• Asian-Australasian Journal of Animal Sciences
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• 제5권4호
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• pp.623-628
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• 1992

Records on weaning (3803) and yearling weight (2990) of beef cattle (Bibos banteng) from the Bali Cattle Improvement Project were examined. A mixed model analysis involving all main non-genetic effects (village, year of birth, season of birth, age of dam, sex of calf, all significant interactions and age at weighing as a covariate) as fixed effects and sire nested within village as a random effect was undertaken. Variance components were estimated by Henderson's Method III. Paternal half-sib components of variance and covariance were used to estimate heritabilities of weaning and yearling weights, as well as their genetic and phenotypic correlations. Heritability estimates ($\pm$ standard error) obtained by Henderson's Method III for weaning and yearling weights were $.11{\pm}.03$ and $.13{\pm}.04$, respectively while the phenotypic and genetic correlations were estimated as .32 and $.64{\pm}.10$, respectively. The parameters estimated in this study were at the lower end of the range of reported values from various breeds. It is concluded that further information should be gathered to assist in estimating genetic parameters for other economic traits of Bali beef cattle and to provide more accurate estimates for weaning and yearling weights. These parameters should then be used to formulate a selection program to enable the genetic improvement of Bali Beef cattle.

• 한국음향학회지
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• 제36권3호
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• pp.218-227
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• 2017

본 논문에서는 배열센서에서 DOA(Direction Of Arrival)를 수행하는 경우 크래머 라오 하한을 이용하여 표적신호가 수신되는 방위오차의 최소분산을 계산하고, 탐지 방위오차 및 위치추정 거리오차를 추정하는 방안을 제시한다. 신호 대 잡음비는 DOA의 정확도 즉, 표적의 탐지 방위오차 및 위치추정 거리오차를 결정한다. 일반적으로 신호대 잡음비는 음원준위, 소음준위, 전달손실, 배열센서의 형상, 빔 조향 방위에 따라 달라진다. 표적의 공간상 상대적 위치와 소음준위가 달라지는 경우, 신호 대 잡음비의 변화에 따른 탐지 방위오차 및 위치추정 거리오차를 확률적으로 추정하는 몬테카를로 시뮬레이션을 수행함으로써, 제안된 방법을 검증하였다.

• 응용통계연구
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• 제27권6호
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• pp.991-1002
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• 2014

Greene (2004a,b), Lee와 Choi (2014) 등의 연구에서 토빗 회귀모형의 최대가능도 추정은 표준오차를 과소추정한다는 것이 알려졌고, 그 원인의 하나는 오차항 분산의 과소 추정에 있다고 한다. 오차항 분산의 과소 추정은 회귀계수에 대한 가설 검정 및 구간추정에 영향을 미칠 뿐 아니라 독립변수들의 주변효과를 추정하는데에도 영향을 미치게 되므로 토빗 회귀모형에 대한 적절한 분석이 수행되려면 최대가능도 추정의 과소 추정 문제를 해결하여야 한다. 일반적으로 무정보 사전분포에 의한 베이지안 추론 방법은 빈도학파들이 요구하는 효율성을 갖는 경우가 많다. 본 연구에서도 무정보 사전분포에 의한 베이지안 추론을 적용하여, 베이지안 방법론이 SUR 토빗 회귀모형에서 최대가능도 추정의 과소 추정 문제를 해결할 수 있는 하나의 대안이 될 수 있다는 것을 보였다.

• 한국음향학회지
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• 제19권7호
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• pp.34-41
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• 2000

본 논문에서는 장거리 통신시스템의 적응 반향제거 기에서 퍼지제어 시스템에 기초한 새로운 동시통화 검출방법을 제안하였다. 이 방법에서, 동시통화 검출을 위한 퍼지추론의 두 입력 변수는 근단 신호와 실제 반향 신호가 더해진 소요 신호와 에러 신호 사이의 상호상관계수와 소요 신호와 추정된 신호 사이의 상호상관계수를 사용하였다. 퍼지 제어기에서 사다리꼴 소속함수로 퍼지화하고 if-then 추론규칙을 이용하여 max-min 합성하였으며, 합성된 결과를 무게중심법에 의하여 디퍼지화한 값으로 동시통화와 반향경로 변화 그리고 동시통화시 반향경로 변화를 검출하도록 하였다. 퍼지 동시통화 검출기는 기존의 알고리듬보다 동시통화와 반향경로 변화를 추정할 수 있었으며 동시통화시에 반향경로 변화에도 좋은 성능을 보였다.

• 국제지역연구
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• 제18권3호
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• pp.175-201
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• 2014

본 논문은 연구개발투자의 지방경제에 대한 효과와 관련한 논문이다. 연구개발투자는 구체적으로 피투자자의 고용증대와 생산성 증대를 통해 지역경제에 기여한다. 투자를 받은 기업과 기관(피투자자)은 증가된 생산성과 매출액, 고용증가로 만족할 수 있다. R&D 차원에서는 중앙정부 R&D 펀드나 기업의 자체투자 등이 해당된다. 혁신은 기업 내에서만 존재하는 것이 아니라 regional innovation도 연구대상이며 연구개발투자가 한 지역에서 중앙정부 펀드건 기업체 자금이건 진행되면 지역 내 연구 인력, 연구기관 등이 작동되고 성과로 지역 내에 특허, 지적 재산권 등이 증가될 것으로 가정할 수 있다. 좀 더 진전된 긍정적인 효과는 지역산업과 내재적인 관계에서 출발한다. 이 연구는 한국의 panel 데이터를 사용하여 연구개발투자의 지방경제에 대한 효과와 관련한 실증분석 사항이다. Lag 종속변수를 가진 Autoregressive 모형을 통해 Dynamic Panel 추정치가 구해졌으며, Da Silva 방법을 사용하여 혼합된 Variance-component moving average error process가 추정되었다. 연구개발투자는 지역의 생산성을 향상시키는 데 매우 중요한 요소이며 효과의 크기는 한국경제 역사에서 기간에 크게 의존한다.

• 아태비즈니스연구
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• 제13권3호
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• pp.183-208
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• 2022

Purpose - The main purpose of this study is to widely investigate the impact of recent pandemic crises on the synchronization of the world capital markets through 25 stock indices from major developed countries. Design/methodology/approach - This study collects 25 stock indices from major developed countries and the time period is between January 5, 2001 and February 24, 2022. The data sets used in the study include finance.yahoo.com and Investing.com.. The Granger causality analysis, unit-root test, VAR analysis, and forecasting error variance decomposition were hired in order to analyze the data. Findings - First, there are significant inter-relations among 25 countries around recent major pandemic crises(such as SARS, A(H1N1), MERS, and COVID19), which is consistent result with previous literature. Second, COVID19 shows much stronger impact on the world-wide synchronization than other pandemics. Third, the return volatility of each stock market varies, unit root tests show that daily stock index data are unstable while daily stock index returns are stable, and VAR(Vector Auto Regression) analyses presents significant inter-relations among 25 capital markets. Fourth, from the impulse response function analyses, we find that each market affects the other markets for short term periods, about 2~4 days, and no long term effect was not found. Fifth, Granger causality tests show one-side or two-sides synchronization between capital markets and we estimate, through forecasting error variance decomposition method, that the explanatory portions of each capital market on other markets vary from 10 to 80%. Research implications or Originality - The above results all together show that pandemic crises have strong effects on the synchronization of world capital markets and imply that these synchronizations should be carefully considered both in the investment decisions by individual investors and in the financial and economic policies by governments.

• 한국운동역학회지
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• 제28권2호
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• pp.127-134
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• 2018

Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

• Animal Bioscience
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• 제34권7호
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• pp.1100-1104
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• 2021

Objective: The objective of present study was to estimate heritability of non-return rate (NRR) and success of first insemination (SFI) by using the Bayesian approach with Gibbs sampling. Methods: Heifer Traits were denoted as NRR-h and SFI-h, and cow traits as NRR-c and SFI-c. The variance covariance components were estimated using threshold model under Bayesian procedures THRGIBBS1F90. Results: The SFI was more relevant to evaluating success of insemination because a high percentage of animals that demonstrated no return did not successfully conceive in NRR. Estimated heritability of NRR and SFI in heifers were 0.032 and 0.039 and the corresponding estimates for cows were 0.020 and 0.027. The model showed low values of Geweke (p-value ranging between 0.012 and 0.018) and a low Monte Carlo chain error, indicating that the amount of a posteriori for the heritability estimate was valid for binary traits. Genetic correlation between the same traits among heifers and cows by using the two-trait threshold model were low, 0.485 and 0.591 for NRR and SFI, respectively. High genetic correlations were observed between NRR-h and SFI-h (0.922) and between NRR-c and SFI-c (0.954). Conclusion: SFI showed slightly higher heritability than NRR but the two traits are genetically correlated. Based on this result, both two could be used for early indicator for evaluate the capacity of cows to conceive.

• ETRI Journal
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• 제34권4호
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• pp.511-517
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• 2012

The best linear unbiased estimator (BLUE) is most suitable for practical application and can be determined with knowledge of only the first and second moments of the probability density function. Although the BLUE is an existing algorithm, it is still largely unexplored and has not yet been applied to channel estimation in amplify and forward (AF)-based wireless relay networks (WRNs). In this paper, a BLUE-based algorithm is proposed to estimate the overall channel impulse response between the source and destination of AF strategy-based WRNs. Theoretical mean square error (MSE) performance for the BLUE is derived to show the accuracy of the proposed channel estimation algorithm. In addition, the Cram$\acute{e}$r-Rao lower bound (CRLB) is derived to validate the MSE performance. The proposed BLUE channel estimation algorithm approaches the CRLB as the length of the training sequence and number of relays increases. Further, the BLUE performs better than the linear minimum MSE estimator due to the minimum variance characteristic exhibited by the BLUE, which happens to be a function of signal-to-noise ratio.

• Computers and Concrete
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• 제16권5호
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.