• Journal of Animal Science and Technology
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• v.44 no.6
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• pp.669-676
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• 2002

The efficiency of marker assisted selection(MAS) over conventional selection index based sorely on phenotypic records was studied by deterministic simulation model. Parameter combination of heritability and amount of genetic variation due to the markers included in the index was employed. For the index with own phenotypic information vs. the index with own phenotypic plus marker information, the relative efficiency of MAS over the selection with phenotypic records was about 38% high when heritability was low(0.05). However, when heritability was high(50%), the relative efficiency of MAS was vary low and almost negligible. For more practical situation of selection index which included information on own, sire and dam, MAS was less effective than when selection criteria was only on own performance.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.6-13
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• 2021

Cases and studies on the selection method of CTQ are relatively active, but there are few cases or studies on the selection method of CTP which is important in the device industry. In fact, many companies simply select and manage CTP from the point of contact based on their experience and intuition. The purpose of this study is to present an evaluation model and a mathematical decision model for rational and systematic CTP selection to improve the process quality of semiconductor equipment lines. In the evaluation model, AHP (Analytic Hierarchy Process) analysis technique was applied to show objective and quantitative figures, and Fuzzy decision-making model was used to solve the ambiguity and uncertainty in the decision-making process. Decision Value (DV) was presented. The subjects were 22 process factors managed in the Plating Process that the representative equipment line can do. As a result, the evaluation model proposed in this study can support more efficient and effective decision-making for process quality improvement by more objectively measuring the problem of subjective CTP selection in manufacturing sites.

• Progress in Superconductivity and Cryogenics
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• v.24 no.3
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• pp.19-23
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• 2022

We have been developing a magnetic separation device that can be used in low magnetic fields for paramagnetic materials. Magnetic separation of paramagnetic particles with a small particle size is desired for volume reduction of contaminated soil in Fukushima or separation of iron scale from water supply system in power plants. However, the implementation of the system has been difficult due to the needed magnetic fields is high for paramagnetic materials. This is because there was a problem in installing such a magnet in the site. Therefore, we have developed a magnetic separation system that combines a selection tube and magnetic separation that can separate small sized paramagnetic particles in a low magnetic field. The selection tube is a technique for classifying the suspended particles by utilizing the phenomenon that the suspended particles come to rest when the gravity acting on the particles and the drag force are balanced when the suspension is flowed upward. In the balanced condition, they can be captured with even small magnetic forces. In this study, we calculated the particle size of paramagnetic particles trapped in a selection tube in a high gradient magnetic field. As a result, the combination of the selection tube and HGMS (High Gradient Magnetic Separation-system) can separate small sized paramagnetic particles under low magnetic field with high efficiency, and this paper shows its potential application.

• Journal of Korea Technology Innovation Society
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• v.1 no.1
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• pp.117-124
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• 1998

Corporations are pursuing maximum returns from their R&D investment. They are also interested in sound measures to quantify returns. In fact, they use various measures and criteria for measuring returns from the R&D investment. But the fundamental problem is that there is no generic and widely acceptable measures and criteria. To make things more complicated, measures are very powerful and influential to the people in the corporations. Herbert Simon already indicated that people do many things but people usually do their best for the only tasks which are measured. Many researchers, like Chester(1995), are interested in R&D productivity measures and risks because what the company measures really influence R&D people and output. This article present design concepts of the R&D project selection and evaluation system in POSCO(Pohang Iron & Steel Company). This is an output extract from the 6-month joint activities with POSRI(POSCO Research Institute) researchers and POSCO R&D personnel. Process changes, new organizations and new selection and evaluation criteria are developed to improve R&D performance and to enhance technology management of the POSCO. This article covers new selection and evaluation criteria only. We would like to share our experience about how we redesign the selection and evaluation of R&D projects. We also bring insights how we seamlessly integrate 4 different project selection and evaluation steps as a whole. We hope that this case will give you a clue to improve your R&D management.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.306-320
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• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.286-288
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• 2012

파일 공유를 위해 널리 사용되는 BitTorrent는 대표적인 P2P 프로토콜이다. BitTorrent는 전송을 요구한 클라이언트가 작은 단위로 쪼개진 하나의 파일을 다수의 클라이언트들로부터 받는 방식으로 기존의 일대일 P2P 전송방식에 대비하여 빠른 다운로드 속도를 낼 수 있다. 이러한 다운로드 성능을 발휘하기위해 다수의 조각으로 분리 된 파일 조각을 선택하는 Pieces Selection 기법은 매우 중요하다. 이에 본 논문에서는 BitTorrent에서 활용되는 네 가지의 Pieces Selection 기법에 대해 알아보고, 성능 개선을 위한 새로운 기법을 제안한다.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.130-141
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• 2017

Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.

• Knowledge Management Research
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• v.13 no.3
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• pp.55-77
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• 2012

Selecting promising technology is becoming more and more difficult due to the increased number and complexity. In this study, we propose hybrid AHP/DEA-AR method and hybrid AHP/DEA-AR-G method to evaluate efficiency of technology alternatives based on ordinal rating data collected through survey to technology experts in a certain field and select efficient technology alternative as promising technology. The proposed method normalizes rating data and uses AHP to derive weights to improve the credibility of analysis, then in order to avoid basic DEA models' problems, use DEA-AR and DEA-AR-G to evaluate efficiency of technology alternatives. In this study, we applied the proposed methods to clean technology and compared with the basic DEA models. According to the result of the comparison, we can find that the both proposed methods are excellent in confirming most efficient technology, and hybrid AHP/DEA-AR method is much easier to use in the process of technology selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3908-3920
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• 2015

In this paper, performance analysis of full-duplex (FD) relay selection under decode-and-forward (DF) relaying mode is carried out by taking into account several critical factors, including the distributions of the received signal-to-noise ratio (SNR) and the outage probability of wireless links. The tradeoff between the FD and half-duplex (HD) modes for relay selection techniques is also analyzed, where the former suffers from the impact of residual self-interference, but the latter requires more channel resources than the former (i.e., two orthogonal channels are required). Furthermore, the impact of optimal power allocation (OPA) on the proposed relay-selection scheme is analyzed. Particularly, the exact closed-form expressions for outage probability of the proposed scheme over Rayleigh fading channels are derived, followed by validating the proposed analysis using simulation. Numerical results show that the proposed FD based scheme outperforms the HD based scheme by more than 4 dB in terms of coding gain, provided that the residual self-interference level in the FD mode can be substantially suppressed to the level that is below the noise power.