA)[5] and multi class Support Vector Machine (SVM) are used to learn a user model. Based on the feedback from the users the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Information dissemination

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IDEAS: Intrusion detection based on emotional ants for sensors

IDEAS: Intrusion detection based on emotional ants for senso...

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5th International Conference on Intelligent Systems Design and applications, ISDA '05

作者： Banerjee, Soumya Grosan, Crina Abraham, Ajith Dept. of Computer Applications Institute of Management Studies India Department of Computer Science Babeş-Bolyai University Cluj-Napoca 3400 Romania School of Computer Science and Engineering Chung-Ang University Korea Republic of

ISBN: (纸本)0769522866

Due to the wide deployment of sensor networks recently security in sensor networks has become a hot research topic. Popular ways to secure a sensor network are by including cryptographic techniques or by safeguarding sensitive information from unauthorized access/manipulation and by implementing efficient intrusion detection mechanisms. This paper proposes a novel ant colony based intrusion detection mechanism which could also keep track of the intruder trials. The IDEAS technique could work in conjunction with the conventional machine learning based intrusion detection techniques to secure the sensor networks. The algorithm is presented and illustrated by simulating a sensor network. © 2005 IEEE.

关键词： computer crime

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A secure image steganography using LSB, DCT and compression techniques on raw images 3

A secure image steganography using LSB, DCT and compression ...

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3rd International Conference on Intelligent Sensing and Information Processing, ICISIP 2005

作者： Raja, K.B. Chowdary, C.R. Venugopal, K.R. Patnaik, L.M. Department of Computer Science Engineering Bangalore University Bangalore560001 India Microprocessor Applications Laboratory Indian Institute OfScience Bangalore560012 India

ISBN: (纸本)0780395883

Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works. © 2005 IEEE.

关键词： Frequency domain analysis

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A dynamic Migration Model for self-adaptive Genetic Algorithms

A dynamic Migration Model for self-adaptive Genetic Algorith...

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6th International Conference on Intelligent Data engineering and Automated Learning - IDEAL 2005

作者： Srinivasa, K.G. Sridharan, K. Shenoy, P. Deepa Venugopal, K.R. Patnaik, Lalit M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore 560001 India Department of CSE Sunny Buffalo United States Microprocessor Applications Laboratory Indian Institute of Science India

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA). © Springer-Verlag Berlin Heidelberg 2005.

关键词： Genetic algorithms

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