• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.281-283
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• 2009

Forming Community is important to manage and provide the service in Ubiquitous Environments including embedded tiny computers. Community Computing is that members constitute the community and cooperate. A mutual exclusion problem occurs when many processors try to use one resource and race condition happens. In the expanded concept, a group mutual exclusion problem is that processors in the same group can share the resource but processors in different groups cannot share. As mutual exclusion problems might be in community computing environments, we propose algorithm which improves the execution speed using RMS (resource management service). In this paper describes proposed algorithm and proves its performance by experiments, comparing proposed algorithm with previous method using quorum-based algorithm.

• Journal of Software Engineering Society
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• v.25 no.4
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• pp.69-74
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• 2012

This article suggests applicability to community detection algorithm from module recovering process of software architecture through compare to software clustering metric and community dectection metric. in addition to, analyze mutual relation and difference between separated module and measurement value of typical clustering algorithms and community detection algorithms. and then only sugeested several kinds basis that community detection algorithm can use to recovering module view of software architecture and, by so comparing measurement value of existing clustering metric and community algorithms, this article suggested correlation of two result data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3041-3063
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• 2018

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1055-1067
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• 2019

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1273-1278
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• 2008

In community computing environments, various members cooperate with each other systematically for attaining each community's goals. Because community computing environments are organized on the basis of PAN (Personal Area Network), each member commonly uses the power of batteries. If one member in community uses up the power of battery and does not operate normally, the community will not be able to provide the ultimate service goals for its users and be terminated finally. Therefore, it is necessary for accurate community operation to prevent a specific member's lifetime from terminating, as checking each member's power consumption in real-time. In this paper, we propose WEL (WEighted Leach) algorithm for optimizing lifetime of the members in community.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.163-167
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• 2008

본 논문에서는 광범위 Community Computing 환경에서의 Community Computing Network의 에너지 효율과 신뢰성을 높이기 위한 수정된 SPaC(Simple Packet Combining)를 제안한다. 제안하는 수정된 SPaC는 같은 패킷을 두 개 이상의 오류가 있는 패킷을 이용하여 에러를 복구하는 기존의 SPaC를 수정하여 특정 threshold 값을 사용하여 감청 시 CPU의 처리량을 줄이고 패리티 패킷을 이용하여 높은 신뢰성과 보다 향상된 에너지 효율을 가진다.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.43-48
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• 2016

We propose plain fingerprint classification based on a core stochastic algorithm that effectively uses a core stochastic model, acquiring more fingerprint minutiae and direction, in order to increase matching performance. The proposed core stochastic algorithm uses core presence/absence and contains a ridge direction and distribution map. Simulations show that the fingerprint classification accuracy is improved by more than 14%, on average, compared to other algorithms.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.55-61
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• 2009

This paper presents a modified Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm to configure Community Energy Systems(CESs) in the distribution system. The CES obtains electric power from its own Distributed Generations(DGs) and purchases insufficient power from the competitive power market, to supply power for customers contracted with the CES. When there are two or more CESs in a network, the CESs will continue the competitive expansion to reduce the total operation cost. The particles of the proposed PDPSO algorithm have magnitude and phase angle values, and move within a circle area. In the case study, the results by PDPSO algorithm was compared with that by the conventional DPSO algorithm.

• International Journal of Contents
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• v.17 no.4
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• pp.79-90
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• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.75-82
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• 2012

In this paper, an improved community detection algorithm based on the modularity is proposed. The existing algorithm does not consider the information that the nodes have in checking the possible modularity increase, hence the computation may be inefficient. The proposed algorithm computes the node degree (weight) and sorts them in non-increasing order. By checking the possible modularity value increase for the nodes in the nonincreasing order of node weights, the algorithm finds the final solution more quickly than the existing algorithm does. Through the computational experiments, it is shown that the proposed algorithm finds a modularity as good as the existing algorithm obtains.