• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.188-194
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• 2006

Three-dimensional topological data is essential for 3D modeling and application such as emergency management and 3D network analysis. This paper reviewed current 3D topological data model and developed a method to construct 3D topological node-relation data structure from 2D computer aided design (CAD) data. The method needed two steps with medial axis-transformation and topological node-relation algorithms. Using a medial-axis transformation algorithm, the first step is to extract skeleton from wall data that was drawn polygon or double line in a CAD data. The second step is to build a topological node-relation structure by converting rooms to nodes and the relations between rooms to links. So, links represent adjacency and connectivity between nodes (rooms). As a result, with the conversion method 3D topological data for micro-level sub-unit of each building can be easily constructed from CAD data that are commonly used to design a building as a blueprint.

• Journal of KIISE:Information Networking
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• v.35 no.4
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• pp.251-267
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• 2008

In this paper, we present a distributed topology control algorithm for constructing an optimized topology having a minimal search-time in unstructured peer-to-peer network. According to the proposed algorithm, each node selects the best nodes having higher hit-ratio than other nodes as many as the number being exponentially proportional to the hit-ratio of the node itself, and then it connects to them. The ensemble behavior of the proposed algorithm is very similar to the recrystrallizing phenomenon that is observed in nature. There is a partial order relationship among the hit-ratios of most nodes of constructed topology. Therefore once query message visits a node, it has a higher hit-ratio than the node that was visited last by the message. The query message even sent from freeloader can escape to the node having high hit-ratio by one hop forwarding, and it never revisits any freeloader again. Thus the search can be completed within a limited search time. We also propose the Chain-reactive search scheme using the constructed topology. Such a controlled multicasting reduces the query messages by 43 percent compared to that of the naive Gnutella using broadcasting, while it saves the search time by 94 percent. The search success rate of the proposed scheme is 99 percent.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.355-358
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• 2009

센서 네트워크에 대한 연구가 활발히 이루어지면서 센서 네트워크 보안에 대한 문제점이 많이 야기되고 있다. 무선 센서 네트워크에서는 개방된 환경에서 제한적인 자원을 가지는 노드들로 구성되어 있다. 개방된 환경에 배치된 노드들은 공격자에게 쉽게 노출되어질 수 있다. 공격자는 노드를 물리적으로 포획하여 데이터 인증에 사용하는 인증키와 같은 보안 정보들을 획득할 수 있다. 공격자는 포획된 노드를 통하여 허위 보고서로 무선 센서 네트워크에 쉽게 삽입시킬 수 있다. 이는 허위 보고서로 인한 혼란 및 위조 정보의 전달과정에서 발생하는 에너지 고갈 등의 문제점을 유발시키게 된다. 이러한 허위 보고서를 조기에 탐지 및 폐기하기 위하여 동적 여과 프로토콜(DEF: Dynamic En-route Filtering scheme)이 제안되었다. DEF에서 인증키를 재배포 하는 주기는 보안 강도와 비용을 트레이드-오프 하는 관계에 놓여있으므로 매우 중요하다. 본 논문에서는 센서네트워크에서 동적 여과 프로토콜의 인증키 재배포 주기를 결정하는 기법을 제안한다. 배포된 노드들의 위상변화, BS까지 도달한 허위보고서 비율, 공격자에게 포획된 노드의 수 등을 고려하여 재배포 여부를 결정하고 재배포가 결정되면 각 클러스터 헤드들에게 재배포를 명령하게 된다.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.407-417
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• 2014

An arirang is the most famous Korean folk song and was registered in UNESCO(Unitied Nations Educational, Scientific and cultural Organization) as an intangible cultural heritage in 2012. Most arirangs are composed of text and refrain parts. Genealogy of the arirang was classified in refrain patterns by using multiple sequence alignment algorithm. There are two different refrain patterns, slow and fast melodies. Of 106 arirangs, 38 and 68 arirangs contain fast and slow melodies, respectively. 73 arirangs and 104 their key words were extracted from bipartate arirang network that composed of arirangs, text works, and their relationships. The correlation among the arirangs was analyzed from the selected arirangs and key words by using pairwise comparison matrix. Also, analysis of correlation among the arirnags was performed by stepwise removal of the single degree nodes from the bipartate arirang network In this study, arirangs were analyzed in genealogy and correlation among arirangs by using informatic algorithm and network technology, in which arirang research will be constructed a stepping stone for the popularization and globalization of the arirangs.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12B
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• pp.1066-1080
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• 2003

In this paper a scalable QoS routing scheme for distributed multimedia applications in a hierarchical wide area network is proposed. The problem of QoS routing is formulated as a multicriteria shortest path problem, known as NP-complete. The proposed hierarchical routing scheme consists of two phases. In Phase 1, every border node periodically pre-computes the QoS distance for the paths between every pair of border nodes in any level of domain hierarchy. This phase is independet of the QoS request from an application. In Phase II, distributed graph construction algorithm is performed to model the network as a graph by retrieving pre-computed QoS distances. The graph is constructed by the on-demand algorithm and contains a part of the network topology which is completely neglected or partially considered by existing routing schemes, thus maintaining more accurate topology information. By using retrieval approach rather than advertising one, no global QoS state information exchange among nodes is needed. In this Phase, distributed partition algorithm for QoS routing problem is also performed, thus eliminating virtual links on the hierarchically complete path.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.357-362
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• 2016

A social network or bio network is one of the complex networks that are formed by connecting specific relationships between interacting objects. Usually, these networks consist of community structures. Automatically detecting the structures is an important technique to understand and control the interaction objects. However, the topologies and structures of the networks change by interactions of the objects, with respect to time. Conventional techniques for finding the community structure have a high computational complexity. Additionally, the methods inefficiently deal with repeated computation concerning graph operation. In this paper, we propose an incremental technique for detecting interaction-based communities in dynamic networks. The proposed technique is able to efficiently find the communities, since there is an awareness of changed objects from the previous network, and it can incrementally reuse the previous community structure.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.383-393
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• 2018

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.