• Journal of the Korean Society of Tobacco Science
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• v.13 no.2
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• pp.59-65
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• 1991

There was no a difference of genetic analysis among methods(means, joint scaling test, 3 Parameter model) . The magnitude of additive effects generally paralleled the magnitude of difference between parental means and appeared to be more independent from non-allelic interaction than did dominance effects, whereas the magnitude of dominance effects were inflated by non-allelic interaction. Additive effects were significant for all characteristics observed and it was a major effects in inheritance of number of leaves. Dominance effects were higher than additive effects for plant height, days to flower, flesh leaf weight per plant, curing rate, total alkaloid and total nitrogen.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2003.11a
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• pp.355-359
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• 2003

In this paper, an identification method for a first order dead time process is proposed. This method used the genetic algorithm for parameter identification of process. The proposed method gives a better identification result than the existing methods under step testing. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• Journal of Animal Science and Technology
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• v.48 no.6
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• pp.759-766
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• 2006

This study was conducted to investigate the genetic correlations among the traits used to select young bulls and proven bulls in Hanwoo Performance and Progeny Test Program in Korea. For the estimation of heritabilities and correlations among the growth traits of bulls and carcass traits of progeny steers, 2,532 records of performance tested bull calves and 1,819 records of progeny tested steers were collected from Livestock Improvement Main Center (LIMC), National Agricultural Cooperative Federation (NACF). Fixed effects of mixed model for each traits were selected by using stepwise regression analysis and prior values of variance components were estimated by MTDFREML. The prior values of variance components were estimated with pairwise 2 traits model followed by single trait analysis. The estimated heritability of backfat thickness(BF), dressing percentage(DP), loin-eye muscle area(LMA), marbling score(MS) and weight at 12 months(WT12) was 0.51, 0.32, 0.27, 0.33, 0.50 and 0.26, respectively. Genetic correlation of WT12 of bull calves with backfat thickness, carcass weight and loin-eye muscle area of steers was positive correlation as 0.05, 0.35 and 0.21, respectively. However genetic correlation of WT12 with DP and MS showed negative correlation as 󰠏0.09 and 󰠏0.27, respectively and these negative genetic correlations implies that bulls that may be superior in carcass traits can be lost at the first step of selection and current selection method should be modified to solve this problem.

• Journal of agriculture & life science
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• v.44 no.2
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• pp.17-28
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• 2010

This study looked into how much maternal genetic effect influenced on economic traits through estimation of genetic parameter and heritability over swine's economic traits by maternal animal model using GGP farm examination data of total 31,455 swine of Duroc species, Landrace species and Yorkshire species that were born between 2000 and 2008. As a result of significance test over each factor in surveyed all traits, high significance was approved in the effect of breed, gender, the date of swine's birth, the season swine's born, and difference in delivery in every trait (p<0.01). It is considered that it would be possible to get more efficient improvement effect provided correlation between additive genetic effect and maternal genetic effect as well as maternal genetic effect according to breed, traits, and improvement direction are properly considered as negative covariance existed between additive genetic distribution and maternal genetic distribution presumed for traits by each breed and their genetic relation also showed mostly strong negative correlation.

• Animal Bioscience
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• v.36 no.8
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• pp.1156-1166
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• 2023

Objective: Pork belly is a cut of meat with high worldwide demand. However, although the belly is comprised of multiple muscles and fat, unlike the loin muscle, research on their genetic parameters has yet to focus on a representative cut. To use swine breeding, it is necessary to estimate heritability against pork belly traits. Moreover, estimating genetic correlations is needed to identify genetic relationship among the traditional carcass and meat quality traits. This study sought to estimate the heritability of the carcass, belly, and their component traits, as well as the genetic correlations among them, to confirm whether these traits can be improved. Methods: A total of 543 Yorkshire pigs (406 castrated males and 137 females) from 49 sires and 244 dam were used in this study. To estimate genetic parameters, a total of 12 traits such as lean meat production ability, meat quality and pork belly traits were chosen. The heritabilities were estimated by using genome-wide efficient mixed model association software. The statistical model was selected so that farm, carcass weight, sex, and slaughter season were fixed effects. In addition, its genetic parameters were calculated via MTG2 software. Results: The heritability estimates for the 7th belly slice along the whole plate and its components were low to moderate (0.07±0.07 to 0.33±0.07). Moreover, the genetic correlations among the carcass and belly traits were moderate to high (0.28±0.20 to 0.99±0.31). Particularly, the rectus abdominis muscle exhibited a high absolute genetic correlation with the belly and meat quality (0.73±52 to 0.93±0.43). Conclusion: A moderate to high correlation coefficient was obtained based on the genetic parameters. The belly could be genetically improved to contain a larger proportion of muscle regardless of lean meat production ability.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.6
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• pp.600-603
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• 2009

Genetic and phenotypic parameter estimates for measurement traits were obtained from pacific oyster Crassostrea gigas at nine months old. For the growth-related traits among nine months old pacific oyster, heritabilities of shell length, shell height, shell width, total weight, body weight and shell weight were estimated as 0.4855, 0.5248, 0.0884, 0.7236, 0.7726 and 0.6957, respectively. Genetic correlations among the growth-related traits of pacific oyster at nines month old, shell length, shell height, shell width, total weight, body weight, shell weight were showing highly positive correlations. Breeding value on growth-related traits of pacific oyster at nine months old were estimated as shell length -7.044-11.870, shell height -11.380-18.370, shell width -1.234-2.831, total weight -8.339-17.140, body weight -1.813-3.507 and shell weight -4.422-8.837. The results show that there is quite substantial additive genetic variance for measurement traits in pacific oyster that can be exploited through selective breeding.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.3
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• pp.1-8
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• 2009

The objectives of this study are to apply univariate search and genetic algorithm to tank model, and compare the two optimization methods. Hydrologic data of Baran watershed during 1996 and 1997 were used for correction the tank model, and the data of 1999 to 2000 were used for validation. RMSE and R2 were used for the tank model's optimization. Genetic algorithm showed better result than univariate search. Genetic algorithm converges to general optima, and more population of potential solution made better result. Univariate search was easy to apply and simple but had a problem of convergence to local optima, and the problem was not solved although search the solution more minutely. Therefore, this study recommend genetic algorithm to optimize tank model rather than univariate search.