• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.509-515
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• 2013

In this study, the real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was developed for an airfoil shape design. In order to achieve the better aerodynamic characteristics than reference airfoil at landing and cruise conditions, maximum lift coefficient and lift-to-drag ratio were chosen as object functions. Futhermore, the PARSEC method reflecting geometrical properties of airfoil was adopted to generate airfoil shapes. Finally, two airfoils, which show better aerodynamic characteristics than a reference airfoil, were chosen. As a result, maximum lift coefficient and lift-to-drag ratio were increased of 4.89% and 5.38% for first candidate airfoil and 7.13% and 4.33% for second candidate airfoil.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.445-450
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• 2004

This paper describes an optimal design system for multi-disciplinary structural design. An automatic finite element (FE) mesh generation technique, which is based on the fuzzy knowledge processing and computational geometry technique, is incorporated into the system, together with a commercial FE analysis code and a commercial solid modelers. An optimum design solution or satisfactory solutions are then automatically searched using the genetic algorithms modified for real search space, together with the automated FE analysis system. With an aid of genetic algorithms, the present design system allows us to effectively obtain a multi-dimensional solutions. The developed system is successfully applied to the shape design of a micro accelerometer based on a tunnel current concept.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.233-236
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• 2002

An efficient method was developed in this study to obtain optimal stacking sequences, thicknesses, and minimum weights of stiffened laminated composite shells under combined loading conditions and stiffener layouts using genetic algorithms (GAs) and finite element analyses. Among many parameters in designing composite laminates determining a optimal stacking sequence that may be formulated as an integer programming problem is a primary concern. Of many optimization algorithms, GAs are powerful methodology for the problem with discrete variables. In this paper the optimal stacking sequence was determined, which gives the maximum critical buckling load factor and the minimum weight as well. To solve this problem, both the finite element analysis by ABAQUS and the GA-based optimization procedure have been implemented together with an interface code. Throughout many parametric studies using this analysis tool, the influences of stiffener sizes and three different types of stiffener layouts on the stacking sequence changes were throughly investigated subjected to various combined loading conditions.

• Computers and Concrete
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• v.21 no.5
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• pp.513-523
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• 2018

Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NU-ITI database.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.60-66
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• 2005

Pod shape of twenty soybean (Glycine max L. Merrill) genotypes was evaluated quantitatively by image analysis using elliptic Fourier descriptors and their principal components. The closed contour of each pod projection was extracted, and 80 elliptic Fourier coefficients were calculated for each contour. The Fourier coefficients were standardized so that they were invariant of size, rotation, shift, and chain code starting point. Then, the principal components on the standardized Fourier coefficients were evaluated. The cumulative contribution at the fifth principal component was higher than $95\%$, indicating that the first, second, third, fourth, and fifth principal components represented the aspect ratio of the pod, the location of the pod centroid, the sharpness of the two pod tips and the roundness of the base in the pod contour, respectively. Analysis of variance revealed significant genotypic differences in these principal components and seed number per pod. As the principal components for pod shape varied continuously, pod shape might be controlled by polygenes. It was concluded that principal component scores based on elliptic Fourier descriptors yield seemed to be useful in quantitative parameters not only for evaluating soybean pod shape in a soybean breeding program but also for describing pod shape for evaluating soybean germplasm.

• Clinical and Experimental Reproductive Medicine
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• v.43 no.2
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• pp.59-81
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• 2016

It is well established that there is a heritable element of susceptibility to chronic human ailments, yet there is compelling evidence that some components of such heritability are transmitted through non-genetic factors. Due to the complexity of reproductive processes, identifying the inheritance patterns of these factors is not easy. But little doubt exists that besides the genomic backbone, a range of epigenetic cues affect our genetic programme. The inter-generational transmission of epigenetic marks is believed to operate via four principal means that dramatically differ in their information content: DNA methylation, histone modifications, microRNAs and nucleosome positioning. These epigenetic signatures influence the cellular machinery through positive and negative feedback mechanisms either alone or interactively. Understanding how these mechanisms work to activate or deactivate parts of our genetic programme not only on a day-to-day basis but also over generations is an important area of reproductive health research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.1-11
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• 2008

Stiffness estimation of a shear building due to local damages is usually achieved though structural analysis based on the assumed material properties and idealized numerical modeling of structure. Conventional numerical modeling, however, frequently causes an inevitable error in the structural response and this makes it difficult to exactly predict the damage state in structure. To solve this problem, this paper introduces a damage detection technique for shear building using genetic algorithm. The introduced algorithm evaluates the damage in structure using a flexibility matrix since the flexibility matrix can exactly be obtained from the field test in spite of using a few lower dynamic modes of structure. The introduced algorithm is expected to be more effectively used in damage detection of structures rather than conventional method using the stiffness matrix. Moreover, even in cases when an accurate measurement of structural stiffness cannot be expected, the proposed technique makes it possible to estimate the absolute change in stiffness of the structure on the basis of genetic algorithm. The validity of the proposed technique is demonstrated though numerical analysis using OPENSEES.

• Korean Journal of Organic Agriculture
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• v.12 no.4
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• pp.451-461
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• 2004

In an animals, identification system has been widely used by ear tag with dummy code and blood typing for parernity. Also, genotyping methods were using for useful mean of individual identification for live animals. In the case of genotyping estimation of gene in population of korean native chicken. In this study, we tested for development of genetic markers used it possible to determination of individual identification system. The candidate genetic markers were used already bow 10 of microstalite DNA sequence information in chromosome No. 1 and 14. Result of analysis for genotyping, the number of alleles of those microstatelites DNA was shown minimal 3 to 12 and the heterozygote expression frequency range was shown from 0.617 to 0.862. In our result, effective number of allele for each microsatellites DNA was shown 3~7, and the accuracy of individual identification was shown nearly 100%, when used with 6 genetic marker. This study was about genotyping method for identification used specific genetic marker form microsatellite DNA in the brand marketing of korean native chicken. Our results suggest that genotyping method used specific genetic marker from microsatellite DNA might be very useful for determination of individual identification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.243-246
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• 2004

배낭 문제는 단순한 것 같지만 조합형 특성을 가진 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 GA(Genetic algorithms)를 이용하였으나 지역해에 빠질 수 있어 잘못된 해를 찾거나 찾지 못하는 문제점을 갖고 있다. 본 논문에서는 이러한 문제점들을 해결하기 위해 막대한 병렬성과 저장능력을 가진 DNA 컴퓨팅 기법에 DNA에 기반한 변형된 GA인 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optmization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 GA를 이용한 것 보다 초기 문제 표현에서 우수한 적합도를 생성했으며, 빠른 시간내에 우수한 해를 찾을 수 있었다.