• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.646-655
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• 2008

A main issue about production management of shipyards is to efficiently manage the work in process and logistics. However, so far the management of a transporter for moving building blocks has not been efficiently performed. To solve the issues, optimal block transporting scheduling system is developed for minimizing of the travel distance without overload of a transporter. To implement the developed system, a hybrid optimization algorithm for an optimal block transportation scheduling is proposed by combining the genetic algorithm and the ant algorithm. Finally, to evaluate the applicability of the developed system, it is applied to a block transportation scheduling problem of shipyards. The result shows that the developed system can generate the optimal block transportation scheduling of a transporter which minimizes the travel distance without overload of the transporter.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.332-340
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• 2016

Seed size traits are controlled by multiple genes in crops and determine grain yield, quality and appearance. However, the molecular mechanisms controlling the size of plant seeds remain unclear. We performed functional analysis of BrPATL4 encoding Sec14-like protein to determine the genetic architecture of seed size, shape and their association analyses. We used 60 $T_3$ transgenic rice lines to evaluate seed length, seed width and seed height as seed size traits, and the ratios of these values as seed shape traits. Pleiotropic effects on general architecture included small seed size, erect panicles, decreased grain weight, reduced plant height and increased sterility, which are common to other mutants deficient in gibberellic acid (GA) biosynthesis. To test whether BrPATL4 overexpression is deleterious for GA signal transduction, we compared the relative expression of GA related gene and the growth rate of second leaf sheath supplied with exogenous $GA_3$. Overexpression of BrPATL4 did not affect GA biosynthesis or signaling pathway, with the same response shown under GA treatment compared to the wild type. However, the causal genes for the small seed phenotype (D1, SRS1, and SRS5) and the erection of panicles showed significantly decreased levels in mRNA accumulation compared to the wild type. These results suggest that the overexpression of BrPATL4 can control seed size through the suppression of those genes related to seed size regulation. Although the molecular function of BrPATL4 is not clear for small seed and erect panicles of BrPALT4 overexpression line, this study provides some clues about the genetic engineering of rice seed architecture.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.1
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• pp.77-83
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• 2016

Pipe working is consist of design, making and installation. Pipe line is consist of spool pipes which are made in fabrication shop. And these spool pipes installation in shipyard. Spool pipes are designed based 2D Drawings(ISO Drawing), so spool pipes are not considered working area, that wake decreasing working efficiency and delay working time. In this paper, suggest make spool pipe design method using analysis working area about 3D CAD model and genetic algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.1
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• pp.124-130
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• 2009

Estimating genetic interaction effects in animal genomics would be one of the most challenging studies because the phenotypic variation for economically important traits might be largely explained by interaction effects among multiple nucleotide sequence variants under various environmental exposures. Genetic improvement of economic animals would be expected by understanding multi-locus genetic interaction effects associated with economic traits. Most analyses in animal breeding and genetics, however, have excluded the possibility of genetic interaction effects in their analytical models. This review discusses a historical estimation of the genetic interaction and difficulties in analyzing the interaction effects. Furthermore, two recently developed methods for assessing genetic interactions are introduced to animal genomics. One is the restricted partition method, as a nonparametric grouping-based approach, that iteratively utilizes grouping of genotypes with the smallest difference into a new group, and the other is the Bayesian method that draws inferences about the genetic interaction effects based on their marginal posterior distributions and attains the marginalization of the joint posterior distribution through Gibbs sampling as a Markov chain Monte Carlo. Further developing appropriate and efficient methods for assessing genetic interactions would be urgent to achieve accurate understanding of genetic architecture for complex traits of economic animals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• Horticulture, Environment, and Biotechnology : HEB
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• v.59 no.6
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• pp.889-897
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• 2018

Currently, there is a lack of genetic markers capable of effectively detecting polymorphisms in Clematis. Therefore, we developed new markers to investigate inter- and intraspecific diversity in Clematis. Based on the complete chloroplast genome of Clematis terniflora, simple sequence repeats were explored and primer pairs were designed for all ten adequate repeat regions (cpSSRs), which were tested in 43 individuals of 11 Clematis species. In addition, the nuclear ITS region was sequenced in 11 Clematis species. Seven cpSSR loci were found to be polymorphic in the genus and serve as markers that can distinguish different species and be used in different genetic analyses, including cultivar identification to assist the breeding of new ornamental cultivars.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1558-1565
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• 2020

Objective: The objective of this study was to characterize the number of loci affecting growth traits and the distribution of single nucleotide polymorphism (SNP) effects on growth traits, and to understand the genetic architecture for growth traits in Hanwoo (Korean cattle) using genome-wide association study (GWAS), genomic partitioning, and hierarchical Bayesian mixture models. Methods: GWAS: A single-marker regression-based mixed model was used to test the association between SNPs and causal variants. A genotype relationship matrix was fitted as a random effect in this linear mixed model to correct the genetic structure of a sire family. Genomic restricted maximum likelihood and BayesR: A priori information included setting the fixed additive genetic variance to a pre-specified value; the first mixture component was set to zero, the second to 0.0001×σ²_g, the third 0.001×σ²_g, and the fourth to 0.01×σ²_g. BayesR fixed a priori information was not more than 1% of the genetic variance for each of the SNPs affecting the mixed distribution. Results: The GWAS revealed common genomic regions of 2 Mb on bovine chromosome 14 (BTA14) and 3 had a moderate effect that may contain causal variants for body weight at 6, 12, 18, and 24 months. This genomic region explained approximately 10% of the variance against total additive genetic variance and body weight heritability at 12, 18, and 24 months. BayesR identified the exact genomic region containing causal SNPs on BTA14, 3, and 22. However, the genetic variance explained by each chromosome or SNP was estimated to be very small compared to the total additive genetic variance. Causal SNPs for growth trait on BTA14 explained only 0.04% to 0.5% of the genetic variance Conclusion: Segregating mutations have a moderate effect on BTA14, 3, and 19; many other loci with small effects on growth traits at different ages were also identified.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.2 s.152
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• pp.139-147
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• 2007

The ultimate goal of structural design is to find the optimal design results which satisfies both safety and economy at the same time. Optimum design has been studied for the last several decades and is being studied. in this study, an optimum algorithm which is based on the genetic algorithm has been applied to the multi-object problem to obtain the optimum solutions which minimizes structural weight and construction cost of panel blocks in ship structures at the same time. Mathematical problems are dealt at first to justify the reliability of the present optimum algorithm. And then the present method has been applied to the panel block model which can be found in ship structures. From the present findings it has been seen that the present optimum algorithm can reasonably give the optimum design results.

• Steel and Composite Structures
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• v.15 no.5
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• pp.519-538
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• 2013

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.660-665
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• 2004

In this study, a new architecture and comprehensive design methodology of genetically optimized Multi-layer Fuzzy Neural Networks (gMFNN) are introduced and a series of numeric experiments are carried out. The gMFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gMFNN. The consequence part of the gMFNN is designed using PNN. The optimization of the FNN is realized with the aid of a standard back-propagation learning algorithm and genetic optimization. The development of the PNN dwells on the extended Group Method of Data Handling (GMDH) method and Genetic Algorithms (GAs). To evaluate the performance of the gMFNN, the models are experimented with the use of a numerical example.