• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.67-80
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• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• 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 the Korean Society of Combustion
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• v.1 no.1
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• pp.31-39
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• 1996

An equilibrium analysis was carried out to determine principal species in the incineration of hazardous waste, which was assumed as a compound of hydrocarbon fuel, chlorine, sulfur, and heavy metals, and their behaviors with variation of temperature, chlorine and sulfur concentrations. Calculated results showed that the most important parameter influencing the principal species was temperature. Chlorine concentration affected on mole fractions of the species, especially at high temperature. Existence of sulfur had a significant effect on the species at low temperature, regardless of surfur concentration. Generally, principal species at high temperature were chlorides and oxides, while the principal species at low temperature were sulfides. As temperature increased, mole fractions of the principal species increased at low temperature, however, mole fractions of some metal species decreased at high temperature.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.347-360
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• 2017

The problem of cylindrical cavity expansion incorporating deformation dependent of intermediate principal stress in rock or soil mass is investigated in the paper. Assumptions that the initial axial total strain is a non-zero constant and the axial plastic strain is not zero are defined to obtain the numerical solution of strain which incorporates deformation-dependent intermediate principal stress. The numerical solution of plastic strains are achieved by the 3-D plastic potential functions based on the M-C and generalized H-B failure criteria, respectively. The intermediate principal stress is derived with the Hook's law and plastic strains. Solution of limited expansion pressure, stress and strain during cylindrical cavity expanding are given and the corresponding calculation approaches are also presented, which the axial stress and strain are incorporated. Validation of the proposed approach is conducted by the published results.

• 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.

• Wind and Structures
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• v.7 no.5
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• pp.333-344
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• 2004

This paper illustrates application of the proper orthogonal decomposition (POD) and the autoregressive (AR) model to simulate large wind pressures due to gusts on a low-rise building. In the POD analysis, the covariance of the ensemble of large wind pressures is employed to calculate the principal modes and coordinates. The POD principal coordinates are modeled using the AR process, and the fitted AR models are employed to generate the principal coordinates. The generated principal coordinates are then used to simulate large wind pressures. The results show that the structure characterizing large wind pressures is well represented by the dominant eigenmodes (up to the first fifteen eigenmodes). Also, wind pressures with large peak values are simulated very well using the dominant eigenmodes along with the principal coordinates generated by the AR models.

• 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 Korean Society of Safety
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• v.34 no.5
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• pp.95-102
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• 2019

Non-destructive methods such as rebound hardness method and ultrasonic method are widely studied for evaluating the physical properties, condition and damage of concrete, but are not suitable for detecting delamination and cracks near the surface due to various constraints of the site as well as the accuracy. Therefore, in this study, the impact resonance method was applied to detect the separation cracks occurring near the surface of the concrete slab and bridge deck. As a next step, the principal component analysis were performed by extracting various features using the FFT data. As a result of principal component analysis, it was analyzed that the reliability was high in distinguishing defects in concrete. This feature extraction and application of principal component analysis can be used as basic data for future use of machine learning technique for the better accuracy.

• The Pure and Applied Mathematics
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• v.30 no.4
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• pp.397-406
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• 2023

Let R be a commutative ring with identity, X be an indeterminate over R, and R[X] be the polynomial ring over R. In this paper, we study when R[X] is a radically principal ideal ring. We also study the t-operation analog of a radically principal ideal domain, which is said to be t-compactly packed. Among them, we show that if R is an integrally closed domain, then R[X] is t-compactly packed if and only if R is t-compactly packed and every prime ideal Q of R[X] with Q ∩ R = (0) is radically principal.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.235-246
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• 2024

In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.