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Engineering Applications of Neural Networks 2011

引用

丛书名： IFIP Advances in Information and Communication Technology

2011年

作者： Lazaros S. Iliadis Chrisina Jayne

ISBN: (数字)9783642239571

ISBN: (纸本)9783642239564

The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The first volume includes the papers that were accepted for presentation at the EANN 2011 conference. They are organized in topical sections on computer vision and robotics, self organizing maps, classification/pattern recognition, financial and management applications of AI, fuzzy systems, support vector machines, learning and novel algorithms, reinforcement and radial basis function ANN, machine learning, evolutionary genetic algorithms optimization, Web applications of ANN, spiking ANN, feature extraction minimization, medical applications of AI, environmental and earth applications of AI, multi layer ANN, and bioinformatics. The volume also contains the accepted papers from the Workshop on Applications of Soft Computing to Telecommunication (ASCOTE 2011), the Workshop on Computational intelligence Applications in Bioinformatics (CIAB 2011), and the Second Workshop on Informatics and Intelligent Systems Applications for Quality of Life Information Services (ISQLIS 2011).

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A Biologically Inspired Memory in a Multi-agent Based Robotic Architecture

A Biologically Inspired Memory in a Multi-agent Based Roboti...

引用

international conference on Tools for Artificial intelligence (ICTAI)

作者： Tomas Arredondo Patricio Castillo Pablo Benapres Javiera Quiroz Miguel Torres Electronic Engineering Department Universidad Tecnica Federico Santa Maria (UTFSM) Valparaiso Chile Department of Electrical Engineering Pontificia Universidad Catolica Santiago Chile

We describe a biologically inspired memory in a multi-agent based robotic architecture. In this approach, memory and pattern recognition are intertwined to form a cognitive memory that is used for recognition of objects in a robotics environment. This memory is implemented in a multiple agent behavior based blackboard architecture as an object recognition agent. The agent performance is tested against a standard dataset with satisfactory results. The system is currently installed in a mobile robotic platform where its capabilities and applications are explored.

关键词： Robots Computer architecture Training machine vision pattern recognition Biology Image segmentation

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A Novel Linear Cellular Automata-Based Data Clustering Algorithm

引用

4th international Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： de Lope, Javier Maravall, Dario Univ Politecn Madrid Cognit Robot Grp Dept Artificial Intelligence E-28040 Madrid Spain Univ Politecn Madrid Dept Appl Intelligent Syst E-28040 Madrid Spain

ISBN: (纸本)9783642213441

In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insights from both social segregation models based on Cellular Automata Theory, where the data items themselves are able to move autonomously in lattices, and also from Ants Clustering algorithms, particularly in the idea of distributing at random the data items to be clustered in lattices. We present a series of experiments with both synthetic and real datasets in order to study empirically the convergence and performance results. These experimental results are compared to the obtained by conventional clustering algorithms.

关键词： Cellular Automata machine Learning pattern recognition Data Mining Data Clustering Social Segregation Models Ants Clustering

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Social Robotics - Third international conference, ICSR 2011, Proceedings

Social Robotics - Third International Conference, ICSR 2011,...

引用

3rd international conference on Social Robotics, ICSR 2011

ISBN: (纸本)9783642255038

The proceedings contain 23 papers. The topics discussed include: interaction scenarios for HRI in public space;MAWARI: a social interface to reduce the workload of the conversation;design of robust robotic proxemic behavior;effects of gesture on the perception of psychological anthropomorphism: a case study with a humanoid robot;eight lessons learned about non-verbal interactions through robot theater;proxemic feature recognition for interactive robots: automating metrics from the social sciences;children interpretation of emotional body language displayed by a robot;making robots persuasive: the influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power;BEHAVE: a set of measures to assess users' attitudinal and non-verbal behavioral responses to a robot's social behaviors;and initial formation of trust: designing an interaction with Geminoid-DK to promote a positive attitude for cooperation.

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Unsupervised Feature Selection and Category Formation for Generic Object recognition

Unsupervised Feature Selection and Category Formation for Ge...

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14th international conference on Computer Analysis of Images and patterns (CAIP)

作者： Madokoro, Hirokazu Tsukada, Masahiro Sato, Kazuhito Akita Prefectural Univ Dept Machine Intelligence & Syst Engn Yurihonjo City Akita Japan

ISBN: (纸本)9783642236716

This paper presents an unsupervised method for selection of feature points and object category formation without previous setting of the number of categories. For unsupervised object category formation, this method has the following features: selection of target feature points using One Class-SVMs (OC-SVMs), generation of visual words using Self-Organizing Maps (SOMs), formation of labels using Adaptive Resonance Theory-2 (ART-2), and creation and classification of categories for visualizing spatial relations between them using Counter Propagation Networks (CPNs). Classification results of static images using a Caltech-256 object category dataset demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

关键词： Self organizing maps

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Unravelling Bueno De Mesquita's group decision model

Unravelling Bueno De Mesquita's group decision model

引用

作者： Scholz, Jason B. Calbert, Gregory J. Smith, Glen A. C3I Division Defence Science and Technology Organisation PO Box 1500 Edinburgh SA Australia

ISBN: (纸本)9789898425409

The development of societies of human and machine agents should benefit from an understanding of human group decision processes. Political Scientist and Professor, Bruce Bueno De Mesquita has made significant claims for the predictive accuracy of his computational model of group decision making, receiving much popular press including newspaper articles, books and a television documentary entitled "The New Nostradamus". Despite these and many journal and conference publications related to the topic, no clear elicitation of the model exists in the open literature. We expose and present the model by careful navigation of the literature and illustrate the soundness of our interpretation by replicating De Mesquita's own results. We also discuss concerns regarding model sensitivity and convergence.

关键词： Decision making

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2nd international conference on Autonomous and Intelligent Systems, AIS 2011

引用

2nd international conference on Autonomous and Intelligent Systems, AIS 2011

ISBN: (纸本)9783642215377

The proceedings contain 40 papers. The special focus in this conference is on Autonomous and Intelligent Systems. The topics include: Thermal dynamic modeling and control of injection moulding process;analysis of future measurement incorporation into unscented predictive motion planning;Nonlinear maneuvering control of rigid formations of fixed wing UAVs;intelligent control system design for a class of nonlinear mechanical systems;trends in the control schemes for bilateral teleoperation with time delay;bilateral teleoperation system with time varying communication delay: Stability and convergence;online incremental learning of inverse dynamics incorporating prior knowledge;experimental comparison of model-based and model-free output feedback control system for robot manipulators;P-map: An intuitive plot to visualize, understand, and compare variable-gain PI controllers;small tree probabilistic roadmap planner for hyper-redundant manipulators;sufficient conditions for global synchronization of continuous piecewise affine systems;question type classification using a part-of-speech hierarchy;exploring wikipedia’s category graph for query classification;combination of error detection techniques in automatic speech transcription;Developing a secure distributed OSGi cloud computing infrastructure for sharing health records;extreme learning machine with adaptive growth of hidden nodes and incremental updating of output weights;face recognition based on kernelized extreme learning machine;detection and tracking of multiple similar objects based on color-pattern;Argo Vehicle Simulation of Motion Driven 3D LIDAR Detection and Environment Awareness;signal processing and pattern recognition for eddy current sensors, used for effective land-mine detection;A fuzzy logic approach for indoor mobile robot navigation using UKF and customized RFID Communication;human-machine learning for intelligent aircraft systems.

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A New Intrusion Detection System Based on Rough Set Theory and Fuzzy Support Vector machine

A New Intrusion Detection System Based on Rough Set Theory a...

引用

international Workshop on Intelligent Systems and Applications, ISA

作者： Lei Li Ke-nan Zhao Pattern Recognition and Intelligence System College of Automation Nanjing University of Posts and Telecommunications Nanjing China

Nowdays, IDS (Intrusion Detection System) is a hot topic in the information security. The main function of IDS is distinguishing and predicting normal or abnormal behaviors. This paper is to propose a model used on IDS, it is based on rough set (RS) theory and fuzzy support vector machine (FSVM). Firstly, the model set rough set as a preprocessor of FSVM. Rough set can reduce dimensions of attributes and filter some invasion behaviors which are esay to identify. Secondly, less attributes selected by RS are input FSVM to train and classify, this method can improve operational speed of FSVM. For this model, FSVM uses an effective Fuzzy Membership Function based on the affinity among sample points to select an appropriate fuzzy membership to reduce the effects of outliers. Finally, Experimental results will show that the RS-FSVM performs the best recognition ability, indicating that RS-FSVM can serve as a promising model for intrusion detection system.

关键词： Support vector machines Intrusion detection Accuracy Training Computational modeling Feature extraction Approximation methods

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Automated object recognition and pattern matching analysis of runways using surface track data

Automated object recognition and pattern matching analysis o...

引用

international conference on Electronic Computer Technology

作者： Rahiman, M. Abdul Mahalekshmi Monisha, C. Remya, S. Kumar, G. Manoj Rajasree, M.S. Karpagam University Coimbatore India Computer Science LBSITW Trivandrum India Dept. of CSE LBS Inst. of Tech. for Women Trivandrum Kerala India Computer Science CET Trivandrum Kerala India

ISBN: (纸本)9781424486779

Accurate and timely information is critical for the safe landing of aircraft in now-a-days. The goal of an EMAS (Engineered Materials Arresting System) is to avoid aircraft overrun with no human injury and minimal aircraft damage. Although many techniques have been developed for the analysis of object detection, relatively few researchers have considered image analysis as an aid to aircraft landing. In one system, image intensity edges are used to detect the sides of a runway in an image sequence, and the 3-dimensional position and orientation of the runway can be estimated. For better detection and extraction of objects from an aerial image, a fuzzy network system is used. In another system integration of biologically and geometrically inspired approaches for detecting objects from hyper spectral and/or multispectral (HS/MS), multi-scale, multiplatform imagery is used. But the drawback of these technologies is similarity in the top view of runways and building and roads or other objects. We propose a new method to detect and track the runway using pattern matching and texture analysis using digital images from cameras mounted on the aircraft. In order to detect runway from aerial image edge detection algorithms are used. In this paper the edge detection techniques used are Hough Transform, Canny Filter and Sobel Filter algorithms which lead to efficient detection of runways from aerial images. © 2011 IEEE.

关键词： Feature extraction

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Emotion recognition Using Image Processing and Principal Component Analysis

Emotion Recognition Using Image Processing and Principal Com...

引用

2011 3rd IEEE international conference on Information Management and Engineering(ICIME 2011)

作者： Lam Choong Kee Sameem Abdul Kareem Woo Chaw Seng Department of Artificial Intelligence University of Malaya KualaLumpur Department of Artificial Intelligence University of Malaya Kuala Lumpur

ISBN: (纸本)9781612842387

Emotion,which can be best elicited by facial expression,contains much communicative information and it should be considered in human machine interface *** primary universal emotions,anger,disgust,fear,happiness,sadness,and surprise,are *** facial features,the shapes of eyebrows and mouth are extracted and the developed dataset was applied using the principal component analysis to reveal the more simple structure by reducing the data dimension and *** processing which consists of image acquisition,image enhancement,image thresholding,edge detection and feature extraction has been *** transformed PCA dataset is used to learn and recognize the emotion patterns using artificial neural network *** the findings,the classification accuracy has been improved as much as 10 percent after the geometrical facial feature data has been applied principal component analysis,where 80.8 percent accuracy for PCA dataset while the original dataset only achieves 71.2 ***,the training time and the number of hidden units have been decreased as much as 0.05 seconds and 1 unit respectively after the PCA applied.

关键词： Emotion recognition image processing principal component analysis geometrical facial feature appearance facial feautre artificial neural network

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