• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.77-86
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• 2005

본 논문에서는 분류오차를 추출하고 학습하여 분류성능을 개선하는 LVQ 학습 알고리즘을 설계하였다. 제안된 LVQ학습 알고리즘은 초기기준백터의 학습을 위해 SOM을 이용하고, LVQ 출력뉴런의 부류지정을 위하여 out-star 학습법을 사용하는 학습네트워크이다. 분류오차가 발생되는 패턴백터로 추출하기 위하여 오차유발조건을 제안하였고, 이 조건을 이용하여 분류오차를 유발시키는 입력패턴벡터로 구성되는 패턴백터공간을 구성하여 분류오차가 발생되는 패턴백터를 학습시키므로 분류오차수를 감소시키고, 패턴분류성능을 개선하였다. 제안된 학습알고리즘의 성능을 검증하기 위하여 Fisher의 Iris 데이터와 EMG 데이터를 학습백터 및 시험 백터로 사용하여 시뮬레이션 하였고, 제안된 학습방식의 분류 성능은 기존의 LVQ와 비교되어 기존의 학습방식보다 우수한 분류성공률을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(5)
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• pp.273-276
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• 2002

This paper has design of ECG pattern classification system using decision of fuzzy IF-THEN rules and neural network. each fuzzy IF-THEN rule in our classification system has antecedent lingustic values and a single consequent class. we use a fuzzy reasoning method based on a single winner rule in the classification phase. this paper in, the MIT/BIH arrhythmia database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, we can effectively pattern classification by application of learned from neural networks.

• Journal of Electrical Engineering and information Science
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• 제3권3호
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• pp.342-347
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• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전력기술부문
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• pp.89-91
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• 2001

This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.

• International Journal of Contents
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• 제2권3호
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• pp.11-17
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• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.289-293
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• 2005

패턴 분류는 실세계의 객체를 표현한 다양한 형태의 패턴 정보를 추출하여, 이것이 어떤 부류(클래스)인가를 결정하는 것이다. 패턴 분류 기술은 데이터 마이닝, 산업 자동화나 업무자동화를 위한 컴퓨터 응용 소프트웨어 기술로서 현재 다양한 분야에서 활용되고 있다. 패턴 분류 기술의 최대 목표는 분류 성능 향상이며 이것을 위해 지난 40년간 많은 연구자들이 다양한 접근 방법들을 시도해 왔다. 주로 이용되는 단일 분류 방법들로는 패턴들의 확률적 추론에 기반한 베이즈 분류기, 결정 트리, 거리함수를 이용하는 방법, 신경망, 군집화 등이 있으나 대용량 다차원 데이터를 분석하기에는 효율적이지 못하다. 따라서 상호 보완적인 여러 분류기들을 사용해 결합을 통하여 성능 향상에 도움을 주고 있는 다중 분류기 시스템에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 다중 SVM(Support Vector Machine) 분류기에 관한 기존 연구의 문제점을 지적하고 새로운 모델을 제안한다. SVM을 다중 클래스 분류기로 확장하기 위해 일대다 정책을 기반으로 하여 각각의 SVM 출력값을 비선형 패턴을 갖는 신호로 간주하고 이를 신경망에 학습하여 최종 분류 성능 결과를 결합하는 모델인 BORSE(Bootstrap Resampling SVM by Ensemble)를 제안한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2846-2848
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• 2005

This paper proposes a pattern classification algorithm for the defect distribution of Hard Disk Drive (HDD). In the HDD productions, the defect pattern of defective HDD set is important information to diagnosis of defective HDD set. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A fuzzy inference system is proposed, the inputs of which are 5 characteristic values and the outputs are the possibilities that the input pattern is classified to the standard patterns. Classification result is the pattern with maximum possibility. The proposed algorithm is implemented with a PC system for defective HDD sets and shows its effectiveness.

• 한국공작기계학회논문집
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• 제16권6호
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• pp.62-70
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• 2007

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.