• Journal of the Korean Society of Clothing and Textiles
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• v.14 no.3 s.35
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• pp.208-215
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• 1990

The purpose of this study is to provide the fundamental data of a dummy design for more suitable ready made clothing by making a pattern of somatic types and analyzing their morphological characteristics in accordance with different pattern of somatic types. The side view silhouettes of 90 junior high school girls of age $13\~16$ in seoul urban area were measured by means of the plan photographing and the low data were examined by principal component analysis, while the principal component analysis was applied and three components were extracted and then interpreted to explain to variation of the form of the body. Using three components respectively the cluster analysis was carried out and the subject classified into 4 cluster The following outcomes are obtained. . The results of principal component analysis of this study would be turned out the three; 1) The first principal component shows the degree of erectness or stoop of the figure. 2) The second principal component was a stature length or a growth rate. 3) The third principal component was the obesity component. 2. The results of cluster analysis by using three principal component analysis would be turned out the four cluser; 1) Cluster 1 ($29\%$ of the total) is characterized with lower stature. 2) Cluster 2 ($21\%$ of the total) is characterized with backward somatotype, and the highest leg. 3) Cluster 3 ($23\%$ of the total) is thicked back of neck. 4) Cluster 4 ($27\%$ of the total) is characterized with forward somatotype, and highest stature, height.

• Korean Journal of Food Science and Technology
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• v.26 no.1
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• pp.57-61
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• 1994

Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.135-145
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• 2017

Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.

• Bulletin of the Korean Chemical Society
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• v.24 no.9
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• pp.1345-1350
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• 2003

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.933-942
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• 2013

Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.

• 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.

• 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.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.60.2-60
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• 2001

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.637-644
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• 1997

Sometimes, the first principal component may come logically from the established knowledge and premises. For example, for the high school students' test scores of Korean, English, Mathematics, Social Study, and Science, it is natural to define the first principal component as the average of all subject scores. In such cases, we need to respect both the background knowledge and the data exploration. The aim of this study is to find the remaining components in principal component analysis of multivariate data when the first principal component is defined a priori by the researcher. Moreover, we study related matrix decomposition and their application to the graphical display.