• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.

• 한국항공우주학회지
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• 제30권6호
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• pp.7-13
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• 2002

본 논문에서는 새로운 형태의 혼합형 유전 알고리즘을 제안하고 성능을 검증한 후 30kW 피치제어 가변 풍력발전시스템의 블레이드 설계와 피치제어 최적화에 적용하여 주어진 Weibull 분포함수에서 동력을 최대화하는 최적의 블레이드 시위 및 비틀림각의 분포와 작동범위내에서 동력을 일정하게 유지하기 위한 최적의 피치각을 결정하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2486-2489
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• 2002

Pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.587-589
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• 1997

In this paper we design the model following ${\mu}$-synthesis control system for optimal fuel-injection of diesel engine using genetic algorithms. To do this, we give gain and dynamics parameters to the weighting functions and apply genetic algorithms with reference model to the optimal determination of weighting functions that are given by D-K iteration method which can design ${\mu}$-synthesis controller in the state space. These weighting functions are optimized simultaneously in the search domain selected adequately. The effectiveness of this ${\mu}$-synthesis control system for fuel-injection is verified by computer simulation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.116-119
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• 1998

Abstract-In paper, an operation support software for combustion control of refuse incineration plant is developed using fuzzy model and genetic algorithm. It has two major modules which are simulation module and optimal operation module. In simulation module modelling is performed to obtain fuzzy model of the refuse incineration plant and obtained fuzzy model predicts outputs of the plant when inputs are given. This module can be used to obtain control strategy, and train and enhance operators' skill by simulating the plant. And in optimal operation module, genetic algorithm searches and finds out optimal control inputs over all possible solutions in respect to desired outputs. In order to testify proposed operation support software, computer simulation was carried out.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.590-595
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• 2003

본 논문에서는 mGA를 이용해 축구로봇의 속도를 제어하는 새로운 기법을 제안하였다 축구 로봇의 목표를 최소 시간 내에 도착하기 위해 속도제어에 크게 영향을 미치는 거리 오차와 각도 오차 등의 비율을 나타내는 각종 파라미터가 포함되어 있는 제어 함수를 제안하였다. 이들 파라미터들을 mGA을 이용하여 최적의 값들을 탐색함으로써 변화되는 환경 속에서도 로봇의 목적지에 최소 시간 내에 이동하도록 속도제어 전략을 제안한다.

• 대한전기학회논문지:전력기술부문A
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• 제52권1호
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• pp.58-61
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• 2003

A loop power system has a nonlinear characteristics. Also it is very hard to analyse through a equation if a discontinuous characteristic of the ULTC is added to a system. However, the problem which is hard to analyse by equations can acquire the useful result with what use the genetic algorithm (GA) which is a multi-point search program. In this paper, we proved through a simulation that the proposed method can reduce an operation frequency of tap changers and improving the quality of voltage of the buses by decreasing the deviation between the actual voltage and the reference voltage through the coordinated control of the ULTC that use GA in the loop power system.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of the Korean Wood Science and Technology
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• 제51권3호
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• pp.222-237
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• 2023

Eucalyptus pellita is one of the fast-growing tree species and has become predominant in Indonesian forest plantations. Meanwhile, tree breeding programs with clone development are the best way to provide greater genetic advantages. A better understanding of genetic control on growth and basic density in E. pellita is important for increasing wood productivity and quality. In this study, growth characteristics (tree height, diameter, and volume), basic density and its genetic parameters (heritability, genetic gain and genetic correlation) were determined. The number of clones tested in both trials was 50, divided into 5 blocks, and 5 trees/plot. The results showed that there were significant differences in growth and basic density among clones. There was an interaction between genetics and the environment further indicating the existence of unstable clones. The high heritability was found in tree height (0.82-0.86), diameter (0.82-0.90), and basic density (0.91-0.93). This implies that E. pellita has good opportunities for genetic improvement to increase wood productivity and quality. In addition, the results of genetic correlations among growth characteristics (height, diameter, and volume) and basic density showed positive moderate to highly significant value. It is suggested that these characters may be used to the advantage of the breeder for bringing improvement in these traits simultaneously. Therefore, this study provides important information of the genetic improvement of wood quality in E. pellita in Indonesia.