• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.306-311
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• 2009

We initially obtained human diseases-related proteins dataset from the OMIM and the SWISS PROT and then constructed disease-related protein-protein interaction network. The protein network contains 40 hub proteins such as CALM1, ACTB and ABL2. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived 38 diseasomal proteins, including APP, ABL1 and STAT1. We previously demonstrated that hub proteins in the network tend to be diseasomal proteins in the disease-related protein sub-networks. However, we found that 18% hubs are only diseasomal proteins in the whole disease network. At this point, we could not elucidate difference in the hub-diseasomal proteins tendency between sub0network and whole network. In spite of we still have unsolved problems, our results elucidate that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.165-169
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• 2006

문자메시지 네트워크는 웹페이지 검색네트워크와는 위상적으로 많이 다르기 때문에 상이한 특성을 가지고 있음을 보였다. 네트워크가 성장함에 따라 강력한 허브를 구성하는 네트워크는 소수의 노드에 연결이 집중되는 특성을 가질 수 있다. 문자메시지 네트워크는 시간이 흐름에 따라서 네트워크의 밀도가 높아지는 속도가 높다는 특징을 가지고 있다. 두 개의 크기가 다른 문자메시지 네트워크에 대한 실험을 통하여 적어도 작은 집단 내의 메시지네트워크는 크기가 증가함에 따라서 허브의 집중도가 power law분포가 아닌 선형적인 특징을 가지고 있음을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2656-2664
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• 2015

IoT management hub has to request the WLAN AP list of the accessible areas in a place that is installed, or choose a specific WLAN AP you want to connect in order to be connected to the Internet. the specific method also is required to input the password when the security key was set. As that way, IoT management hub needs both the display device and the input device to see the list of WLAN AP and to input the security key. If the IoT management hub is consist of them, It is difficult to achieve the objective of the miniaturization and cost reduction. In this paper, we propose a method to connect to a WLAN AP network using a smart-phone without the display device and the input device.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1036-1041
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• 2006

본 연구에서는 허브와 스포크의 입지선정과 차량 경로가 통합된 hub-and-spoke 네트워크 설계문제를 다룬다. Hub-and-spoke 네트워크는 대량화와 공동화를 통해 물류효율화를 실현하기 위한 대표적인 구조로 물류시스템에서 흔히 사용되고 있다. 이러한 물류시스템에서 물류비용의 절감과 고객서비스 향상을 위한 효율적인 수송네트워크 설계는 매우 중요하다. 전통적인 hub-and-spoke 네트워크 설계문제에서 각 스포크의 위치와 화물량이 미리 주어진 상황에서 허브의 입지를 결정하였다. 하지만 스포크 역시 스포크가 담당하는 고객들의 위치와 담당 영역에 따라 그 위치와 수, 그리고 화물량이 변할 수 있다. 또한 정확한 비용산출을 위해서는 스포크에서 고객으로의 수집과 배달을 위한 차량경로가 함께 고려되어야 한다. 다루는 수송망 설계문제는 상호 관련성 있는 여러 부분문제가 결합된 통합문제로써 이를 해결하는 방법으로 기존의 발견적 방법에 의한 순차적 기법은 한계가 있다. 본 연구에서는 공생 진화알고리듬 기반의 방법론을 채용하여 다루는 수송망 설계문제를 동시에 통합적으로 해결할 수 있는 알고리듬을 개발한다. 실험을 통해 개발한 알고리듬의 우수성과 그 적용성을 보인다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.1045-1050
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• 2007

단백질 상호작용 네트워크의 데이터의 양이 증가함에 따라 이를 보다 쉽게 분석하기 위해 고속으로 시각화 방법이 요구되고 있다. 본 논문은 방대한 단백질 상호작용 네트워크에서 물리적 관계도가 높은 단백질을 중심으로 다단계에 걸쳐 스프링-포스(spring force)레이아웃 기법을 적용하여 그래프를 시각화하는 방법을 제안한다. 본 논문에 따른 단백질 상호작용 네트워크에서 시각화하는 방법은 물리적 관계도가 높은 단백질을 선정하는 단계, 선정된 단백질을 기반으로 네트워크를 합병하는 단계 및 합병된 노드들을 확장하는 단계를 거쳐 시각화하는 것을 특징으로 한다. 이에 따라, 단백질 상호작용 네트워크를 균형 상태의 그래프로 표현하고 고속으로 시각화할 수 있다는 장점이 있다.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.114-120
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• 2009

In this study, we employed the idea that disease-related proteins tend to be work as an important factor for architecture of the disease network. We initially obtained 43 atopy-related proteins from the Online Mendelian Inheritance in Man (OMIM) and then constructed atopy-related protein interaction network. The protein network can be derived the map of the relationship between different disease proteins, denoted disease interaction network. We demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks. From constructed the disease-protein bipartite network, we derived three diseasomal proteins, CCR5, CCL11, and IL/4R. Although we use the relatively small subnetwork, an atopy-related disease network, it is sufficient that the discovery of protein interaction networks assigned by diseases will provide insight into the underlying molecular mechanisms and biological processes in complex human disease system.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.327-345
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• 2008

The main purposes of this study are to construct a Korean science network from journal contributions data of Korean researchers, and to analyze the structure and characteristics of the network. First of all, the association matrix of 140 scholarly domains are calculated based on the number of contributions in common journals, and then the Pathfinder network algorithm is applied to those matrix. The resulting network has several hubs such as 'Biology', 'Korean Language & Linguistics', 'Physics', etc. The entropy formula and several centrality measures for the weighted networks are adopted to identify the centralities and interdisciplinarity of each scholarly domain. In particular, the date hubs, which have several weak links, are successively distinguished by local and global triangle betweenness centrality measures.

• 한국지구과학회:학술대회논문집
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• 2004.02a
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• pp.50-51
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• 2004

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.1-13
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• 2007

In this research, we found out that bridging nodes have great effect on the robustness of protein-protein interaction networks. Until now, many researchers have focused on node's degree as node's essentiality. Hub nodes in the scale-free network are very essential in the network robustness. Some researchers have tried to relate node's essentiality with node's betweenness centrality. These approaches with betweenness centrality are reasonable but there is a positive relation between node's degree and betweenness centrality value. So, there are no differences between two approaches. We first define a bridging node as the node with low connectivity and high betweenness value, we then verify that such a bridging node is a primary factor in the network robustness. For a biological network database from Internet, we demonstrate that the removal of bridging nodes defragment an entire network severally and the importance of the bridging nodes in the network robustness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.137-146
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• 2018

The purpose of this study was to derive and analyze the hub facilities that occupy major positions in the vehicle movement networks of livestock facilities. For this purpose, this study used the KAHIS data provided by Animal and Plant Quarantine Agency. The hub facilities were derived from the degree centrality & betweenness centrality. The analysis results are summarized as follows. First, in a livestock facility's vehicle movement network, there are a small number of hub facilities with very high centrality indicator values compared to other facilities. Second, the hub facilities based on the degree centrality are the feed factory, the milk collecting center, slaughterhouse, slaughterhouse for chicken, and livestock markets. Third, the hub facilities based on the betweenness centrality are the livestock markets, the feed factory, and slaughterhouse. Fourth, hub facilities based on the degree centrality are concentrated in a particular area, but the hub facilities based on betweenness centrality are distributed relatively evenly.