• Journal of Korean Society of Forest Science
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• v.105 no.4
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• pp.414-421
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• 2016

Fourteen Acer pseudosieboldianum populations in South Korea were used to estimate genetic diversity, genetic differentiation and genetic relationships using seven AFLP primer combinations. The average of effective alleles ($A_e$), the proportion of polymorphic loci (%P) and Shannon's diversity index (I) was 1.4, 82.2% and 0.358, respectively. The expected heterozygosity ($H_e$) under Hardy-Weinberg equilibrium was 0.231 and the expected heterozygosity (Hj) from Bayesian inference was 0.253. The level of genetic diversity was moderate compared to those of Genus Acer and lower than those of other species having similar ecological niche and life history. The inbreeding coefficient within populations ($F_{IS}$) from Bayesian method was 0.712 and it could be influenced by selfing or biparental inbreeding to induce homozygote excess. The level of genetic differentiation was 0.107 from AMOVA (${\Phi}_{ST}$) and 0.110 from Bayesian method (${\Phi}^{II}$). The genetic differentiation was lower than those of other species having similar ecological niche and life history. Ulleungdo population had the lowest level of genetic diversity and was genetically the most distinct population from others in the study. We consider that founder effect and genetic drift might be occurred to reduce genetic diversity and then the geographical isolation might interrupt gene flow to aggravate it.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.43-51
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• 2008

Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, ions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an $m{\times}n$ matrix form in which m is the number of input variables.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.23-27
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• 1997

This paper introduces the structural design optimization of a high speed machining center using multi-step optimization combined with G.A.(Genetic Algorithm) and Weighted Method. In this case, the design problem is to find out the best design variables which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously. Dimensional thicknesses of the thirteen structural members of the machine structure are adopted as design variables. The first step is the cross-section configuration optimization, in which the area moment of inertia of the cross-section for each structural member is maximized while its area is kept constant The second step is a static design optimization, In which the static compliance and the weight of the machine structure are minimized under some dimensional and safety constraints. The third step IS a dynamic design optimization, where the dynamic compliance and the structure weight are minimized under the same constraints. After optunization, static and dynamic compliances were reduced to 62.3% and 95.7% Eorn the initial design, while the weight of the moving bodies are also in the feaslble range.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.313-320
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• 2014

A tensegrity structure consists of a set of continuous cables in tension and a set of discontinuous struts in compression. The tensegrity structure can be classified into self-stressed and pre-stressed pin-jointed structure. A key step in the design of tensegrity structures is the determination of their equilibrium configuration, known as form-finding. In this paper, three effective methods are presented for form-finding of tensegrity structures. After performing form-finding process, a set of force density and corresponding topology results can be obtained. Then the force density method combined with a genetic algorithm is adopted to uniquely define a single integral feasible set of force densities. Numerical examples are presented that demonstrate the excellent performance of the algorithms.

• Structural Engineering and Mechanics
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• v.39 no.1
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• pp.147-168
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• 2011

To ensure safety and long term performance, structural control has rapidly matured over the past decade into a viable means of limiting structural responses to strong winds and earthquakes. Nonlinear response history analysis requires rigorous procedure to compute seismic demands. Therefore the simplified nonlinear analysis procedures are useful to determine performance of the structure. In this investigation, application of improved capacity demand diagram method in the control of structural system is presented for the first time. Developed pole assignment method (DPAM) in structural systems control is introduced. Genetic algorithm (GA) is employed as an optimization tool for minimizing a target function that defines values of coefficient matrices providing the placement of actuators and optimal control forces. The ground acceleration is modified under induced control forces. Due to this, performance of structure based on improved nonlinear demand diagram is selected to threshold of nonlinear behavior of structure. With small energy consumption characteristics, semi-active devices are especially attractive solutions for limiting earthquake effects. To illustrate the efficiency of DPAM, a 30-story steel moment frame structure employing the semi-active control devices is applied. In comparison to the widely used linear quadratic regulation (LQR), the DPAM controller was shown to be just as effective and better in the reduction of structural responses during large earthquakes.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.45-56
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• 2019

A modified mechanical model of pre-pressed spring self-centering energy dissipation (PS-SCED) brace is proposed, and the hysteresis band is distinguished by the indication of relevant state variables. The MDOF frame system equipped with the braces is formulated in an incremental form of linear acceleration method. A multi-objective genetic algorithm (GA) based brace parameter optimization method is developed to obtain an optimal solution from the primary design scheme. Parameter sensitivities derived by the direct differentiation method are used to modify the change rate of parameters in the GA operator. A case study is conducted on a steel braced frame to illustrate the effect of brace parameters on node displacements, and validate the feasibility of the modified mechanical model. The optimization results and computational process information are compared among three cases of different strategies of parameter change as well. The accuracy is also verified by the calculation results of finite element model. This work can help the applications of PS-SCED brace optimization related to parameter sensitivity, and fulfill the systematic design procedure of PS-SCED brace-structure system with completed and prospective consequences.

• Korean Journal of Ichthyology
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• v.22 no.2
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• pp.115-120
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• 2010

Genetic variation and population structure of the slender bitterling Acheilognathus lanceolatus of Korea (the Han, Geum, Dongjin, Seomjin and Nakdong Rivers) and Japan (the Katsura River) were assessed by amplified fragment length polymorphism (AFLP) analysis. Five combinations of selective primers generated 345~374 DNA fragments, of which 55~131 were polymorphic. The Nakdong River population had the highest genetic diversity and the Han River population had the lowest genetic diversity. Dendrogram based on the distance matrix revealed that individuals from each population consistently clustered together and bifurcated into two distinct clades (or population groups) composed of the Han, Geum, Dongjin and Seomjin River populations and of the Nakdong and Katsura River populations, supported with high bootstrap values. The pairwise genetic differentiation ($F_{ST}$) estimates showed that the six populations were genetically well differentiated (P<0.01). The analysis of molecular variance (AMOVA) after partitioning the six populations into two population groups revealed very strong biogeographic structuring between them with 25.49% of total variance (P<0.01). Taken together, the AFLP markers clearly divided six A. lanceolatus populations into two population groups.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1986-1989
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• 2003

This paper proposes the information retrieval improvement for the Web using the structure and hyperlinks of HTML documents along with user profile. The method bases on the rationale that terms appearing in different structure of documents may have different significance in identifying the documents. The method partitions the occurrence of terms in a document collection into six classes according to the tags in which particular terms occurred (such as Title, H1-H6 and Anchor). We use genetic algorithm to determine class importance values and expand user query. We also use this value in similarity computation and update user profile. Then a genetic algorithm is used again to select some terms from user profile to expand the original query. Lastly, the search engine uses the expanded query for searching and the results of the search engine are scored by similarity values between each result and the user profile. Vector space model is used and the weighting schemes of traditional information retrieval were extended to include class importance values. The tested results show that precision is up to 81.5%.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.146-153
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• 2005

In this paper, a multi-step optimization using genetic algorithm with variable penalty function is introduced to the structural design optimization of a grinding machine. The design problem, in this study, is to find out the optimum configuration and dimensions of structural members which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously under several design constraints such as dimensional constraints, maximum deflection limit, safety criterion, and maximum vibration amplitude limit. The first step is shape optimization, in which the best structural configuration is found by getting rid of structural members that have no contributions to the design objectives from the given initial design configuration. The second and third steps are sizing optimization. The second design step gives a set of good design solutions having higher fitness for lightweight and minimum static compliance. Finally the best solution, which has minimum dynamic compliance and weight, is extracted from the good solution set. The proposed design optimization method was successfully applied to the structural design optimization of a grinding machine. After optimization, both static and dynamic compliances are reduced more than 58.4% compared with the initial design, which was designed empirically by experienced engineers. Moreover the weight of the optimized structure are also slightly reduced than before.