• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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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.

• 소프트웨어공학소사이어티 논문지
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• 제25권4호
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• pp.69-74
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• 2012

본 논문은 소프트웨어 클러스터링 기법과 커뮤니티 검출 기법의 비교를 통하여 아키텍쳐 모듈 복원 프로세스에 커뮤니티 검출 알고리즘의 적용가능성을 제시한다. 또한, 대표적인 클러스터링 알고리즘과 커뮤니티 검출 알고리즘의 값과 나눠진 모듈간의 상관관계와 차이점을 분석한다. 이를 통하여 커뮤니티 검출 알고리즘이 소프트웨어 아키텍쳐 모듈 뷰 복원에 활용되어질 수 있다는 몇 가지 근거를 제시하였고, 기존의 클러스터링 결과와 커뮤니티 알고리즘의 결과치를 비교함으로써, 서로의 결과 데이터가 어떠한 연관성을 가지는지 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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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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• 제15권5호
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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.

• 전기학회논문지
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• 제57권7호
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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.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2008년도 제38차 하계학술발표논문집 16권1호
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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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• 제5권1호
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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.

• 조명전기설비학회논문지
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• 제23권9호
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• pp.55-61
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• 2009

본 논문에서는 구역전기사업자를 구성하는데 적용하기 위해, 기존의 최적화 기법인 Discrete Particle Swarm Optimization (DPSO) 알고리즘을 개량한 Phasor DPSO (PDPSO) 알고리즘을 새롭게 제시한다. 구역전기사업자는 전력구입 뿐만 아니라 전력판매도 가능하고, 미리 계약한 수용가의 전력부하에게 전력을 공급할 의무가 있다. 하나의 배전계통에 다수의 구역전기사업자가 존재할 경우, 해당 배전계통 내의 모든 수용가에게 최소의 운영비용으로 전력을 공급하기 위해서는 다수 구역전기사업자 간에 구성형태를 조정할 필요가 있다. 이에 적용할 최적화 기법으로 본 논문은 PDPSO 알고리즘을 제안하며, 제안된 알고리즘의 각 개체는 기존의 다변수 벡터 대신 크기와 위상각으로 이루어진 다변수 페이저 값을 갖는다.

• International Journal of Contents
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• 제17권4호
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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.

• 인터넷정보학회논문지
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• 제13권3호
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• pp.75-82
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• 2012

본 논문에서는 modularity를 기반으로 한 향상된 커뮤니티 추출 알고리즘을 제안한다. 기존의 알고리즘은 modularity 값을 증가시키는 커뮤니티를 구축할 때 노드가 갖고 있는 정보를 고려하지 않음으로써, 계산을 비효율적으로 반복하여 수행한다. 제안하는 알고리즘은 노드의 degree(weight)를 계산하고 그것을 내림차순으로 정렬하고, 정렬된 순서대로 modularity 값의 증가여부를 확인함으로써, 반복되는 계산과정을 줄여 기존의 알고리즘보다 빠르게 최종 결과를 도출해낸다. 실험계산을 통해 제안하는 알고리즘이 더 짧은 시간 내에, 기존알고리즘이 구한 modularity 값보다 같거나, 향상된 값을 찾는다는 것을 보인다.