• Journal of Korean Association for Spatial Structures
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• v.16 no.1
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• pp.43-51
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• 2016

A novel vibration control method for vibration reduction of a spacial structure subjected to earthquake excitation was proposed in this study. Generally, spatial structures have various vibration modes involving high-order modes and their natural frequencies are closely spaced. Therefore, in order to control these modes, a spatially distributed MTMDs (Multiple TMDs) method is proposed previously. MR (Magnetorheological) damper were used to enhance the control performance of the MTMDs. Accordingly, MSTMDs (Multiple Smart TMDs) were proposed in this study. An arch structure was used as an example structure because it has primary characteristics of spatial structures and it is a comparatively simple structure. MSTMDs were applied to the example arch structure and the seismic control performance were evaluated based on the numerical simulation. Fuzzy logic control algorithm (FLC) was used to generate command voltages sent for MSTMSs and the FLC was optimized by genetic algorithm. Based on the analytical results, it has been shown that the MSTMDs effectively decreased the dynamic responses of the arch structure subjected to earthquake loads.

• Journal of Navigation and Port Research
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• v.28 no.5
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• pp.435-441
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• 2004

This paper deals with modelling and identification of a shipboard stabilized satellite antenna system using the optimal neural network structure. It is difficult for shipboard satellite antenna system to control and identification because of their approximating ability of nonlinear function So it is important to design the neural network with optimal structure for minimum error and fast response time. In this paper, a neural network structure using genetic algorithm is optimized And genetic algorithm is also used for identifying a shipboard satellite antenna system It is noticed that the optimal neural network structure actually describes the real movement of ship well. Through practical test, the optimal neural network structure is shown to be effective for modelling the shipboard satellite antenna system.

• Korean Journal of Computational Design and Engineering
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• v.18 no.6
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• pp.439-450
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• 2013

In the case of a floating offshore structure such as FPSO(Floating, Production, Storage, and Offloading unit), many equipment should be installed in the limited space, as compared with an onshore structure. Recently, the requirement for an optimal layout method of the structure has been raised. Thus, a layout method of the floating offshore structure was proposed in this study. First, an optimization problem for layout design was mathematically formulated, and then an optimization algorithm was implemented based on the genetic algorithm in order to solve it. To evaluate the applicability of the proposed method, it was applied to examples ofFPSO topsides and an offshore wind turbine. As a result, it was shown that the proposed method can be applied to layout design of the floating offshore structure.

• Journal of Korean Society of Forest Science
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• v.90 no.1
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• pp.43-54
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• 2001

Genetic differentiation of populations is resulted from the environmental and the genetic effects, and the interactions between them. Whereas, the major factors influencing to the genetic differentiation within populations are the gene flow induced by seed or pollen dispersial, the microsite heterogeneity, and the density-dependent distribution of individuals. For the purpose of studying spatial genetic structure and the distribution pattern of Korean pines(Pinus koraiensis), we set up one $100{\times}100m$ plot at a Korean pine stand in Quercus mongolica community on Mt. Jumbong in Korea. To estimate the coefficient of spatial autocorrelation as Moran's index and an analogue, simple block distance, isozyme markers were analyzed in 325 Korean pines. For 11 polymorphic loci observed in 9 enzyme systems, the average percentage of polymorphic loci, the observed and expected heterozygocity were 72.2% 0.200, and 0.251, respectively. It was revealed the excess of homozygotes was observed in the plot, which suggests that here may be more number of consanguineous trees than expected. On the basis of isozyme genotypes observed in this study, 325 trees were classified into 147 groups in which the maximum number of trees for one group was 34. From the distance class of 24-32m, the genetic heterogeneity began to increase. The variation of simple block distance against the growth performance by tree height and diameter also showed the same trend at 24~32m class. According to high fixation index(F=0.204), the spatial genetic structure within a stand, the analysis of the growth performance, and the distribution patterns of identical genotypes, we inferred that the genetic structure of a Korean pine stand in Mt. Jumbong has been maintained rather density-dependent mechanism than the gene flow, such as the pollen dispersial or the heavy input of seeds following the forest gaps. The genetic patchy size was determined between 24~32m, which suggests that the selection of individuals for the ex situ conservation of Korean pine in Mt. Jumbong may be desirable to be made with the spatial distance over 37 meters between trees.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2876-2878
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimal Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. The study is illustrated with the ACI Distance Relay Data for application to power systems.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4625-4635
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• 2022

A low-energy dielectric loaded accelerator with a non-uniform, multi-segment structure is studied and optimized. So far, no analytical solution is provided for such structures. Also, due to the existing nonlinear behavior and a large number of geometric parameters, the problem of numerical optimizations is complex. For this reason, a method is presented to design and optimize such structures using the Genetic Algorithm (GA). Moreover, the GA output results are compared with Trust Region (TR) and Nelder-Mead Simplex (NMS) methods. Comparative results show that the GA is more efficient in achieving optimization goals and also has a higher speed than the two other methods. Finally, an optimized accelerating tube is integrated into a proper coupler. Then, the accelerator is simulated for full electromagnetic investigations using the CST suite of codes. This design leads to a structure with a power of about 80 kW in the X-band, which delivers electrons to the output energy in the range of 300-459 kV. The length and outer diameter of the accelerating tube obtained are 10 cm and 1 cm, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.6
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• pp.902-912
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• 2020

Objective: Mongolia is one of a few countries that supports over 25 million goats, but genetic diversity, demographic history, and the origin of goat populations in Mongolia have not been well studied. This study was conducted to assess the genetic diversity, phylogenetic status and population structure of Mongolian native goats, as well as to discuss their origin together with other foreign breeds from different countries using hypervariable region 1 (HV1) in mtDNA. Methods: In this study, we examined the genetic diversity and phylogenetic status of Mongolian native goat populations using a 452 base-pair long fragment of HVI of mitochondrial DNA from 174 individuals representing 12 populations. In addition, 329 previously published reference sequences from different regions were included in our phylogenetic analyses. Results: Investigated native Mongolian goats displayed relatively high genetic diversities. After sequencing, we found a total of 109 polymorphic sites that defined 137 haplotypes among investigated populations. Of these, haplotype and nucleotide diversities of Mongolian goats were calculated as 0.997±0.001 and 0.0283±0.002, respectively. These haplotypes clearly clustered into four haplogroups (A, B, C, and D), with the predominance of haplogroup A (90.8%). Estimates of pairwise differences (Fst) and the analysis of molecular variance values among goat populations in Mongolia showed low genetic differentiation and weak geographical structure. In addition, Kazakh, Chinese (from Huanghuai and Leizhou), and Arabian (Turkish and Baladi breeds) goats had smaller genetic differentiation compared to Mongolian goats. Conclusion: In summary, we report novel information regarding genetic diversity, population structure, and origin of Mongolian goats. The findings obtained from this study reveal that abundant haplogroups (A to D) occur in goat populations in Mongolia, with high levels of haplotype and nucleotide diversity.

• Journal of Sericultural and Entomological Science
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• v.37 no.1
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• pp.10-15
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• 1995

Genetic characterization of Sri Lankan silkworm bivoltine population has not been at-tempted so far, since its sporadic introduction of bivoltine strains into the island, starting from the 1950's. Genetic structure of Sri Lankan population of mulberry silkworm Bombyx mori was investigated through estimation of general (GCA) and specific combining ability(SCA) and heritability(${h^2}_B$), on the economic quantitative characters from leading 8 inbreds and their 28 F1's in a half diallel cross, in an attempt to utilize the estimates in determination of future breeding methods and to predict the breeding value over the phenotypic value. It was found that the breeding population of the bivoltine silkworm in Sri Lanka has still maintained considerable amounts of additive gene action as well as nonadditive. For some time in the future, both breeding strategies of "selection without inbreeding" and also "inbreeding followed by crossing" should therefore be effective in genetic improvement of economic characters investigated. In addition, superior combiners in general and in specific F1′s were identified for each of 6 economic characters, to be immediately utilized in selection and also in cross breeding programs in Sri Lanka.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.