• 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.

• Human Ecology Research
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• v.56 no.6
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• pp.603-617
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• 2018

This study analyzed research trends in the field of Practical Arts (Technology & Home Economics) education. From 958 articles published between 2010 and 2018 in the Journal of Korean Practical Arts Education (JKPAE), Journal of Korean Home Economics Education Association (JHEEA), and Korean Journal of Technology Education Association (KJTEA), 958 keywords were extracted and analyzed using NetMiner 4. When the general network structure was analyzed, keywords such as practical arts education, curriculum, textbook, home economics education, and students were high in the degree centrality and closeness centrality, and textbook, practical arts education, curriculum, student, home economics education, and invention were high in the node betweenness centrality. The cluster analysis showed that a four-cluster solution was most appropriate: cluster 1, technology and experiential learning activities; cluster 2, curriculum studies and practical problem; cluster 3, relationships; and cluster 4, creativity and character education. The three journals showed differences in the knowledge network structure: The topics of JKPAE and JKHEEA focused on general content knowledge and curriculum, while the topics of KJTEA were spread across invention and creativity education, and curriculum studies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.308-314
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• 2022

This study deals with the technology Convergence Analysis by IPC Code-Based Social Network Analysis of Healthcare Patents filed in Korea. The relationship between core technologies is visualized using Social Network Analysis. At the subclass level of healthcare patents, 1,155 cases (49.4%) of patents with complex IPC codes were investigated, and as a result of Social Network Analysis on them, the IPC codes with the highest Degree Centrality were A61B, G16H, and G06Q, in that order. The IPC codes with the highest Betweenness Centrality are in the order of A61B, G16H, and G06Q. In addition, it was confirmed that healthcare patents consist of two large technology clusters. Cluster-1 corresponds to related business models centered on A61B, G16H and G06Q, and Cluster-2 is consisting of H04L, H04W and H04B. The technology convergence core pairs of the healthcare patent is [G16H-A61B] and [G16H-G06Q] in Cluster-1, and [H04L-H04W] in Cluster-2. The results of this study can contribute to the development of core technologies for healthcare patents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3477-3485
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• 2012

This research focused on the cluster analysis for the vitalization of the innovation cluster, Gwanggyo Technovalley. The study was performed based on both theoretical study and quantitative and qualitative study approaches. Particularly, questionnaire survey was performed for the cluster analysis of the innovation cluster. The major determinants for vitalization of the innovation cluster, Gwanggyo Technovalley can be summarized as follows; the strategy formulation for the development of the innovation cluster, the enhancement of the host institution capability and gradual enlargement of the role of the host institution. In terms of the needs of times, this study regarding the cluster analysis for the vitalization of the innovation cluster, Gwanggyo Technovalley is anticipated to be a good reference for the R&D organizations and technology cluster participants in coming years.

• Journal of Korea Technology Innovation Society
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• v.6 no.3
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• pp.373-390
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• 2003

With limited resources, proper allocation of the national R&D budget is very crucial matter for reinforcing the national competence, and the importance of selecting strategic R&D fields have been increasingly emphasized by technology policy-makers and CTOs. This paper deals with technology relevance/cluster analysis, which measures technological dependency and relevancy among technologies, and how it can be used for selecting the strategic R&D fields especially satisfying SMEs(small and medium enterprises)' specific needs. As a result of this study, technology-product tree composed of 7 major technology fields, 22 clusters, 41 groups, 335 core-need technologies and hundreds of related business items are produced that can be used for designing SMEs' R&D/business portfolio as well as R&D investment decision-making of the Ministry of Small and Medium Business Administration.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.2
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• pp.51-57
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• 2016

The aim of this study is to examine of the cluster approach to ensure high rates of innovation, information and communication enterprises of information technology cluster in order to enhance the competitiveness of regions. Keeping with the previous literature, the present research determined that the novelty of the problem, concerning of the creation IT clusters as drivers of new generation, i.e. a kind of platform of "startup accelerators" through the creation of previously not existing in the country high-tech industries and sectors of the economy. The study employs system approach involves to determine prospective directions of the formation of clusters of IT industry, also applies structured approach to shows relationships between elements of cluster systems (participants of cluster), as well as focusing on some aspects of cluster development such as networking. Based on this analysis we have proposed to create clusters in regions, which can play the role of translator's innovations at the periphery of the country. This research shows that formation of IT clusters is one of the most successful tools to avoid of dependence of Kazakhstan from raw materials.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.998-1007
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• 2018

The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.20-25
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• 2016

The parallel computer cluster system has been known as the powerful tool to solve a complex physical phenomenon numerically. The numerical analysis of large size of Li-ion battery pack, which has a complex physical phenomenon, requires a large amount of computing time. In this study, the numerical analyses were conducted for comparing the computing efficiency between the single workstation and the parallel cluster system both with multicore CPUs'. The result shows that the parallel cluster system took the time 80 times faster than the single work station for the same battery pack model. The performance of cluster system was increased linearly with more CPU cores being increased.

• Journal of Microbiology and Biotechnology
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• v.21 no.11
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• pp.1127-1142
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• 2011

The aim of this investigation was to select effective Pseudomonas sp. strains that can enhance the productivity of soybean-wheat cropping systems in Vertisols of Central India. Out of 13 strains of Pseudomonas species tested in vitro, only five strains displayed plant growth-promoting (PGP) properties. All the strains significantly increased soil enzyme activities, except acid phosphatase, total system productivity, and nutrient uptake in field evaluation; soil nutrient status was not significantly influenced. Available data indicated that six strains were better than the others. Principal component analysis (PCA) coupled cluster analysis of yield and nutrient data separated these strains into five distinct clusters with only two effective strains, GRP3 and HHRE81 in cluster IV. In spite of single cluster formation by strains GRP3 and HHRE81, they were diverse owing to greater intracluster distance (4.42) between each other. These results suggest that the GRP3 and HHRE81 strains may be used to increase the productivity efficiency of soybean-wheat cropping systems in Vertisols of Central India. Moreover, the PCA coupled cluster analysis tool may help in the selection of other such strains.

• International Journal of Contents
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• v.16 no.4
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• pp.39-49
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• 2020

The goal of this research was to understand the patterns of multidimensional engagement in MOOCs. An email with an online survey link was sent to enrollees in an MOOC course. The survey included 35 questions asking about engagement, teaching presence, and learning persistence. The items were validated in the literature, revised for the MOOC setting, reviewed by four professionals in the field of educational technology, and used in the study. A heterogeneous group of 170 individuals gathered through convenience sampling participated in the study. With cluster analysis of the engagement data, three groups were identified: Cluster1, 2, and 3. Cluster 1 scored high on behavioral, emotional, and cognitive engagement. Cluster 2 scored high on behavioral aspects but low on emotional and cognitive engagement. Cluster 3 scored low on behavioral and cognitive engagement but high on emotional aspects. The study addressed cluster-specific learner characteristics and differences in perceived teaching presence and learning persistence. Design strategies pertaining to each cluster were further discussed. These strategies may guide instructors and practitioners in the design and management of MOOCs and should be further validated through future studies.