• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4991-4996
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• 2010

Clustering method in wireless sensor network is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. Even though cluster group model is based on clustering, it differs from previous method that reducing the total energy consumption by separating energy overload to cluster group head and cluster head. In this thesis, I calculate the optimal cluster group number and cluster number in this kind of cluster group model according to threshold of energy consumption model. By using that I can minimize the total energy consumption in sensor network and maximize the network lifetime. I also show that proposed cluster group model is better than previous clustering method at the point of network energy efficiency.

• Atmosphere
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• v.16 no.4
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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.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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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.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.189-213
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• 2017

This study seeks to critically examine the significance and limits of the cluster adaptive cycle model for analysis of cluster evolution and to propose research issues for future analysis of cluster evolution based on this critical examination. Until the 1980s, research on industrial complexes including clusters was based on a 'static perspective' that focuses on the aspect of economic space at a specific point in time, but the research paradigm has recently shifted to a 'dynamic perspective' focusing on 'evolution' of 'complex adaptive systems'. As a result, the adaptive cycle model has attracted attention as an analysis tool of dynamically evolving clusters. However, the cluster adaptive cycle model has emerged by being appropriately modified and expanded according to the properties of the cluster and its evolution. The cluster adaptive cycle model is a comprehensive analysis framework that identifies the characteristics of cluster evolution in terms of resource accumulation, interdependence, and resilience and classifies cluster evolution paths into six different categories. Nevertheless, there is still a need for further discussion and supplementation in terms of theoretical and empirical research to expand and deepen the model. Therefore, research issues for future analysis of cluster evolution are to specify and elaborate the cluster evolution model, to emphasize the concept of resilience, and to verify the applicability and usefulness of the model through empirical research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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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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• v.22 no.1
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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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• v.19 no.2
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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.

• Proceedings of the KSPS conference
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• 2007.05a
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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.

• Journal of the Korean Society of Food Culture
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• v.21 no.4
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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.

• Journal of Environmental Science International
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• v.24 no.6
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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.