• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.76-81
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• 1998

This paper presents an on-line scheme for parameter estimation of continuous-time systems, based on the model adjustment technique and the genetic algorithm technique. To deal with the initialisation and unmeasurable signal problems in on-line parameter estimation of continuous-time systems, a discrete-time model is obtained for the linear differential equation model and approximations of unmeasurable states with the observable output and its time-delayed values are obtained for the nonlinear state space model. Noisy observations may affect these approximation processes and degrade the estimation performance. A digital prefilter is therefore incorporated to avoid direct approximations of system derivatives from possible noisy observations. The parameters of both the model and the designed filter are adjusted on-line by a genetic algorithm, A set of simulation works for linear and nonlinear systems is carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.301-305
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• 1996

$\mu$ theory can handle the parametric uncertainty and produces more non-conservative controller than H$_{\infty}$ control theory. However an existing solution of the theory, D-K iteration, creates a controller of huge order and cannot handle the real or mixed real-complex perturbation sets. In this paper, we use genetic algorithms to solve these problems of the D-K iteration method. The Youla parameterization is used to obtain all stabilizing controllers and the genetic algorithms determines the values of the state feedback gain, the observer gain, and Q parameter to minimize $\mu$, the structured singular value, of given system. From an example, we show that this method produces lower order controller which controls a real parameter-perturbed plant than D-K iteration method.

• Toxicological Research
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• v.4 no.2
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• pp.131-142
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• 1988

The post-mitochondrial liver fractions (S-9) were prepared from rats and hamsters which have been treated with Aroclor 1254 (PCB) and the capacities of these S-9 fractions to generate mutagenic metabolites from several well known procarcinogens have been compared. Benzo(a)pyrene (B(a)P), 3-methylcholanthrene (3-MC), Aflatoxin B1(AFB1), 2-acetylamino-fluorene(AAF), and 2-aminofluorene (AF) were employed as promutagens in the Salmonella reverse mutation tests. Results showed that the rat and hamster S-9 fractions had differential abilities to produce mutagenic metabolites from a given promutagen.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.500-509
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• 2015

This paper presents the single objective design optimization of plate-fin heat sink equipped with fan cooling system using Genetic Algorithm. The proper heat sink and fan model are selected based on the previous studies. And the thermal resistance of heat sinks and fan efficiency during operation are calculated according to specific design parameters. The objective function is combination of thermal resistance and fan efficiency which have been taken to measure the performance of the heat sink. And Decision making procedure is suggested considering life time of semiconductor and Fan Operating cost. And also Analytical Model used for optimization is validated by Fluent, Ansys 13.0 and this model give a quite reasonable and reliable design.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.728-731
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• 1997

This paper proposes the systematic auto-tuning method for fuzzy controller using genetic algorithm(GA). In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge and relies to a great extent on heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controller can be degraded in the case of plant parameter variations or unpredictable incident which the designer may have ignored. Proposed genetic algorithm searches the optimal rule structure, parameters of membership functions and scaling factors simultaneously and automatically by a new genetic coding format. Inverted pendrum system is provided to show the advantages of the proposed method.

• Journal of Forest and Environmental Science
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• v.25 no.3
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• pp.195-204
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• 2009

Agrobacterium-mediated genetic transformation has been widely used for the production of genetically modified transgenic plants to obtain specific desired traits. Most of the molecular mechanisms that underlie the transformation steps have been well elucidated over the years. However, a few steps, such as nuclear targeting, T-DNA integration, and Agrobacterium-plant proteins involved remain largely obscure and are still under extensive studies. This review describes the major steps involved in the molecular mechanism of Agrobacterium-mediated transformation and provides insight in the recent developments in studies on the Agrobacterium-mediated genetic transformation system. Some factors affecting the transformation efficiency are also briefly discussed.

• IE interfaces
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• v.20 no.1
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• pp.33-40
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• 2007

In this paper, we consider a scheduling problem of shipyard sub-assembly process. We introduce a skid conveyor system in a shipbuilding company. We develop a mathematical model and a genetic algorithm for shipyard sub-assembly process. The objective of the scheduling is to minimize the makespan which is the final completion time of all jobs. Numerical experiments show that the genetic algorithm performs efficiently.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.571-573
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• 1995

This paper presents a method for optimal dispatch which minimizes transmission losses and improves voltage profile of power systems using genetic algorithm based on the mechanism of natural genetics and natural selection. The constraints are VAR sources(transformer tap, generator voltage magnitude and shunt capacitor/reactor), load bus voltages and generator reactive power. Real variable-based genetic algorithms which can save coding times and maintain the accuracy are applied for optimal dispatch of reactive power. The genes of genetic algorithm consisted of integers for considering discrete VAR sources. A efficient operator for crossover is proposed to consider the effect of close genes. The algorithm proposed can apply to problems for large scale power systems with multi-variables and complex nonlinear functions efficiently. The proposed method is applied to IEEE 30 buses model system to show its effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.115-121
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• 2007

In this paper, we study flowshop scheduling problems with availability constraints. In such problems, n jobs have to be scheduled on m machines sequentially under assumption that the machines are unavailable during some periods of planning horizon. The objective of the problem is to find a non-permutation schedule which minimizes the makespan. As a solution procedure, we propose an improved genetic algorithm which utilizes a look-ahead schedule generator to find good solutions in a reasonable time Computational experiments show that the proposed genetic algorithm outperforms the existing genetic algorithm.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.58-65
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• 2001

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 he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.