• Title, Summary, Keyword: pattern recognition

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A Study on Word Recognition Using Neural-Fuzzy Pattern Matching (뉴럴-퍼지패턴매칭에 의한 단어인식에 관한 연구)

  • 이기영;최갑석
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.29B no.11
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    • pp.130-137
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    • 1992
  • This paper presents the word recognition method using a neural-fuzzy pattern matching, in order to make a proper speech pattern for a spectrum sequence and to improve a recognition rate. In this method, a frequency variation is reduced by generating binary spectrum patterns through associative memory using a neural network, and a time variation is decreased by measuring the simillarity using a fuzzy pattern matching. For this method using binary spectrum patterns and logic algebraic operations to measure the simillarity, memory capacity and computation requirements are far less than those of DTW using a conventional distortion measure. To show the validity of the recognition performance for this method, word recognition experiments are carried out using 28 DDD city names and compared with DTW and a fuzzy pattern matching. The results show that our presented method is more excellent in the recognition performance than the other methods.

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Neural Network Design for Spatio-temporal Pattern Recognition (시공간패턴인식 신경회로망의 설계)

  • Lim, Chung-Soo;Lee, Chong-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.11
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    • pp.1464-1471
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    • 1999
  • This paper introduces complex-valued competitive learning neural network for spatio-temporal pattern recognition. There have been quite a few neural networks for spatio-temporal pattern recognition. Among them, recurrent neural network, TDNN, and avalanche model are acknowledged as standard neural network paradigms for spatio-temporal pattern recognition. Recurrent neural network has complicated learning rules and does not guarantee convergence to global minima. TDNN requires too many neurons, and can not be regarded to deal with spatio-temporal pattern basically. Grossberg's avalanche model is not able to distinguish long patterns, and has to be indicated which layer is to be used in learning. In order to remedy drawbacks of the above networks, unsupervised competitive learning using complex umber is proposed. Suggested neural network also features simultaneous recognition, time-shift invariant recognition, stable categorizing, and learning rate modulation. The network is evaluated by computer simulation with randomly generated patterns.

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Comparison of invariant pattern recognition algorithms (불변 패턴인식 알고리즘의 비교연구)

  • 강대성
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.8
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    • pp.30-41
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    • 1996
  • This paper presents a comparative study of four pattern recognition algorithms which are invariant to translations, rotations, and scale changes of the input object; namely, object shape features (OSF), geometrica fourier mellin transform (GFMT), moment invariants (MI), and centered polar exponential transform (CPET). Pattern description is obviously one of the most important aspects of pattern recognition, which is useful to describe the object shape independently of translation, rotation, or size. We first discuss problems that arise in the conventional invariant pattern recognition algorithms, or size. We first discuss problems that arise in the coventional invariant pattern recognition algorithms, then we analyze their performance using the same criterion. Computer simulations with several distorted images show that the CPET algorithm yields better performance than the other ones.

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Emergent damage pattern recognition using immune network theory

  • Chen, Bo;Zang, Chuanzhi
    • Smart Structures and Systems
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    • v.8 no.1
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    • pp.69-92
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    • 2011
  • This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

Chemometric Tool of Chromatographic Pattern Recognition for the Analysis of Complex Mixtures

  • Park, Man-Ki;Park, Jeong-Hill;Cho, Jung-Hwan;Kim, Na-Young;Kang, Jong-Seong
    • Archives of Pharmacal Research
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    • v.15 no.4
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    • pp.376-378
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    • 1992
  • A chemical tool was developed for the analysis of complex mixtures such as crude drugs by the method of pattern recognition. Pattern recognition was accomplished by a multiple reference peak identification method and three kinds of outlier statistics. This tool was tested on the analysis of synthetic mixtures.

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EMG Pattern Recognition based on Evidence Accumulation for Prosthesis Control

  • Lee, Seok-Pil;Park, Sand-Hui
    • Journal of Electrical Engineering and information Science
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    • v.2 no.6
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    • pp.20-27
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    • 1997
  • We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

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The Pattern Recognition System Using the Fractal Dimension of Chaos Theory

  • Shon, Young-Woo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.2
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    • pp.121-125
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    • 2015
  • In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.

A Study on Feature Projection Methods for a Real-Time EMG Pattern Recognition (실시간 근전도 패턴인식을 위한 특징투영 기법에 관한 연구)

  • Chu, Jun-Uk;Kim, Shin-Ki;Mun, Mu-Seong;Moon, In-Hyuk
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.9
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    • pp.935-944
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    • 2006
  • EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network

  • Chang, Wen-Yeau
    • Journal of Electrical Engineering and Technology
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    • v.9 no.1
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    • pp.293-300
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    • 2014
  • This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. Since an insulation defect, such as one resulting from PD, would have a corresponding particular pattern, pattern recognition of PD is significant means to discriminate insulation conditions of high-voltage electrical apparatus. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of cast resin current transformer (CRCT) models. These tests used artificial defects created in order to produce the common PD activities of CRCTs by using feature vectors of field-test PD patterns. The significant features are extracted by using nonlinear principal component analysis (NLPCA) method. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach is efficient and reliable.

Real-Time Bus Reconfiguration Strategy for the Fault Restoration of Main Transformer Based on Pattern Recognition Method (자동화된 변전소의 주변압기 사고복구를 위한 패턴인식기법에 기반한 실시간 모선재구성 전략 개발)

  • Ko Yun-Seok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.11
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    • pp.596-603
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    • 2004
  • This paper proposes an expert system based on the pattern recognition method which can enhance the accuracy and effectiveness of real-time bus reconfiguration strategy for the transfer of faulted load when a main transformer fault occurs in the automated substation. The minimum distance classification method is adopted as the pattern recognition method of expert system. The training pattern set is designed MTr by MTr to minimize the searching time for target load pattern which is similar to the real-time load pattern. But the control pattern set, which is required to determine the corresponding bus reconfiguration strategy to these trained load pattern set is designed as one table by considering the efficiency of knowledge base design because its size is small. The training load pattern generator based on load level and the training load pattern generator based on load profile are designed, which are can reduce the size of each training pattern set from max L/sup (m+f)/ to the size of effective level. Here, L is the number of load level, m and f are the number of main transformers and the number of feeders. The one reduces the number of trained load pattern by setting the sawmiller patterns to a same pattern, the other reduces by considering only load pattern while the given period. And control pattern generator based on exhaustive search method with breadth-limit is designed, which generates the corresponding bus reconfiguration strategy to these trained load pattern set. The inference engine of the expert system and the substation database and knowledge base is implemented in MFC function of Visual C++ Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and pattern recognition solution based on diversity event simulations for typical distribution substation.