• Journal of the Korean Data and Information Science Society
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• 제8권2호
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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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• 제14권4호
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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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• 한국데이터정보과학회 2002년도 춘계학술대회
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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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• 제17권1호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권6호
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• pp.741-749
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• 2004

컴포넌트 기반 개발에서 컴포넌트 사용자는 개발 목적에 맞추어 컴포넌트를 맞춤 할 필요가 있다. 컴포넌트는 그 내부에 블랙박스특성을 갖는 부분과 화이트박스특성을 갖는 부분이 공존하고, 맞춤으로 인해 화이트박스 부분이 변형되며, 이때 발생하는 오류는 블랙박스 부분과 화이트박스 부분의 상호작용을 통해 나타나게 된다. 블랙박스 부분과 화이트박스 부분 사이의 통합 테스트인 맞춤 테스트를 위해서 새로운 테스트 기법이 요구된다. 또한 테스트 기법이 비용 절감 효과를 노리는 컴포넌트 기반 개발에 사용되기 위해서는 효율적인 테스트 데이타의 선정이 요구된다. 따라서 본 논문에서는 컴포넌트 아키덱쳐로 COM(Component Object Model)을 대상 컴포넌트로 선정하고, 다양한 COM 컴포넌트들의 분석을 통하여 효율적인 테스트 데이타를 선정하는 맞춤 테스트 기법을 제안한다. 본 논문에서는 제안하는 기법이 선정하는 테스트 데이타가 오류 감지 능력에 있어서 효과적이라는 것을 실험을 통해 평가한다. 또한 본 기법을 실제 대규모 컴포넌트 기반 시스템인 샤모아에 적용하는 예제를 수행함으로써, 본 기법이 컴포넌트 기반 시스템의 일원으로서 실제 동작하는 COM 컴포넌트의 맞춤을 테스트할 수 있음을 보인다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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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 the Korean Data and Information Science Society
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• 제14권2호
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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.

• 구강회복응용과학지
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• 제31권2호
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• pp.112-125
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• 2015

구강 내의 모습을 재현하는 복제모델을 만드는 것은 치과 진료에서 가장 중요한 과정이며 정확성과 효율성이 동시에 만족되어야 하는 과정이다. 현재 기술이 발전함에 따라 치과 진료에서도 디지털화가 이루어지고 있다. 이러한 것을 가능하게 하는 가장 중요한 작업 중 하나가 바로 구강 내의 모습을 3차원적으로 재구성하는 디지털화이다. CAD/CAM 시스템의 3가지 성분 (1) data capture component (digitizers), (2) design component (CAD software), (3) manufacturing component (CAM)중 가장 기본이 되며 뒤의 과정에 막대한 영향을 끼치는 것이 data capture component 즉 구강 스캐너이다. 이 논문은 Pubmed와 Google Scholar에서 최근 5년 전 연구 논문들을 기초로 하여, 각각의 스캐너의 구동원리와 스캐너들 간의 정확성, 현재 구강 스캐너가 치과 영역에서 적용되고 있는 분야와 그 정도를 분석하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제13권3호
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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.

• 소음진동
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• 제6권1호
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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.