• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.115-123
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• 2011

Distribution system loss minimization re-configuration is 0-1 planning problem, and the number of combinations requiring searches is extremely large when dealing with typical system scales. For this reason, the application of a genetic algorithm (GA) seems a tractive to solve this problem. Although Genetic algorithms are a type of random number search method, they incorporate a multi-point search feature and are therefore superior to one-point search techniques. The incorporate of GAs for solving large combinational problem has received wide attention. Further, parallel searching can be performed and the optimal solution is more easily reach ed. In this paper, for improving GA convergence characteristics in the distribution system loss minimization re-configuration problem, a chromosome "Limited Life" concept is introduced. Briefly, considering the population homogenization and genetic drift problems, natural selection is achieved by providing this new concept, in addition to natural selection by fitness. This is possible because individuals in a population have an age value. Simulation were carried out using a model system to check this method's validity.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.27-35
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• 2021

Artificial life is used in various fields of applied science by evaluating natural life-related systems, their processes, and evolution. Research has been actively conducted to evolve physical body design and behavioral control strategies for the dynamic activities of these artificial life forms. However, since co-evolution of shapes and neural networks is difficult, artificial life with optimized movements has only one movement in one form and most do not consider the environmental conditions around it. In this paper, artificial life that co-evolve bodies and neural networks using predator-prey models have environmental adaptive movements. The predator-prey hierarchy is then extended to the top-level predator, medium predator, prey three stages to determine the stability of the simulation according to initial population density and correlate between body evolution and population dynamics.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Journal of the Korea Society of Computer and Information
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• v.5 no.4
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• pp.41-47
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• 2000

In modern dynamic management environment, there is growing recognition that information & knowledge management systems are essential for CEO's efficient/effective decision making. As a key component to cope with this current, we suggest the management performance data-mining system based on IT(Information Technology). This system measures management performance that is considered with both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. The relationship between management performance and 85 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied the explanation-based Gas(Genetic Algorithms) that consider predictability, understanability (lucidity) and reasonability factors simultaneously. To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1239-1247
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• 2009

Study the gene about economical characteristic of human disease or domestic animal is a matter of grave interest, preserve and elevation of gene of Korea cattle is key subject. Studies have been done on the gene of Korea cattle using EST based SNP map, but it is based on statistical model, therefore there are difference between real position and statistical position. These problems are solved using both EST_based SNP map and Gene on sequence by Lee et al. (2009b). We have used multifactor dimensionality reduction(MDR) method to study interaction effect of statistical model in general. But MDR method cannot be applied in all cases. It can be applied to the only case-control data. So, method is suggested E-MDR method using CART algorithm. Also we identified interaction effects of single nucleotide polymorphisms(SNPs) responsible for average daily gain(ADG) and marbling score(MS) using E-MDR method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.325-333
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• 2016

This paper proposes controller design algorithms for a ceramic soldering iron temperature control system, and reports their effectiveness in a control experiment. Because the responses of the ceramic soldering iron temperature to the control input are non-linear and very slow, precise modeling and controller design is difficult. In this study, the temperature characteristics of a ceramic soldering iron are represented by TSK fuzzy models consisting of TSK fuzzy rules. In the fuzzy rules, the premise variable is the control input and the consequences are the transfer functions. The transfer functions in the fuzzy model were obtained from the step input responses. As the responses of the ceramic soldering iron temperature are very slow, it is difficult to obtain the complete step input responses. This paper proposes a genetic algorithm to obtain the transfer functions from an incomplete step input responses, and showed its effectiveness in examples. This paper also reports a fuzzy controller design method from the TSK fuzzy model and examples. The proposed methods were applied to the temperature control experiments of ceramic iron. The TSK fuzzy model consisted of 7 TSK fuzzy rules, and the consequences were PI controllers. The experimental results of the proposed fuzzy PI controller were superior to the linear controller and were as good as in previous studies using a fuzzy PID controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.457-463
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• 2015

A gas turbine engine plays an important role as a prime mover that is used in the marine transportation field as well as the space/aviation and power plant fields. However, it has a complicated structure and there is a time delay element in the combustion process. Therefore, an elaborate mathematical model needs to be developed to control a gas turbine engine. In this study, a modeling technique for a gas generator, a PLA actuator, and a metering valve, which are major components of a gas turbine engine, is explained. In addition, sub-models are obtained at several operating points in a steady state based on the trial running data of a gas turbine engine, and a method for controlling the engine speed is proposed by designing an NPID controller for each sub-model. The proposed NPID controller uses three kinds of gains that are implemented with a nonlinear function. The parameters of the NPID controller are tuned using real-coded genetic algorithms in terms of minimizing the objective function. The validity of the proposed method is examined by applying to a gas turbine engine and by conducting a simulation.

• Journal of the Society of Naval Architects of Korea
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• v.36 no.4
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• pp.137-146
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• 1999

Inspection becomes to be important in the safety of structure and economical viewpoint, because structural damage accompanies lots of economical cost and social problems. Especially ship structure is composed of a lot of members and it is impossible to inspect all members continuously. The purpose of this paper is to get optimal inspection plan containing inspection time and method. Crack is one of major modes on the structural failure and can lead to collapse of structure. In this paper, the deteriorating process, which contains inspection to detect the crack before the propagation to large crack, is idealized as Markov chain model. Genetic algorithm is also used to accomplish the optimization of inspection plan. Especially, the probabilistic characteristics of cracks are changed, because ship is operating in corrosive environments and the scantling of structural members is reduced due to corrosion. Non-stationary Markov chain model is used to represent the process of corrosion in structural members. In this paper, the characteristics of indivisual inspection plan are compared by numerical examples for the change of corrosion rate, the cost due to scheduled system down and target failure probability. From the numerical example, it can be seen that the improvement of fatigue life for the members with short fatigue life is the most effective way in order to reduce total maintenance cost.

• Korean Journal of Environmental Biology
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• v.25 no.4
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• pp.349-355
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• 2007

Phylogeny of the species mainly from the genus Amynthas in family Megascolecidae was inferred at the molecular level using ITS regions in rDNA. With 26 species of earthworms from 10 genera in 2 families, a stretch comprising the 3'-end of the 18S rRNA, ITS1, 5.8S rRNA, ITS2, and 5' end of 28S rRNA was amplified by applying the primers ITS-1, ITS-2. Phylogenetic analyses of nucleotide sequences with a help of MP, NJ, and QP yielded 5 groups similarly. Genus Amynthas was separated largely into two groups, Korean and Philippine origins. Species grouped into the 1st were Amynthas jirensis, A. agrestis, A. gucheonensis, A. sopaikensis, A. bubonis, A. multimaculatus, A. koreanus, A. dageletensis, A. heteropodus, A. odaesanensis, Pontoscolex sp., Pheretima sp. 1, and Dendropheretima banahawensis. Amynthas halconensis, A. isarogensis, A. mindrooensis, Pithemera sp. 2, Pithmera sp. 1, and Pleionogaster sp. clustered into one clade forming the 2nd group. Polypheretima sp. 1 and polypheretima. sp. 2 stayed closely together representing a separate monophyletic status, forming the 3rd group, apart from species in other genera. Archipheretima sp. falls into the 4th group. Distinct morphological characteristics from Archipheretima also coinsides with its branching away from others in the previously reported molecular analyses. Similar to Perionyx excavatus that has been selected as an outgroup, Aporrectodea tuberculata also showed a long branch in the phylogram, but it differed from other 24 species included in the analyses. Unlike others, for example, its habitat is very closely related to that of man.