• Korean Journal of Remote Sensing
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• v.4 no.1
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• pp.49-60
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• 1988

Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.617-622
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• 2021

Let R be a commutative ring with identity, R[X] the polynomial ring over R and S a multiplicative subset of R. Let U = {f ∈ R[X] | f is monic} and let N = {f ∈ R[X] | c(f) = R}. In this paper, we show that if S is an anti-Archimedean subset of R, then R is an S-Noetherian ring if and only if R[X]_U is an S-Noetherian ring, if and only if R[X]_N is an S-Noetherian ring. We also prove that if R is an integral domain and R[X]_U is an S-principal ideal domain, then R is an S-principal ideal domain.

• International Journal of Knowledge Content Development & Technology
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• v.8 no.4
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• pp.75-98
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• 2018

In this study, the network analysis of researchers, institutions, and research principal agent was conducted to understand structure characteristics of international cooperation research project implemented from 1997 to 2018. The network of researchers and institutions were decentralized structure. On the other hands, the network of research principal agent was centralized structure. The Soul National University is the leading organization of international cooperation research project. In terms of research principal agent, corporation is the leading principal agent. In additions, the results of the network centroid analysis of the researchers and institutions were correlated with the research funds. As a result, it was confirmed that the network centroid of research organization was linearly related to research funds.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1474-1478
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• 2009

The cooperation and trust among the project participants play a critical role in the success or failure of any delivery system in construction industry. But it is very difficult to establish trust between an owner and a contractor when rational people only pursue only their own material self-interest. Based on the principal-agent theory, this paper will introduce the altruistic behavior into the traditional principal-agent model, and model the reciprocal behavior between the owner and contractor. We will show that both the owner and the contractor benefit from their reciprocal behavior, and hence trust establishing between them is possible. More importantly, we will proof that the higher the project uncertainty is, the more important trust establishing is.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1035-1036
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• 2010

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.33-42
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• 1999

A method of detecting influential observations in principal factor analysis is suggested. it is based on a perturbation of the empirical distribution function and an adoption of the local influence method. An illustrative example is given.

• 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

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.226-226
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• 2004

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.785-798
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• 2017

This study was conducted to evaluated the surface(10 variables) and bottom(10 variables) water quality, and sediment(3 variables) in the cage fish farm off Baegyado in Gamak Bay using a multivariate analysis from January 2013 to November 2014. Generally, the environmental data did not show a certain tendency by months during two years investigated. The pairwise simple correlation matrices among variables were also shown. The first four principal components of the surface water in 2013 explain 93% of the total sample variance; the first principal component($z_1$) showed the freshwater inflow and/or precipitation, $z_2$, $z_3$ and $z_4$ related to freshwater inflow and/or precipitation, organic matters and eutrophy, respectively; the first four principal components of the bottom water in 2013 explain 93% of the total sample variance; the $z_1$, $z_2$ and $z_4$ related to freshwater inflow and/or precipitation, and $z_3$ water temperature. In 2014, at the surface water the first three principal components explain 87%; the $z_1$, $z_2$ and $z_3$ related to water temperature, eutrophy and freshwater inflow and/or precipitation, respectively; at the bottom water the first three principal components explain 93%; $z_1$, $z_2$ and $z_3$ related to water temperature, freshwater inflow and/or precipitation and eutrophy. Half of the principal components related to freshwater inflow and/or precipitation.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.113-118
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• 1996

Principal component analysis(PCA) is an essential technique for data compression and feature extraction, and has been widely used in statistical data analysis, communication theory, pattern recognition, and image processing. Oja(1992) found that a linear neuron with constrained Hebbian learning rule can extract the principal component by using stochastic gradient ascent method. In practice real data often contain some outliers. These outliers will significantly deteriorate the performances of the PCA algorithms. In order to make PCA robust, Xu & Yuille(1995) applied statistical physics to the problem of robust principal component analysis(RPCA). Devlin et.al(1981) obtained principal components by using techniques such as M-estimation. The propose of this paper is to investigate from the statistical point of view how Xu & Yuille's(1995) RPCA works under the same simulation condition as in Devlin et.al(1981).