• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.680-699
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• 2015

The use of web service has been increased rapidly, with an increase in the number of available services, finding the exact service is the challenging task. Service discovery is the most significant job to complete the service discoverers needs. In order to achieve the efficient service discovery, we focus on designing a cluster based service discovery model for service registering and service provisioning among all mobile nodes in a mobile ad hoc network (MANETs). A dynamic backbone of nodes (i.e. cluster heads) that forms a service repository to which MANET nodes can publish their services and/or send their service queries. The designed model is based on storing services with their service description on cluster head nodes that are found in accordance with the proposed cluster head election model. In addition to identifying and analyzing the system parameters for finding the effectiveness of our model, this paper studies the stability analysis of the network, overhead of the cluster, and bandwidth utilization and network traffic is evaluated using analytic derivations and experimental evaluation has been done.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.109-113
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• 2007

Second language learners' variable degree of production difficulty according to the cluster type has previously been accounted for in terms of sonority distance between adjacent segments. As an alternative to this previous model, I propose a Phonetically Based Consonant Cluster Acquisition Model (PCCAM) in which consonant cluster markedness is defined based on the articulatory and perceptual factors associated with each consonant sequence. The validity of PCCAM has been tested through Korean speakers' production of English consonant clusters.

• 한국산학기술학회논문지
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• 제11권12호
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• pp.4991-4996
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• 2010

무선 센서 네트워크에서 클러스터링 기법은 클러스터를 형성하여 데이터를 통합한 후 한 번에 전송해서 에너지를 효율적으로 사용하는 기법이다. 클러스터 그룹 모델은 클러스터링에 기반을 두지만 이전의 기법과 달리 클러스터 헤드에 집중된 에너지 과부하를 클러스터 그룹 헤드와 클러스터 헤드로 분산시켜서 전체 에너지 소모량을 줄인다. 본 논문에서는 이러한 클러스터 그룹 모델에서 에너지 소모 모델의 임계값에 따라 최적의 클러스터 그룹 수와 클러스터 수를 구하고 이를 이용하여 센서 네트워크 전체 에너지 소모량을 최소화하고 네트워크 수명을 최대화한다. 실험을 통하여 제안된 클러스터 그룹 모델이 이전의 클러스터링 기법보다 네트워크 에너지 효율이 향상되었음을 보였다.

• 한국지능시스템학회논문지
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• 제27권1호
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• pp.35-41
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• 2017

본 연구는 모델기반설계 기법을 이용하여 자동차 클러스터의 감시 및 제어를 하는 스테이션을 설계한다. 설계 도구로 매트랩 GUI(Graphic User Interface), M 프로그램, 시뮬링크(simulink), 스테이트 플로우(state flow), 툴박스(tool box)를 사용하여 실제 자동차 클러스터 시스템과 연동하여 자동차에서 들어오는 경고, 인터럽트 등의 각종 정보 등을 감시한다. 감시 수단으로는 PC(Personal Computer) 스테이션을 사용하여 자동차 클러스터 설계 시 툴 박스의 인터페이스 명령함수가 실제 자동차 클러스터 시스템과 연동하게 한다. 따라서, 기존의 텍스트 방식과 달리 모델기반설계로 개발된 자동차 클러스터 시스템은 각 기능 및 알고리즘을 블록과 상태플로우로 프로그램에 따라 작성하기 때문에 알고리즘의 수정이나 기능 추가가 용이하며, 또한, PC를 통해 모니터 상에서 동작 알고리즘을 검증하기 때문에 클러스터의 개발과 수정에 따른 많은 시간과 비용을 절감할 수 있는 효과를 준다.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.53-64
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• 2019

The purpose of this study is to find out the fast delivery route that several drones return a truck again after departing from it for delivery locations at each cluster while the truck goes through the cluster composed of several delivery locations. The main issue is to reduce the total delivery time composed of the delivery time by relatively slow trucks via clusters and the sum of maximum delivery times by relatively fast drones in each cluster. To solve this problem, we use a three-step heuristic approach. First, we cluster the nearby delivery locations with minimal number of clusters satisfying a constraint of drone flight distance to set delivery paths for drones in each cluster. Second, we set an optimal delivery route for a truck through centers of the clusters using the TSP model. Finally, we find out the moved centers of clusters while maintaining the delivery paths for the truck and drones and satisfying the constraint of drone flight. distance in the two-dimensional region to reduce the total delivery time. In order to analyze the effect of this study model according to the change of the number of delivery locations, we developed a R-based simulation prototype and compared the relative efficiency, and performed paired t-test between TSP model and the cluster-based models. This study showed its excellence through this experimentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2302-2324
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• 2014

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

• 응용통계연구
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• 제25권3호
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• pp.447-456
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• 2012

For a sensitive survey in which the population is comprised of several clusters with a quantitative attribute, we present an additive quantitative randomized response model by cluster sampling that adapts a two-stage cluster sampling instead of a simple random sample based on Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's one. We also derive optimum values for the number of 1st stage clusters and the optimum values of observation units in a 2nd stage cluster under the condition of minimizing the variance given constant cost. We can see that Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of cluster sampling.

• 제어로봇시스템학회논문지
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• 제7권12호
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• 한국경제지리학회지
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• 제20권2호
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• pp.189-213
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• 2017

본 연구는 클러스터 진화의 분석에 있어서 클러스터 적응주기 모델의 의의와 한계점을 비판적으로 검토하고, 이를 토대로 향후 클러스터 진화 분석을 위한 연구 과제를 제시하고자 하였다. 1980년대 이전까지 클러스터를 비롯한 산업집적지 연구는 특정 시점에서 경제 공간의 양상에 주목하는 '정태적 관점'을 기초로 이루어졌지만, 최근에는 '복잡적 응계'의 '진화'에 주목하는 '동태적 연구'로 패러다임이 전환되었다. 이에 역동적 지속적으로 진화하는 클러스터에 적절한 분석도구로 적응주기 모델이 주목받게 되었으나, 클러스터 및 그 진화의 속성에 맞게 수정 및 보완되어 클러스터 적응주기 모델이 등장하게 되었다. 클러스터 적응주기 모델은 자원축적, 상호의존성 그리고 회복력의 측면에서 클러스터 진화의 특성을 규명하고, 클러스터 진화 경로를 6가지로 구분하여 살펴 볼 수 있는 포괄적인 분석틀이지만, 모델의 확대 및 심화를 위해서 이론적 경험적 연구 측면에서 더욱 활발한 논의와 보완이 요구된다. 따라서 향후 클러스터 진화 분석에 있어서의 연구 과제로는 클러스터 진화 모델의 구체화 및 정교화, 회복력 개념의 강조 그리고 경험적 연구를 통한 모델의 적용가능성과 유용성의 검증을 제시하고자 한다.