• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.281-296
• /
• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1272-1274
• /
• 2003

In this paper, we present a new algorithm for high dimensional data reduction based on wavelet decomposition and Genetic Algorithm (GA). Comparative results show the superiority of our algorithm for dimensionality reduction and accuracy improvement.

• The Magazine of the IEIE
• /
• v.42 no.2
• /
• pp.25-32
• /
• 2015

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.23 no.5
• /
• pp.475-482
• /
• 2010

In this paper, an efficient two-level parallel domain decomposition algorithm is suggested to solve large-DOF structural problems. Each subdomain is composed of the coarse problem and local problem. In the coarse problem, displacements at coarse nodes are computed by the iterative method that does not need to assemble a stiffness matrix for the whole coarse problem. Then displacements at local nodes are computed by Multi-Frontal Sparse Solver. A parallel version of PCG(Preconditioned Conjugate Gradient Method) is developed to solve the coarse problem iteratively, which minimizes the data communication amount between processors to increase the possible problem DOF size while maintaining the computational efficiency. The test results show that the suggested algorithm provides scalability on computing performance and an efficient approach to solve large-DOF structural problems.

• Journal of Broadcast Engineering
• /
• v.17 no.6
• /
• pp.924-932
• /
• 2012

Decomposition of a stereo signal into the primary and ambient components is a key step to the stereo upmix and it is often based on the principal component analysis (PCA). However, major shortcoming of the PCA-based method is that accuracy of the decomposed components is dependent on both the primary-to-ambient power ratio (PAR) and the panning angle. Previously, a modified PCA was suggested to solve the PAR-dependent problem. However, its performance is still dependent on the panning angle of the primary signal. In this paper, we proposed a new PCA-based primary-ambient decomposition algorithm whose performance is not affected by the PAR as well as the panning angle. The proposed algorithm finds scale factors based on a criterion that is set to preserve the powers of the mixed components, so that the original primary and ambient powers are correctly retrieved. Simulation results are presented to show the effectiveness of the proposed algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.6
• /
• pp.690-699
• /
• 2014

Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

• Journal of Korean Institute of Industrial Engineers
• /
• v.12 no.1
• /
• pp.133-138
• /
• 1986

In this paper, we are modeling the problem of the reliability evaluation in the network with delay. The triconnected decomposition and factoring algorithm for the network reliability, known as the most efficient algorithm, does not work in this constrained problem. So, we propose some ideas that reduce the above constrained problem to the general network reliability problem. We also present an algorithm for the reliability evaluation in the network with delay based on these ideas.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.995-998
• /
• 2002

In this paper, we present a fast algorithm for hand gesture recognition of a human from an image by using the directivity information of the fingers. To implement a fast recognition system, we applied the morphological shape decomposition. A proposed gesture recognition algorithm has been tested on the 300 ${\times}$ 256 digital images. Our experiments using image acquired image camera have shown that the proposed hand gesture recognition algorithm is effective.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.22 no.1
• /
• pp.1-24
• /
• 1997

In this paper, we have proposed new solution methods to solve TSLP (two-stage stochastic linear programming) problems. One solution method is to combine the analytic center concept with Benders' decomposition strategy to solve TSLP problems. Another method is to apply an idea proposed by Geoffrion and Graves to modify the L-shaped algorithm and the analytic center algorithm. We have compared the numerical performance of the proposed algorithms to that of the existing algorithm, the L-shaped algorithm. To effectively compare those algorithms, we have had computational experiments for seven test problems.

• Journal of KIISE:Software and Applications
• /
• v.27 no.6
• /
• pp.583-594
• /
• 2000

The thinning process which is commonly used in extracting skeletons from handwritten Hangul characters has a problem of distorting the original pattern shapes. This paper proposes a method of skeleton extraction using a shape decomposition algorithm. We decompose the character pattern into a set of near convex parts using a shape decomposition algorithm. From the shape-decomposed pattern, we detect the joint parts and extract the skeletons from the parts incident to the joint parts. Then the skeletons not incident to the joint parts are extracted. Finally, the process of skeleton extension is performed to ensure the connectivity. We setup five criteria for the comparison of quality of skeletons extracted by our method and the thinning based method. The comparison shows the superiority of our method in terms of several criteria.