• Journal of Korean Society for Atmospheric Environment
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• v.6 no.2
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• pp.155-160
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• 1990

During the period from Mar. 1985 to Feb. 1988, airborne particulate matters were collected and size fractionated by the ANdersen high volume air sampler in Seoul. The concentrations of several heavy metals (Pb, Cu, Zn, Fe, Mn) and benzo(a)pyrene were determined to investigate the size distributions and seasonal variations. And with respect to seven components in the total suspended particulate (TSP), the factor analysis was performed for three groups such as the coarse particles (> 2 $\mu$m), fine particles (< $\mu$m) and TSP. As a result of factor analysis by using the varimax method, the chemical components in the TSP were able to characterize with two principal factors. The first factor, F1 was considered to be a factor indicating the contribution of natural sources and the second factor, F2 was a factor indicating the degree of artificial sources. Each components in the TSP was divided into two main groups of components originated from soil and/or road dust and pollutants originated from automobiles and/or human work.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.163-170
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• 2003

This empirical study takes the activation approach for the competitive power of Korean shipping companies in the Korea-China liner routes. Data for this study were collected from Korea/ China/ 3rd flag shipping companies through the 500 questionnaires. The data of 250 respondents were analyzed statistically to verify the hypotheses and to induce Regression Equation which could predicts the influencing level of the determinants to competitive advantage for Korean shipping companies on Korea-China Liner Shipping Routes. Factor Analysis/ Cronbach's Alpha/ Principal Analysis/ Multiple Regression Analysis were used in order to test the hypotheses for the empirical study.

• Journal of Animal Science and Technology
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• v.52 no.3
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• pp.167-174
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• 2010

The objectives of this study were to quantify the combination values of the principal components and factors calculated using body measurements of Hanwoo (Korean Cattle) and estimate their heritabilities. The technique of multivariate analysis was used to reduce a large number of variables to a smaller number of new variables and characterize cattle according to body shape. The analyses were performed using 1,979 cattle at 12 months of age and 936 cattle at 24 months of age. The data for the analyses was obtained from progeny tests performed on Korean Cattle for 6 years from 2003 to 2008. The phenotypic correlations among these traits were estimated to range from 0.32 to 0.90 at 12 months of age and from 0.21 to 0.82 at 24 months of age. The first principal components (PC1s) indicated a weighed average of overall body measurements, accounting for 99.91% of the total variation for both periods of test. The two first PCs had positive coefficients for all body measurements. The major sources of PC, such as chest girth (CG), body length (BL), rump height (RH), and wither height (WH) were similar for both test periods. The heritabilities for PC1, the first factor score (FS1), and the second factor score (FS2) were estimated by multivariate REML method. The estimated heritabilities for PC1, FS1, and FS2 were 0.33, 0.38, and 0.40, respectively, at 12 months of age and 0.26, 0.76, and 0.58 at 24 months of age. Further studies are needed to determine whether the heritabilities of FS1 and FS2 at 24 months of age were overestimated.

• Journal of Soil and Groundwater Environment
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• v.27 no.6
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• pp.22-36
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• 2022

Water resources in Jeju Island are dependent virtually entirely on groundwater. For groundwater resources, drought damage can cause environmental and economic losses because it progresses slowly and occurs for a long time in a large area. Therefore, this study quantitatively evaluated groundwater level fluctuations using principal component and cluster analyses for 42 monitoring wells in Jeju Island, and further identified the types of groundwater fluctuations caused by drought. As a result of principal component analysis for the monthly average groundwater level during 2005-2019 and the daily average groundwater level during the dry season, it was found that the first three principal components account for most of the variance 74.5-93.5% of the total data. In the cluster analysis using these three principal components, most of wells belong to Cluster 1, and seasonal characteristics have a significant impact on groundwater fluctuations. However, wells belonging to Cluster 2 with high factor loadings of components 2 and 3 affected by groundwater pumping, tide levels, and nearby surface water are mainly distributed on the west coast. Based on these results, it is expected that groundwater in the western area will be more vulnerable to saltwater intrusion and groundwater depletion caused by drought.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.5
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• pp.463-469
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• 1998

Factor analysis was applied as a multivariate statistical technique to official genetic evaluations of type classification traits for 1,265,785 Holstein cows and 10,321 sires computed from data collected between August 1982 and June 1994 in Canada. Type traits included eighteen linear descriptive traits and eight major score card traits. Principal components of the factor analysis showed that only five factors explain the information of the genetic value of linear descriptive traits for both cows and sires. Factor 1 included traits related to mammary system, like texture, median suspensory, fore attachment, fore teat placement and rear attachment height and width. Factor 2 described stature, size, chest width and pin width. These two factors had a similar pattern for both cows and sires. In constrast, Factor 3 for cows involved only bone-quality, while in addition for sires, Factor 3 included foot angle, rear legs desirability and legs set. Factor 4 for cows related to foot angle, set of rear leg and leg desirability, while Factor 4 related to loin strenth and pin setting for sires. Finally, Factor 5 included loin strength and pin setting for cows and described only pin setting for sires. Two factors only were required to describe score card traits of cows and sires. Factor 1 related to final score, feet and legs, udder traits, mammary system and dairy character, while frame/capacity and rump were described by Factor 2. Communality estimates which determine the proportion of variance of a type trait that is shared with other type traits via the common factor variant were high, the highest ${\geq}$ 80% for final score, stature, size and chest width. Pin width and pin desirability had the lowest communality, 56% and 37%. Results indicated shifts in emphasis over the twelve-year period away from udder traits and dairy character, and towards size, scale and width traits. A new system that computes fmal score from type components has been initiated.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.31-50
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• 1999

We revisit the problems of testing three-factor classifica-tion models with a single observation per cell. A common approach in analyzing such nonreplicated data is to omit the highest order in-teraction and regard it as error. This paper discusses the use of a multiplicative model(See and Smith 1996 and 1998) which is applied on residuals in order to separate the variablility due to three-factor interaction from what is counted as random error. in particualr to test the significance of the interaction term we derived an approxi-mated distribution of the likelihood ratio test statistic based on the quadrilinear model known as Tucher's three-mode principal compo-nent model. The derivation utilizes the distribution of the eignevalues of the Wishart matrix.

• Environmental Engineering Research
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• v.19 no.3
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.

• 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 Construction Engineering and Project Management
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• v.5 no.3
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• pp.1-9
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• 2015

Project cost is an important concern in any construction project. Although there has been a lot of studies on factors affecting the cost of construction projects, there seems no consensus as what cost factors have direct influence on the cost of civil engineering projects. This study therefore aims to bridge the current knowledge gap by examining quantity surveyors' perception of the factor structure among nineteen costing attributes identified based on literature review. Questionnaire was used to elicit responses from quantity surveyors working in the Hong Kong construction industry. Principal component analysis is conducted to extract the factor structure of the cost attributes and the attributes are grouped into three factor components, namely the contract management factor, the project management factor and the monetary value factor. Understanding these cost impact factors could be crucial in managing civil engineering projects, since it allows the project stakeholders and quantity surveyors to take precautionary steps to identify the cost management problems and areas for improvement and could even help to avoid cost deviations in engineering projects.