• Asian-Australasian Journal of Animal Sciences
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• v.29 no.12
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• pp.1696-1701
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• 2016

Japanese quail is still used as a model for poultry research because of their usefulness as laying, meat, and laboratory animals. Microsatellite markers are the most widely used molecular markers, due to their relative ease of scoring and high levels of polymorphism. The objective of the research was to determine genetic diversity and population genetic structures of selected Japanese quail lines (high body weight 1 [HBW1], HBW2, low body weight [LBW], and layer [L]) throughout 15th generations and an unselected control (C). A total of 69 individuals from five quail lines were genotyped by fifteen microsatellite markers. When analyzed profiles of the markers the observed ($H_o$) and expected ($H_e$) heterozygosity ranged from 0.04 (GUJ0027) to 0.64 (GUJ0087) and 0.21 (GUJ0027) to 0.84 (GUJ0037), respectively. Also, $H_o$ and $H_e$ were separated from 0.30 (L and LBW) to 0.33 (C and HBW2) and from 0.52 (HBW2) to 0.58 (L and LBW), respectively. The mean polymorphic information content (PIC) ranged from 0.46 (HBW2) to 0.52 (L). Approximately half of the markers were informative ($PIC{\geq}0.50$). Genetic distances were calculated from 0.09 (HBW1 and HBW2) to 0.33 (C and L). Phylogenetic dendrogram showed that the quail lines were clearly defined by the microsatellite markers used here. Bayesian model-based clustering supported the results from the phylogenetic tree. These results reflect that the set of studied markers can be used effectively to capture the magnitude of genetic variability in selected Japanese quail lines. Also, to identify markers and alleles which are specific to the divergence lines, further generations of selection are required.

• Journal of Animal Science and Technology
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• v.57 no.5
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• pp.19.1-19.6
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• 2015

The objective of this study was to estimate variance components and genetic parameters for growth curve parameters in Guilan sheep. Studied traits were parameters of Brody growth model which included A (asymptotic mature weight), B (initial animal weight) and K (maturation rate). The data set and pedigree information used in this study were obtained from the Agricultural Organization of Guilan province (Rasht, Iran) and comprised 8647 growth curve records of lambs from birth to 240 days of age during 1994 to 2014. Marginal posterior distributions of parameters and variance components were estimated using TM program. The Gibbs sampler was run 300000 rounds and the first 60000 rounds were discarded as a burn-in period. Posterior mean estimates of direct heritabilities for A, B and K were 0.39, 0.23 and 0.039, respectively. Estimates of direct genetic correlation between growth curve parameters were 0.57, 0.03 and -0.01 between A-B, A-K and B-K, respectively. Estimates of direct genetic trends for A, B and K were positive and their corresponding values were $0.014{\pm}0.003$ (P < 0.001), $0.0012{\pm}0.0009$ (P > 0.05) and $0.000002{\pm}0.0001$ (P > 0.05), respectively. Residual correlations between growth curve parameters varied form -0.52 (between A-K) to 0.48 (between A-B). Also, phenotypic correlations between growth curve parameters varied form -0.49 (between A-K) to 0.47 (between A-B). The results of this study indicated that improvement of growth curve parameters of Guilan sheep seems feasible in selection programs. It is worthwhile to develop a selection strategy to obtain an appropriate shape of growth curve through changing genetically the parameters of growth model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.133-144
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• 2006

In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.

• Journal of Animal Science and Technology
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• v.58 no.10
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• pp.38.1-38.6
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• 2016

Background: Despite the importance of relationships between somatic cell score (SCS) and currently selected traits (milk, fat and protein yield) of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013. Results: Marginal posterior means of heritability estimates for SCS ($0.03{\pm}0.002$) were distinctly lower than those for milk ($0.204{\pm}0.006$), fat ($0.096{\pm}0.004$) and protein ($0.147{\pm}0.005$) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative ($-0.177{\pm}0.007$, $-0.165{\pm}0.008$ and $-0.152{\pm}0.007$ between SCS and milk, fat, and protein yield, respectively), slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from $0.01{\pm}0.039$ to $0.11{\pm}0.036$. This genetic opposition was distinctly higher for protein than for fat. Conclusion: Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS (i.e., single-trait selection for increased milk production will also increase SCS).

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1169-1182
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• 2016

Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1269-1276
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• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1773-1795
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• 2020

Unmanned aerial vehicles (UAVs) network are a very vibrant research area nowadays. They have many military and civil applications. Limited bandwidth, the high mobility and secure communication of micro UAVs represent their three main problems. In this paper, we try to address these problems by means of secure clustering, and a security clustering algorithm based on integrated trust value for UAVs network is proposed. First, an improved the k-means++ algorithm is presented to determine the optimal number of clusters by the network bandwidth parameter, which ensures the optimal use of network bandwidth. Second, we considered variables representing the link expiration time to improve node clustering, and used the integrated trust value to rapidly detect malicious nodes and establish a head list. Node clustering reduce impact of high mobility and head list enhance the security of clustering algorithm. Finally, combined the remaining energy ratio, relative mobility, and the relative degrees of the nodes to select the best cluster head. The results of a simulation showed that the proposed clustering algorithm incurred a smaller computational load and higher network security.