• 전자공학회논문지B
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• 제28B권12호
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• pp.75-82
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• 1991

We proposed an optimal parallel implementation of an optimization neural network with linear increase of speedup by using multicomputer system and presented performance analysis model of the system. We extracted the temporal-and the spatial-parallelism from the optimization neural network and constructed a parallel pipeline processing model using the parallelism in order to achieve the maximum speedup and efficiency on the CSP architecture. The results of the experiments for the TSP using the Transputer system, show that the proposed system gives linear increase of speedup proportional to the size of the optimization neural network for more than 140 neurons, and we can have more than 98% of effeciency upto 16-node system.

• Structural Engineering and Mechanics
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• 제70권1호
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• pp.81-96
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• 2019

In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (${\mu}+{\lambda}$) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.8-9
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• 2006

This paper presents a new methodology to calculate an optimal solution of equilibrium to power system differential algebraic equations. It employs a nonlinear interior point method for solving the optimization formulation, which includes dynamic equations representing two-axis synchronous generator models with AVR and speed governing control, algebraic equations, and steady-state nonlinear loads. Equilibrium optimization (EOPT) is useful for diverse purposes in power system analysis and control with consideration of the system frequency constraint.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.571-576
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• 2001

Hydroforming, the new production technology, has been used to manufacture many parts of vehicle in the recent auto industry. When Hydroforming is applied, it is possible to make parts simplification and flexible alteration of section shape in many advantages such as weight reduction, number of parts reduction or performance improvement. This research into shape optimization which reduces number of parts and weight maintaining performance was achieved. In this paper, the COWL CROSS BAA and MT'g BAKT parts of A car STEERING support SYSTEM was introduced by using Hydroforming.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.377-382
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• 2004

This paper presents a novel optimization method for the air- and gas-supplying network comprised of several air compression systems and air and gas streams in an industrial chemical plant. The optimization is based on the hybrid model developed by Han and $Han^1$ for predicting the power consumption of a compression system. A constrained optimization problem was formulated to minimize the total electric power consumption of all the compression systems in the air- and gas-supplying network under various operating constraints and was solved using a successive quadratic optimization algorithm. The optimization approach was applied to an industrial terephthalic acid manufacturing plant to achieve about 10% reduction in the total electric power consumption under varying ambient conditions.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.870-875
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• 2004

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, the robust design strategy is developed based on the DACE and the global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the system. The robustness is determined by the DACE model to reduce the real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 한국생산제조학회지
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• 제8권2호
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• pp.27-34
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• 1999

The growth-strain method was used for shape optimization, which carries out the optimization by distributing uniformly the distributed parameter such as von Mises stress and shear strain energy density. Shape optimization is carried out by iteration of stress analysis and growth strain analysis. In this study, the effect of growth ratio in the method was investigated and then the range of the adequate value of the growth ratio was determined. Also the growth-strain method was improved by applying the linear PID control theory in order to control volume required by a designer. Finally, an automatic shape optimization system was built up by the improved growth-strain method with a commercial software using finite element method. The effectiveness and practicability of the developed shape optimization system was verified by some examples.

• 전기학회논문지
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• 제57권5호
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• pp.748-754
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• 2008

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, a global optimization technique, which employing the chaos search algorithm, is applied to solve optimal capacitor placement problem with reducing computational effort and enhancing global optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.

• Journal of Ubiquitous Convergence Technology
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• 제2권1호
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• pp.40-50
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• 2008

The problem of assigning customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. For this combinatorial optimization problem, standard optimization methods take a large computation time and so genetic algorithms (GA) and ant colony optimization (ACO) can be used to obtain the best and/or optimal assignment of customers to satellite channels. In this paper, we present a comparative study of GA and ACO to this problem. Various issues related to genetic algorithms approach to this problem, such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. We also discuss an ACO for this problem. In ACO methodology, three strategies, ACO with only ranking, ACO with only max-min ant system (MMAS), and ACO with both ranking and MMAS, are considered. A comparison of these two approaches (i,e., GA and ACO) with the standard optimization method is presented to show the advantages of these approaches in terms of computation time.

• 한국CDE학회논문집
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• 제14권3호
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• pp.207-216
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• 2009

Modern engineering design problems involve complexity of disciplinary coupling and difficulty of problem formulation. Multidisciplinary design optimization can overcome the complexity and design optimization software or frameworks can lessen the difficulty. Recently, a growing number of new multidisciplinary design optimization techniques have been proposed. However, each technique has its own pros and cons and it is hard to predict a priori which technique is more efficient than others for a specific problem. In this study, a software system has been developed to directly solve MDO problems with minimal input required. Since the system is based on MATLAB, it can exploit the optimization toolbox which is already developed and proven to be effective and robust. The framework is devised to change an MDO technique to another as the optimization goes on and it is called a reconfigurable MDO framework. Several numerical examples are shown to prove the validity of the reconfiguration idea and its effectiveness.