• Journal of the korean Society of Automotive Engineers
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• v.15 no.1
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• pp.81-88
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• 1993

A direct treatment of the min-max type objective function of the dynamic response optimization problem is proposed. Previously, the min-max type objective function was transformed to an artificial design variable and an additional point-wise state variable constraint function was imposed, which increased the complexity of the optimization problem. Especially, the design sensitivity analysis for the augmented Lagrangian functional with the suggested treatment is established by using the adjoint variable method and a computer program to implement the proposed algorithm is developed. The optimization result of the proposed treatment are obtained for three typical problems and compared with those of the previous treatment. It is concluded that the suggested treatment in much more efficient in the computational effort than the previous treatment with giving the similar optimal solutions.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.144-147
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• 2000

A numerical method for the solution of one-dimensional inverse heat conduction problem is established and its performance is demonstrated with computational results. The present work introduces the maximum entropy method in order to build a robust formulation of the inverse problem. The maximum entropy method finds the solution that maximizes the entropy functional under given temperature measurement. The philosophy of the method is to seek the most likely inverse solution. The maximum entropy method converts the inverse problem to a non-linear constrained optimization problem of which constraint is the statistical consistency between the measured temperature and the estimated temperature. The successive quadratic programming facilitates the maximum entropy estimation. The gradient required fur the optimization procedure is provided by solving the adjoint problem. The characteristic feature of the maximum entropy method is discussed with the illustrated results. The presented results show considerable resolution enhancement and bias reduction in comparison with the conventional methods.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.157-165
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• 2009

In this paper, we investigate a dynamic spectrum sharing problem for the centralized uplink cognitive radio networks using orthogonal frequency division multiple access. We formulate a large-scale joint rate and power allocation as an optimization problem under quality of service constraint for secondary users and interference constraint for primary users. We also suggest admission control to nd a feasible solution to the optimization problem. To implement the resource allocation on a large-scale, we introduce a notion of using the conservative factors $\alpha$ and $\beta$ depending on the outage and violation probabilities. Since estimating instantaneous channel gains is costly and requires high complexity, the proposed algorithm pursues a practical and implementation-friendly resource allocation. Simulation results demonstrate that the large-scale joint rate and power allocation incurs a slight loss in system throughput over the instantaneous one, but it achieves lower complexity with less sensitivity to variations in shadowing statistics.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.85-94
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• 2000

In this paper, a robust H$\infty$ optimal tuning problem of a structure-specified PSS is investigated for power systems with parameter variation and disturbance uncertainties. Genetic algorithm is employed for optimization method of PSS parameters. The objective function of the optimization problem is the H$\infty$-norm of a closed loop system. The constraint of the optimization problem are based on the stability of the controller, limits on the values of the parameters and the desired damping of the dominant oscillation mode. It is shown that the proposed H$\infty$ PSS tuned using genetic algorithm is more robust than conventional PSS.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.225-228
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• 2004

The generalized knapsack problem, or gknap is the combinatorial optimization problem of optimizing a nonnegative linear functional over the integral hull of the intersection of a polynomially separable 0 - 1 polytope and a knapsack constraint. Among many potential applications, the knapsack, the restricted shortest path, and the restricted spanning tree problem are such examples. We prove via the ellipsoid method the equivalence between the fully polynomial approximability and a certain pseudo-polynomial separability of the gknap polytope.

• Structural Engineering and Mechanics
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• v.7 no.6
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• pp.533-550
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• 1999

Optimal design of raft-pile foundations is examined by combining finite element technique and the optimization approach. The piles and soil medium are modeled by three dimensional solid elements while the raft is modelled by shell elements. Drucker-Prager criterion is adopted for the soil medium while the raft and the piles are assumed to be linear elastic. For the optimization process, the approximate semi-analytical method is used for calculating constraint sensitivities and a constraint approximation method which is a combination of the extended Bi-point approximation and Lagrangian polynomial approximation is used for predicting the behaviour of the constraints. The objective function of the problem is the volume of materials of the foundation while the design variables are raft thickness, pile length and pile spacing. The generalized reduced gradient algorithm is chosen for solving the optimization process. It is demonstrated that the method proposed in this study is promising for obtaining optimal design of raft-pile foundations without carrying out a large number of analyses. The results are also compared with those obtained from the previous study in which linear analysis was carried out.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.561-571
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• 2022

An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. In the assembly, a flexure mount attached to an optical mirror should be flexible in the radial direction, but rigid for the remaining degrees of freedom for supporting the mirror rigidly and suppressing the wavefront error of the optical mirror. This work presents an optimal design of the flexure mount using topology optimization with thermal stress constraint. By simplifying the optical mirror assembly into finite shell elements, topology optimization model was built for efficient design and good machinability. The stress at the boundary between the optical mirror and the mount together with the first natural frequency were applied as constraints for the optimization problem, while the objective function was set to minimize the strain energy. As a result, we obtained the optimal design of the flexure mount yielding the improved wavefront error, proper rigidity, and machinability.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.1
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• pp.49-56
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• 2008

Reliability has been considered as an important design measure in various industrial systems. We discuss a reliability optimization problem with component choices (ROP-CC) subject to a budget constraint. This problem has been known as a NP-hard problem in the reliability design fields. Several researchers have been working to find the optimal solution through different heuristic methods. In this paper, we describe our development of simulated annealing (SA) and tabu search (TS) algorithms and a reoptimization procedure of the two algorithms for solving the problem. Experimental results for some examples are shown to evaluate the performance of these methods. We compare the results with the solutions of a previous study which used ant system (AS) and the global optimal solution of each example obtained through an optimization package, CPLEX 9.1. The computational results indicate that the developed algorithms outperform the previous results.

• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.9-16
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• 2004

This study presents results of aerodynamic wing optimization under uncertainties. To consider uncertainties, an alternative strategy for reliability-based design optimization(RBDO) is developed. The strategy utilizes a single loop algorithm and a sequential approximation optimization(SAO) technique. The SAO strategy relies on the trust region-SQP framework which validates approximated functions at every iteration. Further improvement in computational efficiency is achieved by applying the same sensitivity of limit state functions in the reliability analysis and in the equivalent deterministic constraint calculation. The framework is examined by solving an analytical test problem to show that the proposed framework has the computational efficiency over existing methods. The proposed strategy enables exploiting the RBDO technique in aerodynamic design. For the aerodynamic wing design problem, the solution converges to the reliable point satisfying the probabilistic constraints.

• Korean Management Science Review
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• v.28 no.3
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• pp.15-29
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• 2011

In this paper, we consider a system working at both primary service and secondary service. A server can switch between the primary service and the secondary service or it can be assigned to secondary service as a dedicated server. A service policy is characterized by the number of servers dedicated to the secondary service and a rule for switching the remaining servers between two services. The primary service system is modelled as a Markovian queueing system and the throughput is a function of the number of servers, buffer capacity, and service policy. And the secondary service system has a service level requirement strategically determined to perform the service assigned. There is a revenue obtained from throughput and costs due to servers and buffers. We study the problem of simultaneously determining the optimal total number of servers, buffers, and service policy to maximize profit of the system subject to both an expected customer waiting time constraint of the primary service and a service level constraint of the secondary service and develop an algorithm which can be successfully applied with the small number of computations.