...Abstract In recent years fuzzy set theory has emerged as a transcendental tool to deal with environmental engineering application having uncertainty, ambiguity and subjectivity. Analysis of surface water quality plays significant role in environmental impact assessment studies. For qualitative description of surface water quality, number of physical, chemical and biological parameters are taken into consideration, allotted a weightage factor and calculated into an index called water quality index (WQI). Water quality index uses crisp set to analyse water contaminants and hence deals with standing boundary conditions. This paper illustrates use of fuzzy inference system for analysing physical and chemical parameters to assess surface water quality. A water quality index calculated with fuzzy inference system has been developed and discussed. Introduction Determination of status of water quality of a river or any other water sources is highly indeterminate. The current method of determining water quality index which is in practice utilizes statistical approach and is not precise in most of the time. Nowadays environmental protection and water quality management has become an important issue in public policies throughout the world. Moreover, government is concerned about the quality of their environmental resources because of the complexity in water quality data sets. Many countries have introduced a scheme for river water quality monitoring and assessment, examining...
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...Beginning 1. In this paper。。 2. This paper proceeds as follow. 3. The structure of the paper is as follows. 4. In this paper, we shall first briefly introduce fuzzy sets and related concepts 5. To begin with we will provide a brief background on the Introduction 1. This will be followed by a description of the fuzzy nature of the problem and a detailed presentation of how the required membership functions are defined. 2. Details on xx and xx are discussed in later sections. 3. In the next section, after a statement of the basic problem, various situations involving possibility knowledge are investigated: first, an entirely possibility model is proposed; then the cases of a fuzzy service time with stochastic arrivals and non fuzzy service rule is studied; lastly, fuzzy service rule are considered. Review 1. This review is followed by an introduction. 2. A brief summary of some of the relevant concepts in xxx and xxx is presented in Section 2. 3. In the next section, a brief review of the .... is given. 4. In the next section, a short review of ... is given with special regard to ... 5. Section 2 reviews relevant research related to xx. 6. Section 1.1 briefly surveys the motivation for a methodology of action, while 1.2 looks at the difficulties posed by the complexity of systems and outlines the need for development of possibility methods. Body 1. Section 1 defines the notion of robustness, and argues for its importance. 2. Section 1 devoted...
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...Fuzzy Control Kevin M. Passino Department of Electrical Engineering The Ohio State University Stephen Yurkovich Department of Electrical Engineering The Ohio State University An Imprint of Addison-Wesley Longman, Inc. Menlo Park, California • Reading, Massachusetts Don Mills, Ontaria • Sydney • Bonn • Harlow, England • Berkeley, California • Amsterdam • Mexico City ii Assistant Editor: Laura Cheu Editorial Assistant: Royden Tonomura Senior Production Editor: Teri Hyde Marketing Manager: Rob Merino Manufacturing Supervisor: Janet Weaver Art and Design Manager: Kevin Berry Cover Design: Yvo Riezebos (technical drawing by K. Passino) Text Design: Peter Vacek Design Macro Writer: William Erik Baxter Copyeditor: Brian Jones Proofreader: Holly McLean-Aldis Copyright c 1998 Addison Wesley Longman, Inc. All rights reserved. No part of this publication may be reproduced, or stored in a database or retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Printed simultaneously in Canada. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and AddisonWesley was aware of a trademark claim, the designations have been printed in initial caps or in all caps. MATLAB is a registered trademark of The MathWorks...
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...20 Unified Power Flow Controllers Ali Feliachi West Virginia University Azra Hasanovic West Virginia University 20.1 20.2 20.3 20.4 20.5 20.6 20.7 Introduction Power Flow on a Transmission Line UPFC Description and Operation Series Converter: Four Modes of Operation • Automatic Power Control Karl Schoder West Virginia University UPFC Modeling UPFC Steady-State or Load Flow Model • UPFC Dynamic Model • Interfacing the UPFC with the Power Network Control Design UPFC Basic Control Design • Power System Damping Control through UPFC Using Fuzzy Control Case Study Test System • Tracking Real and Reactive Power Flows • Operation under Fault Conditions Conclusion 20.1 Introduction An electric power system is an interconnection of generating units to load centers through high-voltage electric transmission lines. It consists of generation, transmission, and distribution subsystems, which used to belong to the same electric utility in a given geographical area. But, currently, the electric power industry is in transition from large, vertically integrated utilities providing power at regulated rates to an industry that will incorporate competitive companies selling unbundled power at possibly lower rates. With this new structure, which will include separate generation, distribution, and transmission companies with an open-access policy, comes the need for tighter control strategies. The strategies must maintain the level of reliability that consumers not...
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...T.C BAHÇEŞEHİR ÜNİVERSİTESİ DEVELOPING AN EXPERT-SYSTEM FOR DIABETICS BY SUPPORTING WITH ANFIS Master Thesis ALİ KARA İSTANBUL, 2008 T.C BAHÇEŞEHİR ÜNİVERSİTESİ INSTITUTE OF SCIENCE COMPUTER ENGINEERING DEVELOPING AN EXPERT-SYSTEM FOR DIABETICS BY SUPPORTING WITH ANFIS Master Thesis Ali KARA Supervisor: ASSOC.PROF.DR. ADEM KARAHOCA İSTANBUL, 2008 T.C BAHÇEŞEHİR ÜNİVERSİTESİ INSTITUTE OF SCIENCE COMPUTER ENGINEERING Name of the thesis: Developing an Expert-System for Diabetics by supporting with ANFIS Name/Last Name of the Student: Ali Kara Date of Thesis Defense: Jun .09. 2008 The thesis has been approved by the Institute of Science. Prof. Dr. A. Bülent ÖZGÜLER Director ___________________ I certify that this thesis meets all the requirements as a thesis for the degree of Master of Science. Assoc. Prof. Dr. Adem KARAHOCA Program Coordinator ____________________ This is to certify that we have read this thesis and that we find it fully adequate in scope, quality and content, as a thesis for the degree of Master of Science. Examining Committee Members Assoc.Prof.Dr. Adem KARAHOCA Prof.Dr. Nizamettin AYDIN Asst.Prof.Dr. Yalçın ÇEKİÇ Signature ____________________ ____________________ ____________________ ii To my father ACKNOWLEDGEMENTS This thesis is dedicated to my father for being a role model in front of my educational life. I would like to express my gratitude to Assoc. Prof. Dr. Adem Karahoca, for not only being such...
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...Speed Control of Induction Motor using Fuzzy Logic Approach A PROJECT THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In Electrical Engineering By Varuneet Varun (Roll 108EE011) G. Bhargavi (Roll 108EE026) Suneet Nayak (Roll 108EE044) Under Supervision of Prof. Kanungo Barada Mohanty Department of Electrical Engineering National Institute of Technology, Rourkela Rourkela- 769008, Odisha © 2011 - 2012 National Institute of Technology Rourkela Certificate This is to certify that the work contained in this thesis, titled “SPEED CONTROL OF INDUCTION MOTOR USING FUZZY LOGIC APPROACH” submitted by Varuneet Varun, G. Bhargavi and Suneet Nayak is an authentic work that has been carried out by them under my supervision and guidance in partial fulfillment for the requirement for the award of Bachelor of Technology Degree in Electrical Engineering at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other University/ Institute for the award of any Degree or Diploma. Place: Rourkela Date: 11th May, 2012 Dr. Kanungo Barada Mohanty Associate Professor Department of Electrical Engineering National Institute of Technology Rourkela – 769008 i Acknowledgment We are grateful to The Department of Electrical Engineering for giving us the opportunity to carry out this project, which is an integral...
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...Edge detection using Fuzzy Logic and Automata Theory Title Page By Takkar Mohit Supervisor A Thesis Submitted to In Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electronics & Communication December 2014 . Table of Contents Title Page i CERTIFICATE ii COMPLIANCE CERTIFICATE iii THESIS APPROVAL CERTIFICATE iv DECLARATION OF ORIGINALITY v Acknowledgment vi Table of Contents vii List of Figures x Abstract xiii Chapter 1 Introduction 1 1.1 Edge Detection: Analysis 3 1.1.1 Fuzzy Logic in Image Processing 4 1.1.2 Fuzzy Logic for Edge Detection 5 1.1.3 Cellular Learning Automata 6 Chapter 2 Literature Review 7 2.1 Edge Detection: Methodology 7 2.1.1 First Order Derivative Edge Detection 7 2.1.1.1 Prewitts Operator 7 2.1.1.2 [pic] Sobel Operator 8 2.1.1.3 Roberts Cross Operator 11 2.1.1.4 Threshold Selection 11 2.1.2 Second Order Derivative Edge Detection 11 2.1.2.1 Marr-Hildreth Edge Detector 11 2.1.2.2 Canny Edge Detector 12 2.1.3 Soft Computing Approaches to Edge Detection 13 2.1.3.1 Fuzzy Based Approach 14 2.1.3.2 Genetic Algorithm Approach 14 2.1.4 Cellular Learning Automata 15 Chapter 3 Fuzzy Image Processing 18 3.1 Need for Fuzzy Image Processing 19 3.2 Introduction to Fuzzy sets and Crisp sets 20 3.2.1 Classical sets (Crisp sets) 20 3.2.2 Fuzzy sets 21 3.3 Fuzzification 22 3.4 Membership Value Assignment 22 3.5...
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...Expert systems are computer applications that combine computer equipment, software, and specialized information to imitate expert human reasoning and advice. As a branch of artificial intelligence, expert systems provide discipline-specific advice and explanation to their users. While artificial intelligence is a broad field covering many aspects of computer-generated thought, expert systems are more narrowly focused. Typically, expert systems function best with specific activities or problems and a discrete database of digitized facts, rules, cases, and models. Expert systems are used widely in commercial and industrial settings, including medicine, finance, manufacturing, and sales. As a software program, the expert system integrates a searching and sorting program with a knowledge database. The specific searching and sorting program for an expert system is known as the inference engine. The inference engine contains all the systematic processing rules and logic associated with the problem or task at hand. Mathematical probabilities often serve as the basis for many expert systems. The second component—the knowledge database—stores necessary factual, procedural, and experiential information representing expert knowledge. Through a procedure known as knowledge transfer, expertise (or those skills and knowledge that sustain a much better than average performance) passes from human expert to knowledge engineer. The knowledge engineer actually creates and structures the knowledge...
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...used for complex problem-solving and decision-support techniques in real-time business applications. The business applicability of AI techniques is spread across functions ranging from finance management to forecasting and production. In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. In addition, the amount of data to be analyzed has increased substantially. AI technologies help enterprises reduce latency in making business decisions, minimize fraud and enhance revenue opportunities. Definition of AI AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications. There are many definitions that attempt to explain what Artificial Intelligence (AI) is. I like to think of AI as a science that investigates knowledge and intelligence, possibly the intelligent application of knowledge. Knowledge and Intelligence are as fundamental as the universe within which they exist, it may turn out that they are more fundamental. One of the aims of AI is said to be the investigation of human cognition and AI is part of Cognitive Science. AI is really an investigation into the creation of intelligence...
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...intelligent machines, especially intelligent computer programs. It includes people,procedure,hardware,software,data and knowledge needed to develop computer systems and machines that demonstrated characteristics of intelligence.It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Intelligence is basically the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.There is no solid definition of intelligence relating it to human intelligence. The problem is that it cannot yet be characterize in general what kinds of computational procedures can be called intelligent. We understand some of the mechanisms of intelligence and not others. Sometimes Artificial Intelligence is about simulating human intelligence but not always.On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI textbooks define the field as "the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy who coined the term in 1956,defines...
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...essay在帮助学生应付大学学业的过程中，我发现很多学生对大学作业有着一些误会。这些错误的观念不仅导致他们花费大量的时间在作业上，也导致他们从来不能获得理想的作业分数。很多学生花一个星期在一篇2000字的Essay上，自己感觉已经竭尽全力，但是交作业的时候还是惴惴不安，祈祷一帆风顺，成绩却还是差强人意。其实，作业并非那么困难，只要你掌握作业的技巧，明白大学的要求，以及老师的阅读习惯，就会获得很好的分数。以下是本人总结的十个完成大学作业的误区。如果同学们不能从这些误区走出来，那么大学的学业仍然是非常困难。 误区1：写essay或者report要从introduction写起 写作业如同吃鱼，要从鱼身吃起，而不是鱼头。 我看到很多学生对着Introduction这个heading苦思冥想，毫无头绪。学生写了删除，删除后又重写，反反复复，就introduction就可以写上一天。这是一个错误的做法。 不管essay或者report，introduction都要放到最后写。原因有二： 1， 学生在不知道主体论述部分写什么内容的情况下，Introduction是不可能很快的完成，因为Introduction经常包含对内容的大致介绍。 2， Introduction的分重很低，只要简单明了，让老师知道大概的论述方向，就达到了目标和效果。 误区二：先做Research，再开始写 写作业象看病，小病自己解决，大病再找医生。 以前在大学读书，因为工作的原因，经常需要很短的时间去完成essay。譬如说，在一个很难的research essay上，我只用了四个小时。旁边的同学总是以为我是不上进的学生——他们认为，一个好学生，不可能只花四个小时在作业上。最后成绩出来，30分的作业，班上80%的中国学生只拿了9分，而我拿了22分。很多同学，包括KIWI，都来问我怎么写的，因为这是很著名的杀手课。 其实，我的方法很简单。看到作业的题目，我首先是自己去想，按照常识和上课的内容，去思考大致的观点。每当想到一个观点，我就写下来，并且解释这个观点。且思考且动笔，在没有做research的情况下，我已经写好了700字。 当思维“弹尽粮绝”的时候，我开始做Research。有了自己思考的基础，做Research有很清晰的方向。堆堆填填，缝缝补补，一个1500字的essay就写出来了。 先做research再动笔这个习惯有个很大的弊端：你所看到的一些文章，里面的观点可能是不重要的观点，或者是老师不强调的观点。这些观点写上去，老师不欣赏，只会给你很糟糕的分数。我看到很多学生辛辛苦苦做了research，老师却大笔一划，写上“irrelevant”。老师非常希望学生的作业出现他/她所希望看到的东西。否则，他/她怎么会给出一个好的分数？ 误区三：英文好，才能够完成好的作业 世界上最畅销的产品，不一定是最好的产品。同样的道理，最高分的作业，不一定是最博学的学生写出来的。 诚然，语言能力好的同学阅读速度更快，表达能力更高，因此完成作业的效率比较高。然而，如果不掌握作业技巧，譬如说文章的结构和论述方式，语言能力好的同学也未必能够拿到好的成绩，至少未必能够拿到理想的分数。反之，语言能力不好的学生，未必不能够在作业中拿到好的分数。 好的作业，总是让老师觉得这个学生懂得很多。如何影响老师对作业的观感？语言固然重要，段落的结构、解释观点的方法、副标题等等，都是很重要的环节，虽...
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...Framework and Infrastructure...................................................................................................................... 3 MODELLING AND APPLICATION OF DSS TO TRAFFIC MANAGEMENT .......................................................... 8 MSTEM By (Hasan, 2010) .......................................................................................................................... 8 Fuzzy Logic Application by (Almejalli, Dahal, & Hossain, 2006) .............................................................. 12 MultiAgent Modelling by (Ossowski, et al., 2005) .................................................................................. 15 Conclusion ................................................................................................................................................... 18 References .................................................................................................................................................. 19 Table of Figures Figure 1: Basic Variables and relationships in transportation system analysis ............................................ 3 Figure 2: Hassan M.K Framework ................................................................................................................. 4 Figure 3: Abstract dialogue between DSS and DM....
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...CSE- 401 DISTRIBUTED SYSTEMS [3 1 0 4] 1. Distributed System Models: Introduction , Examples , Architecture models , Fundamental models (1.1,1.2,1.4, 2.1-2.3 of Text1 ) ..2hrs 2. Interprocess Communication, Distributed Objects and Remote Invocation: Introduction , External data representation and marshalling, Communication models, Communication between distributed objects , Remote procedure call Case study: Interprocess communication in UNIX, Java RMI . (4.1-4.6, 5.1-5.5 of Text1) ..6hrs 3. Operating System Introduction , Operating system layer, Processes and threads, Communication and invocation, Architecture (6.1-6.6 of Text1) ..4hrs. 4. Distributed File Systems and Name Services: Introduction , File service architecture, Name services, Domain Name System, Directory and directory services. Case study: Sun network file system, Global name service. (8.1-8.3, 9.1-9.4 of Text1) …6hrs 5. Synchronization: Clock Synchronization, Physical clocks, Logical clocks, Global state (5.1-5.3 of Text2) ..5hrs 6. Transactions&...
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...UNIVERSITY OF KERALA B. TECH. DEGREE COURSE 2008 ADMISSION REGULATIONS and I VIII SEMESTERS SCHEME AND SYLLABUS of COMPUTER SCIENCE AND ENGINEERING B.Tech Comp. Sc. & Engg., University of Kerala 2 UNIVERSITY OF KERALA B.Tech Degree Course – 2008 Scheme REGULATIONS 1. Conditions for Admission Candidates for admission to the B.Tech degree course shall be required to have passed the Higher Secondary Examination, Kerala or 12th Standard V.H.S.E., C.B.S.E., I.S.C. or any examination accepted by the university as equivalent thereto obtaining not less than 50% in Mathematics and 50% in Mathematics, Physics and Chemistry/ Bio- technology/ Computer Science/ Biology put together, or a diploma in Engineering awarded by the Board of Technical Education, Kerala or an examination recognized as equivalent thereto after undergoing an institutional course of at least three years securing a minimum of 50 % marks in the final diploma examination subject to the usual concessions allowed for backward classes and other communities as specified from time to time. 2. Duration of the course i) The course for the B.Tech Degree shall extend over a period of four academic years comprising of eight semesters. The first and second semester shall be combined and each semester from third semester onwards shall cover the groups of subjects as given in the curriculum and scheme of examination ii) Each semester shall ordinarily comprise of not less than 400 working periods each of 60 minutes duration...
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...except where fully reference direct quotations have been included, no aspect of this submission has been copied from any other source. I declare that all other works cited in this submission have been appropriately referenced. I understand that any act of Academic Dishonesty such as plagiarism or collusion may result in the non-award of my degree. Signature…………………………………………………………………………………… Date………………………………………………………………………………………….. Table of Contents Abstract 3 1. Introduction 3 2. General process of customer requirements management 3 2.1. Requirement Elicitation 5 3. Kansei mining 5 3.1. Kansei Engineering 5 3.2. Kansei mining method 6 3.3. Case study 7 3.4. Method analysis 8 4. Picture Sorting and ART2 Network 8 4.1. Sorting technique 9 4.2. The ART2 Network 10 4.3. Case study 11 4.4. Method analysis 12 5. Conclusion 13 6. References 14 Figure Index Figure 1 - Customer requirement management process (Jiao & Chen, 2006) 4 Figure 2 - Perceptual design elements for mobile phones (Jiao, Zhang, & Helander, 2006) 7 Figure 3 -Procedure of specified picture sorts for product design (Shieh, Yan, & Chen, 2008) 10 Figure 4 - Customer attributes hierarchy derived from the sorting process (Shieh, Yan, & Chen, 2008) 12 Table index Table 1- Perceived by testing groups (Jiao, Zhang, & Helander, 2006) 8 Table 2 - Definitions of terminology used in sorting techniques (Shieh, Yan, & Chen, 2008) 9 Table 3...
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