• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.51-69
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• 1997

Recently Lee et al[5] developed an approximation algorithm for the performance evaluation of the open queueing networks with blocking. This algorithm, which solves the exponential queueing networks with general configuration is developed based on the symmetrical decomposition approach and is reported to have many advantages over the previous algorithmsf. In addition to being very accurate, this algorithm is reported to be quite simple, pretty fast and solves very general configurations. In this study, we show that if a network has a tree-like configurations, the algorithm developed by Lee at al, always converges to the unique solution. To prove the theoretical results pertaining to the algorithm, some properties associated with symmetrical decomposition approach are exploited. The results obtained in this study such as the proofs of convergence of the algorithm as well as uniquences of the solution would contribute to the theoretical study for the non-tandem configurating of open queueing network.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.121-125
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• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• 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.

• Structural Engineering and Mechanics
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• v.42 no.4
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• pp.449-469
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• 2012

For generally damped linear systems with repeated eigenvalues and defective eigenvectors, this study provides a decomposition method based on residue matrix, which is suitable for engineering applications. Based on this method, a hybrid approach is presented, incorporating the merits of the modal superposition method and the residue matrix decomposition method, which does not need to consider the defective characteristics of the eigenvectors corresponding to repeated eigenvalues. The method derived in this study has clear physical concepts and is easily to be understood and mastered by engineering designers. Furthermore, this study analyzes the applicability of step-by-step methods, including the Newmark beta and Runge-Kutta methods for dynamic response calculation of defective systems. Finally, the implementation procedure of the proposed hybrid approach is illustrated by analyzing numerical examples, and the correctness and the effectiveness of the formula are judged by comparing the results obtained from the different methods.

• 전기의세계
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• v.21 no.4
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• pp.39-44
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• 1972

This paper describes a new approach for minimizing working memory spaces without loosing too much amount of computing time in the analysis of power system faults. This approach requires the decomposition of alrge power system into several small groups of subsystems, forms individual bus impedance matrics, store them in the auxiliary memory, later assembles them to the original total system by algorithms. And also the approach uses techniques for diagonalizing primitive impedances and expanding the system bus impedance matrices by adding a fault bus. These scheme ensures a remarkable savings of working storage and continous computations of fault currents and voltages with the voried fault locations.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.112-122
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• 2009

In this paper, we present a novel space-frequency adaptive image restoration approach using vaguelette-wavelet decomposition (VWD). The proposed algorithm classifies a degraded image into flat and edge regions by using spatial information of the wavelet coefficient. For reducing the noise we perform an adaptive wavelet shrinkage process. At edge region candidates, we adopt entropy approach for estimating the noise and remove it by using relative between sub-bands. After shrinking wavelet coefficients process, we restore the degraded image using the VWD. The proposed algorithm can reduce the noise without affecting the sharpness details. Based on the experimental results, the proposed algorithm efficiently proved to be able to restore the degraded image while preserving details.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.813-831
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• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.90-97
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• 2009

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, and lower multi-path distortion. To further utilize vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subchannel and bit allocation among users. In this paper, we propose an 0-1 integer programming model formulating the optimal subchannel and bit allocation problem of the multiuser OFDM. We employ a dual-decomposition method that provides a tight linear programming (LP) relaxation bound. Simulation results are provided to show the effectiveness of the 0-1 integer programming model. MATLAB simulation on a system employing M-ary quardarature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multi-paths are carried with the optimal subchannel and bit allocation solution generated by 0-1 integer programming model.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.243-252
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• 2001

The points of failure of a decomposition process are defined to be the union of the points of failure from two component point processes for software reliability systems. Because sampling from the likelihood function of the decomposition model is difficulty, Gibbs Sampler can be applied in a straightforward manner. A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For model determination, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. A numerical example with a simulated data set is given.