• 상하수도학회지
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• 제18권4호
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• pp.453-460
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• 2004

The objective of this research is to develop the water quality management model to achieve the water quality goal and the minimization of the waste load abatement cost. Most of existing water quality management model can calculate BOD and DO. In addition to those variables, N and P are included in the management model of this study. With a genetic algorithm, calculation results from the mathematical water quality model can be used directly in this management model. Developed management model using genetic algorithm was applicated for the Youngsan River basin. To verify the management model, water quality and pollution source of the Youngsan River had been investigated. Treatment types and optimum treatment costs of the existing and planned WWTPs in the baisn were calculated from the model. The results of genetic algorithm indicate that Kwangju and Naju WWTP have to do the advanced treatment to achieve the water quality goal about BOD, DO and TP. Total annual treatment cost including the upgrade cost of existing WWTPs in the Youngsan River basin was about 50.3 billion Won.

• Architectural research
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• 제8권2호
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• pp.43-48
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• 2006

The appropriate fleet estimation of the excavation equipment is a major factor in the determination of the cost and time requirements of a project. But the decision of what kind of equipment selected is often based on heuristic methods or trial and error in Korea. Thus, this study proposes a prototype model that uses genetic algorithms to select fleet estimation of loaders (backhoe) and trucks used in excavation work. To verify the applicability of this model, the case study was performed. And the result of the genetic model was compared with that of the trial & error method. The use of the genetic model suggested this study required 44days, 2 units of backhoes, 7 units of trucks, and a total cost of 171,839,756 won. With the estimated fleet number of equipment, the minimum cost of excavation work can be calculated, taking account of the time-cost trade-off. By utilizing this prototype model, the efficiency of excavation work can be improved.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.696-702
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• 2006

Flutter characteristics of composite curved wing were investigated in this study. The efficient and robust system for the flutter optimization of general composite curved wing models has been developed using the coupled computational method based on both the standard genetic algorithm and the micro genetic algorithms. Micro genetic algorithm is used as an alternative method to overcome the relatively poor exploitation characteristics of the standard genetic algorithm. The present results show that the micro genetic algorithm is more efficient in order to find optimized lay-ups for a composite curved wing model. It is found that the flutter stability of curved wing model can be significantly increased using composite materials with proper optimum lamination design when compared to the case of isotropic wing model under the same weight condition.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Computers and Concrete
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• 제23권4호
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.615-620
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• 2001

This paper addresses the design of sliding model fuzzy-model-based controller using genetic algorithms. In general, the construction of fuzzy logic controllers has difficulties for the lack of systematic design procedure. To release this difficulties, the sliding model fuzzy-model-based controllers was presented by authors. In this proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Although, the stability and the performance is guaranteed by the proposed method, some design parameters have to be chosen by the designer manually. This problem can be solved by using genetic algorithms. The proposed method tunes the parameters of the controller, by which the reasonable accuracy and the control effort is achieved. The validity and the efficiency of the proposed method are verified through simulations.

• 한국농공학회논문집
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• 제51권3호
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• pp.1-8
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• 2009

The objectives of this study are to apply univariate search and genetic algorithm to tank model, and compare the two optimization methods. Hydrologic data of Baran watershed during 1996 and 1997 were used for correction the tank model, and the data of 1999 to 2000 were used for validation. RMSE and R2 were used for the tank model's optimization. Genetic algorithm showed better result than univariate search. Genetic algorithm converges to general optima, and more population of potential solution made better result. Univariate search was easy to apply and simple but had a problem of convergence to local optima, and the problem was not solved although search the solution more minutely. Therefore, this study recommend genetic algorithm to optimize tank model rather than univariate search.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.331-334
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• 1996

It is a well-known problem that the adaptive control has a poor transient response. In order to improve this problem, the scheme that model-reference adaptive control (MRAC) uses the genetic algorithm (GA) in the search for parameters is proposed. Use genetic algorithm (GA) in the searching for controller's parameters set and conventional gradient method for fine tuning. And show the reduction of the oscillations in transient response comparing with the conventional MRAC.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.29-32
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• 2000

HMM(Hidden Markov Model)은 시간적인 정보를 토대로 하는 수학적인 방법으로서 문자인식에 많이 사용되어지고 있다. 그런데 HMM이 적용되고자 하는 문제에서 사용되어지는 상태 수와 HMM에서 사용되어지는 parameter들은 처음에 결정되는 값들에 의해서 상당히 많은 영향을 받게 된다. 따라서 한글의 특성을 이용한 HMM의 상태 수를 결정한 후 결정되어진 각각의 HMM parameter들을 Genetic Algorithm을 이용하였다. Genetic Algorithm은 매개변수 최적화 문제에 대하여 자연의 진화 원리를 마땅한 알고리즘으로 선택, 교배, 돌연변이 연산을 이용하여 최적의 개체를 구하게 된다. 여기서는 HMM에서의 Viterbi Algorithm을 적합도 검사에 사용하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제28권9호
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.