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15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

作者： Valy, Dona Verleysen, Michel Sok, Kimheng Catholic Univ Louvain ICTEAM Inst Louvain La Neuve Belgium Inst Technol Cambodia Dept Informat & Commun Engn Phnom Penh Cambodia

ISBN: (纸本)9781509009817

Line segmentation is very crucial in handwritten text recognition/analysis task. A new text line extraction scheme based on a data clustering algorithm is proposed. Our approach starts by determining the number of lines and setting up text line mid points' initial positions using a modified piece-wise projection profile technique. We apply afterwards competitive learning algorithm to adaptively move those mid points according to the geometrical information of connected components in the document page to form lines. Borders between text lines are defined so that they can be used to separate touching components that spread over multiple lines. The proposed method is robust in handling documents with skewed, fluctuated, or discontinued text lines. Experimental evaluations were made on a data set of Khmer ancient palm leaf manuscripts.

关键词： handwritten document analysis text line segmentation competitive learning algorithm

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A new competitive learning algorithm for vector quantization based on the neuron winning probability

A new competitive learning algorithm for vector quantization...

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IEEE International Conference on Intelligent Processing Systems

作者： Xu, Y Yan, GQ Chen, HX Dai, YS Changchun Inst Posts & Telecomm Dept Sci & Technol Changchun Peoples R China

ISBN: (纸本)0780342534

Neural network competitive learning algorithms are widely used for vector quantization. In this paper, some typical competitive learning algorithms have been specially investigated, analyzed and their performances have also been evaluated. A new competitive learning algorithm based on the neuron winning probability is presented for vector quantization. Unlike the traditional competitive learning algorithms where only one neuron will win and learn in each competition, every neuron in the proposed probability sensitive competitive learning algorithm (PSCL) will win to some extent, depending on its winning probability and adjustment of distortion distance to the input vector. The new algorithm is shown to be efficient to overcome the problem of neuron underutilization.

关键词： neural nets unsupervised learning vector quantisation probability signal processing competitive learning algorithm vector quantization neuron winning probability neural network competitive learning algorithms probability sensitive competitive learning algorithm distortion distance input vector neuron underutilization

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competitive learning: A New Meta-Heuristic Optimization algorithm

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2018年第8期27卷

作者： Afroughinia, Afshin Moghaddam, Reihaneh Kardehi Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran

This work proposes a new powerful meta-heuristic optimization algorithm in education process called competitive learning (CLA). The algorithm is benchmarked on 8 well-known test functions, and the results are verified by a comparative study with some meta-heuristic optimization methods including: Imperialist competitive algorithm (ICA), Teaching-learning-Based Optimization (TLBO), Grey Wolf algorithm (GWO), Particle Swarm Optimization (PSO) and Genetic algorithm (GA). Analyzing the findings, it is shown that the CLA algorithm is able to provide more accurate results than other well-known meta-heuristic ones. Also, those results applied to famous unimodal and multimodal benchmarks show CLA is efficient in improving accuracy as well as computational speed.

关键词： Optimization meta-heuristic algorithm competitive learning algorithm

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A new feature extractor invariant to intensity, rotation, and scaling of color images

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INFORMATION SCIENCES 2006年第14期176卷 2097-2119页

作者： Sookhanaphibarn, Kingkarn Lursinsap, Chidchanok Chulalongkorn Univ Fac Sci Dept Math Adv Virtual & Intelligent Comp Ctr Bangkok 10330 Thailand

This paper proposes a new method for extracting the invariant features of an image based on the concept of principal component analysis and a competitive learning algorithm. The proposed algorithm can be applied to binary, gray-level, or colored-texture images with a size greater than 256 x 256 pixels. In addition to translation, scaling, and rotation invariant extraction, the extraction of a feature invariant to color intensity can be implemented by using this method. In our experiment, the proposed method shows the capability to differentiate images having the same shape but different colored textures. The experimental results report the effectiveness of this technique and its performance as measured by recognition accuracy rate and computational time. These results are also compared with those obtained by classical techniques. (C) 2005 Elsevier Inc. All rights reserved.

关键词： translation scaling and rotation transformation intensity invariance invariant object recognition (IOR) colored-texture images competitive learning algorithm principal component analysis (PCA) rotational direction algorithm

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Extract Tacit Knowledge in the Learner Model of the Smart Tutoring System

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN learning 2020年第4期15卷 235-240页

作者： Hibbi, Fatima-Zohra Abdoun, Otman Haimoudi, El Khatir Abdelmalek Essaadi Univ Larache Morocco Abdelmalek Essaadi Univ Dept Comp Sci Pluridisciplinary Fac Larache Larache Morocco Abdelmalek Essaadi Univ Dept Comp Sci Larache Morocco

Knowledge management (KM) is one of the main factors that have become extremely popular in recent years. KM is the processes which people explain information data using scientific and technological media and summarize it into concepts and rules to generate knowledge. This later can be implicit or explicit one. The aim of this contribution is to convert the tacit knowledge into explicit using Metaheuristics techniques. This paper aims to develop a model for converting tacit knowledge into explicit knowledge, using the Metaheuristics algorithm for the E-learning platform. For that purpose, the knowledge conversion process will respect the following steps: define the source of tacit knowledge and their methods, classify the tacit knowledge, then we evaluate the implicit knowledge conversion.

关键词： Knowledge Management Metaheuristics Tacit Knowledge competitive learning algorithm

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Characterization of deep water turbidite channels and submarine fan lobes using artificial intelligence;Case study of Frem Field deep offshore Niger Delta

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JOURNAL OF AFRICAN EARTH SCIENCES 2020年 167卷 103852-103852页

作者： Obafemi, S. Oyedele, K. F. Omeru, T. Bankole, S., I Univ Lagos Dept Geosci Lagos Nigeria

Two lithofacies and fluid discriminating seismic attributes are integrated using Artificial Intelligence (AI) via Unsupervised Artificial Neural Network (UANN) to characterize the architecture of deep water turbidite channels and submarine fan lobes across a hydrocarbon bearing reservoir within the Frem Field deep-water Niger Delta. A data-based approach including reservoir identification, environment of deposition prediction, seismic attribute analysis and finally UANN using the competitive learning algorithm (CLA) was used to match patterns from the two seismic attributes in order to reduce and capture uncertainties inherent with characterization of turbidite sands within the stratigraphic and structurally complex deep-water Niger Delta. One hydrocarbon bearing reservoir (Sand R001) with excellent reservoir quality was identified from the wireline logs interpretation after which gamma ray logs motifs as well as root mean square (RMS) amplitude and sweetness attributes imaging revealed the environment of deposition of the sand as an inner fan channel within a complex system of several channels and submarine fan lobes. Discreet facies map generated from the CLA enabled a better definition of the architecture, orientation and trend of the sand and lobate nature of the submarine fans lobes associated with the reservoir. The resulting output led to an enhanced characterization of the architectural patterns of the reservoir as well as associated deep-water facies in terms of reservoir architecture and orientation. The discreet facies map also revealed both northeast southwest and northwest southeast orientation of turbidite channels and submarine fan lobes and indicates the channels serves as feeders to the lobate submarine fan systems. The study has shown the efficacy of AI in enhancing deep water architectural patterns via pattern matching of facies and fluid related seismic attributes using CLA and thereby shows the method is effective in reducing uncertainties inher

关键词： Artificial intelligence Unsupervised artificial neural network competitive learning algorithm Seismic attributes Facies Uncertainties

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Determination of Gender and Age Based on Pattern of Human Motion Using AdaBoost algorithms

Determination of Gender and Age Based on Pattern of Human Mo...

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12th International FIRA RoboWorld Congress

作者： Handri, Santoso Nomura, Shusaku Nakamura, Kazuo Nagaoka Univ Technol Top Runner Incubat Ctr Acad Ind Fus Niigata Japan Nagaoka Univ Technol Dept Management & Informat Syst Sci Niigata Japan

ISBN: (纸本)9783642039829

Automated human identification by their walking behavior is a challenge attracting much interest among machine vision researchers. However, practical systems for such identification remain to be developed. In this study, a machine learning approach to understand human behavior based on motion imagery was proposed as the basis for developing pedestrian safety information systems. At the front end, image and video processing was performed to separate foreground from background images. Shape-width was then analyzed using 2D discrete wavelet transformation to extract human motion features. Finally, an adaptive boosting (AdaBoost) algorithm was performed to classify human gender and age into its class. The results demonstrated capability of the proposed systems to classify gender and age highly accurately.

关键词： human motion gender and age CHIAN competitive learning algorithm

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Fuzzy multi-model based adaptive predictive control and its application to thermoplastic injection molding

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2001年第2期79卷 263-272页

作者： Li, MZ Yang, Y Gao, FR Wang, FL Hong Kong Univ Sci & Technol Dept Chem Engn Kowloon Hong Kong Peoples R China

Many chemical processes are inherently nonlinear. A single linear model is ineffective for these processes. Several local linear models may be developed for different operating conditions. A combination of these local models, through a fuzzy logic representation, results in an overall model for a wider operation range. In this paper, on-line improvements and a fuzzy multimodel have been proposed for predictive control implementation. Firstly, assuming that the premises of the fuzzy rules keep their original structures, the linear parameters in the rule consequents are on-line updated by a weighted recursive least squares algorithm at each sample interval. Secondly, a batch learning algorithm is proposed to tune the fuzzy rule premises using a competitive learning algorithm. The effectiveness of the proposed improvements is demonstrated with experimental applications to the filling velocity control of thermoplastic injection molding.

关键词： fuzzy multi-model predictive control weighted recursive least squares algorithm competitive learning algorithm injection molding

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Abrupt Shot Boundary Detection algorithm Based on Fuzzy Clustering Neural Network

Abrupt Shot Boundary Detection Algorithm Based on Fuzzy Clus...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Shujuan Shen Information Engineering College Zhengzhou University Zhengzhou,China Jianchun Cao Department of Information Engineering Yellow River Conservancy Technical Institute Kaifeng,China

A novel model of fuzzy clustering neural network is discussed,which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model,an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature *** experimental results obtained demonstrate that the algorithm is feasible and efficient.

关键词： fuzzy clustering neural network competitive learning algorithm abrupt shot boundary detection

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