• Journal of Animal Science and Technology
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• 제65권6호
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• pp.1180-1193
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• 2023

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.

• 동력기계공학회지
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• 제17권5호
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• Asian-Australasian Journal of Animal Sciences
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• 제12권7호
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• pp.1015-1018
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• 1999

Single trait mixed models have been dominantly utilized for genetic evaluation of the reproductive traits in swine. However employing multiple trait approach may lead to more accurate genetic evaluations. For 5 litter size and litter weight traits of Danish Landrace, genetic parameters were estimated with a multiple trait mixed model. The heritability estimates were 0.02, 0.03, 0.03, 0.05, and 0.07, respectively for litter size at birth, litter size born alive, litter weight at birth, litter size at weaning, and litter weight at weaning. Negative genetic correlations were all positive. The litter weight at birth showed genetic antagonism with litter size born alive (-0.65) and litter size at weaning (-0.31), but positive with litter size at birth (0.47) and litter weight at weaning (0.31). The estimates of environmental correlations were larger than their corresponding genetic correlation estimates except for those between litter weight at birth and the other four traits. This study recommends simultaneous selection for two or more traits with multivariate mixed models in order to improve overall economic response.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제27회 추계학술대회논문집
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• pp.285-288
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• 2006

유전자 알고리즘은 자연선택과 유전법칙을 적용하여 최적해를 탐색하는 방법으로, 본 연구에서 항공기용 가스터빈 엔진의 결함 진단을 위한 학습 알고리즘으로 사용되었다. 성능 저하를 고려한 구성 요소는 압축기, 가스발생기 터빈, 동력 터빈이며, 설계점에서 엔진의 구성 요소에 대하여 복합 결함 진단을 수행하였다. 그 결과, 압축기, 가스발생기터빈, 동력터빈모두 10% 이내의 오차로 예측됨을 확인하였다.

• Journal of Forest and Environmental Science
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• 제32권2호
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• pp.103-112
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• 2016

To compare seed components of plus trees, seed powder ground after seedcoat removal was analyzed for two oak species, i. e., Quercus monglica (white oak) and Quercus variabilis (red oak), which are typical oak trees in Korea but have different fruiting characteristics. Thus we aimed at analyzing and comparing many ingredients including minerals, sugars, etc. Two species were similar to each other in the content of water, crude ash, crude protein and carbohydrates, but crude lipid content in Q. variabilis was 2.5 times higher than that in Q. mongolica. Crude proteins of Clone 124 was 1.5 times higher than that of Clone 75 in Q. mongolica. Crude lipid content showed the highest value in Clone 0511 of Q. variabilis, and more phosphate and iron was found in Q. monglica than in Q. variabilis. Glucose showed 85.4% and 88.3% on average of the total monosacchrides in two species, and galactose and arabinose were also found. In the content of phosphate, iron, and crude lipid, differences were found between two species and among clones of two species.

• 산업경영시스템학회지
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• 제33권2호
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• 한국정보처리학회논문지
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• 제6권2호
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• pp.326-334
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• 1999

컴퓨터 시스템의 성능을 분석하고 평가하는 방법에는 결함허용(fault tolerance)과 결함회피(fault avoidance) 기법이 있다. 소프트웨어 신뢰성을 향상시키기 위하여 소프트웨어 결함허용 기법 중에서 가장 객관적이고 정량적으로 평가받는 것이 NVP(N-version Programming)기법이다. 이 기법에서 신뢰도를 추정하는 모델로 이항분포를 사용하는데 이 추정 모델은 각 컴포넌트의 신뢰도의 값들이 동일하다는 한계점이 있었다. 본 논문에서는 기존모델의 문제점을 해결하기 위하여 GA (Genetic Algorithms)를 적용하였다. GA를 적용하여 최적화 시뮬레이터를 구현하고 시뮬레이션을 수행해서 비교 분석 및 평가하였다. 그 결과 전체 시스템의 신뢰도를 일정 수준 이상 유지하면서 각 컴포넌트 신뢰도를 최적화 할 수 있었고, 도한 시스템 신뢰도에 가장 적합한 최적의 수를 추정할 수 있었다.

• Smart Structures and Systems
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• 제15권1호
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3242-3249
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• 2022

As Kori unit 1 is undergoing the decommissioning process, estimating the disposal amount of waste from the decommissioned nuclear reactor has become one of the challenging issues. Since the waste disposal amount estimation depends on the packing of the waste, it is highly desirable to optimize the waste packing plan. In this study, we developed an efficient scheme for packing waste component segments. The scheme consists of 1) preparing three-dimensional models of segments, 2) orienting each segment in such a way to minimize the bounding box volume, and 3) applying hybrid genetic algorithm to pack the segments in the disposal containers. When the packing solution converges in the algorithm, it comes up with the number of containers used and the placement of segments in each container. The scheme was applied to Kori-1 reactor pressure vessel. The required number of containers calculated by the developed scheme was 24 compared to 42 that was the estimation of the prior packing plan, resulting in disposal volume savings by more than 40%. The developed method is flexible for applications to various packing problems with waste segments from different cutting options and different sizes of containers.

• Fisheries and Aquatic Sciences
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• 제14권4호
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• pp.295-301
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• 2011

We investigated the genetic structure of Korean and Japanese ayu Plecoglossus altivelis populations by examining 669 individuals from 14 populations using three microsatellite loci. Genetic variation did not differ significantly among the populations examined in terms of allelic number and heterozygosity. Korean populations were genetically close to each other, implying that persistent gene flow has occurred in these populations. This suggests that eastern populations in Korea form a single large population and all of the Korean populations are distinct from the Japanese populations. Pairwise population $F_{ST}$ estimates, principal component analyses, and a neighbor-joining tree showed that genetic separation between the southern and pooled eastern coast populations was probably influenced by restricted gene flow. Hierarchical analysis of molecular variance (AMOVA) revealed a weak but significant genetic structure among three ayu groups (eastern and southern coasts of Korea and the Japan coast), and no genetic variation within groups. The estimated genetic population structure and potential applications of microsatellite markers may aid in the proper management of ayu populations.