• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.247-256
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• 1999

In this paper, a methodology for optimal design of PID controller using the modified genetic algorithm has been proposed to improve the transient stability at system fault in HVDC transmission system, mathematical model preparation for stability analysis, and supplementary signal control by an optimal PID controller using the modified genetic algorithm(MGA). The propriety was verified through computer simulations regarding transient stability. It means that the application of MGA-PID controller in HVDC transmission system can contribute the propriety to the improvement of the transient stability in HVDC transmission system and the design of MGA-PID controller has been proved indispensible when applied to HVDC transmission system.

• Animal Bioscience
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• v.34 no.4
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• pp.471-481
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• 2021

Objective: A comprehensive study was conducted to study the effects of partition of variance on accuracy of genetic parameters and genetic trends of economic traits in Vanaraja male line/project directorate-1 (PD-1) chicken. Methods: Variance component analysis utilizing restricted maximum likelihood animal model was carried out with five generations data to delineate the population status, direct additive, maternal genetic, permanent environmental effects, besides genetic trends and performance of economic traits in PD-1 chickens. Genetic trend was estimated by regression of the estimated average breeding values (BV) on generations. Results: The body weight (BW) and shank length (SL) varied significantly (p≤0.01) among the generations, hatches and sexes. The least squares mean of SL at six weeks, the primary trait was 77.44±0.05 mm. All the production traits, viz., BWs, age at sexual maturity, egg production (EP) and egg weight were significantly influenced by generation. Model four with additive, maternal permanent environmental and residual effects was the best model for juvenile growth traits, except for zero-day BW. The heritability estimates for BW and SL at six weeks (SL6) were 0.20±0.03 and 0.17±0.03, respectively. The BV of SL6 in the population increased linearly from 0.03 to 3.62 mm due to selection. Genetic trend was significant (p≤0.05) for SL6, BW6, and production traits. The average genetic gain of EP40 for each generation was significant (p≤0.05) with an average increase of 0.38 eggs per generation. The average inbreeding coefficient was 0.02 in PD-1 line. Conclusion: The population was in ideal condition with negligible inbreeding and the selection was quite effective with significant genetic gains in each generation for primary trait of selection. The animal model minimized the over-estimation of genetic parameters and improved the accuracy of the BV, thus enabling the breeder to select the suitable breeding strategy for genetic improvement.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.720-734
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• 2023

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the M_e, the higher the accuracy of EBV.

• Korean Journal of Agricultural Science
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• v.42 no.3
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• pp.215-222
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• 2015

This paper sketches the strategies and designs for monitoring system of swine genetic improvement. The system should reflect every side of pig production. The system leads us to assess the efficiency of pig production and the scope of the system includes not only nucleus, multiplying and commercial herds, but also packing and processing sectors. For more accurate statistics, data for this monitoring system must be collected from all above mentioned areas, but not by random sampling. Futhermore, data analysis results including seedstocks and distribution information of genetic trend should be included in the system. The schema of knowledge database system could be employed in the system. The monitoring system in the final destination would unify the systems derived from various sources and provide any solution in swine industry including pig breeding.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.305-313
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• 2002

The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.9
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• pp.1225-1233
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• 2017

Objective: Calving ease (CE) is a complex reproductive trait of economic importance in dairy cattle. This study was aimed to investigate the genetic merits of CE for Holsteins in Korea. Methods: A total of 297,614 field records of CE, from 2000 to 2015, from first parity Holstein heifers were recorded initially. After necessary data pruning such as age at first calving (18 to 42 mo), gestation length, and presence of sire information, final datasets for CE consisted of 147,526 and 132,080 records for service sire calving ease (SCE) and daughter calving ease (DCE) evaluations, respectively. The CE categories were ordered and scores ranged from CE1 to CE5 (CE1, easy; CE2, slight assistance; CE3, moderate assistance; CE4, difficult calving; CE5, extreme difficulty calving). A linear transformation of CE score was obtained on each category using Snell procedure, and a scaling factor was applied to attain the spread between 0 (CE5) and 100% (CE1). A sire-maternal grandsire model analysis was performed using ASREML 3.0 software package. Results: The estimated direct heritability ($h^2$) from SCE and DCE evaluations were $0.11{\pm}0.01$ and $0.08{\pm}0.01$, respectively. Maternal $h^2$ estimates were $0.05{\pm}0.02$ and $0.04{\pm}0.01$ from SCE and DCE approaches, respectively. Estimates of genetic correlations between direct and maternal genetic components were $-0.68{\pm}0.09$ (SCE) and $-0.71{\pm}0.09$ (DCE). The average direct genetic effect increased over time, whereas average maternal effect was low and consistent. The estimated direct predicted transmitting ability (PTA) was desirable and increasing over time, but the maternal PTA was undesirable and decreasing. Conclusion: The evidence on sufficient genetic variances in this study could reflect a possible selection improvement over time regarding ease of calving. It is expected that the estimated genetic parameters could be a valuable resource to formulate sire selection and breeding plans which would be directed towards the reduction of calving difficulty in Korean Holsteins.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1813-1821
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• 2002

The aim of this study was to identify useful secondary traits for estimating genetic ability of milk production traits. We investigated the value of using plasma metabolites concentrations. Two hundred and nineteen cattle out of 271 had only milk production traits records (G1), 33 had only metabolites records (G2), and 19 had both milk production traits and metabolites records (G3). Fifty two calves with metabolites records (G2 and G3) were born from 1992 to 1997. Forty three calves (29 females, 14 males) were used from 10 to 90 d of age and the others (3 females, 6 males) from 10 to 60 d of age. A total of 566 records of milk yield, fat yield and protein yield for 240 to 305 d on 238 heads (G1 and G2) were collected The collected blood samples were divided into three age groups: AG1, 10 to 30 d; AG2, 40 to 60 d; and AG3, 70 to 90 d. Heritabilities of milk yield, fat yield and protein yield were $0.45{\pm}0.04$, $0.50{\pm}0.04$ and $0.38{\pm}0.04$, respectively. Heritability of plasma glucose concentration at AG1 was $0.45{\pm}0.08$. Genetic correlations between plasma glucose concentration and milk yield, fat yield and protein yield were -$0.35{\pm}0.28$, $0.64{\pm}0.24$ and $0.36{\pm}0.35$, respectively. When the plasma glucose concentration at AG1 was used to estimate genetic ability of these milk production traits, reliability of milk yield of animals without milk record increased 8.2%, fat yield increased 24.2% and protein yield increased 9.5%. Heritability of plasma total cholesterol concentration at AG3 was $0.83{\pm}0.04$. Genetic correlation between plasma total cholesterol concentration and milk yield, fat yield and protein yield were $0.58{\pm}0.21$, $0.42{\pm}0.20$ and $0.45{\pm}0.22$, respectively. When the plasma total cholesterol concentration at AG3 was using to estimate genetic ability of these milk production traits, reliability of milk yield of animals without milk record increased 19.0%, fat yield increased 9.6%, and protein yield increased 13.5%. The annual genetic gain is in proportion to the reliability of selection. These results show that the plasma metabolite concentrations would be useful for improvement of genetic ability for milk production traits in the genetic improvement in herd of cows, where half of the animals selected are from a herd without its own milk record.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.4
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• pp.336-346
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• 2013

Amaranth (Amaranthus sp. L.) is an important group of plants that includes grain, vegetable, and ornamental types. Centers of diversity for Amaranths are Central and South America, India, and South East Asia, with secondary centers of diversity in West and East Africa. The present study was performed to determine the genetic diversity and population structure of 75 amaranth accessions: 65 from South America and 10 from South Asia as controls using 14 SSR markers. Ninety-nine alleles were detected at an average of seven alleles per SSR locus. Model-based structure analysis revealed the presence of two subpopulations and 3 admixtures, which was consistent with clustering based on the genetic distance. The average major allele frequency and polymorphic information content (PIC) were 0.42 and 0.39, respectively. According to the model-based structure analysis based on genetic distance, 75 accessions (96%) were classified into two clusters, and only three accessions (4%) were admixtures. Cluster 1 had a higher allele number and PIC values than Cluster 2. Model-based structure analysis revealed the presence of two subpopulations and three admixtures in the 75 accessions. The results of this study provide effective information for future germplasm conservation and improvement programs in Amaranthus.