• 대한조선학회논문집
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• 제34권1호
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• pp.41-49
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• 1997

작은 파 기울기(Ak=0.01)를 갖는 Stokes 파/평판의 경계층 및 반류에 대하여 엄밀 및 근사 자유표변 경계조건을 적용하여 Navier-Stokes 식을 수치해석하였다. 거시유동에서는 외부 흐름 압력 변화가 유선방향의 속도 성분을 가속 혹은 감속시키며 교차 유동의 방향 변화를 야기시킨다. 특히 반류는 역(逆)압력기울기 영역에서보다 순(順)압력기울기 영역에서 그 반응이 크며 정수 중에서의 값을 평균치로 하지 않고 편향되는 반류 편향 특성을 갖는다. 미시유동에서는 자유표면 경계조건이 경계층 및 근접/중간 반류영역에서 큰 영향을 미친다. 차수크기 추정은 계산 결과와 잘 일치하였으며 근사 자유표변 경계조건은 상당한 오차를 유발시킴을 알 수 있다.

• 패션비즈니스
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• 제16권2호
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• pp.151-162
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• 2012

The industry-wide development of digital technologies has also affected the textile and fashion industries immensely. The applications of 3D technology, virtual reality, and/or augmented reality systems have helped to create novel fashion brands based on the marriage of IT and textile/fashion industries. 3D digital virtual clothing systems have been developed to help the textile and fashion industries in terms of the planning, manufacturing, marketing and sales sectors. So far, most of the development effort for the 3d virtual clothing systems has been focused on the woven fabrics. The characteristics of woven fabrics differ from those of knitted fabric. Since the physical structures and mechanical properties of the knitted fabrics are definitely different from those of woven fabrics, the simulation process for the knitted fabrics should follow different approaches. The loops in a knitted fabric deform easily. The deformation results in a readily stretchable fabric appearance. Cloth simulation mostly employs models that approximate the mechanical properties of linear elastic planes. This simulation scheme does not, however, describe well enough the behavior of knitted fabrics, which deviate largely from the linear isotropic material characteristics. This study aims at characterizing the tensile deformation and surface textures of a knitted fabric product. Tensile deformation curves for the wale, course, and bias direction are analyzed. The surface texture of the knitted fabric is analyzed by using a 3-dimensional scanning device.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of Animal Science and Technology
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• 제65권4호
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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.