• Survey Research
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• v.11 no.1
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• pp.1-18
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• 2010

We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.955-966
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• 2010

Canonical correlation biplot is a useful biplot for giving a graphical description of the data matrix which consists of the association between two sets of variables, for detecting patterns and displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data, most biplots are not directly applicable. To solve this problem, we estimate the missing data using the median, mean, EM algorithm and MCMC imputation methods according to missing rates. Even though we estimate the missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we use a RMS(root mean square) which was proposed by Shin et al. (2007) and PS(procrustes statistic) for measuring and comparing the shape variability between the original biplots and the estimated biplots.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.73-87
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• 1999

본 연구에서는 다양한 종류의 공정을 수행할 수 있는 가공장비와 자동화된 물류 운반시스템으로 구성된 FMS의 효율적 운영을 위해 셀 제조방식의 개념을 도입한 다수의 유연셀 형성 방법을 제시하고 있다. 서로 독립적인 유연셀의 형성을 위해 FMS의 장점인 공정유연성을 최대한 활용할 수 있도록 기계-공정 행렬과 부품-공정 행렬에 기초한 2 종류의 새로운 유사도 계수와 예외적 요소의 수를 최소화하기 위한 셀 형성 알고리즘을 개발하였다. 두 행렬들은 다공정 수행가능기계간 유사도와 기계셀과 부품간 비처리 능력지수 산출의 기초자료로 사용된다. 알고리즘은 예외적 요소의 수를 최소로 하면서 셀의 수를 최대로 하는 정리에 기초하여 공정을 기계에 할당하며, 다수의 대체경로가 유연셀내에서 형성될 수 있도록 크게 2 단계로 구성되어 있다. 마지막으로 수치예제와 함께 예외적 요소의 발생수를 척도로 하여 기존의 방법들과 비교, 평가하고 있다.

• Information and Communications Magazine
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• v.31 no.11
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• pp.35-45
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• 2014

주성분 분석 기법(PCA)는 가장 널리 사용되는 데이터 차원 감소 (dimensionality reduction) 기법으로 알려져 있다. 하지만 데이터에 이상점 (outlier)가 존재하는 환경에서는 성능이 크게 저하된다는 단점을 가지고 있다. Rank-Sparsity(Robust PCA) 기법은 주어진 행렬을 low-rank 행렬과 저밀도(sparse)행렬의 합으로 분해하는 방식으로, 이상점이 많은 환경에서 PCA기법을 효과적으로 대체할 수 있는 알고리즘으로 알려져 있다. 본 고에서는 RPCA 기법을 간략히 소개하고, 그의 적용분야, 및 알고리즘에 관한 연구들을 대해서 알아본다.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.770-783
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• 2012

In this paper, we propose a novel structure recovery algorithm in the projective space using image feature points. We use normalized image feature coordinates for the numerical stability. To acquire an initial value of the structure and motion, we decompose the scaled measurement matrix using the singular value decomposition. When recovering structure and motion in projective space, we introduce matrix decomposition constraints. In the reconstruction procedure, a nonlinear iterative optimization technique is used. Experimental results showed that the proposed method provides proper accuracy and the error deviation is small.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.2
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• pp.193-201
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• 2004

The aim of this study is focused on getting an almost exact solution which is the simplicity and exactness of an axisymmetrically loaded cylindrical shell. This method replaces the finite element method which is a very powerful tool for analysis of any kind of structure which has an arbitrary shape, but is still a numerical analysis. Instead, this study uses the method of distribution of end actions which is a kind of iteration technique to implement the leading matrix method. The distribution and carry-over factors of a cylinder are calculated by the theory of a differential equation of a beam on an elastic foundation. The results are satisfactory when this method is applied to a cylinder that is subjected to a concentrated load and hydrostatic pressure when compared with the BEF analogy separately.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.101-108
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• 2017

As the quantum gate matrix is a $r^{n+1}{\times}r^{n+1}$ dimension when the radix is r, the number of control state vectors is n, and the number of target state vectors is one, the matrix dimension with increasing n is exponentially increasing. If the number of control state vectors is $2^n$, then the number of $2^n-1$ unit matrix operations preserves the output from the input, and only one can be performed the unitary operation to the target state vector. Therefore, this paper proposes a new method of function embedding that can replace $2^n-1$ times of unit matrix operations with deterministic contribution to matrix dimension by arithmetic power switch of the unitary gate. The proposed function embedding method uses a binary literal switch with a multivalued threshold, so that a general purpose hybrid MCU gate can be realized in a $r{\times}r$ unitary matrix.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1263-1270
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• 2010

Sixty patients were divided into three groups. Each group of twenty persons had fed on different diet foods over 5 weeks. Cholesterol had been measured repeatedly five times at an interval of a week during 5 weeks. It resulted from mixed model analysis of repeated measurements data that homogeneous toeplitz covariance matrix pattern was selected as the optimal covariance pattern. The correlations between measurements of different times for the covariance matrix are somewhat highly correlated as 0.64-0.78. Based upon the homogeneous toeplitz covariance pattern model, the time effect was found to be highly significant, but the treatment effect and treatment-time interaction effect were found to be insignificant.

• Journal of Power System Engineering
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• v.7 no.4
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• pp.49-54
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• 2003

스프링은 가장 널리 이용되어지고 있는 기계요소이다. 본 논문에서는 원추형 코일스프링의 강성을 구하기 위하여, 빔요소를 이용한 유한요소법을 사용하였다. 가상일의 법칙을 이용하였고, 코일스프링의 하중벡터를 압축 분포하중으로 대체하였다. 하중의 증가에 의한 절점에서의 변위는 유한요소법를 이용하여 계산하였다. 단계법으로 결점의 변위를 중첩하여 전체 강성행렬을 구하였다. 유한요소법에 의한 해석치는 실험치와 잘 일치하였다. 본 논문에서 제시한 프로그램을 사용하여, 스프링 강성과 응력을 예측할 수 있을 것으로 사료된다.