• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.12
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• pp.589-594
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• 2000

The fuzzy controllers have been applied to the power system stabilizer due to its excellent properties on the nonlinear systems. But the design process of fuzzy controller requires empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This process is time consuming task. This paper presents an parameters tuning method of the fuzzy power system stabilizer using the genetic algorithm and simulated annealing(SA). The proposed method searches the local minimum point using the simulated annealing algorithm. The proposed method is applied to the one-machine infinite-bus of a power system. Through the comparative simulation with conventional stabilizer and fuzzy stabilizer tuned by genetic algorithm under various operating conditions and system parameters, the robustness of fuzzy stabilizer tuned by proposed method with respect to the nonlinear power system is verified.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.59-64
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• 2006

The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved in order to achieve the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties). The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and design of mill requirement. To achieve this objectives, a new teaming method with neural network to improve the accuracy of rolling force prediction in hot rolling mill is developed. Also, Genetic Algorithm(GA) is applied to select the optimal structure of the neural network and compared with that of engineers experience. It is shown from this research that both structure selection methods can lead to similar results.

• Journal of Acupuncture Research
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• v.19 no.5
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• pp.189-198
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• 2002

Objective : To research the trend of the study related to rheumatoid arthritis(RA) and to establish the hereafter direction of the study on RA. Methods : We have selected 12 medical report about RA in Arthritis & Rheumatism and Journal of Rheumatology recently, reviewed them, and investigated their methods. Results & Conclusions : The pattern of study was as follows: physical & serologic research in 2 articles, immunologic research in 7 articles, and genetic research in 3 articles. There is now evidence of the benefit of treatment early in the disease course and evidence of the impact of treatment on outcomes. New classes of therapeutic agents have also been introduced. Wherever possible, these revised guidelines are evidence-based. By the above results, it would be needed further research on RA mechanism related immunologic and genetic process.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.6
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• pp.167-174
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• 2013

High speed rotor systems with magnetic bearings have been the subject of much research in recent years due to the potential for active vibration control. In this thesis, optimal design was conducted for an 8-pole heteropolar magnetic bearing used in the flexible rotor of a turbo blower. In connection with bearing stiffness, this optimal design process was conducted using a genetic algorithm(GA), which is based on natural selection and genetics. The maximum stiffness of the magnetic bearing-rotor was found by considering the critical speeds of the flexible rotor. As a result, the magnetic bearings were optimized to have maximum stiffness.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.502-504
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of FIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate FIR filter parameter using the genetic algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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• pp.13-20
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• 2021

Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• The Plant Pathology Journal
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• v.25 no.4
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• pp.303-316
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• 2009

Genetic variation among the base isolates and monosporidial strains derived from these isolates of Tilletia indica- the causal agent of Karnal bunt (KB) in wheat, was analyzed by morphological, growth behaviors and RAPD-ISSR based molecular polymorphism. Genetic make up of fungal cultures vary among each other. The magnitude of variation in KBPN group is less (narrow genetic base) when compared to the other groups KB3, KB9 and JK (broad genetic base) reflecting that variability is a genetically governed process. The generation of new variation with different growth characteristics is not a generalized feature and is totally dependant on the original genetic make-up of the base isolate generating new monosporidial strains. Thus, it can be concluded that monosporidial strains derived from mono-teliosporic isolate, consists of genetically heterogeneous population. The morphological and genetic variability further suggests that the variation in T. indica strains is predominantly derived through the genetic rearrangements through para sexual means.