• Korean Journal of Ecology and Environment
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• v.52 no.4
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• pp.285-292
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• 2019

Our study was based on the long-term surveys with respect to the major reservoirs located in the Yeongsan and Seomjin river basins. A total of 45 survey sites have been surveyed four times a year from 2008 to 2017. We identified 166 zooplankton species, including 127 rotifers, 26 cladocerans, and 13 copepods. Mean population density and species number of small reservoirs were higher than those of mid and large reservoirs. Considering outliers exceeding the 90th percentile between species occupancy and mean abundance, 10 of 11 habitat generalists were rotifers, and Bosmina longirostris was the only cladoceran. Habitat specialist consisted of three species of rotifers and emerged from one to three survey sites. According to the modularity results, it was found that the survey sites covering the entire river basins were characterized into five groups, which was similar to the classification by maximum water surface areas(MWSA). The result of the eigenvector centrality showed that the size of MWSA had a greater impact on the similarity of zooplankton community structure between reservoirs than the difference in distance between reservoirs. In the case of survey points in near dam or estuary bank of Juam and Youngsan reservoirs, modularity class were separated from other internal survey points of those. Given that the zooplankton interactions may contribute to freshwater functions more than species diversity. These topological features provide new insight into studying zooplankton distribution patterns, their organization and impacts on freshwater-associated function.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.133-154
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• 2016

Interdisciplinary approach has been recognized as one of key strategies to address various and complex research problems in modern science. The purpose of this study is to investigate the interdisciplinary characteristics and structure of the field of big data. Among the 1,083 journals related to the field of big data, multiple Subject Categories (SC) from the Web of Science were assigned to 420 journals (38.8%) and 239 journals (22.1%) were assigned with the SCs from different fields. These results show that the field of big data indicates the characteristics of interdisciplinarity. In addition, through bibliographic coupling network analysis of top 56 journals, 10 clusters in the network were recognized. Among the 10 clusters, 7 clusters were from computer science field focusing on technical aspects such as storing, processing and analyzing the data. The results of cluster analysis also identified multiple research works of analyzing and utilizing big data in various fields such as science & technology, engineering, communication, law, geography, bio-engineering and etc. Finally, with measuring three types of centrality (betweenness centrality, nearest centrality, triangle betweenness centrality) of journals, computer science journals appeared to have strong impact and subjective relations to other fields in the network.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.607-614
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• 2014

This article aims to explore and discuss research trends in global cruise industry using keyword network analysis. We visualize keyword networks in each of four groups of 1982-1999, 2000-2004, 2005-2009, 2010-2014 based on the top 20 keyword nodes' degree centrality and betweenness centrality which are selected among four centrality measurements, comparing them with frequency order. The article shows that keyword frequency collected from 240 articles published in international journals is subject to Zipf's law and nodes degree distribution also exhibits power law. We try to find out research trends in global cruise industry to change some important keywords diachronically, visualizing several networks focusing on the top two keywords, cruise and tourism, belonging to all the four year groups, with high degree and betweenness centrality values. Interestingly enough, a new node, China, connecting the top most keywords, appears in the most recent period of 2010-2014 when China has emerged as one of the rapid development countries in global cruise industry. Therefore keyword network analysis used in this article will be useful to understand research trends in global cruise industry because of increase and decrease of numbers of network types in different year groups and the visual connection between important nodes in giant components.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.529-535
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• 2015

Biological networks have been handled with the static concept. However, life phenomena in cells occur depending on the cellular state and the external environment, and only a few proteins and their interactions are selectively activated. Therefore, we should adopt the dynamic network concept that the structure of a biological network varies along the flow of time. This concept is effective to analyze the progressive transition of the disease. In this paper, we applied the proposed method to Alzheimer's disease to analyze the structural and functional characteristics of the disease network. Using gene expression data and protein-protein interaction data, we constructed the sub-networks in accordance with the progress of disease (normal, early, middle and late). Based on this, we analyzed structural properties of the network. Furthermore, we found module structures in the network to analyze the functional properties of the sub-networks using the gene ontology analysis (GO). As a result, it was shown that the functional characteristics of the dynamics network is well compatible with the stage of the disease which shows that it can be used to describe important biological events of the disease. Via the proposed approach, it is possible to observe the molecular network change involved in the disease progression which is not generally investigated, and to understand the pathogenesis and progression mechanism of the disease at a molecular level.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.923-925
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• 2012

대용량 고차원의 생물학 데이터가 매우 빠른 속도로 생산되는 현재, 단순히 고전적인 알고리즘들로는 풀 수 없는 문제들을 맞이하게 되었다. 이러한 문제들의 경우 시스템 생물학의 관점으로 다양한 생물 데이터의 융합을 통하여 접근할 경우 효율적으로 Computational Infeasibility(계산 불가능)를 해결함은 물론 그 해석 및 새로운 정보 획득에 매우 유리하다. 인간 DNA의 고차원 SNP 정보들의 군집화 및 질병 발현 패턴 분석은 그 조합의 수가 입력 데이터의 차원수에 따라 지수적(Exponentially)으로 증가하지만 PPI(단백질 상호작용) 네트워크 정보에 결합하여 필요한 중요부위를 선택적으로 이용할 경우 효율적으로 필요 SNP들의 선택 및 이로 인한 공간 축소가 가능하다.

• Journal of the Korean Society for information Management
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• v.32 no.4
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• pp.77-106
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• 2015

By document co-citation analysis with Korean Citation Index (KCI) data, this study accurately identified the research fronts and hot topics in Korean library and information science (LIS) from 2004 to 2013. 159 core papers in LIS domain and their citations are scraped manually from Korean Citation Index web site. In the cluster analysis and network analysis, 159 core papers were grouped into 27 clusters with multiple papers and 8 singlton clusters. Among the 27 clusters which have multple papers, 'LIS education' cluster was the largest with 16 core papers, and 'citation analysis & intellectual structure analysis' cluster had the strongest citation impact according to the ehs-index. Closer observation of the citations to the core papers in each research front showed that 67.5% of the citations were made by LIS research papers and 32.5% of the citations were made by non-LIS research papers. Considering the share of citations and the citation impact growth index, 'local documentation', 'citation analysis & intellectual structure analysis', and 'research trends analysis' were identified as the most emerging research front in Korean library and information science. The analytical methods used in this study have great potential in discovering the characteristics of research fronts in Korean interdisciplinary research domains.

• Journal of IKEEE
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• v.25 no.3
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• pp.425-429
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• 2021

Vehicle platooning in automated connected vehicle systems is an efficient transportation operation model that not only significantly reduces computational load and networking overhead of the central system but also improves traffic flow. For efficient platoon group management, it is important to maintain the platoon group size appropriately and to control the merge request of a new vehicle and other group member vehicle. In this paper, we present a merger control scheme that accepts or rejects merge requests based on the current group size and the priority of vehicles. The proposed method was analyzed and validated through mathematical models based on Markov chains. Performance evaluation shows that the proposed scheme properly manages the load of the central system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.131-139
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• 2010

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.151-165
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• 2004

The purpose of the study was to explore the potential of the Cluster Analysis and the Community Analysis of Social Network Analyses family in job-task analysis for curriculum design. These two multivariate analysis techniques were expected to bring us relevant and scientific information as well as inspiration in investigating the structure and nature of job system, which are critical in developing relevant curriculum. To pursue the purpose mentioned above, qualitative and quantitative data were collected from "S" Corporate, a major large high-tech manufacturing company, and analyzed by relevant analytic procedures. Results indicate that there are discrepancies between formal job structures and actual ones. Following Community analysis showed that the presence of center-marginal structure along with clustering structure in the current job formation. Interpretations of the results of the study are provided in light of past research and additional data collected from the study. Implications of the study are also discussed along with suggestions for future research.