• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.571-580
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• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.175-184
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• 2013

Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.

• Korean Institute of Interior Design Journal
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• v.21 no.5
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• pp.280-288
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• 2012

The objective of this study was to analyze the characteristics of traditionality expressions at modernized Chinese restaurant in Hong Kong. As a case study, the study examined 12 modernized Chinese restaurants in Hong Kong. The gathered data were categorized and examined according to the ways of traditionality expressions, which included reproduction, transformation, and reinterpretation of traditional components. Each of the components was measured for the amount of traditional or modernity expression on a five-point scale. The five-point scoring system put an emphasis on tradition; 1 point was given to principal modernity(modernity: 90-100% + tradition: 0-10%), 2 points were given to principal modernity + auxiliary tradition(modernity: 70-90% + tradition: 10-30%), 3 points were given to the same ratio between tradition and modernity(modernity: 40-60% + tradition: 40-60%), 4 points were given to principal tradition + auxiliary modernity(modernity: 10-30% + tradition: 70-90%), and 5 points were given to principal tradition(modernity: 0-10% + tradition: 90-100%). The analysis performed according to those criteria and methodologies led to the following findings and conclusions: TM style, in which modernity was principal, usually did transformation and reinterpretation of traditionality. As for the design attributes of the styles, the TM style, they processed a majority of the spatial components as modern or reinterpretation of traditionality, which would be easily considered to be modern without careful observation, and applied a small amount of direct reproduction or transformation, which gives out a direct hint at traditionality, to attract more attention. Many of the spatial components did not express traditionality directly, expressing it indirectly or metaphorically. Traditionality was expressed in a small number of the spatial components, thus serving as a focus or impact point in the given space.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.320-325
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• 2010

This paper is about the method and procedure to register the candidate sequentially in the face recognition system using the PCA(Principal Components Analysis). We use the method to update the principal components sequentially with the SKL algorithm which is improved R-SVD algorithm. This algorithm enable us to solve the re-training problem of the increase the candidates number sequentially in the face recognition using the PCA. Also this algorithm can use in robust tracking system with the bright change based to the principal components. This paper proposes the procedure in the face recognition system which sequentially updates the principal components using the SKL algorithm. Then we compared the face recognition performance with the batch procedure for calculating the principal components using the standard KL algorithm and confirms the effects of the forgetting factor in the SKL algorithm experimentally.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.311-314
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• 2004

To identify the effect of geology and land use, the hydrogeochemical and multivariate statitstical analysis were executed for stream water collected in headwater region of Daecheong reservoir. Hydrogeochemical analysis was showed the effect of weathering process such as dissolution of calc-silicate minerals to hydrochemistry of stream water with contrasting geology. Cluster and principal components analysis can also help to identify the source of dissolved components in stream water.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.24-28
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• 2009

This study was conducted to evaluate the growth characters and yield components of 18 collected sesame cultivars to get basic information on the variation for the sesame breeding using principal component analysis. All characters except days to flowering, days to maturity and 1,000 seed weight showed significantly different. Seed weight per 10 are showed higher coefficient of variance. Capsule bearing stem length and liter weight showed positive correlation with seed yield per 10 are. The principal components analysis grouped the estimated sesame cultivars into four main components which accounted for 83.7% of the total variation at the eigenvalue and its contribution to total variation obtained from principal component analysis. The first principal component ($Z_1$) was applicable to increase plant height, capsule bearing stem length and 1,000-seed weight. The second principal component ($Z_2$) negatively correlated with days to flowering and maturity by which it was applicable to shorten flowering and maturity date of sesame. At the scatter diagram, Yangbaek, Ansan, M1, M2, M4, M7 and M9 were classified as same group, but M10, Yanghuk, Kanghuk, M5, M6, M12 and M13 were classified as different group. This results would be helpful for sesame breeder to understand genetic relationship of some agronomic characters and select promising cross lines for the development of new sesame variety.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.120-126
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• 2007

A principal components analysis of the entire median HRIRs in the CIPIC HRTF database reveals that the individual HRIRs can be adequately reconstructed by a linear combination of several orthonormal basis functions. The basis functions cover the inter-individual and inter-elevation variations in median HRIRs. There are elevation-dependent tendencies in the weights of basis functions, and the basis functions can be ordered according to the magnitude of standard deviation of the weights at each elevation. We propose a HRIR customization method via tuning of the weights of 3 dominant basis functions corresponding to the 3 largest standard deviations at each elevation. Subjective listening test results show that both front-back reversal and vertical perception can be improved with the customized HRIRs.

• Bulletin of the Korean Chemical Society
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• v.28 no.4
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• pp.557-560
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• 2007

The metabolic discrimination of safflowers from various geographical origins was performed using 1H nuclear magnetic resonance (NMR) spectroscopy followed by principal components analysis. With a combination of these techniques, safflower samples from different origins could be discriminated using the first two principal components (PC) of the 1H NMR spectra of the 50% methanol fractions. PC1 and PC2 accounted cumulatively for 91.3% of the variation in all variables. The major peaks in the 1H NMR spectra that contributed to the discrimination were assigned to fatty acid (terminal CH3), lactic acid, acetic acid, choline derivatives, glycine, and safflower yellow derivatives. In this study, we suggest that various types of safflower can be discriminated using PCA and 1H NMR spectra.