• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.57-65
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• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1008-1015
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• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.523-530
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• 2007

This paper proposes a new stochastic optimization algorithm for hidden Markov models (HMMs) used as a recognizer of automatic lipreading. The proposed method combines a global stochastic optimization method, the simulated annealing technique, and the local optimization method, which produces fast convergence and good solution quality. We mathematically show that the proposed algorithm converges to the global optimum. Experimental results show that training HMMs by the method yields better lipreading performance compared to the conventional training methods based on local optimization.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.700-702
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• 2004

A local-step optimization method is proposed to supplement the global-step optimization methods which adopt online update mode of internal weights and error energy as stop criterion in learning of multilayer perceptrons (MLPs). This optimization method is applied to the standard online error backpropagation(EBP) and the performance is evaluated for a speaker verification system.

• Journal of Magnetics
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• v.18 no.1
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• pp.74-79
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• 2013

This paper proposes a sampling-based optimization method for electromagnetic design problems, where design sensitivities are obtained from the elaborate surrogate models based on the universal Kriging method and a local window concept. After inserting additional sequential samples to satisfy the certain convergence criterion, the elaborate surrogate model for each true performance function is generated within a relatively small area, called a hyper-cubic local window, with the center of a nominal design. From Jacobian matrices of the local models, the accurate design sensitivity values at the design point of interest are extracted, and so they make it possible to use deterministic search algorithms for fast search of an optimum in design space. The proposed method is applied to a mathematical problem and a loudspeaker design with constraint functions and is compared with the sensitivity-based optimization adopting the finite difference method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.39-47
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• 2010

Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.6
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• pp.607-612
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• 2012

The wear problem of wheel flange occurs at sharp curves of rail. This paper proposes a procedure for optimum design of a wheel profile wherein flange wear is reduced by improving an interaction between wheel and rail. Application of optimization method to design problem mainly depends on characteristics of design space. This paper compared local optimization method with global optimization according to sensitivity value of objective function for design variables to find out which optimization method is appropriable to minimize wear of wheel flange. Wheel profile is created by a piecewise cubic Hermite interpolating polynomial and dynamic performances are analyzed by a railway dynamic analysis program, VAMPIRE. From the optimization results, it is verified that the global optimization method such as genetic algorithm is more suitable to wheel profile optimization than the local optimization of SQP (Sequential Quadratic Programming) in case of considering the lack of empirical knowledge for initial design value.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.183-186
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• 1995

A method is presented for optimization of Universal Learning Networks (ULN), where, together with gradient method, Simulated Annealing (SA) is employed to elude local minima. The effectiveness of the method is shown by its application to control of a crane system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.902-907
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• 1993

An efficient dynamic control algorithm that outperforms existing local torque optimization techniques for redundant manipulators is presented. The method resolves redundancy at the acceleration level. In this method, a systematic switching technique as a trade-off means between local torque optimization and global stability is proposed based on the stability condition proposed by Maciejewski [1]. Comparative simulations on a three-link planar arm show the effectiveness of the proposed method.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.