• 한국의류학회지
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• 제14권3호
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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.

• 한국식품과학회지
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• 제26권1호
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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.

• 응용통계연구
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• 제30권1호
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• pp.135-145
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• 2017

주성분 분석(principal component analysis; PCA)은 서로 상관되어 있는 다변량 자료의 차원을 축소하는 대표적인 기법으로 많은 다변량 분석에서 활용되고 있다. 하지만 주성분은 모든 변수들의 선형결합으로 이루어지므로, 그 결과의 해석이 어렵다는 한계가 있다. sparse PCA(SPCA) 방법은 elastic net 형태의 벌점함수를 이용하여 보다 성긴(sparse) 적재를 가진 수정된 주성분을 만들어주지만, 변수들의 그룹구조를 이용하지 못한다는 한계가 있다. 이에 본 연구에서는 기존 SPCA를 개선하여, 자료가 그룹화되어 있는 경우에 유의한 그룹을 선택함과 동시에 그룹 내 불필요한 변수를 제거할 수 있는 새로운 주성분 분석 방법을 제시하고자 한다. 그룹과 그룹 내 변수 구조를 모형 적합에 이용하기 위하여, sparse 주성분 분석에서의 elastic net 벌점함수 대신에 계층적 벌점함수 형태를 고려하였다. 또한 실제 자료의 분석을 통해 제안 방법의 성능 및 유용성을 입증하였다.

• Bulletin of the Korean Chemical Society
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• 제24권9호
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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.

• 정보보호학회논문지
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• 제24권5호
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• pp.1013-1022
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• 2014

상관관계 전력 분석(Correlation Power Analysis, CPA)은 암호장비에서 알고리즘이 수행될 때 누설되는 전력 소비 신호와 알고리즘의 중간 계산 값의 상관도를 이용하여 비밀키를 추출하는 부채널 공격 방법이다. CPA는 누설된 전력 소비의 시간적인 동기 또는 잡음에 의해 공격 성능이 영향을 받는다. 최근 전력 분석의 성능 향상을 위해 다양한 신호 처리 기술이 연구되어지고 있으며, 그 중 주성분 분석 기반의 신호 압축 기술이 제안되었다. 주성분 분석 기반의 신호 압축은 주성분 선택 방법에 따라 분석 성능에 영향을 주기 때문에 주성분 선택은 중요한 문제이다. 본 논문에서는 CPA의 성능 향상을 위해 전력 소비와의 상관도가 높은 주성분을 선택하는 주성분 선택 기법을 제안한다. 또한 각 주성분이 갖는 특징이 다르다는 점을 이용한 주성분 기반 CPA 분석 기법을 제안하고, 기존 방법과 제안하는 방법의 실험적인 분석을 통해 공격 성능이 향상됨을 보인다.

• 응용통계연구
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• 제26권6호
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• pp.933-942
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• 2013

본 연구에서는 시뮬레이션 방법을 사용해서 다양한 조건에서 주성분분석이 얼마나 잘 요인 구조를 복원할 수 있는지를 공통요인분석과 비교하여 체계적으로 평가하였다. 이 연구에서 요인 대 변수 비율, 공통성, 그리고 표본크기를 실험변수로 설정하였다. 주성분분석은 표본의 크기가 200개 이하인 경우 공통적으로 공통요인분석에 비해 더 우수한 요인구조의 복원력을 보여주었다. 특히, 요인 당 변수 수가 적은 경우, 주성분분석은 50개의 표본에서도 만족할 만한 수준의 요인복원능력을 보여주었다. 이와 더불어 공통성 수준 또한 낮은 경우 필요한 표본수는 100개로 늘어난다. 본 연구결과는 요인추출방법으로서 주성분분석의 선택의 근거를 제시하고 타당한 사용에 관한 가이드라인을 제시해 준다.

• 한국농공학회논문집
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• 제55권1호
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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.

• 한국환경보건학회지
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• 제31권2호
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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.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2001년도 Bulletin of The 5th Asian Design Conference International Symposium on Design Science
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• pp.60.2-60
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• 2001

• Communications for Statistical Applications and Methods
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• 제4권3호
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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.