• 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.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.23-31
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• 2003

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.213-217
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• 2003

In this paper, we analysize the noise robustness of MLPs(Multilayer perceptrons). Also, as a preprocessing stage of MLPs to improve noise robustness, we consider the ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise redunction effect using PCA or ICA, we verify the noise robustness of MLPs through handwritten-digit recognition simulations.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.51-55
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• 2007

중요한 정보를 저장하고 있는 서버 및 개인용 컴퓨터를 위협하는 바이러스가 현실적인 문제로 대두되고 있다. 범용 바이러스 탐지기법을 위해 주성분 분석(PCA)을 사용하여 휴리스틱 접근으로 바이러스 탐지 능력을 높일 수 있는 명령어 집합을 찾았고, PCA의 결과좌표 분포에 따라 정상파일인 경우 90%의 분류, 바이러스파일에 대하여 85%의 분류 능력을 확인하였다.

• Information and Communications Magazine
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• v.31 no.11
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• pp.35-45
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• 2014

주성분 분석 기법(PCA)는 가장 널리 사용되는 데이터 차원 감소 (dimensionality reduction) 기법으로 알려져 있다. 하지만 데이터에 이상점 (outlier)가 존재하는 환경에서는 성능이 크게 저하된다는 단점을 가지고 있다. Rank-Sparsity(Robust PCA) 기법은 주어진 행렬을 low-rank 행렬과 저밀도(sparse)행렬의 합으로 분해하는 방식으로, 이상점이 많은 환경에서 PCA기법을 효과적으로 대체할 수 있는 알고리즘으로 알려져 있다. 본 고에서는 RPCA 기법을 간략히 소개하고, 그의 적용분야, 및 알고리즘에 관한 연구들을 대해서 알아본다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1679-1686
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• 2009

Odor sensing system that is electronic nose device and its signal processing technique has potential to become a critical service for the people who require tangibility of sense of smell in the multimedia communication. PCA(Principal Component Analysis) have been used for dimensionality reduction and visualization of multivariate measurement data. PCA is good for estimating importance value by variance of data but, have some limitation for getting meaningful representation from odor sensing system. This paper explain about how to analyze the data of odor sensing system by ICA(Independent Component Analysis). We show that ICA can give better result like sensor drift analysis, dimensionality reduction and data representation by improved discrimination.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.304-311
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• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.252-258
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• 2022

In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.