• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.108-119
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• 2015

This study intends to propose a non-oriented DEA based game cross-efficiency approach for supplier selection. With a discussion on the choice of DEA models and approaches that are most appropriate for supplier selection, we propose a game cross efficiency model based upon the non-oriented variable returns-to-scale RAM DEA by adapting the existing game cross efficiency model based upon the oriented constant returns-to-scale CCR DEA. We develop the RAM game cross efficiency model and a convergent iterative solution procedure to find the best game cross efficiency scores that constitute a Nash equilibrium. We illustrate the proposed model with two data sets of supplier selection, and demonstrate that significantly different results are obtained when compared with the existing approaches.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.320-326
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• 2016

In existing literature on multiple-input multiple-output (MIMO) relaying communication systems, antenna selection is often implemented by maximizing the channel capacity or the output single-to-noise ratio (SNR). In this paper, we propose an energy-efficient low-complexity antenna selection scheme for MIMO relaying communication systems. The proposed algorithm is based on beamforming and maximizing the Frobenius norm to jointly optimize the transmit power, number of active antennas, and antenna subsets at the source, relaying and destination. We maximize the energy efficiency between the link of source to relay and the link of relay to destination to obtain the maximum energy efficiency of the system, subject to the SNR constraint. Compared to existing antenna selection methods forMIMO relaying communication systems, simulation results demonstrate that the proposed method can save more power in term of energy efficiency, while having lower computational complexity.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.190-196
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• 1999

Continuation power flow has been developed to remove the ill-condition problem caused by singularity of power flow Jacobian at and near at steady-state voltage instability point in conventional power flow. Continuation power flow consists of predictor and corrector. In prddictor, the direction vector at the resent solution is caluculated and the initial guess of next solution is determined at the distance of step length. The selection of step length is a very important part, since computational speed and convergence performance are both greatly affected by the choice of the step length. This paper presents the practical step length selection algorithm using the reactive power generation sensitivith. In numulation, the proposed algorithm is compared with step length selection algorithm using TVI(tangent vector index).

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Korean Journal of Microbiology
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• v.50 no.1
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• pp.1-7
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• 2014

Site-specific incorporation of unnatural amino acids (SSIUA) into protein can be achieved in vivo by coexpression of an orthogonal pair of suppressor tRNA and engineered aminoacyl-tRNA synthetase (ARS) that specifically ligates an unnatural amino acid to the suppressor tRNA. As a step to develop the SSIUA technique in Escherichia coli, here we established a new 2-step screening system that can be used for selecting an ARS variant(s) that ligates an unnatural amino acid to a suppressor tRNA. A positive selection system consists of chloramphenicol acetyl transferase gene containing an amber mutation at the $27^{th}$ residue, and efficiently concentrated amber suppressible ARS with a maximum enrichment factor of $9.0{\times}10^5$. On the other hand, a negative selection system was constructed by adding multiple amber codons in front of a lethal gene encoding the control of cell death B toxin (ccdB) which acts as an inhibitory protein of bacterial topoisomerase II. Amber suppression of ccdB by an orthogonal pair of Saccharomyces cerevisiae tyrosyl-tRNA synthetase (TyrRS) and an amber suppressor tRNA significantly inhibits bacterial growth. This selection system was also able to efficiently remove amber suppressible ARS which could ligate natural amino acids to the suppressor tRNA. Thus, sequential combination of these two selection systems might be able to function as a powerful tool for selecting an ARS variant that specifically ligates an unnatural amino acid to the suppressor tRNA from an ARS mutant pool.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.640-645
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• 2016

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

• The Korean Journal of Zoology
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• v.26 no.2
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• pp.95-106
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• 1983

Selections for rapid and slow walking behavior were carried out with the populations, drived from Oregon-R and lethal free strain of Drosophila melanogaster. The behavior was measured by means of connected test-tube apparatus. The populations responded effectively to the artificial selection, and it reached the selection plateau after 7 generations. The realized heritability for the first 10 generations was estimated to be about $9\\sim14%$ for the rapid walking behavior, and those for slow walking behavior was about $11\\sim16%$. The results of hybridization analysis between selected populations at generations 8 and 10 indicated that some polygenes showing a slow walking behavior were partially dominant over polygenes controlled rapid trait. The populations selected for rapid and slow walking behavior were relaxed after 10 generations of selection. The response to natural selection of rapid population was completely returned to their neutral states after only 5 generations. Such phenomena would be explained by the genetic homeostasis resulted from an action of natural selection. However, the slow population did not make any difference from walking scores of their original artificial selection. It seems reasonable to assume that the slow walking behavior was possibly controlled by a major gene.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.162-164
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• 2007

This paper presents a HVDC site selection algorithm to increase transfer capability using VSC HVDC system which can control active power as well as reactive power. Using normal powerflow results and simple index $k_r$ the HVDC site selection algorithm is enhanced and more tightly-coupled transmission lines are identified in a domain of generators.

• Management Science and Financial Engineering
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• v.1 no.1
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• pp.67-90
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• 1995

In planning an assembly system, choosing the proper assembly sequence is one of the most important decisions because it significantly affects the costs associated with the assembly process. This paper deals with the selection of assembly sequences in flexible assembly systems. The selection criterion is the minimization of makespan to complete all assembly products. This problem is formulated as a "modified FAS scheduling problem" (MFASSP) and its scheduling procedure is described. The experimental results show that the procedure is very efficient for both quality of solution and computation time.

• Korean Management Science Review
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• v.13 no.2
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• pp.147-161
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• 1996

Due-date is an important factor in Flexible Manufacturing System scheduling. Even though most of researchers have focused part selection and loading problem using fixed due-date assignment rules, FMSs consist of multi-function machines which facilitate alternative processes. This research investigates interactions of three dispatching mechanisms, three NSS (Next Station Selection) rules and four due-date assignment rules using simulation. Both cost-based and time-based performance measures are considered in this research.