• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.438-441
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• 2004

In this study, we introduce a noble neurogenetic approach to the design of fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and Fuzzy-Set based Neural Networks (FSNN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out by using GAs, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based FSNN. The developed approach is applied to a nonlinear system such as an inverted pendulum where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.

• Journal of Aquaculture
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• v.14 no.1
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• pp.9-16
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• 2001

Characterization of a single pass of cDNA sequence, an expressed sequence tag (EST) has been a fast growing activity in fish genomics. Despite its relatively short history, fish EST databases (dbESTs) have already begun to play a significant role in bridging the gaps in our knowledge on the gene expression in fish genome. This review provides a brief description of the technology for establishing fish dbESTs, its current status, and implication of the ESTs to aquaculture and fisheries science with particular emphasis on the discovery of novel genes for transgenic application, the use of polymorphic EST markers in genetic linkage mapping and the evaluation of signal-responsive gene expression.

• ETRI Journal
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• v.34 no.5
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• pp.696-705
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• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.339-339
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• 2005

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.335-341
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• 2004

소축척지도제작을 위한 자동화 처리 과정에서는 기하학적 및 논리적 오류가 발생하고, 기하학적 오류는 많은 부분에서 자동화 처리가 가능하나 논리적 오류는 여러 가지 경우에 대하여 자동 판단이 곤란한 경우가 많기 때문에 대부분 수작업으로 이루어지고 있는 실정이다. 따라서, 본 연구는 수치지도를 이용한 일반화 처리 후의 지형도 제작시에서 나타나는 여러 가지 문제 중 도로과장화로 인한 오류문제를 해결하기 위하여 도로와 건물 폴리곤간의 겹침위치를 자동 탐색하고, 이를 자동처리하기 위한 프로그램을 유전자 알고리즘을 이용하여 개발하였다. 그 결과 지도제작 과정에서 발생하는 오류를 해결할 수 있었고, 지도제작 자동화율을 향상시킬 수 있었다.

• 한국지형공간정보학회:학술대회논문집
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• 2004.10a
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• pp.107-112
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• 2004

소축척 수치지도 및 지형도 제작을 위한 자동화 처리 과정에서는 기하학적 및 논리적인 오류가 발생하고 있고, 기하학적 오류는 많은 부분에서 자동화 처리가 가능하나 논리적 오류는 논리적으로 자동 판단이 곤란한 경우가 많기 때문에 대부분 수작업으로 이루어 지고 있는 실정이다. 따라서, 본 연구에서는 논리적 오류문제를 해결하기 위하여 폴리곤 겹침에 대해서 도로와 건물 폴리곤간의 겹침위치를 탐색하고, 이를 처리하기 위해 유전자알고리즘을 이용하여 프로그램을 개발하였다. 그 결과 지도제작 과정에서의 논리적 오류에 대한 자동화가 가능하도록 하였고, 그로 인한 지도제작 오류를 최소화할 수 있었다.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• Journal of Ship and Ocean Technology
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• v.5 no.3
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• pp.27-35
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• 2001

In this research, hybrid method with case-based reasoning and rule-based reasoning is applied. Using case-based reasoning, design experts'experience and know-how are effectively represented in order to obtain a proper configuration of midship section in the initial ship design stage. Since there is not sufficient domain knowledge available to us, traditional case-adaptation algorithms cannot be applied to our problem, i.e., creating the configuration of midship section. Thus, new case-adaptation algorithms not requiring any domain knowledge are developed antral applied to our problem. Using the knowledge representation of DnV rules, rule-based reasoning can perform deductive inference in order to obtain the scantling of midship section efficiently. The results from the case-based reasoning and the rule-based reasoning are examined by comparing the results with various conventional methods. And the reasonability of our results is verified by comparing the results wish actual values from parent ship.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.179-182
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• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.