• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.27-33
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• 2007

Recently, a new hard problem on a two dimensional vector space called vector decomposition problem (VDP) was proposed by M. Yoshida et al. and proved that it is at least as hard as the computational Diffe-Hellman problem (CDHP) on a one dimensional subspace under certain conditions. However, in this paper we present the VDP relative to a specific basis can be solved in polynomial time although the conditions proposed by M. Yoshida on the vector space are satisfied. We also suggest strong instances based on a certain type basis which make the VDP difficult for any random vector relative to the basis. Therefore, we need to choose the basis carefully so that the VDP can serve as the underlying intractable problem in the cryptographic protocols.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.3
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• pp.173-178
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• 2006

For the design of multivariable fuzzy control systems decomposition of control rules is a efficent inference method since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper we define indices of applicability which decides whether the decomposition method can be applied to a multivariable fuzzy system or not.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.8
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• pp.585-592
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• 1989

This paper presents an efficient algorithm for the reactive planning of transmission network under normal operating conditions. The optimal operation of a power system is a prerequisite to obtain the optimal investment planning. The operation problem is decomposed into a P-optimization module and a Q-optimization module, but both modules use the same objective function of generation cost. In the investment problem, a new variable decomposition technique is adopted which can operate the operation and the investment variables. The optimization problem is solved by using the gradient projection method (GPM).

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.127-131
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• 2011

Even if there are no causing factors such as car crash and road works, traffic congestion come from traffic growth on the road. In this case, estimation of traffic flow helps find the solution of traffic congestion problem. In this paper, we present a optimization model which used on traffic equilibrium problem and studied the problem of inverting shortest path sets for complex traffic system. And we also develop pivotal decomposition algorithm for reliability function of complex traffic system. Several examples are illustrated.

• Korean Management Science Review
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• v.32 no.1
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• pp.49-63
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• 2015

This paper investigates an aircraft-target assignment problem in consideration of deconfliction. The aircraft-target assignment problem is the problem to assign available aircrafts and weapons to targets that should be attacked, where the objective function is to minimize the total expected damage of aircrafts. Deconfliction is the way of dividing airspaces for aircraft flight to ensure the safety while performing the mission. In this paper, mixed integer programming model is suggested, where it considers deconfliction between aircrafts. However, the suggested MIP model is non-linear and limited to get solution for large size problem. The 2-phase decomposition model is suggested for efficiency and computation, where in the first phase target area is divided into sectors for deconfliction and in the second phase aircrafts and weapons are assigned to given targets for minimizing expected damage of aircraft. The proposed decomposition model shows outperforms the model developed for comparison in the computational experiment.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.953-960
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• 2014

In Shamir's (t,n)-threshold based secret image sharing schemes, there exists a problem that the secret image can be reconstructed when an arbitrary attacker becomes aware of t secret image pieces, or t participants are malicious collusion. It is because that utilizes linear combination polynomial arithmetic operation. In order to overcome the problem, we propose a secret image sharing scheme using matrix decomposition and adversary structure. In the proposed scheme, there is no reconstruction of the secret image even when an arbitrary attacker become aware of t secret image pieces. Also, we utilize a simple matrix decomposition operation in order to improve the security of the secret image. In experiments, we show that performances of embedding capacity and image distortion ratio of the proposed scheme are superior to previous schemes.

• Journal of Magnetics
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• v.19 no.4
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• pp.385-392
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• 2014

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.1
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• pp.17-26
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• 2014

This paper presents development of an improved domain decomposition method for large scale structural problem that aims to provide high computational efficiency. In the previous researches, we developed the domain decomposition algorithm based on augmented Lagrangian formulation and proved numerical efficiency under both serial and parallel computing environment. In this paper, new computational analysis by the proposed domain decomposition method is performed. For this purpose, reduction in computational time achieved by the proposed algorithm is compared with that obtained by the dual-primal FETI method under serial computing condition. It is found that the proposed methods significantly accelerate the computational speed for a linear structural problem.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.37-53
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• 2009

In this paper the problem of calculating the probability that the responses of a wind-excited structure exceed specified thresholds within a given time interval is considered. The failure domain of the problem can be expressed as a union of elementary failure domains whose boundaries are of quadratic form. The Domain Decomposition Method (DDM) is employed, after being appropriately extended, to solve this problem. The probability estimate of the overall failure domain is given by the sum of the probabilities of the elementary failure domains multiplied by a reduction factor accounting for the overlapping degree of the different elementary failure domains. The DDM is extended with the help of Line Sampling (LS), from its original presentation where the boundary of the elementary failure domains are of linear form, to the current case involving quadratic elementary failure domains. An example involving an along-wind excited steel building shows the accuracy and efficiency of the proposed methodology as compared with that obtained using standard Monte Carlo simulations (MCS).

• Korean Management Science Review
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• v.5 no.1
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• pp.56-70
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• 1988

In this study, We reviewed the solution methods (for the heuristic and optimization method) for the single-item dynamic lot-sizing problem, and improved the efficiency (speed and optimality) of the conventional heuristic method by utilizing the inventory decomposition property. The iventory decomposition property decomposes the given original problem into several independent subproblems without violating the optimality conditions. Then we solve each decomposed subproblems by using the conventional heuristics such as LTC, LUC, Silver-Meal etc. For testing the efficiency of the proposed decomposition method, we adopted the data sets given in Kaimann, Berry and Silver-Meal. The computational results show that the suggested problem solving framework results in some promising effects on the computation time and the degree of optimality.