• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.1-5
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• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.355-358
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• 2004

본 논문에서는 사람의 얼굴과 지문을 이용하여 실시간 다중 생체인식 시스템 구현을 제안하였다. 얼굴인식에서는 이미지의 크기를 축소하기 위해 Wavelet Transform을 이용하였으며, 특징 값을 찾아내기 위한 방법으로는 얼굴인식에서 많이 사용되는 LDA(Linear Discriminant Analysis)를 이용하였다. 또한, 지문인식에서는 지문의 중심점을 찾아 가버 변환을 하고, 이로부터 섹터별 변량을 특징 값으로 사용하였으며, 인식 성능을 향상시킬 수 있는 상관도가 높은 지문 3개를 기준 데이터로 등록하였다. 마지막 단계로 두 가지의 생체정보를 모두 사용할 수 있도록 퍼지를 이용하여 얼굴인식의 결과와 지문인식의 결과를 융합하였으며, 단일 생체정보를 이용했을 때의 단점을 다중 생체인식 시스템을 구현함으로서 우수한 성능을 보이는 시스템을 구현하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.759-764
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• 2005

In this paper we describe a face recognition using EBGM(Elastic Bunch Graph Matching) method. Usally, the PCA and LDA based face recognition method with the low-dimensional subspace representation use holistic image of faces, but this study uses local features such as a set of convolution coefficients for Gabor kernels of different orientations and frequencies at fiducial points including the eyes, nose and mouth. At pre-recognition step, all images are represented with same size face graphs and they are used to recognize a face comparing with each similarity for all images. The proposed algorithm has less computation time due to simplified face graph than conventional EBGM method and the fuzzy matching method for calculating the similarity of face graphs renders more face recognition results.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.57-64
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• 2007

One of the cause of insulation failure in power cable is well known by electrical treeing discharge. This is occurred for imposed continuous stress at cable. And this event is related to safety, reliability and maintenance. In this paper, throughout analysis of partial discharge(PD) distribution when occurring the electrical tree, is studied for the purpose of knowing of electrical treeing discharge characteristics according to defects. Own characteristic of tree will be differently processed in each defect and this reason is the first purpose of this paper. To acquire PD data, three defective tree models were made. And their own data is shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. And if other defects (void, metal particle) exist internal power cable then their characteristics are shown very different. This result Is related to the time of breakdown and this is importance of cable diagnosis. And classification method of PD sources was studied in this paper. It needs select the most useful method to apply PD data classification one of the proposed method. To meet the requirement, we select methods of different type. That is, neural network(NN-BP), adaptive neuro-fuzzy inference system and PCA-LDA were applied to result. As a result of, ANFIS shows the highest rate which value is 98 %. Generally, PCA-LDA and ANFIS are better than BP. Finally, we performed classification of tree progress using ANFIS and that result is 92 %.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.616-626
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.423-433
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• 2008

The surface curvatures extracted from the face contain the most important personal facial information. In particular, the face shape using the depth information represents personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple face regions using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area and has to take into consideration of the orientated frontal posture to normalize. Multiple areas are extracted by the depth threshold values from reference point, nose tip. And then, we calculate the curvature features: principal curvature, gaussian curvature, and mean curvature for each region. The second step of approach concerns the application of eigenface and Linear Discriminant Analysis(LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each region. In the experimental results, using the depth threshold value 40 (DT40) show the highest recognition rate among the regions, and the maximum curvature achieves 98% recognition rate, incase of fuzzy integral.