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Engineering Applications of Neural Networks 2011

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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Driver recognition based on dynamic handgrip pattern on steeling wheel

Driver recognition based on dynamic handgrip pattern on stee...

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2011 12th ACIS international conference on Software Engineering, Artificial Intelligence Networking and Parallel Distributed computing, SNPD 2011

作者： Chen, Ronghua She, Mary Fenghua Sun, Xiangping Kong, Lingxue Wu, Yuchuan Institute for Technology Research and Innovation Deakin University Geelong VIC Australia School of Mechanical Engineering and Automation Wuhan Textile University Wuhan China

ISBN: (纸本)9780769544755

this paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. the experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design. © 2011 IEEE.

关键词： Automobile steering equipment

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A kind of shape-color moment invariants

A kind of shape-color moment invariants

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12th international conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2011

作者： Gong, Ming Hu, Ping Cao, Weiguo Li, Hua National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Science Beijing 100190 China

ISBN: (纸本)9780769544977

Shape or color based moment invariants are conventional pattern sensitive features in the object recognition and image description. However, the existing moment invariants are not robust because they handle simplified cases, such as single impact of shape transformation, single color transformation, and a simple combination of them. In this paper, we propose a kind of shape-color moment invariants (SCMIs), taking complicated shape and color transformation into account. It is applicable to more complicated cases, such as changing viewpoints, different illuminations, different camera white balance modes, and different color spaces. the integral-based SCMI construction framework is extensible and general. We evaluate the technique through comprehensive experiments. © 2011 IEEE.

关键词： Color

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Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

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12th IEEE international conference on Information Reuse and Integration (IRI)/1st international Workshop on Issues and Challenges in Social computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

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New trends in musical genre classification using optimum-path forest

New trends in musical genre classification using optimum-pat...

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12th international Society for Music Information Retrieval conference, ISMIR 2011

作者： Marques, C. Guilherme, I.R. Nakamura, R.Y.M. Papa, J.P. Dep. of Statistics Applied Math. and Computation UNESP - Univ Estadual Paulista Rio Claro SP Brazil Department of Computing UNESP - Univ Estadual Paulista Bauru SP Brazil

ISBN: (纸本)9780615548654

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 international Society for Music Information Retrieval.

关键词： Feature extraction

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Computation pattern driven reuse of manual optimizations for GPGPUs

Computation pattern driven reuse of manual optimizations for...

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2011 12th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2011

作者： Xu, Shixiong Han, Dongni Chen, Li Key Laboratory of Computer System and Architecture Institute of Computing Technology University of Chinese Beijing China

ISBN: (纸本)9780769545646

the wide application of General Purpose Graphic Processing Units (GPGPUs) results in large manual efforts on porting and optimizing algorithms on them. However, most existing automatic ways of generating GPGPU code fail to conduct optimization strategies regarding a specific computation and to reuse constantly evolving manual optimizations. In this paper, we present a computation pattern driven approach for computation-specific GPGPU code generation and optimization, which in turn reuses manual optimizations to a certain extent. We suggest language extensions to OpenMP, high-level data structure attributes, in order to assist the process of computation pattern matching and to help give users intuitive performance tuning parameters in the view of data structure attributes. We illustrate the feasibility of this approach through three important computation dwarfs, which are dense matrix, sparse matrix, and structured mesh computation in scientific computing. We also build a prototype OpenMP-to-CUDA translator that consists of computation pattern recognition and code template instantiation. the experimental results demonstrate the performance benefits of computation pattern driven method. To our best knowledge, it is the first work on reusing manual optimizations for GPGPUs with computation pattern driven approach. © 2011 IEEE.

关键词： Data structures

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Driver recognition Based on Dynamic Handgrip pattern on Steeling Wheel

Driver Recognition Based on Dynamic Handgrip Pattern on Stee...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Ronghua Chen Mary Fenghua She Xiangping Sun Lingxue Kong Yuchuan Wu Institute for Technology Research and Innovation Deakin University Geelong VIC Australia School of Mechanical Engineering and Automation Wuhan Textile University Wuhan China

关键词： Vehicles Wheels Wavelet transforms Feature extraction Grasping pattern recognition

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Computation pattern Driven Reuse of Manual Optimizations for GPGPUs

Computation Pattern Driven Reuse of Manual Optimizations for...

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IEEE international conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Shixiong Xu Dongni Han Li Chen Key Laboratory of Computer System and Architecture Institute of Computing Technology Graduate University of Chinese Academy of Sciences Beijing China

关键词： Data structures Optimization pattern matching Sparse matrices Manuals Libraries Tuning

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A Kind of Shape-Color Moment Invariants

A Kind of Shape-Color Moment Invariants

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IEEE international conference on Computer-Aided Design and Computer Graphics

作者： Ming Gong Ping Hu Weiguo Cao Hua Li Graduate University Chinese Academy of Sciences Beijing China National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

关键词： Image color analysis Shape Color Mathematical model Lighting Cameras Additives

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pattern classification with missing data: a review

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NEURAL computing & APPLICATIONS 2010年第2期19卷 263-282页

作者： Garcia-Laencina, Pedro J. Sancho-Gomez, Jose-Luis Figueiras-Vidal, Anibal R. Univ Politecn Cartagena Dpto Tecnol Informac & Comunicac Murcia 30202 Spain Univ Carlos III Madrid Dpto Teoria Senal & Comunicac Madrid 28911 Spain

pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. the aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

关键词： pattern classification Missing data Neural networks Machine learning

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