• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.54-68
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• 1995

In this paper, we develop detailed algorithms for implementing the so-called Limited Column Generation procedure for Local Access Telecommunication Network (LATN) Design problem. We formulate the problem into a tree-partitioning problem with an exponential number of variables. Its linear programming relaxation has all integral vertices, and can be solved by the Limited Column. Generation procedure in just n pivots, where n is the number of nodes in the network. Prior to each pivot, an entering variable is selected by detecting the Locally Most Violated (LMV) reduced cost, which can be obtained by solving a subproblem in pseudo-polynomial time. A critical step in the Limited Column Generation is to find all the LMV reduced costs. As dual variables are updated at each pivot, the reduced costs have to be computed in an on-line fashion. An efficient implementation is developed to execute such a task so that the LATN Design problem can be solved in O(n$^{2}$H), where H is the maximum concentrator capacity. Our computational experiments indicate that our algorithm delivers an outstanding performance. For instance, the LATN Design problem with n = 150 and H = 1000 can be solved in approximately 67 seconds on a SUN SPARC 1000 workstation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1082-1087
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• 2008

Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.219-221
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• 2012

The concave utility in the Network Utility Maximization (NUM) problem is only suitable for elastic flows. In networks with multiclass traffic, the utility can be concave, linear, step or sigmoidal. Hence, the basic NUM becomes a nonconvex optimization problem. The current approach utilizes the standard dual-based decomposition method. It does not converge in case of scarce resource. In this paper, we propose an algorithm that always converges to a local optimal solution to the nonconvex NUM after solving a series of convex approximation problems. Our techniques can be applied to any log-concave utilities.

• International Journal of Contents
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• v.12 no.3
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2560-2567
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• 2000

When spatially dense velocity distribution is measured by a scanning laser Doppler vibrometer, the Fourier transform method provides the real and imaginary parts of the mode shapes in the form of a polynomial. However the Fourier transform method is often impractical because the independent decomposition property of cosine and sine components into real and imaginary parts, respectively, does not hold due to the leakage problem which commonly occurs in the Fourier transform of harmonic signals. To deal with this problem, a Hilbert transform method is newly proposed in this article. The proposed method is free from the leakage problem and relatively robust to the scanning error. A simulation example is provided to verify the effectiveness of this method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2350-2364
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• 2014

This paper addresses the problem of resource allocation in amplify-and-forward (AF) relayed OFDM based cognitive radio networks (CRNs). The purpose of resource allocation is to maximize the overall throughput, while satisfying the constraints on the individual power and the interference induced to the primary users (PUs). Additionally, different from the conventional resource allocation problem, the rate-guarantee constraints of the subcarriers are considered. We formulate the problem as a mixed integer programming task and adopt the dual decomposition technique to obtain an asymptotically optimal power allocation, subcarrier pairing and relay selection. Moreover, we further design a suboptimal algorithm that sacrifices little on performance but could significantly reduce computational complexity. Numerical simulation results confirm the optimality of the proposed algorithms and demonstrate the impact of the different constraints.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.420-425
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• 2000

When spatially dense velocity distribution is measured by a scanning laser Doppler vibrometer, the Fourier transform method provides the real and imaginary parts of the mode shapes in the form of a polynomial. However the Fourier transform method is often impractical because the independent decomposition property of cosine and sine components into real and imaginary parts, respectively, does not hold due to the leakage problem which commonly occurs in the Fourier transform of harmonic signals. To deal with this problem, a Hilbert transform method is newly proposed in this article. The proposed method is free from the leakage problem and relatively robust to tire scanning error. A simulation example is provided to verify the effectiveness of this method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.593-600
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• 2003

Structural optimization often requires the evaluation of design sensitivities. The Semi Analytic Method(SAM) fur computing sensitivity is popular in shape optimization because this method has several advantages. But when relatively large rigid body motions are identified for individual elements. the SAM shows severe inaccuracy. In this study, the improvement of design sensitivities corresponding to the rigid body mode is evaluated by exact differentiation of the rigid body modes. Moreover. the error of the SAM caused by numerical difference scheme is alleviated by using a series approximation for the sensitivity derivatives and considering the higher order terms. Finally the present study shows that the refined SAM including the iterative method improves the results of sensitivity analysis in dynamic problems.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.139-144
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• 2011

This paper presents a boundary element application to determine the optimal impressed current densities at defect position on the pipe line. In this protection paint, enough current must be impressed to lower the potential distribution on the metal surface to the critical values. The optimal impressed current densities are determined in order to minimize the power supply for protection. This inverse problem was formulated by employing the boundary element method. Since the system of linear equations obtained was ill-conditioned, including singular value decomposition, conjugate gradient method were applied and the accuracies of these estimation. Several numerical examples are presented to demonstrate the practical applicability of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.15-24
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• 2009

Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.