• Animal cells and systems
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• v.8 no.1
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• pp.41-47
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• 2004

The kallikrein-kinin system affects regulation of blood pressure, and genes encoding for the components of this system have been considered as good candidates for hypertension. To evaluate the relationship between genetic polymorphisms of candidate genes involved in this system and hypertension, we performed case-control studies using genetic markers in Korean normotensives and hypertensives, respectively. By association study, there was a marginal association with hypertension in AA genotype distribution of A1789G polymorphism in the hKLK1 gene (P=0.0754). Thus, this genetic polymorphism may weakly contribute to the susceptibility to hypertension in Koreans. We also observed that significant linkage disequilibrium exists among three polymorphic sites in the hKLK1 gene studied, suggesting that the three genetic polymorph isms can be useful as genetic markers in clinical association studies. Further studies using larger sample sizes and more genetic markers will be needed to clarify genetic influence of kallikrein-kinin system for hypertension.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.968-971
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• 1996

In this paper, feedback controller is designed for ball-beam system using genetic algorithms. A genetic algorithms are implemented for optimizing gain parameters of feedback controller. We can find optimal point in multi-dimensional search space by using genetic algorithms. Performance of controller is tested by simulation of ball-beam system.

• Journal of Genetic Medicine
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• v.4 no.1
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• pp.1-5
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• 2007

Unprecedented amount of genetic information being generated from the result of Human Genome Project (HGP) and advances in genetic research is already forcing changes in the paradigm of health and disease. The ultimate goal of genetic medicine is to use genetic information and technology to develop new ways of treatment or even prevention of the disease on an individual level for 'personalized medicine'. Genetics is play ing an increasingly important role in the diagnosis, monitoring and management of common multifactorial diseases in addition to rare single-gene disorders. While wide range of genetic testing have provided benefits to patients and family, uncertainties surrounding test interpretation, the current lack of available medical options for the diseases, and risks for discrimination and social stigmatization may remain to be resolved. However an increasing number of genetic tests are becoming commercially available, including direct to consumer genetic testing, yet public is often unaw are of their clinical and social implications. The personal nature of information generated by a genetic test, its power to affect major life decisions and family members, and its potential misuse raise important ethical considerations. Therefore appropriate genetic counseling is needed for patient to be informed with the benefits, limitations and risks of genetic tests, prior to informed consent for the tests. Physician also should be familiar with the legal and ethical issues involved in genetic testing to tell patients how w ell a particular genetic risk factor relates with likelihood of disease, and be able to provide appropriate genetic counseling. Genetic counseling become a mandatory requirement as global standard for many genetic testing such as prenatal diagnosis, presymtomatic DNA diagnostic tests and cancer susceptibility gene test for familial cancer syndrome. In oder to meet the challenge of genetic medicine of 21 century in korean health care system, professional education program and certification board for medical genetics specialist including non-MD genetic counselors should be addressed by medical society and regulatory policy of national health insurance reimbursement for genetic counseling to be in place to promote the implementation of clinical genetic service including genetic counseling for proper genetic testing.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1460-1463
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• 1997

The aim of this paper is to suggest a design method of the model following optimal boiler-turbine H.inf. control system using genetic algorithm. This boiler-turbine H.inf. control system is designed by applying genetic algortihm with reference model to the optimal determination of weighting functions and design parameter .gamma. that are given by Glover-Doyle algornithm whch can design H.inf. contrlaaer in the sate. space. The first method to do this is ghat the gains of weightinf functions and .gamma. are optimized simultaneously by genetic algroithm. And the second method is that not only the gains and .gamma. but also the dynamics of weighting functions are optimized at the same time by genetic algonithm. The effectiveness of this boiler-turbine H.inf. control system is verified and compared with LQG/LTR control system by computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.479-485
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• 2000

This paper presents a analysis method for dynamic characteristics of power system using a Genetic-based Power System Stabilizer(PSS). The proposed PSS parameters are optimized using Genetic Algorithm(GA) in order to maintain optimal operation of generator under the various operating conditions. To decrease the computational time, real variable string is adopted. The results tested on a single machined infinite bus system verify that the proposed controller has better dynamic performance than conventional controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1095-1103
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• 1992

An approach to fuse the fuzzy inference system which is able to deal with imprecise and uncertain information and genetic algorithms which display the excellent robustness in complex optimization problems is presented in this paper. In order to combine genetic algorithms and fuzzy inference engine effectively the new reasoning method is suggested. The efficient identification method of fuzzy rules is proposed through the adjustment of search areas of genetic algorithms. The feasibilty of the proposed approach is evaluated through simulation.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.