• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2419-2435
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• 2023

Command and control networks(C2N) exhibit evident multi-network interdependencies owing to their complex hierarchical associations, interleaved communication links, and dynamic network changes. However, the existing command and control networks do not consider the effects of dependent nodes on the load distribution. Thus, we proposed a command and control networks load allocation strategy based on interdependence strength. First, a new measure of interdependence strength was proposed based on the edge betweenness, which was followed by proposing the inter-layer load allocation strategy based on the interdependence strength. Eventually, the simulation experiments of the aforementioned strategy were designed to analyze the network invulnerability with different initial load capacity parameters, allocation model parameters, and allocation strategies. The simulation indicates that the strategy proposed in this study improved the node survival rate of the interdependent command and control networks model and successfully prevented cascade failures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.443-461
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• 2014

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. For one-hop overlay source routing, when a given primary path suffers from the link failure or performance degradation, the source can reroute the traffic to the destination via a strategically placed relay node. However, the over-heavy traffic passing through the same relay node may cause frequent package loss and delay jitter, which can degrade the throughput and utilization of the network. To overcome this problem, we propose a Load-Balanced One-hop Overlay Multipath Routing algorithm (LB-OOMR), in which the traffic is first split at the source edge nodes and then transmitted along multiple one-hop overlay paths. In order to determine an optimal split ratio for the traffic, we formulate the problem as a linear programming (LP) formulation, whose goal is to minimize the worse-case network congestion ratio. Since it is difficult to solve this LP problem in practical time, a heuristic algorithm is introduced to select the relay nodes for constructing the disjoint one-hop overlay paths, which greatly reduces the computational complexity of the LP algorithm. Simulations based on a real ISP network and a synthetic Internet topology show that our proposed algorithm can reduce the network congestion ratio dramatically, and achieve high-quality overlay routing service.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.99-114
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The analysis items of the 3rd question were and the 4th question were the motivation for applying to college, the academic plan and the career plan. The text mining to the 3rd question showed that the frequency of 'friends' was overwhelmingly high, followed by keywords such as 'thought', 'time', 'opinion', 'activity', and 'club'. In the 4th question, keyword frequency such as 'thought', 'agriculture', 'KNCAF', 'farm', 'father' was high. The result of association rules analysis for each question showed that the relationship with the highest support level, which means the frequency and importance of the rule, was the {friend} <=> {thought}, {thought} <=> {KNCAF}. The confidence level of a correlation between keywords was the highest in the rules of {teacher}=>{friend}, {agriculture, KNCAF}=>{thought}. Also the lift level that indicates the closeness of two words was the highest in the rules of {friend} <=> {teacher}, {knowledge} <=> {professional}. These keywords are found to play a very important roles in analyzing betweenness centrality and analyzing degree centrality between keywords. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Information Systems Review
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• v.19 no.2
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• pp.21-35
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• 2017

The advancement of IT and the "Fourth Industrial Revolution" blurred the boundary between industries. The importance of strategic cooperation between enterprises is emphasized. IT companies must consider their existing business areas and create new territories to drive changes in the industry. They must also secure their competitive edge and manage economic costs to enable them to compete with their global counterparts. By utilizing their resources effectively, these firms can create value through inter-firm cooperation. This study analyzes the collaborative network of global IT companies using social network analysis and examines the effect of this network on firm performance. Collaborative linkages and betweenness centrality, which represent the bridging position of a firm in a network, significantly affect firm performance. This result highlights the importance of the structural position of a firm in a cooperative network of IT companies. This study also characterizes clusters in a network of IT companies. Most of these clusters comprise a combination of IT companies in diverse IT industries. These clusters suggest that these companies engage in multilateral cooperation without boundaries to maximize their business capabilities. This study offers practical implications for establishing a cooperative strategy and framework that can capture business trends in the IT industry from a macroscopic view. This study also visualizes collaborative networks in a multifaceted way using social network analysis to provide researchers and business practitioners with an informative viewpoint.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.