• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.98-103
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• 1995

In this paper, a method is presented for finding an optimal temperature control pattern in microwaveoven using genetic algorithm. Power spectrum of temperature variance of charcoal is obtained and oven system modeling with fuzzy-neural-network is explained. Fan on/off timing is converted to strings in gene pool and then genetic iterations make the power spectrum of simmulated temperature variance of microwave oven closer to that o charcoal.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.624-629
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• 1998

Evolutionary computation techniques can solve search problems using simulated evolution based on the ‘survival of the fittest’. Recently, the genetic programming (GP) which evolves computer programs using the genetic algorithm was introduced. In this paper, the genetic programming technique is used in order to design a rule based controller consisting of condition-action rules for an unknown system. No a priori knowledge about the structure of the controller is needed. Representation of a solution, functions and terminals in GP are analyzed, and a method of constructing a fuzzy logic controller using the obtained rule based controller is described. A simulation example using a nonlinear system shows the validity and efficiency of the proposed method.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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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.

• Journal of Genetic Medicine
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• v.20 no.2
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• pp.39-45
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• 2023

As advanced sequencing technologies continue to uncover an increasing number of variants in genes associated with human genetic diseases, there is a growing demand for systematic approaches to assess the impact of these variants on human development, health, and disease. While in silico analyses have provided valuable insights, it is essential to complement these findings with model organism studies to determine the functional consequences of genetic variants in vivo. Drosophila melanogaster is an excellent genetic model for such functional studies due to its efficient genetic technologies, high gene conservation with humans, accessibility to mutant fly resources, short life cycles, and cost-effectiveness. The traditional GAL4-UAS system, allowing precise control of gene expression through binary regulation, is frequently employed to assess the effects of monoallelic variants. Recombinase medicated cassette exchange or CRISPR-Cas9-mediated GAL4 insertion within coding introns or substitution of gene body with Kozak-Gal4 result in the loss-of-function of the target gene. This GAL4 insertion strategy also enables the expression of reference complementary DNA (cDNA) or cDNA carrying genetic variants under the control of endogenous regulatory cis elements. Furthermore, the CRISPR-Cas9-directed tissue-specific knockout and cDNA rescue system provides the flexibility to investigate candidate variants in a tissue-specific and/or developmental-timing dependent manner. In this review, we will delve into the diverse genetic techniques available in Drosophila and their applications in diagnosing and studying numerous undiagnosed diseases over the past decade.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2513-2515
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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 leaming 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 Butter-Worth analog 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 Butter-Worth analog filter parameter using the genetic algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Animal Systematics, Evolution and Diversity
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• v.24 no.1
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• pp.25-32
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• 2008

The Gold-spotted pond frog, Rana plancyi chosenica, designated as a vulnerable species by IUCN Red list. This species is a typical example facing local population threats and extinction due to human activities in South Korea. A strategic conservation plan for this endangered species is urgently needed. In order to provide information for future conservation planning, accurate information on the genetic diversity and taxonomic status is needed for the establishment of conservation units for this species. In this study, we used a molecular genetic approach using the mitochondrial cytochrome b gene and control region sequences to find the genetic diversity of gold-spotted pond frogs within South Korea. We sequenced the mitochondrial DNA cytochrome b gene and control region of 77 individuals from 11 populations in South Korea, and one from Chongqing, China. A total of 15 cytochrome b gene haplotypes and 34 control region haplotypes were identified from Korean gold-spotted pond frogs. Mean sequence diversity among Korean gold-spotted pond frogs was 0.31% (0.0-0.8%) and 0.51% (0.0-1.0%), respectively. Most Korean populations had at least one unique haplotype for each locus. The Taean, Ansan and Cheongwon populations had no haplotypes shared with other populations. There was a sequence divergence between Korean and Chinese gold-spotted pond frogs (1.3% for cyt b; 2.9% for control region). Analysis of genetic distances and phylogenetic trees based on both cytochrome b and control region sequences indicate that the Korean gold-spotted pond frog are genetically differentiated from those in China.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.