• ETRI Journal
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• v.43 no.2
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• pp.324-331
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• 2021

Space-based automatic dependent surveillance-broadcast (ADS-B) is an important emerging augmentation of existing ground-based ADS-B systems. In this paper, the problem of space-based ultra-long-range reception processing of ADS-B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS-B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high-precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1007-1014
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• 2015

An optimally designed six-phase induction motor (6PIM) is compared with an initial design induction motor having the same ratings. The Genetic Algorithm (GA) method is used for optimization and multi objective function is considered. Comparison of the optimum design with the initial design reveals that better performance can be obtained by a simple optimization method. Also in this paper each design of 6PIM, is simulated by MAXWELL_2D. The obtained simulation results are compared in order to find the most suitable solution for the specified application, considering the influence of each design upon the motor performance. Construction a 6PIM based on the information obtained from GA method has been done. Quality parameters of the designed motors, such as: efficiency, power losses and power factor measured and optimal design has been evaluated. Laboratory tests have proven the correctness of optimal design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.42-45
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• 2002

In this paper, multiphase optimization of machine Tool structure is presented. The final goal is to obtain 1) light weight, 2) statically and dynamically rigid. and 3) thermally stable structure. The entire optimization process is carried out in three phases. In the first phase, multiple static optimization problem with two objective functions is treated using Pareto genetic algorithm. where two objective functions are weight of the structure and static compliance. In the second phase, maximum receptance is minimized using simple genetic algorithm. And the last phase, thermal deflection to moving heat sources is analyzed using Predictor-Corrector Method. The method is applied to a high speed line center design which takes the shape of back-column structure.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.3-12
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• 1995

A controller utilizing fuzzy logic is developed to control the speed of a motor in a washing machine by choosing an appropriate phase. Due to the hardship imposed on obtaining a result from a relation established for inputs, present speed and present rate of speed, and ouput, a phase, of the system that can be tested against an experimental result, it is impossible to apply a genetic algorithm to fine-tune the fuzzy logic controller. To avoid this difficulty, a proper assumption that the parameters of an if-part of a primary fuzzy logic controller have a functional relationship with an error between computed values and experimental ones in made. Setting up of a fuzzy relationship between the parameters and the errors is then achieved through experimentally obtained data. Genetic Algorithm is then applied to this secondary fuzzy logic controller to verify the fuzzy logic. In the verification process, the primary fuzzy logic controller is used in obtaining experimental results. In this way the kind of difficulty in obtaining enough experimental values used to verify the fuzzy logic with genetic algorithm is gotten around. Selection of the parameters that would produce the least error when using the secondary fuzzy logic controller is done with applying genetic algorithm to the then-part of the controller. In doing so the optimal values for the parameters of the if-part of the primary fuzzy logic controller are assumed to be contained. The experimental result presented in the paper validates the assumption.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.145-152
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• 2004

This paper proposes a representation for the embodiment design of mechanical structures and a genetic algorithm suited for the representation. In order to represent early stages and latter stages of the embodiment design, the designs are modeled as simultaneous multi-objective optimization problems of parametric designs for parts and of layout generation for structures. The study, thus, involves genotypes that are adequate to represent phenotypes of the models for the genetic algorithm to solve the given problems. We demonstrate the implementation of the genetic algorithm with the result applied to the gear equipment design.

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.181-200
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• 1994

In the learning phase of multilyer feedforword neural network,there are problems such that local minimum,learning praralysis and slow learning speed when backpropagation algorithm used.To overcome these problems, the genetic algorithm has been used as learing method in the multilayer feedforword neural network instead of backpropagation algorithm.However,because the genetic algorith, does not have any mechanism for fine-tuned local search used in backpropagation method,it takes more time that the genetic algorithm converges to a global optimal solution.In this paper,we suggest a new GA-BP method which provides a fine-tunes local search to the genetic algorithm.GA-BP method uses gradient descent method as one of genetic algorithm's operators such as mutation or crossover.To show the effciency of the developed method,we applied it to the 3-parity bit problem with analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.272-278
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• 2017

In this paper, we consider a single-channel amplitude comparison monopulse system(SCACMS). The monopulse ratio curve(MR-C) of the SCACMS can be controlled by an amplitude difference between sum and different signal, a phase difference and the coefficient of the signal processor. We first propose the SCACMS with multiple variables, and then apply a genetic algorithm to optimize the multiple variables in terms of minimizing a root mean square error. The simulation results show that when three variables of the SCACMS are jointly optimized, the linear region of the MR-C can be extended approximately 187 % compared to that of two variables.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.85-91
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• 2000

This paper proposes a novel method with pseudo-on-line scheme using the optimized look-up table based on the genetic algorithm which does not use the gradient and finds the global optimum of an un-constraint optimization problem. The technique is a pseudo-on-line method that optimally estimates the parameters of fuzzy PID(FPID) controller for systems with non-linearity, using the genetic algorithm. The proposed controller(GFPID) with the auto-tuning function is applied to the on-line and real-time control of speed at 3-phase induction motor, and its computer simulation is carried out. simulation results show that the proposed methodis more excellent that conventional FPID and PID controllers.