• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.466-468
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• 2003

그리드 컴퓨팅은 네트워크 상의 유휴 자원 및 다수의 저성능 자원을 활용함으로써 보다 고 성능의 컴퓨팅 환경을 요구하는 응용 문제를 해결할 수 있다. 따라서, 그리드 컴퓨팅의 자원 관리 시스템의 자원 선택 및 할당 기능은 주어진 응용 문제에 대하여 보다 높은 성능의 그리드 컴퓨팅 환경을 제공하기 위한 매우 중요한 요소이다. 본 논문에서는 보다 효율적으로 자원을 선택하기 위하여 환경 내 자원들의 종합적인 CPU 성능을 평가하는 UC 단위 모델을 제안하고, 보다 효율적인 자원 할당을 위하여 그리디 방식(Greedy Method)을 변형한 최적 자원 우선(8est-Fit-First) 알고리즘을 제안한다. 또한, 기존의 자원선택, 할당방식과 비교하는 모의실험을 통하여 제안하는 모델 및 알고리즘의 향상된 성능을 나타내 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.41-61
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• 2004

The purpose of this paper is to construct $\ulcorner$CIDEAR(Cross Impact-DEA-AR)$\lrcorner$ model which evaluates proposed R&D projects considering cross impact among them and selects proper projects to utilize resources efficiently as well as to maximize efficacy of investments. For this purpose, $\ulcorner$CIDEAR$\lrcorner$ model is designed as the following six steps. $\ulcorner$Decision Theory Evaluation Model$\lrcorner$ is for setting and selecting the evaluation items according to the structured procedure of evaluation system. The priority of items is decided at $\ulcorner$AR Decision Model$\lrcorner$$\ulcorner$Cross Impact Estimation Model$\lrcorner$ is for computing the final probability of success and the result is used to revise the evaluation results of $\ulcorner$Decision Theory Evaluation Model$\lrcorner$. $\ulcorner$Resource Performance Analysis Model$\lrcorner$ classifies the proposed R&D projects on the basis of required resources and expected performance. Consequently, the possibility of bias of project selection can be prevented. $\ulcorner$Priority Oder Decision Model$\lrcorner$ is for computing the efficacy of proposed projects. Finally, $\ulcorner$Efficacy-Efficiency Cause Analysis Model$\lrcorner$ analyzes the structure of efficacy and efficiency of the projects. The major findings and significances of this study are summarized as follows: (1) $\ulcorner$CIDEAR$\lrcorner$ model can deal with the affairs of R&D projects having the characteristics of mutual independence as well as mutual dependence in the point of efficacy and efficiency. Hence, it is possible to evaluate and select R&D projects more accurately. (2) It can be possible to raise the possibility of projects success. R&D manager can use the information for project management because the efficacy-efficiency structure of selected projects can be analyzed. (3) We proved the usefulness of the constructed $\ulcorner$CIDEAR$\lrcorner$ model using an case about twenty-one R&D projects of a leading company of electronic industry in Korea.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.53-61
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• 2004

As remodeling project is progressed in the constraint which is utilization of existing resource, the subject of remodeling and extend of construction are affected by the correlation between building element. Therefore, this research analyze the correlation between component of building element in the design phase and proposed the diagram model for correlation between building element and proposed selection method of alternative plan for optimized combination of building element in remodeling project.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.673-694
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• 2021

In this paper, we intend to study the energy efficiency (EE) optimization for a simultaneous wireless information and power transfer (SWIPT)-based distributed antenna system (DAS). Firstly, a DAS-SWIPT model is formulated, whose goal is to maximize the EE of the system. Next, we propose an optimal resource allocation method by means of the Karush-Kuhn-Tucker condition as well as an ergodic method. Considering the complexity of the ergodic method, a suboptimal scheme with lower complexity is proposed by using an antenna selection scheme. Numerical results illustrate that our suboptimal method is able to achieve satisfactory performance of EE similar to an optimal one while reducing the calculation complexity.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.83-98
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• 2007

The handover between different radio access networks, especially where their coverage overlaps, suffers various complications since the different access networks provide different service characteristics. One way to reduce service interruptions and QoS (i.e., bandwidth, throughput, delay) degradations during the inter-technology handover is to reserve the required resource in advance. The resource reservation algorithm should minimize the handover latency and maximize the resource utilization based on the accurate estimation on mobile's location, velocity, movement pattern and service requirements. In this paper, we propose a resource reservation algorithm based on the mobile terminal velocity and the cell selection probability, which maximizes resource utilization ana reduces network overhead. We compare the proposed algorithm with PMS(Predictive Mobility Support) and VCDS(Velocity and Call Duration Support scheme) based on 3-layer network model under various scenarios.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.337-347
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• 2008

Based on social welfare maximum theory, the optimal scale of power plants entering generation power market being is researched. A static non-cooperative game model for short-term optimization of power plants with different cost is presented. And the equilibrium solutions and the total social welfare are obtained. According to principle of maximum social welfare selection, the optimization model is solved, optimal number of power plants entering the market is determined. The optimization results can not only increase the customer surplus and improve power production efficiency, but also sustain normal profits of power plants and scale economy of power production, and the waste of resource can also be avoided. At last, case results show that the proposed model is efficient.

• Asian Journal of Innovation and Policy
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• v.13 no.1
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• pp.1-28
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• 2024

Enhancing the efficiency of research and development (R&D) is crucial for organizations to remain competitive and generate innovative solutions. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating R&D efficiency. However, traditional DEA models heavily rely on the selection of input and output variables, which can limit their effectiveness. To overcome this dependency and improve the robustness of DEA, this study proposes a novel methodology that integrates machine learning techniques with DEA for determining the most suitable input and output variables. The proposed approach is particularly relevant for specialized R&D fields, such as Radiation Emergency Medicine (REM). REM is a critical domain that deals with the medical and public health consequences of nuclear emergencies. The selection of REM as the focus of this study is motivated by several factors, including the unique challenges posed by the field, the potential for significant societal impact, and the need for efficient resource allocation in emergency situations. By leveraging machine learning algorithms, such as Support Vector Machines (SVM), the proposed methodology aims to identify the most relevant input and output variables for DEA in the context of REM. The integration of machine learning enables the DEA model to capture complex relationships and non-linearities in the data, leading to more accurate and reliable efficiency assessments. The effectiveness of the proposed methodology is demonstrated through a comprehensive evaluation using real-world REM data. The results highlight the superior performance of the machine learning-integrated DEA approach compared to traditional DEA models. This study contributes to the advancement of R&D efficiency assessment in specialized fields and provides valuable insights for decision-makers in REM and other critical domains.

• Journal of Plant Biotechnology
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• v.2 no.2
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• pp.89-96
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• 2000

For large DNA fragment transformation in dicots and monocots, BIBAC2 vector system was applied to Arabidopsis thaliana and Oryza sativa L. cv. Jinmi as a model plant, respectively. For Arabidopsis, the Th1 gene in T23L3 BAC clone whose size is about 90 kb was used as the target gene source for transformation. Because T23L3 BAC clone was originally constructed in pBelloBAC11, the target gene was reconstructed into BIBAC2. As the results of reconstruction, 476 colonies were survived in selection medium containing 40 mg/L kanamycin. In colony hybridization analysis, 24 out of 476 colonies exhibited positive signals. In the pulsed-field gel electrophoresis analysis, 11 out of 24 positive clones exhibited the band at the location of 90 kb. In Southern hybridization, positive signal band at the location of 90 kb was observed in all 11 transformants. Using these verified clones, Agrobacterium-mediated transformation was applied to Arabidopsis thaliana th1-201 mutant for genetic complementation test. Twelve thousands T$_1$ seeds were harvested, and antibiotic selection test is being analyzed to verify whether these seeds were transformed. for rice, COR356 that contains 150 kb human genomic DNA in a BIBAC2 vector was used as the target gene. As the results of transformation, 151 out of 210 co-cultivated calli were survived in selection medium containing 5 mg/L hygromycin, and 45 out of 151 survived calli were regenerated into plants. Transformation efficiency was 21.6%. Progeny test using 71 seeds is being analyzed now. These results provide the potential that large DNA fragments can be transferred into both dicots and monocot by Agrobacterium-mediate d transformation system.