• Electronic Materials Letters
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• v.14 no.6
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• pp.784-792
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• 2018

In this letter, a standard deviation based optimization technique has been applied on High Resolution X-ray Diffraction symmetric and asymmetric scan results to accurately determine the Aluminum molar fraction and lattice relaxation of Molecular Beam Epitaxy grown compositionally graded Aluminum Gallium Nitride (AlGaN)/Aluminum Nitride/Gallium Nitride (GaN) heterostructures. Mathews-Blakeslee critical thickness model has been applied in an alternative way to determine the partially relaxed AlGaN epilayer thicknesses. The coupling coefficient determination has been presented in a different perspective involving sample tilt method by off set between the asymmetric planes of GaN and AlGaN. Sample tilt is further increased to determine mosaic tilt ranging between $0.01^{\circ}$ and $0.1^{\circ}$.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.328-338
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• 2006

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1566-1571
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• 2011

The objective of this paper is to present a quantitative analysis leading to the assessment of optimum terminating impedances in the design of active frequency multipliers. A brief analysis of the basic principal of the GaAs FET frequency multiplier is presented. The analysis is outlined in bias optimization and drive power determination. Utilizing the equivalent circuit model of GaAs FET, we have simulated the optimized load impedance for the maximum output of the active frequency multipliers. The C-class and reverse C-class frequency doublers have been fabricated and the load impedances have been measured. The experimental results are in good agreement with the estimated results in the simulation with the accuracy of 90%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.1
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• pp.111-117
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• 2007

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic Algorithm (GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transforming problem to minimize load generated load point outage in each sub-section. This approach is one of the most difficult procedures and become combination problems. A new approach using GA was developed for this problem. GA is a general purpose optimization technique based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. Test results for the model system with 24 nodes 29 branches are reported in the paper.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.3
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• pp.24-29
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• 2009

In case of the optimization problems that have many design variables, the conventional genetic algorithms(GA) fall into a trap of local minima with high probability. This problem is called the premature convergence problem. To overcome it, the parallel genetic algorithms which adopt the migration mechanism have been suggested. But it is hard to determine the several parameters such as the migration size and the migration interval for the parallel GAs. Therefore, we propose a new method to determine the migration interval automatically in this paper. To verify its validity, it is applied to some traditional mathematical optimization problems and is compared with the conventional parallel GA. It is also applied to the optimal design of the brushless DC motor for an electric wheel chair which is a real world problem and has five design variables.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2556-2564
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• 2002

The design of airfoil had practiced by repeat tests in its first stage, though an airfoil has as been designed based on simulations according to techniques of computational fluid dynamics. Here, using of traditional optimization is unsuitable because a state of flux is hypersensitive to the shape of airfoil. Therefore the paper optimized the shape of airfoil in transonic region using a genetic algorithm (GA). Response surfaces are based on back propagation neural network (BPN) and regression model. Training data of BPN and regression model were obtained by computational fluid dynamic analysis using CFD-ACE, and each analysis has been designed by design of experiments.

• Journal of Ship and Ocean Technology
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• v.2 no.1
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• pp.42-57
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• 1998

Genetic Algorithm (GA) is applied to obtain optimal Inspection plan for fatigue deteriorating structures. The optimization problem is defined so as to minimize inspection cost in the 1ifs-time of the structure under the constraint that the increment of failure probability in each inspection interval is maintained below a target value. Optimization parameters are the inspection timing and the inspection quality. The inspection timing is selected from the discrete intervals such as one year, two years, three years, etc. The inspection quality is selected from the followings; no inspection, normal inspection, sampling inspection or precise inspection. The applicability of the proposed GA approach is demonstrated through the numerical calculations assuming a structure consisting of four member sets. Influences of the level of target failure probability, initial defect condition and stress increase due to plate thickness reduction caused by corrosion on inspection planning are discussed.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.292-298
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• 2012

The epitaxial layer structures for blue InGaN light emitting diodes have been optimized for high brightness applications with the output power levels exceeding 1000 $W/cm^2$ by using a self-consistent finite element method. The light-current-voltage relationship has been directly estimated from the multiband Hamiltonian for wurtzite crystals. To analyze the efficiency droop at high injection levels, the major nonradiative recombination processes and carrier spillover have also been taken into account. The wall-plug efficiency at high injection levels up to several thousand $A/cm^2$ has been successfully evaluated for various epilayer structures facilitating optimization of the epitaxial structures for desired output power levels.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2827-2829
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• 1999

Genetic algorithms were described as a method of solving large-scaled optimization problems with complex constraints. It has overcome their slowness, a major drawback of genetic algorithms using hardware implementation of genetic algorithm processor (GAP). In this study, we proposed GAP effectively connecting the goodness of survival-based GA, steady-state GA, tournament selection. Using Pipeline Parallel processing, handshaking protocol effectively, the proposed GAP exhibits 50% speed-up over survival-based GA which runs one million crossovers per second(1MHz). It will be used for high speed processing such of central processor of EHW, robot control and many optimization problem.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.147-152
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• 2006

The current type amplifier is the device that is used for an actuator as the motor's torque controller. However, it is too difficult to select the parameter value that has the desired output because the current type amplifier's transfer function is too complex. This study concern about the design of the current type amplifier with the desired output. From the modeled transfer function of the current type amplifier, the optimal parameter values of the transfer function can be selected in order to have the desired output using the Real Coded Genetic Algorithm(RCGA). The real circuit is made with the selected parameter value. The step response of the real circuit is in good agreement with the desired step response.