• Journal of Animal Science and Technology
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• v.54 no.6
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• pp.463-469
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• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.91-96
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• 2012

Ad-hoc networks can be used various environment, which it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio (CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In CR based wireless ad-hoc networks, cluster-head decides the existence of primary user using sensing information of primary user from each ad-hoc device. However, it is still defective research to decide cluster head among the a lot of ad-hoc devices. So, in this paper, we show the decision method of cluster head in CR based wireless and detection probabilities of primary user based on decision method of cluster head.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.731-740
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• 2001

Multi-distributed web cluster model expanding conventional cluster system is the cluster system which processes large-scaled work demanded from users with parallel computing method by building a number of system nodes on open network into a single imaginary network. Multi-distributed web cluster model on the structured characteristics exposes internal system nodes by an illegal third party and has a potential that normal job performance is impossible by the intentional prevention and attack in cooperative work among system nodes. This paper presents the mutual authentication protocol of system nodes through key division method for the authentication of system nodes concerned in the registration, requirement and cooperation of service code block of system nodes and collecting the results and then designs SNKDC which controls and divides symmetrical keys of the whole system nodes safely and effectively. SNKDC divides symmetrical keys required for performing the work of system nodes and the system nodes transmit encoded packet based on the key provided. Encryption packet given and taken between system nodes is decoded by a third party or can prevent the outflow of information through false message.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2404-2410
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• 2011

This paper describes the design overview of cluster file system $SANique^{TM}$ based on SAN(Storage Area Network) environment. The design issues and problems of the conventional global buffer manager are also illustrated under a large set of clustered computing hosts. We propose the efficient global buffer management method that provides the more scalability and availability. In our proposed global buffer management method, we reuse the maintained list of lock information from our cluster lock manager. The global buffer manger can easily find and determine the location of requested data block cache based on that lock information. We present the pseudo code of the global buffer manager and illustration of global cache operation in cluster environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.125-129
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• 2015

The most important factor within the wireless sensor network is to have effective network usage and increase the lifetime of the individual nodes in order to operate the wireless network more efficiently. Therefore, many routing protocols have been developed. The LEACH protocol presented by Wendi Heinzelman, especially well known as a simple and efficient clustering based routing protocol. However, because LEACH protocol in an irregular network is the total data throughput efficiency dropped, the stability of the cluster is declined. Therefore, to increase the stability of the cluster head, in this paper, it proposes a stochastic cluster head selection method for improving the LEACH protocol. To this end, it proposes a SH-LEACH(Stochastic Cluster Head Selection Method-LEACH) that it is combined to the HEED and LEACH protocol and the proposed algorithm is verified through the simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2513-2533
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• 2018

In cognitive radio technology, the overall efficiency of communications systems can be improved without allocating additional bands by allowing a secondary system to utilize the licensed band when the primary system, which has the right to use the band, does not use it. In this paper, we propose a fast and reliable common channel initialization protocol without any exchange of initialization messages between the cluster head and the member nodes in cognitive ad-hoc networks. In the proposed method, the cluster and member nodes perform channel-based spectrum sensing. After sensing, the cluster head transmits a system activation signal through its available channels with a predetermined angle difference pattern. To detect the cluster head's transmission channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation for the angle difference sequence of the received signal patterns. This is compared to the predetermined reference angle difference pattern. The join-request and channel-decision procedures are presented in this paper. Performance evaluation of the proposed method is presented in the simulation results.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.90-95
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• 2022

This paper proposes a method to find a tree with the maximum number of terminals that can be connected by a given length when numerous terminals distributed in a cluster form are given to the Euclidean plane R2 with several constraints. First constraint is that a given terminal is distributed in a cluster form, second is that a given length cannot connect all terminals in the tree, and third is that there is no curved connection between each terminal. This paper proposes a method to establish more efficient interconnections within terminals distributed in a cluster form by improving a randomly distributed memetic genetic algorithm. The construction of interconnections has been extensively used in design-related fields, from networking to architecture. Additionally, in real life, the construction of interconnections is mostly distributed in the form of clusters. Therefore, the heuristic algorithm proposed in this paper can be effectively utilized in real life and is expected to provide various cost savings.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.4 no.1
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• pp.14-50
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• 2010

This study focused on the competitiveness analysis of industrial cluster using econometric method and competency analysis method to measure the situation of Seocho-gu industrial cluster and Yangjae R&D cluster at Seoul. The econometric method used the data such as the number of firms, the number of employee, GRDP. The competency analysis method measured the level of agglomeration of cluster for years through LQ(Location quotient) index. In addition, the characteristics and structure of each industry in Secho-gu and Yangjae-dong R&D cluster. The circumstance of location and business, the competency of potential ability of industries were analysed by SWOT. In sum, this study has the following conclusion. First, Seocho-gu and Yangjae-dong region has an advantage in the circumstance for R&D related industry and man power. Second, Seocho-gu and Yangjae-dong region has a good potential for development in R&D related industries. These region has a good relationship the four major R&D related industry of Seoul. Third, these region also has a advantage in the potential development of R&D related researchers in the corresponding industries.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.173-185
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. In this paper, Condor system using K-Means algorithm Compares with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.285-292
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• 2000

In general, exploratory data analysis consists of three processes: i) assessment of clustering tendency, ii) cluster analysis, and iii) cluster validation. This analysis method requiring a number of iterations of step ii) and iii) to converge is computationally inefficient. In this paper, we propose a density function-based approximate fuzzy clustering method with a hierachical structure which consosts of two phases: Phase I is a features(i.e., number of clusters and cluster centers) extraction process based on the tendency assessment of a given data and Phase II is a standard FCM with the cluster centers intialized by the results of the Phase I. Numerical examples are presented to show the validity of the proposed clustering method.