• Design & Manufacturing
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• 제17권3호
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• pp.41-47
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• 2023

This paper presents the development of a controller that can control the temperature of an heating system based on a thermoelectric module. Temperature controller using Peltier has various external factors such as external temperature, characteristics of an aluminum plate, installation location of temperature sensors, and combination method between the aluminum plate and heating element. Therefore, it is difficult to apply the simulation and simulation results of heating system using Peltier at control algorithm. In general, almost temperature controller is using PID algorithm that finds control gain value heuristically. In this paper, it is proposed mathematical model that explain correlate between the temperature of the heating system and input voltage. And then, optimal parameter of estimated thermal model of the aluminum plate are searched by using genetic algorithm. In addition, based on this estimated model, the optimal PID control gain are inferred using a genetic algorithm. All of the sequence are simulated and verified with proposed real system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• 동력기계공학회지
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• 제17권5호
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.293-298
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• 1997

The H$_{\infty}$robust controller fur the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of non-convex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. robust design.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.275-280
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• 2009

A Genetic Algorithm (GA) is a kind of search techniques used to find exact or approximate solutions to optimization and searching problems. This paper discusses the design of a genetic algorithm-based intelligent parking system. This is a search strategy based on the model of evolution to solve the problem of parking systems. A genetic algorithm for an optimal solution is used to find a series of optimal angles of the moving vehicle at a parking space autonomously. This algorithm makes the planning simpler and the movement more effective. At last we present some simulation results.

• 대한임상검사과학회지
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• 제41권3호
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• pp.93-104
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• 2009

This short review was aimed to provide the information for the people who are interested in genetic counselor education and certification system in Korea. A large part of this study is indebted to HJ Kim's articles on the genetic counselor system, the global standards of genetic counseling curriculums, training program accreditation (TPA), and a certification process for genetic counselors (CPGC) in the US and Japan. The US and Japanese educational systems showed a high degree of similarities in curriculum, accreditation, and certification programs. Based upon this review, we hereby propose that the Korean Society for Medical Genetics should take a key role in providing the TPA and CPGC for non-MD genetic counselors. Requirement for the entrance to a Master's degree genetic counseling program should be open to successful four year undergraduate students for all areas, provided the candidates demonstrate the abilities to master the graduate level study in human genetics, statistics, psychology, and other required subjects. Besides accredited program graduates, eligibility for certification should also include the qualified candidates of genetic counseling with no formally approved education, but with a sufficient amount of clinical experience.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.584-589
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• 2001

This paper presents the dynamic parameter design optimization of a suspension seat system using the genetic algorithm. At first, an equivalent 1-D.O.F. mass-spring-damper model of a suspension seat system was constructed for the purpose of its vibration analysis. Vertical vibration response and transmissibility of the equivalent model due to base excitations, which are defined in the ISO's seat vibration test codes, were computed. Furthermore, seat vibration test, that is ISO's damping test, was carried out in order to investigate the validity of the equivalent suspension seat model. Both analytical and experimental results showed good agreement each other. For the design optimization, the acceleration transmissibility of the suspension seat model was adopted as an object function. A simple genetic algorithm was used to search the optimum values of the design variables, suspension stiffness and damping coefficient. Finally, vibration ride performance test results showed that the optimum suspension parameters gives the lowest vibration transmissibility. Accordingly the genetic algorithm and the equivalent suspension seat modelling can be successfully adopted in the vibration ride quality optimization of a suspension seat system.

• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• Journal of Multimedia Information System
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• 제7권2호
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• pp.165-174
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• 2020

This document outlines a new color image encryption technology development. After splitting the original image into 240-bit blocks and modifying the first block by an initialization vector, an improved Feistel circuit is applied, sponsored by a genetic crossover operator and then strong chaining between the encrypted block and the next clear block is attached to set up the confusion-diffusion and heighten the avalanche effect, which protects the system from any known attack. Simulations carried out on a large database of color images of different sizes and formats prove the robustness of such a system.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 영호남 합동 학술대회 및 춘계학술대회 논문집 센서 박막 기술교육
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• pp.111-112
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• 2006

This paper a new method which applies a genetic algorithm for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. In addition, the proposed method introduces a ultra efficient MPP tracking in a solar power generation system.