• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.280-285
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• 2007

다가올 유비쿼터스 시대를 선도할 기술 중 하나인 USN(Ubiquitous Sensor Network)은 산업 전반에 커다란 변화를 가져올 것으로 기대된다. USN은 각종 센서 노드로부터 사물 및 환경 정보를 감지, 저장, 가공, 통합하고 상황인식 정보 및 지식 콘텐츠 생성을 통하여 언제, 어디서. 누구나 원하는 맞춤형 서비스를 자유로이 이용할 수 있는 첨단 지능형사회의 기반 인프라이다. USN과 더불어 이를 활용하기 위한 USN 어플리케이션의 개발들이 늘고 었으며 이에 USN 미들웨어의 필요성 또한 증가하고 있다. USN 디렉토리 서비스는 USN 미들웨어를 구성하는 핵심 서비스 중 하나이며, 지역적으로 분산된 USN 자원에 대한 정보를 관리하고 USN 자원에 대한 검색을 실시간으로 제공하는 서비스이다. USN 자원은 고정된 자원과 이동성을 지닌 자원으로 나눌 수 있다. 하지만 현재 이동성을 지닌 센서 노드(Mobile Sensor Node)를 지원할 수 있는 USN 디렉토리 서비스는 보이지 않고 있다. 본 논문은 이러한 Mobile Sensor Node의 특성을 지원하는 USN 디렉토리 서비스의 모델들을 제시하고 그에 대한 설계 및 구현을 논한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.31-36
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• 2009

Bayesian networks are known as the best tools to express and predict the domain knowledge with uncertain environments. However, bayesian learning could be too difficult to do effective and reliable searching. To solve the problems of overtime demand, the nodes should be arranged orderly, so that effective structural learning can be possible. This paper suggests the classification learning model to reduce the errors in the independent condition, in which a lot of variables exist and data can increase the reliability by calculating the each entropy of probabilities depending on each circumstances. Also efficient learning models are suggested to decide the order of nodes, that has lowest entropy by calculating the numerical values of entropy of each node in K2 algorithm. Consequently the model of the most suitably settled Bayesian networks could be constructed as quickly as possible.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.686-689
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• 2006

무선 센서 네트워크의 사용이 증가함에 따라, 센서 네트워크의 보안에 대한 연구가 중요한 이슈가 되고 있다. 그 중 센서 노드들 사이의 키 설립 방법에 대한 연구가 매우 활발히 진행되었으며, 그에 따라 다양한 연구 결과들이 제시되었다. 하지만 기존의 연구 결과들은 강력한 보안성을 제공하기는 하나 키 설립과정이 매우 복잡하기 때문에 현실적으로 이용되기에는 어렵다는 단점이 있다. 즉, 이는 실제 센서 노드에 대한 구현을 어렵게 하며, 다양한 응용 분야에서 요구하는 보안성의 제공 역시 보장하지 못한다. 이에 본 연구에서는 다중 채널을 사용한 간단한 키 설립 방법을 제시하였다. 제시된 방법은 필요한 저장 공간과 통신 횟수, 계산 횟수를 최소화 한 반면, 다중 채널을 사용함으로서 보안성은 강화하였다. 이는 다양한 분야에서 제시된 방법이 활용될 수 있음을 의미한다. 1장의 서론에 이어 2장에서는 관련연구를 설명한다. 3장에서는 배경지식을 설명하고 4장에서 제안한 프로토콜을 설명한다. 마지막으로 5장에서는 분석을 통하여 이를 증명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.291-292
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• 2009

모바일 장치에서 얻을 수 있는 정보는 의미 있는 다양한 개인 정보를 가지고 있다. 본 논문에서는 모바일 장치에서 얻을 수 있는 정보를 분석하여 특이성을 추론하는 방법을 제안한다. 특이성 추론 방법으로 인과관계의 지식을 모델링하고 표현하며 추론하는 주요 형식화 방법의 하나인 FCM(Fuzzy Cognitive Map)을 사용하였다. 제안된 방법은 모바일 장치에서 얻은 정보와 추론된 특이성을 개념노드로 이용하여 새로운 특이성을 추론하며, 개념노드간의 인과관계를 효율적으로 표현한다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.7
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• pp.53-61
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• 2008

Consider a typical wireless sensor network in which stem nodes form the backbone network of mesh topology while each stem node together with leaf nodes in its vicinity forms a subnetwork of star topology. In such a wireless sensor network, we must heed the following when we design a MAC scheme supporting the packet delivery from a leaf node to a stem node. First, leaf nodes are usually battery-powered and it is difficult to change or recharge their batteries. Secondly, a wireless sensor network is often deployed to collect and update data periodically. Late delivery of a data segment by a sensor node causes the sink node to defer data processing and the data segment itself to be obsolete. Thirdly, extensive signaling is extremely limited and complex computation is hardly supported. Taking account of these facts, a MAC scheme must be able to save energy and support timeliness in packet delivery while being simple and robust as well. In this paper, we propose a version of ALOHA as a MAC scheme for a wireless sensor network. While conserving the simplicity and robustness of the original version of ALOHA, the proposed version of ALOHA possesses a distinctive feature that a sensor node decides between stop and continuation prior to each delivery attempt for a packet. Such a decision needs a stopping rule and we suggest a Bayes stopping rule. Note that a Bayes stopping rule minimizes the Bayes risk which reflects the energy, timeliness and throughput losses. Also, a Bayes stopping rule is practical since a sensor node makes a decision only using its own history of delivery attempt results and the prior information about the failure in delivery attempt. Numerical examples confirm that the proposed version of ALOHA employing a Bayes stopping rule is a useful MAC scheme in the severe environment of wireless sensor network.

• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.71-79
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• 2015

In a new era of Cloud and Big data, how to search the useful data from dynamic huge data pool in a right time and right way is most important at the stage where the information is getting more important. Above all, in the era of s Big Data it is required to design the advanced efficient intelligent Knowledge system which can process the dynamic variable big data. Accordingly in this paper we propose Dynamic personal Knowledge Network as one of the advanced Intelligent system approach. Adopting the human brain function and its neuro dynamics, an Intelligent system which has a structural flexibility was designed. For Structure-Function association, a personal Knowledge Network is made to be structured and to have reorganizing function as connecting the common nodes. We also design this system to have a reasoning process in the extracted optimal paths from the Knowledge Network.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.259-260
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• 2019

이 연구는 텍스트마이닝 및 사회연결망 분석을 통하여 지금까지 축적된 연구주제의 핵심어와 네트워크 지식구조를 확인하여 무용경영 연구의 흐름과 동향을 분석하는데 목적이 있다. 무용경영 연구동향에 관한 텍스트마이닝 분석 결과, 전반적으로 무용경영 연구에서 가장 높은 빈도를 나타낸 특정 토픽으로는 'Performing arts', 'Entrepreneurship', 'Dance', 'Audience development', 'Dance management' 등이 도출되었다. 사회연결망 분석을 실시한 결과, 'Entrepreneurship', 'Dance Marketing', 'Marketing'에서 노드간의 연결성이 높은 것으로 나타났다. 또한 국외에서는 꾸준히 관객개발(audience development)과 공연마케팅(performing arts marketing)이 주요 쟁점으로 다루어져 왔다. 이와 같은 연구동향 및 지식구조 분석을 토대로 이 연구는 보다 확장된 무용경영 연구의 관점을 제안하였다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.247-267
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• 2011

The purpose of this study is to investigate knowledge structure of knowledge organization research area in Korea. The study employed content analysis and network analysis to analyze degree centrality, betweenness, and eigenvector as well as frequency of words. It also analyzes research articles published during the period of 2000-2001. The study can be summarized that the network of keywords of knowledge organization researches is compact and complicated. Cataloging and classification play important roles in the network, and metadata and ontology becomes focal areas in knowledge organization. On the other hand, networks of authorships and authors are broad and fragmented. Collaboration is not active enough.

• Knowledge Management Research
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• v.22 no.1
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• pp.217-242
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• 2021

The purpose of this study is to examine the research trends in the filed of individual knowledge hiding through keyword network analysis. As individuals intentionally hide their knowledge beyond not sharing their knowledge in organizations and the research on knowledge hiding steadily spreads, it is necessary to examine the research trends regarding knowledge hiding behaviors. For keyword network analyses, we collected 346 kinds of 578 keywords from 120 articles associated with knowledge hiding behaviors. We also transformed the keywords to 86 nodes and 667 links by data standardizing criteria and finally analyzed the keyword network among them. Moreover, this study scrutinized knowledge hiding trends by comparing the conceptual model for knowledge hiding based on literature review and the network structure based on keyword network analysis. As results, first, the network centrality degree, knowledge sharing, creativity, and performance was higher than others in Degree, Betweenness, Closeness centrality. Second, this study analyzed ego networks about psychological ownership and individual emotion theoretically associated with knowledge hiding and explored the relationship between variables through comparing with the conceptual model for knowledge hiding. Finally, the study suggested theoretical and practical implications and provided the limitations and suggestions for future research based on study findings.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.