• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.252-258
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• 2007

In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.15-22
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.1013-1022
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• 2014

Correlation Power Analysis (CPA) is a type of Side-Channel Analysis (SCA) that extracts the secret key using the correlation coefficient both side-channel information leakage by cryptography device and intermediate value of algorithms. Attack performance of the CPA is affected by noise and temporal synchronization of power consumption leaked. In the recent years, various researches about the signal processing have been presented to improve the performance of power analysis. Among these signal processing techniques, compression techniques of the signal based on Principal Component Analysis (PCA) has been presented. Selection of the principal components is an important issue in signal compression based on PCA. Because selection of the principal component will affect the performance of the analysis. In this paper, we present a method of selecting the principal component by using the correlation of the principal components and the power consumption is high and a CPA technique based on the principal component that utilizes the feature that the principal component has different. Also, we prove the performance of our method by carrying out the experiment.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.2
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• pp.159-167
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• 2019

In this paper the potential of the principal component analysis(PCA) technique for the application of detecting leaks in water pipe networks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study which were designed to extract a partial set of flow data from the original 24 hour flow data so that the effective outlier detection rate was maximized. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The developed algorithm may be applied in determining further leak detection field work for water distribution blocks that have more than 70% of the effective outlier detection rate. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks by considering series of leak reports happening in a relatively short period.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.538-540
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• 2002

본 논문에서는 실시간 카메라 입력 영상에 적합한 얼굴 검출을 위해 다양한 외부적 환경에 덜 민감한 새로운 알고리즘을 제안한다. 빛이나 조명의 영향에 의한 오류를 방지하기 위해 전처리 과정을 포함시키고 형판 정합방법의 단점을 개선하기 위해 얼굴 인식에서 주로 쓰이는 방법인 주성분 분석(PCA :Principal Component Analyses) 변환을 적용하고. 생성된 주성분(Principal Component)을 선형 판별 분석(LDA: Linear Discriminant Analysis)의 입력으로 사용하는 방법을 통해 얼굴을 검출하도록 하였다. 실험을 위해 실제 환경과 같은 6개 카테고리의 동영상을 중심으로 실험한 결과, 본 논문에서 제안하는 방법이 기존의 PCA만을 이용한 방법보다 좋은 성능을 보여줌을 알 수 있었다.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2332-2338
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• 2011

In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as a sensitive diagnostic tool to detect the presence of peak position shift. In this approach, the moving window is constructed from a small data segment along the wavenumber axis. For each window bound by a narrow wavenumber region, separate PCA analysis was applied. Simulated spectra with complex spectral feature variations were analyzed to explore the possibility of MWPCA technique. This MWPCA-based detection of the peak shift, potentially coupled with 2D correlation analysis to provide additional verification, may offer an attractive solution.

• Journal of Environmental Health Sciences
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• v.31 no.2 s.83
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• pp.134-146
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• 2005

Environmental monitoring system has been adopted and supplemented as inspection measures for the quantitative and qualitative changes of environmental impact assessment (EIA). This study compares the results of environmental impact assessment with the results of post-environmental investigation using a correction and principal component analysis (PCA) in the housing development project. Correlation analysis showed that most of air quality variables including TSP, $PM_{10},\;NO_2$, CO were linearly correlated with each other in the environmental impact assessment and the post-environmental investigation. In the water quality, pH and BOD were well correlated with the DO and SS, respectively. As a result of correlation analysis in the noise and vibration, noise in day and night and vibration in day and night were related to each other between EIA and the post-environmental investigation. From the results of analysis of soil, Cu with Cd, Cu with Pb, and Cd with Pb were related to each other in EIA. Principal component analysis (PCA) showed a powerful pattern recognition that had attempted to explain the variance of a large dataset of inter-correlated variable with a smaller set of independent variables (principal components). Principal component (PC1) and principal component (PC2) were obtained with eigenvalues> 1 summing almost $90\%$ of the total variance in the all of the items(air, water, noise, vibration and soil) in EIA and post-environmental investigation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.28-33
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• 2003

This paper proposes a database index and retrieval method using the PCA(Principal Component Analysis). We perform a scene change detection and key frame extraction from the DC Image constructed by DCT DC coefficients in the compressed video stream that is video compression standard such as MPEG. In the extracted key frame, we use the PCA, then we can make codebook that has a statistical data as a codeword, which is saved as a database index. We also provide retrieval image that are similar to user's query image in a video database. As a result of experiments, we confirmed that the proposed method clearly showed superior performance in video retrieval and reduced computation time and memory space.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.3
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• pp.143-148
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• 2005

This paper presents a hybrid method for recognizing the faces by using principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). PCA is used to whiten the data, which reduces the effects of second-order statistics to the nonlinearities. FP-ICA is applied to extract the statistically independent features of face image. The proposed method has been applied to the problems for recognizing the 20 face images(10 persons * 2 scenes) of 324*243 pixels from Yale face database. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.258-262
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• 2017

The PCA(Principal Component Analysis) algorithm is widely used as a technique of expressing the eigenvectors of the covariance matrix that best represents the characteristics of the data and reducing the high dimensional vector to a low dimensional vector. In this paper, we have developed a personal authentication method based on ECG using principal component analysis. The proposed method showed excellent recognition performance of 98.2 [%] when it was experimented using electrocardiogram data obtained at weekly intervals. Therefore, it can be seen that it is useful for personal authentication by reducing the dimension without changing the information on the variability and the correlation set variable existing in the electrocardiogram data by using the principal component analysis technique.