• 한국경영과학회지
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• 제17권3호
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• pp.59-65
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• 1992

A modified primal-dual affine scaling algorithm for linear programming is presented. This modified algorithm generates an elipsoid containing all optimal dual solutions at each iteration, then checks whether or not a dual hyperplane intersects this ellipsoid. If the dual hyperplane has no intersection with this ellipsoid, its corresponding column must be optimal nonbasic. By condensing these columns, the size of LP problem can be reduced.

• Nuclear Engineering and Technology
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• 제34권5호
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• pp.421-432
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• 2002

As one of the advanced design features of the APR1400, direct vessel injection (DVI) system is being considered instead of conventional cold leg injection (CLI) system. It is known that the DVI system greatly enhances the reliability of the emergency core cooling (ECC) system. However, there is still a dispute on its performance in terms of water delivery to the reactor core during the reflood phase of a large-break loss-of-coolant accident (LOCA). Thus, experimental validation is under progress. In this paper, test results of direct ECC bypass performed in the steam-water test facility tailed MIDAS (Multi-dimensional Investigation in Downcomer Annulus Simulation) are presented. The test condition is determined, based on the preliminary analysis of TRAC code, by applying the ‘modified linear scaling method’with the l/4.93 length scale . From the tests, ECC direct bypass fraction, steam condensation rate and information on the flow distribution in the upper annulus downcomer region are obtained.

• 한국해양공학회지
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• 제18권2호
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• pp.77-82
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• 2004

The purpose of this study is to develop a nesting algorithm, using a genetic algorithm to optimize nesting order, and modified No Fit Polygon(NFP) methodology to place parts with the order generated from the previous genetic algorithm. Various genetic algorithm techniques, which have thus far been applied to the Travelling Salesman Problem, were tested. The partially mapped crossover method, the inversion method for mutation, the elitist strategy, and the linear scaling method of fitness value were selected to optimize the nesting order. A modified NFP methodology, with improved searching capability for non-convex polygon, was applied repeatedly to the placement of parts according to the order generated from previous genetic algorithm. Modified NFP, combined with the genetic algorithms that have been proven in TSP, were applied to the nesting problem. For two example cases, the combined nesting algorithm, proposed in this study, shows better results than that from previous studies.

• Structural Engineering and Mechanics
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• 제35권4호
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• pp.431-457
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• 2010

This article provides a discussion of the mathematic modeling of connections for designing and qualifying structures, systems, and components subject to monotonic or cyclic loading. To characterize the force-deformation behavior of connections under monotonic loading, a review of the Ramberg-Osgood, Richard-Abbott, and Menegotto-Pinto models is conducted, and it is shown that these nonlinear functions can be mathematically derived by scaling up or down a linear force-deformation function. A generalized four-parameter model for simulating connection behavior is investigated to facilitate nonlinear regression analysis. In order to perform seismic analysis of frameworks, a hysteretic model accounting for loading, unloading, and reloading is described using the established monotonic model. For preliminary analysis, a method is provided to quickly determine the model parameters that fit approximately with the observed data. To reach more accurate values of the parameters, the methods of nonlinear regression analysis are investigated and the modified Levenberg-Marquardt and separable nonlinear least-square algorithms are applied in determining the model parameters. Example case studies illustrate the procedure for the computation through the use of experimental/analytical data taken form the literature. Transformation of connection curves from the three-parameter model to the four-parameter model for structural analysis is conducted based on the modeling of connections subject to fire.

• Asian-Australasian Journal of Animal Sciences
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• 제32권12호
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• pp.1836-1843
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• 2019

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.