• Title, Summary, Keyword: pattern classification

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An Improved 2-D Moment Algorithm for Pattern Classification

  • Yoon, myoung-Young
    • Journal of the Korea Industrial Information Systems Research
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    • v.4 no.2
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    • pp.1-6
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    • 1999
  • We propose a new algorithm for pattern classification by extracting feature vectors based on Gibbs distributions which are well suited for representing the characteristic of an images. The extracted feature vectors are comprised of 2-D moments which are invariant under translation rotation, and scale of the image less sensitive to noise. This implementation contains two puts: feature extraction and pattern classification First of all, we extract feature vector which consists of an improved 2-D moments on the basis of estimated Gibbs distribution Next, in the classification phase the minimization of the discrimination cost function for a specific pattern determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on SUN ULTRA 10 Workstation Experiment results reveal that the proposed scheme had high classification rate over 98%.

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The Classification of Tool Wear States Using Pattern Recognition Technique (패턴인식기법을 이용한 공구마멸상태의 분류)

  • Lee, Jong-Hang;Lee, Sang-Jo
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.17 no.7
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    • pp.1783-1793
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    • 1993
  • Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

A New Pattern Classification and the Analysis of the Lung Sound by Using Cepstrum (Cepstrum을 이용한 폐음의 분석 및 패턴 분류)

  • 김종원;김성환
    • Journal of Biomedical Engineering Research
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    • v.15 no.2
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    • pp.159-166
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    • 1994
  • A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

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EXTRACTING INSIGHTS OF CLASSIFICATION FOR TURING PATTERN WITH FEATURE ENGINEERING

  • OH, SEOYOUNG;LEE, SEUNGGYU
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.24 no.3
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    • pp.321-330
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    • 2020
  • Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X3 and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X3 and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

A Study of Pattern Classification System Design Using Wavelet Neural Network and EEG Signal Classification (웨이블릿 신경망을 이용한 패턴 분류 시스템 설계 및 EEG 신호 분류에 대한 연구)

  • Im, Seong-Gil;Park, Chan-Ho;Lee, Hyeon-Su
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.39 no.3
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    • pp.32-43
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    • 2002
  • In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

The Classification of Electrocardiograph Arrhythmia Patterns using Fuzzy Support Vector Machines

  • Lee, Soo-Yong;Ahn, Deok-Yong;Song, Mi-Hae;Lee, Kyoung-Joung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.11 no.3
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    • pp.204-210
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    • 2011
  • This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

  • Liu, XiaoSheng;Liu, Bo;Xu, DianGuo
    • Journal of Electrical Engineering and Technology
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    • v.11 no.2
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    • pp.309-319
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    • 2016
  • This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

A Study on the Improvement of Multitree Pattern Recognition Algorithm (Multitree 형상 인식 기법의 성능 개선에 관한 연구)

  • 김태성;이정희;김성대
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.14 no.4
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    • pp.348-359
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    • 1989
  • The multitree pattern recognition algorithm proposed by [1] and [2] is modified in order to improve its performance. The basic idea of the multitree pattern classification algorithm is that the binary dceision tree used to classify an unknow pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature ordet. So the feature ordering needed in the calssification procedure is simple and the number of features used in the classification procedure is small compared with other classification algorithms. Thus the algorithm can be easily applied to real pattern recognition problems even when the number of features and that of the classes are very large. In this paper, the wighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.

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Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

  • Hwang, Han-Jeong;Lim, Jeong-Hwan;Im, Chang-Hwan
    • Journal of Biomedical Engineering Research
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    • v.33 no.1
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    • pp.15-24
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    • 2012
  • Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

Electromyography Pattern Recognition and Classification using Circular Structure Algorithm (원형 구조 알고리즘을 이용한 근전도 패턴 인식 및 분류)

  • Choi, Yuna;Sung, Minchang;Lee, Seulah;Choi, Youngjin
    • The Journal of Korea Robotics Society
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    • v.15 no.1
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    • pp.62-69
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    • 2020
  • This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.