• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.942-948
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• 2016

WSN based on wireless sensor nodes, Sensor nodes can not be reassigned and recharged if they once placed. Each sensor node comes into being involved to a communication network with its limited energy. But the existing proposed clustering techniques, being applied to WSN environment with irregular dispersion of sensor nodes, have the network reliability issues which bring about a communication interruption with the local node feature of unbalanced distribution in WSN. Therefore, the communications participation of the sensor nodes in the suggested algorithm is extended by 25% as the sensor field divided in the light of the non-uniformed distribution of sensor nodes and a static or a dynamic clustering algorithm adopted according to its partition of sensor node density in WSN. And the entire network life cycle was extended by 14% to ensure the reliability of the network.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.412-415
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• 2011

본 논문에서는 정적 무선 센서 네트워크상의 클러스터링 기법인 EECS(Energy Efficient Clustering Scheme)의 노드와 Base Station간의 거리를 고려한 head 선출 과정에 노드의 이동성 및 미래 위치 예측을 융합하여 확장한 새로운 동적환경상의 클러스터링 기법 EECS-M(Energy Efficient Clustering Scheme in Mobile wireless sensor networks)을 제안한다. 실험을 통하여 EECS-M이 동적 환경상의 LEACH-M, WCA 및 정적 환경상의 EECS, LEACH 클러스터링 알고리즘들에 비해 life time 및 life time 대비 네트워크의 잔여 에너지 측면에서 성능향상을 가진다는 것을 보인다.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.1
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• pp.35-50
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• 2004

This study evaluated the applicability of the static hierarchic clustering model to clustering query results in OPAC. Two clustering methods(Between Average Linkage(BAL) and Complete Linkage(CL)) and two similarity coefficients(Dice and Jaccard) were tested on the query results retrieved from 16 title-based keyword searchings. The precision of optimal dusters was improved more than 100% compared with title-word searching. There was no difference between similarity coefficients but clustering methods in optimal cluster effectiveness. CL method is better in precision ratio but BAL is better in recall ratio at the optimal top-level and bottom-level clusters. However the differences are not significant except higher recall ratio of BAL at the top-level duster. Small number of clusters and long chain of hierarchy for optimal cluster resulted from BAL could not be desirable and efficient.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.196-198
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• 2002

클러스터링 데이터베이스는 높은 가용성과 확장성을 갖으며, 예상치 못한 클라이언트 질의의 증가나 질의 패턴의 변경에 따른 작업부하의 편중에 효율적으로 대처할 수 있는 구조이다. 특히 온라인 확장 기법은 트랜잭션 처리를 중지하지 않고 새로운 노드를 클러스터에 추가하여 데이터를 재구성함으로써 임의의 노드에 질의가 집중되는 문제를 해결할 수 있다. 정적으로 구성된 시스템만으로는 두 대 이상의 서버에 작업량이 집중될 경우 재배치 시 서버 간의 데이터 이동의 반복 현상이 발생되며. 이로 인해 네트웍의 부하와 함께 실시간 트랜적션의 처리에 있어서 응답 시간이 지연되는 문제점이 발생한다. 따라서 본 논문에서는 데이터 이동의 반복 현상을 해결하기 위해 클러스터링 데이터베이스에서 온라인 확장을 고려한 CSB+ 트리 색인의 온라인 재구성 기법을 제안한다. 제안된 기법은 온라인 확장을 통한 동구 노드의 확장으로 데이터 이동의 반복을 막고 새롭게 추가된 노드를 통해 빠르고 효율적인 데이터의 분산을 수행한다 또한 각 시스템의 내부를 CS$B^{+}$ 트리로 구성하여 데이터의 재주성시에도 실시간 트랜잭션에 대한 빠른 응답 시간을 보장한다.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.485-492
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• 2009

In the mobile service environment, patterns generated by temporal synchronization are streamed with different instance values. This paper proposed a variable clustering management method, which manages multiple data streaming dynamically, to support flexible clustering. The method manages synchronization effectively and differently with conventional streaming methods in data streaming environment and manages clustering streaming after the structural presentation level and the fitness presentation level. In the structural presentation level, the stream structure is presented using level matching and accumulation matching, and clustering management is carried out by the management of dynamic segment and static segment. The performance of the proposed method is tested by using k-means method, C/S server method, CDN method, and simulation. The test results showed that the proposed method has better performance than the other methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.277-280
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• 2009

Mobile Ad-hoc Network(MANET) could provide the reliable monitoring and control of a variety of environments for remote place. Mobility of MANET would require the topology change frequently compared with a static network. To improve the routing protocol in MANET, energy efficient routing protocol would be required as well as considering the mobility would be needed. In this paper, we propose a new method that the CACH(Context-aware Clustering Hierarchy) algorithm, a hybrid and clustering-based protocol that could analyze the link cost from a source node to a destination node. The proposed analysis could help in defining the optimum depth of hierarchy architecture CACH utilize. The proposed CACH could use localized condition to enable adaptation and robustness for dynamic network topology protocol and this provide that our hierarchy to be resilient.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.366-368
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• 2009

본 논문에서는 모바일 로봇이 작업하는 공간상에서 빠르고 안전한 최적 경로계획을 수행할 수 있게 하는 가변적 리드 맵을 이용한 장애물 밀집 정보 기반 경로계획을 제안한다. 모바일 로봇이 작업 공간에 대해서 빠르고 안전한 경로계획을 해 클러스터링 기법을 이용하여 정적 및 동적 장애물의 분포에 대한 맵 정보를 재구성하여 정보화 시킨다. 최적의 경로계획을 위해서는 재구성된 장애물 밀집 클러스터 데이터를 이용하여 전통적 기법의 GA 방법을 변형한 최적 경로계획을 수행한다. 제안한 기술의 효율성을 검증하기 위해 그리드 기반 경로계획 중의 하나인 A*알고리즘과 다양한 맵을 이용하여 성능 비교를 수행하였다. 실험결과 제안한 경로계획 기술은 기존 알고리즘 보다 빠른 처리 성능과 동적 장애물이 밀집한 지역을 회피하는 최적 경로계획을 수행함을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.761-764
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• 2020

본 논문에서는 라이프로그 데이터를 기반으로 한 행동인식 결과로부터 일상생활의 활동유형을 분석하는 기술에 대해 제안한다. 실제 일상생활 중에 수집한 가속도 센서 데이터만을 이용하여 분석한 행동인식 결과를 정적-동적 행동으로 분류된 특징 벡터로 나타내었고, 이를 클러스터링하여 6개의 대표 활동유형으로 분류하였다. 50명의 사용자 데이터를 분석하여 정적-동적 활동의 비율에 따른 활동유형을 분류함으로써 실제 라이프로그 데이터로부터 일상생활 활동유형을 확인하였다.