• Title, Summary, Keyword: principal component analysis (PCA)

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A Study on CPA Performance Enhancement using the PCA (주성분 분석 기반의 CPA 성능 향상 연구)

  • Baek, Sang-Su;Jang, Seung-Kyu;Park, Aesun;Han, Dong-Guk;Ryou, Jae-Cheol
    • 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.

Multivariate Time Series Simulation With Component Analysis (독립성분분석을 이용한 다변량 시계열 모의)

  • Lee, Tae-Sam;Salas, Jose D.;Karvanen, Juha;Noh, Jae-Kyoung
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.694-698
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    • 2008
  • In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

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Study on the applicability of the principal component analysis for detecting leaks in water pipe networks (상수관망의 누수감지를 위한 주성분 분석의 적용 가능성에 대한 연구)

  • Kim, Kimin;Park, Suwan
    • 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.

A Study on the Vulnerability Assessment for Agricultural Infrastructure using Principal Component Analysis (주성분 분석을 이용한 농업생산기반의 재해 취약성 평가에 관한 연구)

  • Kim, Sung Jae;Kim, Sung Min;Kim, Sang Min
    • Journal of The Korean Society of Agricultural Engineers
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    • v.55 no.1
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    • pp.31-38
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    • 2013
  • The purpose of this study was to evaluate climate change vulnerability over the agricultural infrastructure in terms of flood and drought using principal component analysis. Vulnerability was assessed using vulnerability resilience index (VRI) which combines climate exposure, sensitivity, and adaptive capacity. Ten flood proxy variables and six drought proxy variables for the vulnerability assessment were selected by opinions of researchers and experts. The statistical data on 16 proxy variables for the local governments (Si, Do) were collected. To identify major variables and to explain the trend in whole data set, principal component analysis (PCA) was conducted. The result of PCA showed that the first 3 principal components explained approximately 83 % and 89 % of the total variance for the flood and drought, respectively. VRI assessment for the local governments based on the PCA results indicated that provinces where having the relatively large cultivation areas were categorized as vulnerable to climate change.

Moving Window Principal Component Analysis for Detecting Positional Fluctuation of Spectral Changes

  • Ryu, Soo-Ryeon;Noda, Isao;Jung, Young-Mee
    • 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.

A Case Study on the Comparison and Assessment between Environmental Impact Assessment and Post-Environmental Investigation Using Principal Component Analysis (주성분분석을 이용한 환경영향평가와 사후환경조사의 비교 및 평가에 관한 사례연구)

  • Cho Il-Hyoung;Kim Yong-Sup;Zoh Kyung-Duk
    • Journal of Environmental Health Sciences
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    • v.31 no.2
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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.

PCA-Based MPEG Video Retrieval in Compressed Domain (PCA에 기반한 압축영역에서의 MPEG Video 검색기법)

  • 이경화;강대성
    • 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.

Face Recognition by Using Principal Component Anaysis and Fixed-Point Independent Component Analysis (주요성분분석과 고정점 알고리즘 독립성분분석에 의한 얼굴인식)

  • Cho, Yong-Hyun
    • 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.

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Nonlinear Feature Extraction using Class-augmented Kernel PCA (클래스가 부가된 커널 주성분분석을 이용한 비선형 특징추출)

  • Park, Myoung-Soo;Oh, Sang-Rok
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.48 no.5
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    • pp.7-12
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    • 2011
  • In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its performance by experiments using data sets in the UCI Machine Learning Repository.

ECG based Personal Authentication using Principal Component Analysis (주성분 분석기법을 이용한 심전도 기반 개인인증)

  • Cho, Ju-Hee;Cho, Byeong-Jun;Lee, Dae-Jong;Chun, Myung-Geun
    • 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.