• 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.

• 대기
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• 제16권4호
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• 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.

• 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.

• 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.

• Journal of Information Technology Applications and Management
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• 제19권2호
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• pp.117-134
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• 2012

Cluster industries are geographically concentrated and inter-connected by the flow of goods and services, which is stronger than the flow linking them to the rest of the economy. Photonics industries are one of the fastest growing high-tech industries in the world today. Especially, the city of Gwangju(South Korea) industrial cluster, a specialized complex in photonics industry, produced remarkable results in developing high-quality technologies since it launched the cluster program in 2005. Gwangju photonics industrial cluster will be ranked top level of the world photonics industry. In this sense, this study is aimed at proposing a new research model in which corporate performance influence factors of photonics industrial cluster (i.e., business environment, cooperative relationship, and industry-university-research institute partnership) affect absorptive capacity positively, leading to corporate performance eventually. This study developed a research model to explain the Korean photonics industrial cluster effects, and collected 91 survey responses from photonics based company managers in industrial cluster complex. To prove the validity of the proposed research model, PLS analysis is applied with valid 91 questionnaires. By employing PLS technique, the measurement reliability and validity of research variables are tested and the path analysis is conducted to do the hypothesis testing. In brief, the finding of this study suggests that corporate performance influence factors of photonics industrial cluster affect absorptive capacity positively, and corporate performance as well.

• 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.

• 한국식생활문화학회지
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• 제21권4호
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• pp.414-419
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• 2006

The purpose of this study was to measure the factor influencing tourist preferences for leaf mustard iimchi. Among 250 questionnaires, 230 questionnaires were utilized for the analysis. Frequencies, conjoint model, max. utility model, BTL model, Logit model, K-means cluster analysis, and one-way ANOVA analysis were used for this study. The findings from this study were as follows. First, the Pearson's R and Kendall's tau statistics showed that the model fitted the data well. Second, it was found that total respondents and three clusters regarded taste and price as the very important factor. Third, it was found that the first cluster most preferred product with light red color, plain package, and mild taste sold at a cheap price in factory. The second cluster most preferred product with light red color, plain package, and moderately pungent taste sold at a expensive price in factory. The third cluster most preferred product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory. Fourth, it was found that the first cluster most preferred simulation product with light red color, shaped package, and mild taste sold at a cheap price in factory. The second cluster most preferred simulation product with light red color, shaped package, and moderately pungent taste sold at a cheap price in factory. The third clutter most preferred simulation product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory.

• 한국환경과학회지
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• 제24권6호
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• pp.793-806
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• 2015

The purpose of this study is to find out the air flow patterns affecting the PM10 concentration in Busan and the potential sources within each trajectory pattern. The synoptic air flow trajectories are classified into four clusters by HYSPLIT model and the potential sources of PM10 are estimated by PSCF model for each cluster from 2008 to 2012. The potential source locations of PM10 are compared with the distribution of PM10 anthropogenic emissions in east Asia developed in 2006 for the NASA INTEX-B mission. The annual mean concentrations of PM10 in Busan decreased from $51ug/m^3$ in 2008 to $43ug/m^3$ in 2012. The monthly mean concentrations of PM10 were high during a spring season, March to May and low during a summer season, August and September. The cluster2 composed of the air trajectories from the eastern China to Busan through the west sea showed the highest frequency, 44 %. The cluster1 composed of the air trajectories from the inner Mongolia region to Busan through the northeast area of China showed the second high frequency, 26 %. The cluster3 and 4 were composed of the trajectories originated in the southeast sea and the east sea of Busan respectively and showed low frequencies. The concentrations of in each cluster were $47ug/m^3$ in cluster1, $56ug/m^3$ in cluster2, $42ug/m^3$ in cluster3 and $37ug/m^3$ in cluster4. From these results, it was proved that the cluster1 and 2 composed of the trajectories originated in the east and northeast area of China were the causes of high PM10 concentrations in Busan. The results of PSCF and CWT model showed that the potential sources of the high PM10 concentrations were the areas of the around Mongolia and the eastern China having high emissions of PM10 from Beijing, Hebei to Shanghai through Shandong, Jiangsu.