• Advances in environmental research
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• 제6권1호
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• pp.53-65
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• 2017

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 15 physico-chemical parameters collected from 7 monitoring stations in a river during the years from 2014 to 2016 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except alkalinity, which is always the most important parameters in contributing to water quality variations for all three seasons.

• 약학회지
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• 제48권1호
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• pp.6-12
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• 2004

Near-infrared spectra of methanol-water mixtures and acetonitrile-water mixtures were acquired to find interactions between solvents widely used for reverse-phase liquid chromatography. Mixtures were prepared to give a series of increasing mole fractions of methanol or acetonitrile in water. Data matrices of acquired spectra were analyzed to determine the proper number of principal components of each mixture system using Malinowski's factor indicator function. Initial guess of score matrix and loading matrix were calculated by nonlinear iterative partial least squares (NIPALS) algorithm for faster computation. Iterative target transform factor analysis (ITTFA) was applied to convert the initial estimation of score matrix to true concentration profile and loading matrix to pure spectra of pure components of the mixtures. In case of methanol-water the number of principal components was found to be 4 and those initial guess of factors were converted to the pure spectra of water methanol and two kinds of complexes. In case of acetonitrile-water the number of pure components of the mixtures was found to be 3 and the pure spectrum of acetonitrile-water complex was found. The nonlinear characteristics of concentration profiles of complexes in the solvent mixtures may give a good criteria in understanding their elution characteristics in reverse-phase liquid chromatogrsphy.

• 응용통계연구
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• 제27권1호
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• pp.31-41
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• 2014

행렬도(biplot)는 이원표 자료행렬(two-way data matrix)의 행과 열을 한 그림에 동시에 나타내는 탐색적 방법으로, 복잡한 다변량 분석 결과를 보다 쉽게 파악할 수 있는 장점이 있다. 특히 주성분인자 행렬도(principal component factor biplot; PCFB)는 인자분석을 통해서 변수들 간의 상호의존 구조를 탐색하기 위한 시각적 도구이다. 자료에 따라 잠재된 변수들이 독립(independent)이고 비가우시안(non-Gaussian) 분포를 가진다는 사전 정보가 있을 때, Jutten과 Herault (1991)가 제안한 독립성분분석(independent component analysis)을 이용한다. 이 경우 주성분법을 이용한 인자분석을 적용하면 원래 변수들의 상호 관계를 잘못 해석할 수도 있다. 따라서 본 논문에서는 자료에 따라 잠재된 변수들이 독립이고 비가우시안 분포를 가진다는 사전 정보가 있을 때, 독립성분분석을 응용하여 원래 변수들 간의 상호 관계를 기하학적으로 살펴볼 수 있는 시각적 도구인 독립성분 행렬도(independent component biplot; ICB)를 제안하려 한다.

• 한국기계가공학회지
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• 제19권11호
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• pp.78-86
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• 2020

A stress analysis was performed to verify the stability of the digging part of a garlic harvester. A finite element analysis was performed to examine the distribution and concentrated loads on the digging part of the blade and contact plate. Moreover, the stability and maximum deformation of the digging part were determined. Under a distributed load, the maximum principal stress, total deformation, and minimum safety factor ranged from 64-128 MPa, 0.35-0.70 mm, and 2.9-5.7, respectively. The analysis results for the distribution load indicated that the maximum stress occurred at the center of the blade. In contrast, under the concentrated load, the maximum principal stress, total deformation, and minimum safety factor ranged from 66-247 MPa, 0.35-0.79 mm, 1.48-5.53, respectively. The analysis results for the concentrated load indicated that stress and deformation were larger toward the edge and center, respectively.

• 유통과학연구
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• 제13권9호
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• pp.103-112
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• 2015

Purpose - This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology - This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results - PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAF will suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions - Recommendations are offered for the best factor analytic practice for empirical research.

• 한국산업융합학회 논문집
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• 제5권1호
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• pp.33-38
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• 2002

This paper proposes an efficient method for improving the performance of polynomial adaline using the dimension reduction of independent variables. The adaptive principal component analysis is applied for reducing the dimension by extracting efficiently the features of the given independent variables. It can be solved the problems due to high dimensional input data in the polynomial adaline that the principal component analysis converts input data into set of statistically independent features. The proposed polynomial adaline has been applied to classify the patterns. The simulation results shows that the proposed polynomial adaline has better performances of the classification for test patterns, in comparison with those using the conventional polynomial adaline. Also, it is affected less by the scope of the smoothing factor.

• Plant Resources
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• 제2권2호
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• pp.75-80
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• 1999

Twenty two varieties of pecan including wild types were classified based on 6 characters measured by principal component analysis score distance. The results are summarized as fellow. Twenty two varieties were classified into 5 groups based in PCA score distance. Five groups were distinctly characterized by many morphological characters. Total variation could be explained by 51%, 95%, 99% with first, third and fifth principal components respectively. Varimax rotation of the factor loading of the first factors indicated that the first component was highly loaded with leaf characters, the second component with fruit characters, but fruit length was negative loaded. The second, the third and the fourths groups of cultivars had very close genetic parentage similarity.

• 한국기계가공학회지
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• 제11권6호
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• pp.36-41
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• 2012

In this study, we turning plastic mold steel (STAVAX) against cutting speed, depth of cut, feed rate using whisker reinforced ceramic tool (WA1). To predict cutting force, analyze principal, radial, feed force with multi-regression analysis. Results are follows: From the analysis of variance, affected factor to cutting force feed rate, depth of cut, cutting speed in order and cutting speed was very small affect to cutting force. From multi-regression analysis, we extracted regression equation and the coefficient of determination$(R^2)$ was 0.9, 0.88, 0.856 at principal, radial and feed force. It means regression equation is significant. From the experimental verification, it was confirmed that principal, radial and feed force was predictable by regression equation.

• Journal of Preventive Medicine and Public Health
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• 제29권2호
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• pp.255-264
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• 1996

정상적으로 일상생활을 영위하는 사람들을 대상으로 스트레스 수준을 가늠하고, 스트레스에 대한 조정기전을 밝히고자 하는 목적으로 개발된 사회 심리적 건강 측정 도구(PWI)의 신뢰도 및 타당도를 평가하기 위하여, 의과 대학생을 대상으로 4주간격으로 두 차례의 설문 조사를 실시하였다. 내적 일치도를 나타내는 Crohnbach's $\alpha$계수가 0.93의 값을 보였다. 4주의 간격을 둔 검사-재검사 신뢰도는 1차조사의 성적과 2차조사의 성적 사이 Pearson상관계 수가 0.72의 값을 보였다. 탐색적 인자 분석을 통해 고유값 1이상의 인자는 13개였다. 인자의 수를 4개로 지정하여 주축인자법과 직교 회전법으로 분석하여 인자 구조를 비교한 결과 첫번째 인자는 거의 일치하였으나, 3번째와 4번째 인자는 거의 일치하지 않았다. 확증적 인자분석을 통해 선행 연구의 4인자 모형과 이 연구의 탐색적 분석 결과에 나타난 4인자 모형의 적합도를 구한 결과 RMR값이 0.1미만인 것을 제외하고는 모든 적합도 지수가 부적합하게 나왔다 각 인자를 측정 변수로 간주하고 그 하부에 이론 변수로서 단일 인자가 존재하는 것으로 구성한 일차원적 단일 인자 모형에 대한 적합도는, 비록 이 모형이 오차 항의 상관성을 지닌 것이긴 하나 적합한 지수를 많이 보여, PWI가 2차원적 측정 구조를 가졌을 가능성을 제시하였다.

• 한국지능시스템학회논문지
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• 제21권3호
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• pp.317-322
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• 2011

한국정보화진흥원에서 개발한 한국형 성인 인터넷 중독 자가진단 척도인 K-척도는 4가지 요인의 20 문항으로 구성되어 있으며, 사용자의 설문응답값으로 인터넷 중독을 진단한다. 기존의 연구는 대부분 인터넷 중독의 원인을 찾으려는 시도였으며, 청소년 대상으로 수집된 표본을 가지고 그들의 인터넷 중독진단이 수행되었다. 본 연구의 목적은 통계 기법의 주성분분석과 데이터마이닝 기법인 의사결정트리를 이용하여 K-척도의 사용자군 분류를 판정하는 주요인을 발견하는 것이다. 실험 결과로부터 K-척도를 구성하는 4가지 요인 중 내성 및 몰입 요인이 성인 인터넷 중독진단에 가장 큰 영향을 주는 요인임을 알 수 있었다.