• Communications of Mathematical Education
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• v.23 no.2
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• pp.349-381
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• 2009

The purpose of this study is to propose issues in selecting gifted children in mathematics by in-depth analysis of the selection process and items. In order to accomplish the purpose, the rate of right and wrong answers were examined based on the reaction of the students by 1st, 2nd and 3rd selection test. Also, the types of the errors were identified for the 2nd and 3rd selection test. According to the study results, the rate of right answers was low in short response questions and essay questions rather than in multiple-choice questions. In addition, the academic achievements were lower in the fields other than number & operations and logic. The conclusion of this study is that following studies regarding selection of gifted children are required linked with the project tasks and programs.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.386-393
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• 2012

Portfolio selection methods based on stochastic receding horizon approach, which were recently reported in the field of financial engineering, can explicitly consider the dynamic characteristics of wealth evolution and various constraints in the process of performing optimal portfolio selection. In view of the theoretical value, versatility, and effectiveness that receding horizon approach has achieved in many engineering problems, dynamic portfolio selection methods based on stochastic receding horizon optimization technique have the possibility of becoming an important breakthrough. This paper observes through theoretical investigations that the SDP(semi-definite program)-based portfolio selection procedure can be simplified, and has obtained meaningful performance on returns from simulation studies applying the simplified version to Korean financial markets.

• Journal of Animal Science and Technology
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• v.56 no.7
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• pp.23.1-23.7
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• 2014

Background: Scanning of the genome for selection signatures between breeds may play important role in understanding the underlie causes for observable phenotypic variations. The discovery of high density single nucleotide polymorphisms (SNPs) provide a useful starting point to perform genome-wide scan in pig populations in order to identify loci/candidate genes underlie phenotypic variation in pig breeds and facilitate genetic improvement programs. However, prior to this study genomic region under selection in commercially selected Berkshire and Korean native pig breeds has never been detected using high density SNP markers. To this end, we have genotyped 45 animals using Porcine SNP60 chip to detect selection signatures in the genome of the two breeds by using the $F_{ST}$ approach. Results: In the comparison of Berkshire and KNP breeds using the FDIST approach, a total of 1108 outlier loci (3.48%) were significantly different from zero at 99% confidence level with 870 of the outlier SNPs displaying high level of genetic differentiation ($F_{ST}{\geq}0.490$). The identified candidate genes were involved in a wide array of biological processes and molecular functions. Results revealed that 19 candidate genes were enriched in phosphate metabolism (GO: 0006796; ADCK1, ACYP1, CAMK2D, CDK13, CDK13, ERN1, GALK2, INPP1; MAK, MAP2K5, MAP3K1, MAPK14, P14KB, PIK3C3, PRKC1, PTPRK, RNASEL, THBS1, BRAF, VRK1). We have identified a set of candidate genes under selection and have known to be involved in growth, size and pork quality (CART, AGL, CF7L2, MAP2K5, DLK1, GLI3, CA3 and MC3R), ear morphology and size (HMGA2 and SOX5) stress response (ATF2, MSRB3, TMTC3 and SCAF8) and immune response (HCST and RYR1). Conclusions: Some of the genes may be used to facilitate genetic improvement programs. Our results also provide insights for better understanding of the process and influence of breed development on the pattern of genetic variations.

• KNOM Review
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• v.21 no.2
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• pp.35-45
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• 2018

As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

• The Journal of Information Systems
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• v.28 no.2
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• pp.25-52
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• 2019

Purpose Despite the recent increase in the use of selection tools using artificial intelligence (AI), far less is known about the effectiveness of them in recruitment and selection research. Design/methodology/approach This paper tests the impact of AI-based initial screening and interview on intentions to apply. We also examine the moderating role of individual difference (i.e., reliability on technology) in the relationship. Findings Using policy-capturing with undergraduate students at a large university in South Korea, this study showed that AI-based interview has a negative effect on intentions to apply, where AI-based initial screening has no effect. These results suggest that applicants may have a negative feeling of AI-based interview, but they may not AI-based initial screening. In other words, AI-based interview can reduce application rates, but AI-based screening not. Results also indicated that the relationship between AI-based initial screening and intentions to apply is moderated by the level of applicant's reliability on technology. Specifically, respondents with high levels of reliability are more likely than those with low levels of reliability to apply for firms using AI-based initial screening. However, the moderating role of reliability was not significant in the relationship between the AI interview and the applying intention. Employing uncertainty reduction theory, this study indicated that the relationship between AI-based selection tools and intentions to apply is dynamic, suggesting that organizations should carefully manage their AI-based selection techniques throughout the recruitment and selection process.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.134-143
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• 2009

In this paper, we propose a new relay selection algorithm for a two-way multiple relay channel. In the two-way multiple relay channel, two users exchange information with each other via multiple relays. The relays use a decode-and-forward or amplify-and-forward protocol, and exploit the combining process of the received packets to reduce the required channel resources. In the multiple relay network, diversity gain is achieved as the number of relays increases, and various schemes are proposed. In this paper, we propose a single best relay selection scheme based on instantaneous channel conditions. First of all, relays obtain the instantaneous channel state information in the handshaking process, and a single best relay is selected in a distributed methods prior to data transmissions. The relay selection metric is proposed so that the end-to-end channel condition is evaluated based on the intantaneous channel state informations. Simulation results show that the proposed relay selection algorithm achieve the increased throughput and diversity order when the number of potential relays is increased.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Clean Technology
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• v.3 no.2
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• pp.13-24
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• 1997

The present paper aims to give basic information to establish zero emission material cycle including the minimization of emissions from industrial production processes and the area in regional scale. Strategies and methodologies to analyze emissions from the production processes and our human activities and to reduce those emissions by refining and/or replacing the unit process with the alternatives are introduced as well. Quantitative evaluation and management systems of any raw materials and the production process are from vie points of treatment are essential. Estabiishment of a process networking for the recycle of discharged non-products materials by the intra-process, trans-process and the trans-industries are proposed. Procedures and priorities to formulate industrial and regional zero emission system are proposed as well.

• Journal of Information Technology Services
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• v.10 no.4
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• pp.295-307
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• 2011

The purpose of this research is to find a better knowledge service R&D management process. There are four basic characteristics in service : intangibility, inseparability, inconsistency, and no inventory. These intrinsic characteristics require us to change the traditional manufacturing sector based R&D management process. It is necessary to develop an effective knowledge service R&D management model to improve the competitiveness of the service industry and Korean economy. A model case knowledge service R&D has been performed to find a better R&D management model. The whole cycle of R&D process, such as planning, selection, performing, and evaluation has been reviewed by real experience. Several important aspects of R&D process are compared between manufacturing R&D and service R&D. A knowledge service R&D management framework has been suggested. The results of this research can be used for building a better service R&D management process.

• Transactions of Materials Processing
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• v.7 no.2
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• pp.127-138
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• 1998

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network) has been considered. The investigated problem involves the ade-quate selection of the drawing die angle and the correspondent reduction rate in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite ele-ment simulation are selected by using the orthogonal array. Also the orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study it is shown that the application of new technique using ANN and Othogonal array table to the process design of metal forming process is useful method.