• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.515-521
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• 2020

Recently, data traffic has exploded due to development of various industries, which causes problems about losing of efficiency and overloaded existing networks. To solve these problems, network slicing, which uses a virtualization technology and provides a network optimized for various services, has received a lot of attention. In this paper, we propose a resource allocation method using credit value. In the method using the clustering technology, an operation for selecting a cluster is performed whenever an allocation request for various services occurs. On the other hand, in the proposed method, the credit value is set by using the residual capacity and balancing so that the slice request can be processed without performing the operation required for cluster selection. To prove proposed method, we perform processing time and balancing simulation. As a result, the processing time and the error factor of the proposed method are reduced by about 13.72% and about 7.96% compared with the clustering method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.197-197
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• 2017

In order to reduce the loss of perilla seeds at harvesting stage, different seeding methods and harvesting time had been applied to upland soil in NICS (National Institute of Crop Science), RDA, Milyang, Korea. The seeding methods were divided into hill and drill seeding, and harvesting time were 40%, 60%, 80%, and 100% color-changing of top cluster perilla (cv Deulsaem) seeds. Higher plant height and lowest flower cluster, thinner culm thickness was observed in drill seeding treated plot than hill seeding treated plot. However, field lodging occurred lower at drill seeding treatment (lodging degree 3) than hill seeding plot (lodging degree 5). Harvesting time affected the loss of perilla seeds and the loss rate of perilla seeds were investigated at different color-changing of top flower cluster. When seeds were harvested at 40%, 60%, 80%, and 100% color-changing of top cluster perilla seeds, the loss rate of hill and drill seeding showed no significant differences however the loss rated of 40, 60, 80, and 100 % color-changing of top flower cluster were 1.9% to 3.0%, 3.8% to 3.9%, 8.0% to 10.2%, and 16.1% to 22.7%, respectively. The harvesting time had no significant effects on the yield of perilla seeds. These results suggest that optimum harvesting time of perilla could be recommend at the 60% color-changing of top cluster to reduce the loss of perilla seeds.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.181-194
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• 2020

Due to the changes in the distribution market, issues related to storage and distribution of agricultural, aquatic and livestock products, and storage and transportation of processed and fresh food are rapidly emerging, and as a result, Cold Chain is naturally receiving attention as one of the logistics services. The purpose of this study is to evaluate the competitiveness of location in the construction of a cold chain cluster centered on the metropolitan area, which has attracted attention in relation to the distribution of cold chains, such as recently refrigerated frozen foods. To this end, this study evaluated the competitiveness of cold chain cluster candidates in the metropolitan area by utilizing the CFPR (Consistent Fuzzy Preference Relations) method that can efficiently extract and quantify expert knowledge. As a result, the location competitiveness was found to be superior to Incheon New Port's hinterland, Gyeonggi South Area (Yongin), Gyeonggi West Area (Gimpo Logistics Complex), and Pyeongtaek Oseong Logistics Complex. In particular, this study extracted the knowledge of refrigerated and refrigerated logistics warehouse operation experts, and conducted detailed competitiveness assessments for cold chain cluster candidates in the metropolitan area, and suggested the optimal cluster candidates. In the future research, it is necessary to classify the questionnaire into the owner, large business group, and public business group, etc., who have the right to purchase and build to secure ownership of the fresh food distribution center.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.9
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• pp.279-292
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• 2014

Energy aware server cluster aims to reduce power consumption at maximum while keeping QoS(quality of service) as much as energy non-aware server cluster. In the existing methods of energy aware server cluster, they calculate the minimum number of active servers needed to handle current user requests and control server power mode in a fixed time interval to make only the needed servers ON. When loads change rapidly, QoS of the existing methods become degraded because they cannot increase the number of active servers so quickly. To solve this QoS problem, we classify load change situations into five types of rapid growth, growth, normal, decline, and rapid decline, and apply five different thresholds respectively in calculating the number of active servers. Also, we use a flexible scheme to adjust the above classification criterion for multi threshold, considering not only load change but also the remaining capacity of servers to handle user requests. We performed experiments with a cluster of 15 servers. A special benchmarking tool called SPECweb was used to generate load patterns with rapid change. Experimental results showed that QoS of the proposed method is improved up to the level of energy non-aware server cluster and power consumption is reduced up to about 50 percent, depending on the load pattern.

• Korean Journal of Breeding Science
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• v.41 no.4
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• pp.444-450
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• 2009

Amplified fragment length polymorphism (AFLP) marker was utilized for evaluation of genetic diversity of 60 pear germplasms. Twenty selective AFLP primer pairs generated a total of 522 polymorphic amplification products. From UPGMA (unweighted pair-group method arithmetic average) cluster analysis by using polymorphic bands, the pear germplasms were divided into four clusters by similarity index of 0.691. The first cluster (I) included European pears belonging to Pyrus communis and wild species such as P. nivalis and P. cordata. The second cluster (II) included Ussurian pea pears belonging to P. betulaefolia and P. fauriei. The third cluster (III) included pea pears belonging to P. calleryana and P. koehnei. Most of germplasms belonging to P. pyrifolia and P. ussuriensis, and interspecific hybrids were included in the fourth (IV) cluster. Therefore pear germplasms originated from East Asia were closely related to P. pyrifolia and P. ussuriensis. Similarity values among the tested pear germplasms ranged from 0.584 to 0.879, and the average similarity value was 0.686.

• Proceedings of the Korean Vacuum Society Conference
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• 1996.02a
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• pp.180-180
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• 1996

• Journal of the Chungcheong Mathematical Society
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• v.16 no.2
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• pp.23-30
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• 2003

In this note, we introduce the concept of the modulus of a curve family in $R^n$ and examine some basic properties. And we study the boundary behavior of quasiconformal mappings on a domain $D{\subset}\bar{R}^n$ and present some geometric applications.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.187-201
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• 2005

With the trend of urban polynucleation, the issue of detecting economic clusters or urban employment centers has been considered as crucial. However, the prior researches had some limitations in detecting economic clusters in the empirical analysis: i.e. inherent inefficiency of density-based clustering methods, difficulty in detecting linear types of spatial clusters and lacks of consideration of economic significance. The purpose of this paper is to propose the clustering method with the procedure of testing statistical and economic significance named as VCEC (Variable Clumping method for Economic Clusters) and to apply it to a case analysis of Erie County, New York, in order to test its validity. By applying a search radius and a total employment as an economic threshold, 'the both statistically and economically significant clusters' were detected in the Erie County, and proved to be efficient.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.265-283
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• 2022

MovieLens data consists of recorded movie evaluations that was often used to measure the evaluation score in the recommendation system research field. In this paper, we provide additional information obtained by clustering user-specific genre preference information through movie evaluation data and movie genre data. Because the number of movie ratings per user is very low compared to the total number of movies, the missing rate in this data is very high. For this reason, there are limitations in applying the existing clustering methods. In this paper, we propose a convex clustering-based method using the pairwise fused penalty motivated by the analysis of MovieLens data. In particular, the proposed clustering method execute missing imputation, and at the same time uses movie evaluation and genre weights for each movie to cluster genre preference information possessed by each individual. We compute the proposed optimization using alternating direction method of multipliers algorithm. It is shown that the proposed clustering method is less sensitive to noise and outliers than the existing method through simulation and MovieLens data application.