• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.503-513
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• 2000

This study treats the criteria, considering the fuzziness occurred by optimization design. And we applied two weighting methods to show the relative importance of criteria. This study develops multi-objective optimization programs implementing plain stress analysis by FEM and discrete optimization design uniformaly. The developed program performs a sample design of 10-member steel truss. This study can carry over the multi-objective optimization based on total system fuzzy-genetic algorithms while performing the stress analysis and optimization design. Especially, when general optimization with unreliable constraints is cannot be solve this study can make optimization design closed to realistic with fuzzy theory.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.710-711
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• 2009

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.1
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• pp.1-9
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• 2009

OFDM(Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OPDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PARR(Peak-to-Average Power Ratio). PTS(Partial Transmit Sequence) scheme can reduce the PAPR by dividing OFDM signal into subblocks and then multiplying the phase weighting factors to each subblocks, but computational complexity for selecting of phase weighting factors increases exponentially with the number of subblocks. Therefore, in this paper, MG-PSO(Modified Greedy algorithm-Particle Swarm Optimization) algorithm that combines modified greedy algorithm and PSO(Particle Swarm Optimization) algorithm is proposed to use for the phase control method in PTS scheme. This method can solve the computational complexity and guarantee to reduce PAPR. We analyzed the performance of the PAPR reduction when we applied the proposed method to telecommunication systems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.3
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• pp.215-221
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• 2012

In this paper, we introduce an optimization method to improve pursuit performance of a pursuer in a pursuit-evasion game (PEG). Pursuers build a probability map and employ a hybrid pursuit policy which combines the merits of local-max and global-max pursuit policies to search and capture evaders as soon as possible in a 2-dimensional space. We propose an episodic parameter optimization (EPO) algorithm to learn good values for the weighting parameters of a hybrid pursuit policy. The EPO algorithm is performed while many episodes of the PEG are run repeatedly and the reward of each episode is accumulated using reinforcement learning, and the candidate weighting parameter is selected in a way that maximizes the total averaged reward by using the golden section search method. We found the best pursuit policy in various situations which are the different number of evaders and the different size of spaces and analyzed results.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.7 s.250
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• pp.700-706
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• 2006

This study presents a numerical procedure to optimize the shape of dimple surface to enhance turbulent heat transfer in a 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 related term and friction loss related term with a weighting factor. full factorial method is used to determine the training points as a mean of design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

• 한국전산유체공학회:학술대회논문집
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• 2001.05a
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• pp.72-77
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• 2001

The computational efficiency of an shape optimization procedure is highly dependent upon the proper selection of shape representation methods and design variables. In this study, shape functions, Bezier and NURBS(non-uniform rational B-splines) curves are selected as configuration generation methods and their efficiencies on the nose shape design of high-speed air vehicles, are compared. The effects of the number of control points, weighting factors and the optimization methods when utilizing the NURBS curves, are investigated. By implementing Bezier and NURBS curves, shapes having lower drag than the optimization case utilizing the shape functions, were obtained, hence it was demonstrated that these curves have better capability in representing the configuration. Efforts will be given to improve the convergence behavior when utilizing the NURBS, hence to reduce the number of Navier-Stokes analysis calculations.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.564-568
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• 1992

A neural optimization network is designed to solve the collsion-free inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.47-52
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• 2007

A material mixing method to obtain an optimal topology for a structure in a thermal environment was suggested. This method is based on Evolutionary Structural Optimization(ESO). The proposed material mixing method extends the ESO method to a mixing several materials for a structure in the multicriteria optimization of thermal flux and thermal stress. To do this, the multiobjective optimization technique was implemented. The overall efficiency of material usage was measured in terms of the combination of thermal stress levels and heat flux densities by using a combination strategy with weighting factors. Also, a smoothing scheme was implemented to suppress the checkerboard pattern in the procedure of topology optimization. It is concluded that ESO method with a smoothing scheme is effectively applied to topology optimization. Optimal topologies having multiple thermal criteria for a printed circuit board(PCB) substrate were presented to illustrate validity of the suggested material mixing method. It was found that the suggested method works very well for the multicriteria topology optimization.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).