• Journal of Korea Technology Innovation Society
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• v.6 no.2
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• pp.175-190
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• 2003

This paper deals with the methods for selecting the key research areas, which fit for the large, multi-disciplinary, and long-term programs by making use of Technology Cluster Analysis. This method is applied to mano-technology field at the level of national R&D program. 56 nano-technologies are analyzed and grouped into three main clusters based on the survey data from 180 experts. Three main clusters are \circled1 naro-materials related cluster, \circled2 naro-device related cluster, and \circled3 naro-bio related cluster. These three clusters are coincided with the focused areas of nano-technology in Korea. Each cluster is analyzed in view of its competence position.

• Journal of the Korean Vacuum Society
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• v.4 no.S2
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• pp.139-147
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• 1995

The mechanism of the ionized cluster beam deposition has been studied using Molecular Dynamics Simulation. The Embedded Atom Method(EAM) potential were used in the simulation. The impact of a Au95-cluster on Au(100) substrate was studied for the impact energies 0.15-10eV/atom. The dependency of the impact energy of cluster beam was observed. For the cluster energy impact of 10eV per atom, the defects on surface were created and the cluster embedded into substrate as an amorphous state. For the energy of 0.5eV per atom, the defect free homoepitaxial growth was observed and atomic scale nucleation was formated, which are in good agreement with experiment. Thus molecular dynamics simulation is very useful to study the mechanism of the ionized cluster beam deposition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.347-356
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• 2009

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

• Journal of Preventive Medicine and Public Health
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• v.33 no.1
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• pp.51-55
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• 2000

Objectives . In large-scale field trials, randomization by cluster is frequently used because of the administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might of otherwise arise. Cluster randomization trials are experiments in which intact social unit, e.g., families, schools, cities, rather than independent individuals are randomly allocated to intervention groups. The positive correlation among responses of subjects from the same cluster is in matter in cluster randomization. This thesis is to compare the results of three randomization methods by standard error of estimator of treatment effect. Methods : We simulated cholesterol data varing the size of the cluster and the level of the correlation in clusters and analyzed the effect of cholesterol-lowering agent. Results : In intra-cluster randomization the standard error of the estimator of treatment effect is smallest relative to that in inter-cluster randomization and that in individual randomization. Conclusions : Infra-cluster randomization is the most efficient in its standard error of estimator of treatment effect but other factor should be considered when selecting a specific randomization method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.17-24
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• 2014

In this paper, we propose a mobile application categorization method using word cluster information. Because the mobile application description can be shortly written, the proposed method utilizes the word cluster seeds as well as the words in the mobile application description, as categorization features. For the fragmented categories of the mobile applications, the proposed method generates the word clusters by applying the frequency of word occurrence per category to K-means clustering algorithm. Since the mobile application description can include some paragraphs unrelated to the categorization, such as installation specifications, the proposed method uses some word clusters useful for the categorization. Experiments show that the proposed method improves the recall (5.65%) by using the word cluster information.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.635-642
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• 2003

The radical development of recent network technology provides the basic foundation which can establish a high speed and cheap cluster system. It is a general trend that conventional cluster systems are built as the system over a fixed level based on stabilized and high speed local networks. A multi-distributed web cluster group is a web cluster model which can obtain high performance, high efficiency and high availability through mutual cooperative works between effective job division and system nodes through parallel performance of a given work and shared memory of SC-Server with low price and low speed system nodes on networks. For this, multi-distributed web cluster group builds a sub-cluster group bound with single imaginary networks of multiple system nodes and uses the web distributed shared memory of system nodes for the effective data transmission within sub-cluster groups. Since the presented model uses a load balancing and parallel computing method of large-scale work required from users, it can maximize the processing efficiency.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.391-401
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• 2011

Consider a cluster, so called a 'son cluster', whose mean and variance is composed of the means and variances of both clusters called as a 'father cluster' and a 'mother cluster'. In this paper, a method for identifying each of three clusters is provided by modeling the relationship with father and mother clusters. Under the normal mixture model, the parameters are estimated via EM algorithm. We were able to overcome the problems of estimation using ECM approximation. Numerical examples show that our method can effectively identify the three clusters, so called a 'family of clusters'.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.169-175
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• 2010

Recently, to improve operating efficiency with the higher in-line rate in automated production lines, a lot of cases of grouping machines and material handling system together to form a cluster has shown frequently. This article addresses the job allocation and operation method of automated material handling for cluster-type production systems. First of all, the control problems of the automated material handling systems are classified into the control problem of inter-cluster material handling system and that of intra-cluster material handling system. Then, a distributed agent-based control scheme is proposed for the former, and an operational control procedure for the latter. Simulation experiment shows that the proposed method is efficient in reducing cycle times and improving utilization of material handling vehicles.

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.234-241
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• 2024

Objectives: Health development is a key element of national development. The goal of improving health development at the societal level will be readily achieved if it is directed from the smallest social unit, namely the family. This was the goal of the Healthy Indonesia Program with a Family Approach. The objective of the study was to analyze variables of family health indicators across all provinces in Indonesia to identify provincial disparities based on the status of healthy families. Methods: This study examined secondary data for 2021 from the Indonesia Health Profile, provided by the Ministry of Health of the Republic of Indonesia, and from the 2021 welfare statistics by Statistics Indonesia (BPS). From these sources, we identified 10 variables for analysis using the k-means method, a non-hierarchical method of cluster analysis. Results: The results of the cluster analysis of healthy family indicators yielded 5 clusters. In general, cluster 1 (Papua and West Papua Provinces) had the lowest average achievements for healthy family indicators, while cluster 5 (Jakarta Province) had the highest indicator scores. Conclusions: In Indonesia, disparities in healthy family indicators persist. Nutrition, maternal health, and child health are among the indicators that require government attention.

• Journal of Korean Society of Forest Science
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• v.95 no.3
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• pp.370-377
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• 2006

This study was conducted to develop an efficient method of sampling design appropriate for the sustainable forest management. For this, data were collected in Yangpyung-Gun, Gyunggi Province based on three different sampling designs such as systematic design, systematic cluster design, and stratified cluster design. Based on evaluation statistics, the sampling designs were compared to select a sampling method fitted to sustainable forest management. It was found that the systematical cluster sampling is the most efficient sampling method in terms of feasibility for sustainable forest management. It was also recommended that the sample plots should be made as a cluster of triangle-shape. The clusters should be consisted of a main plot and three sub-plots. And the sub-plots should be arranged with a distance of 50m from the main plot in the center of cluster.