• 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 Data and Information Science Society
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• v.19 no.3
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Journal of Environmental Science International
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• v.7 no.2
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• pp.171-176
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• 1998

This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 Percente by three factors which were Included In river loadlnwastes from the Nakdong River and rainfalls : 39.1%1, sediment resuspension(13.7BS) and metabolism(12.5%). In the eastern part of estuary In flowing the Nakdong River, river loading factor score(factor 1 Pas higher than that In western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was h19h- 1y related to rainfall season.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.57-64
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• 2000

For composing the structure model of national maritime power system by system structural modeling, in this study, the 50 basic factors are selected by survey of the extensive and through literatures on maritime, sea, maritime power and sea power. And the basic factors are classified into 36 component factors by cluster method. The 9 attributes are extracted by the application of the principle component analysis method, one of the factor analysis method in system engineering, to component factors. In this study, we define the attributes composing the national maritime power system by integrating the result of this study and existed our studies relating to this topic. Which are showed in Table 2. and we show the structure model of national maritime power system in Fig. 3. In Table 2, the 9 attributes are as follows : the fundamental power of maritime, shipping and port power, naval power, fishing power, shipbuilding power, the power of ocean research and development, dependency on seaborne trade, the protection power of ocean environment and the will and inclination of govemment. Also, in the case of evaluating this system, we conform the importance of considering the interactions among the attributes which have strong interactions in structure model of national maritime power system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.153-161
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• 1999

For composing the structure model of national maritime power system by system structural modelling, in this study, the 50 basic factors are selected by survey of the extensive and thorough literatures on maritime, sea, maritime power and sea power. And the basic factors are classified into 36 component factors by cluster method. The 9 attributes are extracted by the application of the principle component analysis method, one of the factor analysis method in system engineering, to component factors. We defined the attributes composing the national maritime power system by integration the result of this study and existed our studies relate to this topic. Which are showed in table 8. and we showed the structure model of national maritime power system in figure 3. In table 8, the 9 attributes are as follows: the fundamental power of maritime, shipping and port power, naval power, fishing power, shipbuilding power, the power of ocean research and development, dependency on seaborne trade, the protection power of ocean environment and the will and inclination of government.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.54-58
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• 2012

This paper is purposed to analyze electric leakage component which can prevent electrical fires on breaker capacity expansion and power failure by operation of ELB(Earth leakage breaker) for stable sizes. In order to analysis for electric leakage component for stable sizes, this paper studied field state investigation which are at stable companies( 10 companies) in cheong-won location to deduce the problems of electric leakage component is analyzed. The field state experiment method is measured with electric leakage component which load part of ELB detected by electric loads(electrical fan, lighting, auto waterer, feeder and halogen lighting) and stable sizes. Results show that electric leakage component suggested in this paper are valuable and usable to electrical fire in leakage current based on environment factor, which will prevent severe damage to human beings and properties and reduce the electrical fires in stable.

• Journal of Environmental Health Sciences
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• v.30 no.5 s.81
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• pp.455-468
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• 2004

The purpose of this paper is to understand the time series and origin of a chemical component and to compare the difference during yellow sand episodes for analysis $PM_{10}$ chemical components in the region of west in Korean Peninsula, 1999-2001. An annual mean concentration of $PM_{10}$ is $29.1\;{\mu}g/m^3$. A monthly mean and standard deviation of $PM_{10}$ concentration are very high in spring but there is no remarkably seasonal variation. Also, water soluble ionic component of $PM_{10}$ be influenced by double more total anion than total cation, be included $NO_{3}^-\;and\;SO_{4}^{2-}$ for the source of acidity and $NH_{4}^+$ to neutralize. Tracer metals of $PM_{10}$ slowly increases caused by emitted for soil and ocean (Fe, Al, Ca, Mg, Na) and Zn, Pb, Cu, Mn for anthropogenic source. According to method of enrichment factor (E.F) and statistics, assuming that the origin of metal component in $PM_{10}$ most of element in the Earth's crust e.g. Mg, Ca, Fe originates soil and Cu, Zn, Cd, Pb derives from anthropogenic sources. The ionic component for $Na^{+}\;Cl^-,\;Mg^{2+}\;and\;Ca^{2+}$ and Mg, Al, Ca, Fe originated by soil component largely increase during yellow sand period and then tracer metal component as Pb, Cd, Zn decrease. According to factor analysis, the first group is ionic component ($Na^+,\;Mg^{2+},\;Ca^{2+}$) and metal component (Na, Fe, Mn and Ni) be influenced by soil. The second group, Mg, Cr also be influenced by soil particle.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.293-298
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• 2015

The vessel encounter data collected from the vessel trajectories in the maritime traffic situation is possible to analyze vessel collision and near-collision risk using statistical method. In this study, analyzing variables extracted from the vessel encounter data using factor analysis, we determine main factors effecting vessel collision risk from vessel encounter data. In order to calculate each factor, it used principal component analysis for factor analysis after normalization and standardization of vessel encounter variables. As a result of the factor analysis, main effect factors are summarized into the vessel approach factor and collision avoidance variance factor.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• Journal of the Korean Home Economics Association
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• v.36 no.12
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• pp.43-54
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• 1998

From the viewpoint of clothing construction, it is necessary to grasp exactly the shapes of the neck and the shouder, such as the line of the neck base, the neck gradient, the shoulder gradient, the shape of the scapular, and the shape of the breast. In this report, factor analysis was applied to 39 items of neck & shoulder level measurements, including stature, weight, but grith, waist girth, to demonstrate the most relevant measurements for collar and bodice pattern designing, and to classify the neck and shoulder level shapes. The subjects investigated were 126 women of the age 20-29. The main results are follows : 1. For factors of body form were extracted by the factor analysis. The 1st principal component can be interpreted as "size" component, the 2nd-3th principal component is "shape" component relating to neck and shoulder level, and the 4th principal component is "shoulder shape" component. 2. With regard to factor loadings, we were able to extract the most relevant measurements for collar and bodice pattern designing. M16, M22, S26, S30, S34, S35, S36, C37, C38, C39.