• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.6
• /
• pp.638-648
• /
• 1990

An effective resolved motion control method of redundant manipulators is proposed to minimize the energy consumption and to increase the dexterity while satisfying the physical actuator constraints. The method employs the neural optimization networks, where the computation of Jacobian matrix is not required. Specifically, end effector movement resulting from each joint differential motion is first separated into orthogonal and tangential components with respect to a given desired trajectory. Then the resolved motion is obtained by neural optimization networks in such a way that 1) linear combination of the orthogonal components should be null 2) linear combination of the tangential components should be the differential length of the desired trajectory, 3) differential joint motion limit is not violated, and 4) weighted sum of the square of each differential joint motion is minimized. Here the weighting factors are controlled by a newly defined joint dexterity measure as the ratio of the tangential and orthogonal components.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.03a
• /
• pp.229-232
• /
• 2008

This study presents a numerical procedure to optimize the shape of stepped circular pin-fins to enhance turbulent heat transfer. The KRG method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The objective function is defined as a linear combination of heat transfer and friction loss related terms with a weighting factor. Ten training points are obtained by Latin Hypercube Sampling for two design variables. Optimum shape has been successfully obtained with the increased objective function.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.10a
• /
• pp.229-232
• /
• 2008

This study presents a numerical procedure to optimize the shape of stepped circular pin-fins to enhance turbulent heat transfer. The KRG method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The objective function is defined as a linear combination of heat transfer and friction loss related terms with a weighting factor. Ten training points are obtained by Latin Hypercube Sampling for two design variables. Optimum shape has been successfully obtained with the increased objective function.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.725-730
• /
• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• 유체기계공업학회:학술대회논문집
• /
• 2004.12a
• /
• pp.285-288
• /
• 2004

This study presents a numerical procedure to optimize the shape of dimple surface to enhance turbulent heat transfer in rectangular channel. The response surface based optimization method is used as an optimization technique with Reynolds-averaged Wavier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to -dimple print diameter ratio, channel height- to- dimple print diameter ratio. and dimple print diameter-to-pitch ratio are chosen as design variables. The objective function is defined as a linear combination of heat transfer coefficient and friction drag coefficient with a weighting factor. Full factorial method is used to determine the training points as a mean of design of experiment.

• The KSFM Journal of Fluid Machinery
• /
• v.12 no.4
• /
• pp.7-13
• /
• 2009

The purposes of present work are to analyze the convective heat transfer with three-dimensional Reynolds-averaged Navier-Stokes analysis, and to optimize shape of the mixing vane using the analysis results. Response surface method is employed as an optimization technique. The objective function is defined as a combination of inverse of heat transfer rate and friction loss. Two bend angles of mixing vane are selected as design variables. Thermal-hydraulic performances have been discussed and optimum shape has been obtained as a function of weighting factor in the objective function. The results show that the optimized geometry improves the heat transfer performance far downstream of the mixing vane.

• Journal of computational fluids engineering
• /
• v.14 no.2
• /
• pp.1-8
• /
• 2009

The purposes of present work are to analyze the convective heat transfer with three-dimensional Reynolds-averaged Navier-Stokes analysis, and to optimize shape of the mixing vane taken tolerance into consideration by using the analysis results. Response surface method is employed as an optimization technique. The objective function is defined as a combination of heat transfer rate and inverse of pressure drop. Two bend angles of mixing vane are selected as design variables. Thermal-hydraulic performances have been discussed and optimum shape has been obtained as a function of weighting factor in the objective function. The results show that the optimized geometry improves the heat transfer performance far downstream of the mixing vane.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.429-430
• /
• 2019

본 논문에서는 최적화 기법에 기반한 지능형 시스템의 재무응용사례를 다룬다. 본 연구에서 제안하는 모형은 대표적인 최적화 기법 중 하나인 시뮬레이티드 어니일링인데 이는 유전자 알고리듬과 유사한 최적화 성능을 가지고 있는 것으로 알려져 있으나 재무분야에서 응용된 사례가 거의 없다. 본 연구에서 제안하는 지능형 시스템은 시뮬레이티드 어니일링과 기계학습 기법을 결합한 것이다. 일반적으로 최적화와 기계학습 기법을 결합하는 방법은 특징선택(feature selection), 특징 가중치 최적화(feature weighting), 사례선택(instance selection), 모수 최적화(parameter optimization) 등의 방법이 있는데 선행연구에서 가장 많이 사용된 것은 특징선택에 두 기법을 결합하는 방식이다. 본 연구에서도 기계학습 기법을 재무 문제에 활용함에 있어서 최적의 특징선택을 위해 시뮬레이티드 어니일링을 결합하는 방식을 사용한다. 본 연구에서 제안된 기법의 유용성을 확인하기 위하여 실제 재무분야의 데이터를 활용하여 예측 정확도를 확인하였으며 그 결과를 통하여 제안하는 모형의 유용성을 확인할 수 있었다.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.1
• /
• pp.65-71
• /
• 1997

In this paper, we propose a new .mu.-controller design method using an equivalent weighting function $W_{\mu}$(s). The proposed mehtod is not guaranteed to converge to the minimum as D-K and .mu.-K iteration method. However, the robust performance problem can be converted into an equivalent $H^{\infty}$ optimization problem of unstructured uncertainty by using an equivalent weightng function $W_{\mu}$(s). Also we can find a .mu.-optimal controller iteratively using an error index $d_{\epsilon}$ of differnce between maximum singular value and .mu.-norm. And under the condition of the same order of scaling functions, the proposed method provides the .mu.-optimal controller with the degree less than that obtained by D-K iteration..

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.461-466
• /
• 1989

This paper describes some properties of a discrete algebraic Riccati equation and its application to $H^{\infty}$ control design. The conditions, under which an input weighting matrix can be found for a negative output weighting matrix in order that a solution P for a discrete algebraic equation may exist, are suggested in case of a stable A. This result is applied to a $H^{\infty}$ controller design for the special case of nonsingular B. It is based on a state feedback control law whose objective is to reduce the effect of input disterbances below a prespecified level. This law requires the solution of a modified algebraic Riccati equation, which provides an method for the $H^{\infty}$ optimization control problem approximately.ly.