• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.127-134
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• 1997

A method is proposed for the choice of the number of principal components in principal component regression based on the predicted error sum of squares. To do this, we approximately evaluate that statistic using a linear approximation based on the perturbation expansion. In this paper, we apply the proposed method to various data sets and discuss some properties in choosing the number of principal components in principal component regression.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.89-95
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• 2002

This paper considers the problem of estimating paramaters of the bivariate exponential distribution with a loaction parameter for a two-component shared parallel system using component data from system-level life test terminated at the time of the prespecified number of system failure. In the system-level life testing, there are three patterns of failure types; 1) both component failed 2) both component censored 3) one is failed and the other is censored. In the third case, we assume that the failure time might be known or unknown. The maximum likelihood estimators are obtained for the case of known/unknown failure time when the other component is censored.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.141-147
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• 2006

BOK-X12-ARIMA has been developed by the Bank of Korea in order to accomodate special features such as lunar effect, labor day and election effect which are intrinsic in Korean seasonal time series. Irregular component resulting from BOK-X12-ARIMA is usually treated as white noise time series. If this shows dependency structure, it may be advisable to incorporate dependency in irregular component into prediction. This article illustrates how to refine point forecast using dependency structure in irregular component.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.741-749
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• 2004

Component users must customize components they obtain from providers, in order to fit them to their own purposes. Normally, a component consists of black-box parts and white-box parts. Component users customize a component by modifying white-box parts of a component, and the customization faults appear through the interaction between black-box parts and white-box parts. Customization testing could be an integration testing of these two parts of a component. Also, customization testing in CBSD should select effective test data to reduce the testing cost, since CBSD aims to reduce the development cost. Therefore, this paper proposes a customization testing technique based on COM architecture through analyzing many COM components, and the technique selects effective test data. This paper evaluates the effectiveness of the test data selected by the proposed technique through an empirical study. It applies the techlique to a large-scale component-based system, Chamois, and it shows that the technique enables us to test customized COM components that run in a real component-based system

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.345-354
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• 2003

An incremental kernel principal component analysis(IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis(KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenvectors should be recomputed. IKPCA overcomes this problem by incrementally updating the eigenspace model. IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the classification problem on nonlinear data set.

• Journal of Dental Rehabilitation and Applied Science
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• v.31 no.2
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• pp.112-125
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• 2015

Making a model that is an accurate replica of the oral structure requires precision and efficiency. Nowadays, rapid technological advances bring digitalization in dentistry. One of the most important works in digital dentistry is three-dimensional modeling of the oral cavity and digitizing the 3D data. Among the three components of CAD/CAM, (1) data capture component (digitizers), (2) design component (CAD software), (3) manufacturing component (CAM), the basic component that has a significant impact on the other processes is the data capture component, i.e. intra-oral scanners. This literature review discusses the principles and clinical use of intra-oral scanners in dentistry based on recent publications of the past 5 years using the PubMed and Google Scholar databases.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.3
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• pp.413-422
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• 2000

In the class of models which include random effects, the variance component estimates are important to obtain accurate predictors and estimators. Variance component estimation is straightforward for balanced data but not for unbalanced data. Since orthogonality among factors is absent in unbalanced data, various methods for variance component estimation are available. REML estimation is the most widely used method in animal breeding because of its attractive statistical properties. Recently, Bayesian approach became feasible through Markov Chain Monte Carlo methods with increasingly powerful computers. Furthermore, advances in variance component estimation with complicated models such as generalized linear mixed models enabled animal breeders to analyze non-normal data.

• Journal of KSNVE
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• v.6 no.1
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• pp.97-106
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• 1996

This paper presents systematic study of the experimental application of a free-interfaced component mode synthesis method. In the free-interfaced component mode synthesis method, an error the to truncated higher modes and neglected ineria loadings on a component from the connected component is inherent. Also, it is difficult to directly use experimental modal data in a modal synthesis method which links experimental model to finite-element model because of many inconsistencies between experimentally obtained and analytically obtained modal vectors and missing degrees-of-freedom (DOFs) such as rotational DOFs. In order to solve these problems, three methods, the first one based on attaching auxiliary weights to the connection points, the second one utillizing the normalization of experimental modal vector, and the third one generating smoothed and expanded experimental mode shapes, are studied in this paper. Finally, the study is illustrated for a flat-plate structure by using simulated and measured experimental data.