• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.209-211
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• 2017

We performed Principle Component Analysis (PCA) over 264 galaxies in the IRAS Revised Bright Galaxy Sample (Sanders et al., 2003) using 12, 25, 60 and $100{\mu}m$ flux data observed by IRAS and 9, 18, 65, 90 and $140{\mu}m$ flux data observed by AKARI. We found that (i)the first principle component was largely contributed by infrared to visible flux ratio, (ii)the second principal component was largely contributed by the flux ratio between IRAS and AKARI, (iii)the third principle component was largely contributed by infrared colors.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.242-249
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• 2000

The fatigue strength of welded aluminum alloy component has been evaluated. Extruded aluminum alloy component Al 6005-T6 was considered. That component could be one of appropriate candidates for floor structure in railway cars. Finite element analysis has been performed to obtain stress distribution in the welded aluminum component. The results of finite element analysis have been applied in designing the experimental setup for fatigue strength evaluation of welded component. Three point bending fatigue test has been employed, until fracture occurs, to evaluate the fatigue strength of the welded component. In addition, the fatigue strength of the component has been compared with that of specimen.

• ETRI Journal
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• v.40 no.5
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• pp.634-642
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• 2018

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.3
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• pp.177-183
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• 2003

Vibration analysis using component mode synthesis method was carried out to identify that to some extent each component contributed to the whole vibration of a powertrain consisting of several components. This analysis helped decide the component to be modified to reduce the powertrain weight, without degrading its current vibration characteristics. As a result, a cylinder block was chosen as a redesign object. Topology optimization analysis was performed to design the topology of the cylinder block whose flange connected with the transmission was chosen to be the design domain. After all, a new prototype of cylinder block was manufactured based on the analysis results for the verification experiment. It was confirmed from the analytical and experimental results that u optimally designed cylinder block had an advantage over the current one in the powertrain weight, with the powertrain vibration characteristics improved slightly.

• Journal of Life Science
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• v.13 no.3
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• pp.343-348
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• 2003

This study was conducted to get basic information on the Korean local corn line collected from Busan City and Kyungnam Province, a total of 49 lines were selected and assessed by the principal component analysis method. In the result of principal component analysis for 7 characteristics, 67.4% and 86.3% of total variation could be appreciated by the first two and first four principal components, respectively. Contribution of characteristics to principal component was high at upper principal components and low at lower principal components. Biological meaning of principal component and plant types corresponding to the each principal component were explained clearly by the correlation coefficient between principal component and characteristics. The first principal component appeared to correspond to the size of plant and ear, and the duration of vegetative growing period. The second principal component appeared to correspond to the number of ear and tiller. But the meaning of the third and fourth principal components were not clear.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.67-71
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• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.980-990
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• 2004

The component-based development methodology aims at the high state of abstraction and the reusability with components larger than classes. It is indispensible to measure the component so as to improve the quality of the component-based system and the individual component. And, the quality of the component should be improved through putting the results into the process of the development. So, it is necessary to study the component metric which can be applied in the stage of the component analysis and design. Hence, in this paper, we propose component cohesion, coupling, independence metrics reflecting the information extracted in the step of component analysis and design. The proposed component metric bases on the similarity information about behavior patterns of operations to offer the component's service. Also, we propose the redesigning process for the improvement of component design. That process uses the techniques of clustering and is for the thing that makes the component as the independent functional unit having the low complexity and easy maintenance. And, we examine that the component design model can be improved by the component metrics and the component redesigning process.

• 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