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Semantic Annotations for Customizing Geographical Maps Based on WordNet

Procedia Technology 2012年 4卷 375-383页

作者： Anthoniraj Amalanathan S. Shanmugam M. Senthilnathan S. Harikumar Assistant Professor of School of Computing Science and Engineering VIT University Vellore-632014 India M.Sc. Student of School of Computing Science and Engineering VIT University Vellore-632014 India

Geographical maps are an ideal technique to present and visualizethe real world information which is retrieved from various resources such as satellite and sensors. These data are effectively used in complex applications like Google and OpenStreet Map. Every location or place in a map is represented by latitude and longitude coordinates. Thislocation based search methodology can be utilized to find distance or path between two places, weather and traffic information, hotel, café and restaurant. This paper proposes an approach to describe and retrieve information by annotating location and places using semantic technologies. It is expected to retrieve more precise results by adding semantic annotation to the location. Users can also customize themap using this method to look up cities, addresses and other map related information.

关键词： WordNet Semantic web Geographic Maps LESK Algorithm Ontology

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Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks

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Procedia engineering 2012年 41卷 1818-1823页

作者： K. Rajeswari V. Vaithiyanathan T.R. Neelakantan Research Scholar. School of Computing SASTRA UniversityThanjavur 613 401India Associate Dean and CTS Chair Professor School of ComputingSASTRA UniversityThanjavur 613 401India Associate Dean and L&T ECC Chair Professor School of Civil EngineeringSASTRA University Thanjavur 613 401India

Feature Selection in Data Mining refers to an art of minimizing the number of inputs under evaluation. An artificial neural network is the simulation of a human brain which learns with experience. Efficiency of a model or a system in terms of cost, time and accuracy will greatly improve if proper features of a system are selected. This proposed method uses Artificial Neural Network for selecting the interesting or important features from the input layer of the network. A Multi Layer Perceptron Neural Network is used for selection of interesting features from a Ischemic Heart Disease (IHD) data base with 712 patients. Initially the number of attributes was 17 and after feature selection the number of attributes was reduced to 12. All combination of features are attempted as inputs of a Neural Network. When the input features is 12 the predicted accuracy during training is high as 89.4% and during testing is high as 82.2%. Further removal of features lowers the accuracy and hence the interesting features selected for prediction is concluded to be as 12 for this IHD data set.

关键词： Data Mining Feature Selection Multi Layer Perceptron Neural Network Ischemic Heart Disease(IHD)

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Abstracts from the Food Allergy and Anaphylaxis Meeting 2016 Abstracts

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CLINICAL AND TRANSLATIONAL ALLERGY 2017年第SUPPL 1期7卷 10-10页

作者： [Anonymous] Department of Pediatrics Children’s Hospital Roubaix France Division of Pulmonology and Allergology Department of Pediatrics Faculty of Medicine and Children’s Hospital Lille France Allergy Vigilance Network Vandoeuvre les Nancy France Université Lille 2 CHU Lille EA 2694 - Santé Publique: épidémiologie et qualité des soins Lille France Biostatistics Unit Maison Régionale de la Recherche Clinique CHRU Lille Lille France Department of Allergology Emile Durkheim Hospital Epinal France Centre d’Epidémiologie sur les Causes Médicales de Décès INSERM CHU de Bicêtre Le Kremlin-Bicêtre France The Allergy Vigilance Network University Hospital Nancy Nancy France Pediatric Pulmonology and Allergy Department Hôpital Jeanne de Flandre CHU Lille Lille France Charité - Universitätsmedizin Berlin Berlin Germany Institute for Social Medicine Epidemiology and Health Economics Charité - Universitätsmedizin Berlin Berlin Germany Institute of Social Medicine Epidemiology and Health Economics Charité Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité – Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergy Allergie-Centrum-Charité Charité-Universiätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité Universitätsmedizin Berlin Berlin Germany University Hospital Basel Basel Switzerland Allergy Unit Department of Dermatology University Hospital Basel Switzerland Universitätsklinikum Salzburg Salzburg Austria Department of Dermatology and Allergy Centre Odense Research Center for Anaphylaxis (ORCA) Odense University Hospital Odense Denmark Odense University Hospital Odense Denmark Serviço de ImunoalergologiaCentro Hospitalar de São João E.P.E. Porto Portugal GRIAC Research Institute University of Groningen University Medical Center Groningen Groningen The Netherlands Serviço de Imunoalergologia Centr

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CFT based optimal PWM strategy for three phase inverter

CFT based optimal PWM strategy for three phase inverter

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International Conference on Power, Control and Embedded Systems (ICPCES)

作者： G.K. Nisha S. Ushakumari Z. V. Lakaparampil Research Scholar is with the Department of Electrical Engineering College of Engineering Trivandrum Kerala India Associate Professor is with the Department of Electrical Engineering College of Engineering Trivandrum Kerala India Associate Director is with the Centre for Development of Advanced Computing (C-DAC) Trivandrum Kerala India

Selective Harmonic Eliminated Pulse Width Modulation (SHEPWM) techniques necessitate the solution of non-linear transcendental equations for achieving optimal PWM strategy. Online solution of these equations is very difficult and the conventional method is to store the offline prepared optimal switching angles in memory, which are to be recalled for online generation of PWM waveforms. The need to achieve continuous voltage and frequency control, especially for AC machines, has imposed large memory requirements and computation time for PWM systems. This paper proposes an effective online method to arrive optimal PWM switching angles based on Curve Fitting Technique (CFT) for three phase inverters. Polynomial functions for optimal switching angles are generated using CFT in MATLAB. The correctness and harmonic performance of the proposed method is verified by comparing the results with the Newton-Raphson (N-R) iterative method.

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Improving the Prediction Rate of Diabetes Diagnosis Using Fuzzy, Neural Network, Case Based (FNC) Approach

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Procedia engineering 2012年 38卷 1709-1718页

作者： Mythili Thirugnanam Praveen Kumar S. Vignesh Srivatsan C.R. Nerlesh Assistant Professor(Sr) School of Computing Science and EngineeringVIT University VelloreTamilNadu632014India Assistant civil surgeon Government Primary Health Centre T.Palur Tamil Nadu 612904 India Bachelor of technology in Information technology School of Information TechnologyVIT UniversityVelloreTamilNadu632014India

In today's world the diabetes mellitus has become a major health problem among the people of all ages. Most of the researchers have proposed different systems to diagnose diabetes, in spite of which the accuracy of the prediction rate of diabetes is not so significant. Most of the techniques suggested were to detect diabetes associated with the parts of the body such as eye, heart or legs. All the developed systems aimed to identify diabetes by increasing the accuracy of the prediction rate but fail to do so in most of the cases as there were some issues that had not been discussed. The existing systems had number of drawbacks like some were only concentrated for women and who were less than 21 years old [5] , some gave greater accuracy if only one data set was used [6] , some needed dataset of very good quality [12] , some needed to standardize the ontology [10] and some even failed to show the performance of the developed systems [7] .So there is an need to develop a diabetes diagnosis system that improves the accuracy of the prediction rate by considering all the factors into account. This paper presents a novel approach for diagnosis of diabetes which has two stages to predict the diabetes status. The initial prediction stage adopts two computational intelligence and knowledge engineering techniques such as fuzzy logic (F), neural network (N) and case based reasoning (C) as an individual approach (FCN). The final prediction stage applies rule based algorithm to the values obtained from the initial stage. The benefit of applying these stages is that the accuracy of prediction rate will be higher when compared to using only the initial prediction stage done by most of the suggested systems for predicting the occurrence of diabetes mellitus.

关键词： Diabetes Fuzzy Logic Neural Network Case Based Reasoning

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Developing nlp-based verbal communication systems for service robots efficiently by addressing different verbal communication tasks separately

Developing nlp-based verbal communication systems for servic...

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IASTED International Conference on Robotics and Applications, RA 2010

作者： Abu-Eid, Valery Al-Masri, Jehad Department of Computing Technologies Spb. Electro Technical University Professor Popov Street Saint Petersburg Russia Department of Mechanical Engineering Zarqa 13115 Jordan

ISBN: (纸本)9780889868625

Having a Verbal Communication System that understands Natural Language without constraining users to key words or key phrases is essential for service robots to be used in real world environments. Developing a system that understands natural language is a complex task and usually comes at a great cost, that's why typically researchers and developers of service robots resort to key words and key phrases instead of developing systems that understand commands in natural language. This research focuses on practical and cost efficient approaches for developing Verbal Communication Systems for service robots that are capable of understanding commands and questions in natural language. The efficiency of the presented approach emerges from applying different NLP disciples and techniques to solve particular issues in the Service Robot Verbal Communication Domain instead of treating the Verbal Communication System for Service Robots as one big problem. The aim of this research is to help other researchers and developers to develop intelligent verbal communication systems that understand natural language for their service robots in time and budget limited environments without sacrificing the quality of robot's verbal communication system.

关键词： Natural language processing systems

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Clustering patient length of stay using mixtures of Gaussian models and phase type distributions

Clustering patient length of stay using mixtures of Gaussian...

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2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009

作者： Garg, Lalit McClean, Sally Meenan, Brian El-Darzi, Elia Millard, Peter School of Computing and Information Engineering University of Ulster Coleraine United Kingdom School of Engineering University of Ulster Jordanstown United Kingdom Harrow School of Computer Science University of Westminster London United Kingdom Emeritus Professor St. George's University of London London United Kingdom

ISBN: (纸本)9781424448784

Gaussian mixture distributions and Coxian phase type distributions have been popular choices model based clustering of patients' length of stay data. This paper compares these models and presents an idea for a mixture distribution comprising of components of both of the above distributions. Also a mixed distribution survival tree is presented. A stroke dataset available from the English Hospital Episode Statistics database is used as a running example. ©2009 IEEE.

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Invited Talk: The Role of Empiricism in Improving the Reliability of Future Software

Invited Talk: The Role of Empiricism in Improving the Reliab...

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Testing: Academic and Industrial Conference - Practice And Research Techniques (TAICPART)

作者： Les Hatton Professor of Forensic Software Engineering and Oakwood Computing Associates Limited Kingston University UK

This talk first bemoan the general absence of empiricism in the evolution of software system building and then go on to show the results of some experiments in attempting to understand how defects appear in software, what factors affect their appearance and their relationship to testing generally. It challenge a few cherished beliefs on the way and demonstrate in no particular order at least the following: 1) the equilibrium state of a software system appears to conserve defect; 2) there is strong evidence in quasi-equilibrated systems for xlogx growth in defects where x is a measure of the lines of code; 3) component sizes in OO and non-OO software systems appear to be scale-free, (this is intimately related to the first two bullet points); 4) software measurements, (also known rather inaccurately as metrics) are effectively useless in determining the defect behaviour of a software system; 5) most such measurements, (including the ubiquitous cyclomatic complexity) are almost as highly correlated with lines of code as the relationship between temperature in degrees Fahrenheit and degrees Centigrade measured with a slightly noisy thermometer. In other words, lines of code are just about as good as anything else when estimating defects; 6) 'gotos considered irrelevant'. The goto statement has no obvious relationship with defects even when studied over very long periods. It probably never did; 7) checklists in code inspections appear to make no significant difference to the efficiency of the inspection; and 8) when you find a defect, there is an increasing probability of finding another in the same component. This strategy is effective up to a surprisingly large number of defects in youthful systems but not at all in elderly systems.

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Implementing linear iterative learning control laws in nonlinear systems

Implementing linear iterative learning control laws in nonli...

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18th Annual Space Flight Mechanics Meeting

作者： Longman, Richard W. Mombaur, Katja D. Professor of Mechanical Engineering Columbia University MC 4703 500 W. 120th St. NY NY 10027 United States 1WR Interdisciplinary Center for Scientific Computing University of Heidelberg Im Neuenheimber Feld 368 69120Heidelberg Germany

ISBN: (纸本)9780877035442

Iterative learning control (ILC) applies to situations when a system performs a tracking maneuver repeatedly. The error in each run is used to adjust the command in the next run, aiming to converge to zero error. Spacecraft applications include making fine pointing equipment follow a precise scan pattern in spite of flexibility effects. Very effective and general ILC design methods have been developed for linear systems. This paper shows how internal numerical differentiation can be used to very easily obtain the needed model information from the nonlinear system differential equations so that these ILC laws can be applied in the neighborhood of the desired nonlinear system output. This allows one to transfer from the linear context to the nonlinear context, the methods that have been developed to keep the control input history well behaved, methods to robustify to model parameter uncertainty, and to robustify to parasitic poles or residual modes.

关键词： Nonlinear systems

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A Study On Iterative Learning Control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of Control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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