• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.8
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• pp.758-764
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• 2015

This paper describes the pattern synthesis of array antenna which conforms to a metallic curved surface formed by the rotation of a quadratic function by using EAGA(Enhanced Adaptive Genetic Algorithm). Three rotated-type conformal surfaces are realized by changing the coefficient of the quadratic function and the pattern of each conformal array antenna is synthesized. In order to reduce the overall time of pattern synthesis, the transformed active element pattern obtained by the active element pattern of the 2-dimensional planar array using Euler angles rotation is utilized instead of the active element pattern of the 3-dimensional conformal array antenna itself. To verify validity of the proposed synthesis procedure, the synthesized patterns using EAGA are compared with those obtained by MWS.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.514-519
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• 2003

The aircraft engine is usually supported by rolling element bearings and has a small damping rate, which is vol y sensitive to external force. The high-performance requirement of the rotors leads to complex assembly designs and are more flexible. Squeeze film dampers (SFDs) are introduced to provide damping while crossing the critical speeds and stability to the rotor s :stem. Hence, the focus of the present investigation is on the decision of an optimal size of the flexible rotor system supported by the squeeze film dampers to minimize the maximum transmitted load and unbalance response over a range operating speeds. The enhanced genetic algorithm (EGA), which was developed by authors, is used in the optimization process. This algorithm is based on the synthesis of a modified genetic algorithm and simplex method. The results show significant benefits in using EGA when compared with nonlinear programming (NLP).

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1123-1125
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• 1999

This paper proposes an optimization technique using Genetic Algorithm(GA) for service restoration in the distribution system. Restoration planning problem can be treated as a combinatorial optimization problem. So GA is appropriate to solve the service restoration problem in the distribution network. But searching capabilities of the GA can be enhanced by developing relevant repairing operation and modifying GA operations. In this paper, we aimed at finding appropriate open sectionalizing switch position for the restoration of distribution networks after disturbances using enhanced GA with repairing operation and modified mutation. Simulation results show that proposed method found the open sectionalizing switches with less out of service area and minimize transmission line losses and voltage drop.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.108-110
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• 2006

A prediction model of charge concentration of silicon nitride (SiN) thin films was constructed by using neural network and genetic algorithm. SIN films were deposited by plasma enhanced chemical vapor deposition and the deposition process was characterized by means of $2^{6-1}$ fractional factorial experiment. Effect of five training factors on the model prediction performance was optimized by using genetic algorithm. This was examined as a function of the learring rate. The root mean squared error of optimized model was 0.975, which is much smaller than statistical regression model by about 45%. The constructed model can facilitate a Qualitative analysis of parameter effects on the charge concentration.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.174-179
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• 2011

This paper focuses on the optimal step-over value for magnetic tool path. Since magnetic flux density is changed according to distance from center of magnetic tool. Enhanced surface roughness is also different according to change of radius. Therefore, to get a identical surface roughness on workpiece, it is necessary to find optimal tool path including step-over. In this study, response surface models for surface roughness according to change of radiuses were developed, and then optimal enhanced surface roughness for each radius was selected using genetic algorithm and simulated annealing to investigate relation between radius and surface roughness. As a result, it found that step-over value of 6.6mm is suitable for MAP of magnesium alloy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.265-266
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.109-115
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• 2003

This study intends to provide an optimum design of flexible matrix composite driveshafts using a genetic algorithm. An objective function is defined as a combination of shaft flexibility, whirling stability and torsional buckling and the design variables are selected as ply angles and the shaft thickness. Results show that the genetic algorithm can successfully find an optimum solution at which the overall performance of the FMC shafts is significantly enhanced

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.12
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• pp.1031-1043
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• 2015

This paper describes the pattern synthesis method of two kinds of conformal array antenna using the Enhanced Adaptive Genetic Algorithm (EAGA). One is the $1{\times}16$ conformal array antenna on a curved cylindrical metallic surface with quadratic function, and the other is the 18-element conformal arrary antenna on a metallic surface obtained by the rotation of a quadratic function curve around the axis. The active element pattern is utilized in the pattern synthesis. Especially for the case of the rotated-type conformal array antenna the transformed active element pattern obtained from the Euler's angle rotation of the active element pattern of the planar concentric array is utilized, which reduces the synthesis time a lot. To verify the validity of the proposed synthesis method the MATLAB results are compared with the MWS results. Furthermore, for the case of $1{\times}16$ conformal array antenna the measured results are compared with the MATLAB synthesized results.