• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.355-360
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• 2015

In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.144-151
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• 2012

Chronic gastritis is the disease that is occuring in one in every 10 persons in Korea. In western medicine, endoscopy is needed to diagnose chronic gastritis, but it causes patients a pain and budget of expense. According to the TEM (Traditional Eastern Medicine), on the other hand, the 'Guan' position of the right wrist is related to a stomach. Thus we can diagnosis chronic gastritis by analyzing of pulse signal. However, pulse signal diagnosis is depended on oriental doctor's knowledge and experience. In this study, a systematic approach is proposed to analyze the computerized pulse signal. The pulse signals are firstly pre-processed, Gaussian model is adopted to fit the pulse signal, and then some related parameters are extracted from the model. Consequently, disease-sensitive parameters are selected by T-test and statistical difference. Finally, the selected parameters are entered into a Fuzzy C-Means (FCM) algorithm for classification. Classification results show that healthy persons and chronic gastritis patients are 95% and 87%, respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.210-212
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• 2007

본 논문에서는 컴퓨터 바이러스 정보 구축과 탐색에 학습기능을 도입함으로 새로 발생하는 바이러스를 찾아내어 대처할 수 있도록 설계된 퍼지 진단 시스템 FDS를 제안한다. FDS에서는 FCM 알고리즘을 사용하여 알려진 정보의 클러스터를 형성하고 이에 전문가의 지식을 포함하는 지식베이스를 구축한다. 진단을 위한 컴퓨터 파일에 대하여 그 파일의 결정 상태를 확인하고 이미 저장된 지식베이스를 바탕으로 바이러스 침입에 대한 정보를 보고하도록 설계되어있다. 이 시스템은 이미 알려진 테스트 데이터와 이전에 알려지지 않은 새로운 테스트 데이터를 실험데이터로 준비하여 그 성능을 테스트 한다. 제안된 시스템이 알려지지 않은 컴퓨터 바이러스의 경우도 효과적으로 진단할 수 있는 타당성을 보이고 있다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.267-273
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• 2004

The advantage of the remote sensing is extraction the information of wide area rapidly. Such advantage is the resource and environment are quick and efficient method to grasps accurately method through the land cover classification of wide area. Accordingly this study is used to the high-resolution (6.6m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data(36 bands).We accomplished FCM classification technique with MLC technique to be general land cover classification method in the content of research. And evaluated the accuracy assessment of two classification method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1863_1864
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 입력개수, 입력변수, 클러스터의 개수를 PSO알고리즘(Particle Swarm Optimization)을 사용하여 최적화 시켰다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1365-1366
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• 2015

본 논문에서는 HOG 특징을 이용한 다항식 방사형 기저함수 신경회로망 기반 숫자 인식 시스템의 설계를 제안한다. 제안한 숫자 인식 시스템은 HOG 특징을 이용하여 숫자를 입력 데이터로 사용하기 위해 특징을 계산한다. 다항식 방사형 기저 함수 신경회로망은 고차원 데이터의 입-출력 형태를 갖는 클래스를 분류하는데 용이하며, 활성함수의 중심점 및 분포상수는 Fuzzy C-Means(FCM) 알고리즘에 의해 초기 값을 설정한다. 또한 제안한 분류기의 최적화를 위해 Particle Swarm Optimization(PSO)를 사용하여 최적화된 분류기의 성능을 비교한다. 숫자 인식을 위하여 공인 데이터베이스인 MNIST handwritten digit database를 사용하여 분류기의 성능을 평가하고 분석한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1355-1356
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• 2015

본 논문에서는 Convolution기법 기반 RBFNNs 패턴 분류기를 사용한 2차원 얼굴인식 시스템을 설계한다. 제안된 방법은 특징 추출과 차원축소를 하는 컨볼루션 계층과 부분추출 계층을 교대로 연결하여 2차원 이미지를 1차원의 특징 배열로 만든다. 그 후, 만들어진 1차원의 특징 배열을 RBFNNs 패턴 분류기의 입력으로 사용하여 인식을 수행한다. RBFNNs의 조건부에는 FCM 클러스터링 알고리즘을 사용하며 연결가중치는 1차 선형식을 사용하였다. 또한 최소 자승법(LSE : Least Square Estimation)을 사용하여 다항식의 계수를 추정하였다. 제안된 모델의 성능을 평가하기 위해 CMU PIE Database를 사용한다.