• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.331-343
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• 2014

We propose two estimating procedures to analyze clustered interval-censored data with an informative cluster size based on a marginal model and investigate their asymptotic properties. One is an extension of Cong et al. (2007) to interval-censored data and the other uses the within-cluster resampling method proposed by Hoffman et al. (2001). Simulation results imply that the proposed estimators have a better performance in terms of bias and coverage rate of true value than an estimator with no adjustment of informative cluster size when the cluster size is related with survival time. Finally, they are applied to lymphatic filariasis data adopted from Williamson et al. (2008).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.26-32
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• 2002

The objectives of this paper are twofold. First is to introduce a model for representing documents with semantics relatedness using rough sets but with tolerance relations instead of equivalence relations (TRSM). Second is to introduce two document hierarchical and nonhierarchical clustering algorithms based on this model and TRSM cluster-based information retrieval using these two algorithms. The experimental results show that TRSM offers an alterative approach to text clustering and information retrieval.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.261-268
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• 2001

With the rapid growth of the Internet, various web-based businesses are creating a new environment in an imaginary space. However, this expanding Internet and user increase cause an overflow of transmission and numerous subordinate problems. To solve these problems, a parallel cluster system is produced using different methods. This thesis recommends a multi0distribution cluster group. It constructs a MPP dynamic distribution sub-cluster group using numerous low-priced and low-speed systems. This constructed sub-cluster group is then connected with a singular virtual IP to finally serve the needs of clients (users). This multi-distribution cluster group consists of an upper structure based on LVS and a dynamic serve cluster group centered around an SC-server. It conducts the workloads required from users in a parallel process. In addition to the web service, this multi-distribution cluster group can efficiently be utilized for the calculations which require database controls and a great number of parallel calculations as well as additional controls with result from the congestion of service.

• Journal of Korea Technology Innovation Society
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• v.17 no.4
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• pp.820-844
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• 2014

Various innovation theories, such as innovation system, innovation cluster, triple helix model, are different in their focus. However they all emphasize the interaction between innovation actors in order to generate, diffuse, and appropriate technological innovations successfully. This study analyzes how the interaction of innovation actors in Daedeok Innopolis has been changed since the introduction of innovation cluster policy like the designation of Daedeok Innopolis. Based on the analysis of survey data, Innopolis statistics, and patent joint-application data, we come to the conclusions that the Daedeok Innopolis has characteristics of multi-level governance structure, in which innovation cluster, i.e. Daedeok Innopolis, regional innovation system, and national innovation system directly overlap under the framework of innovation system. In addition, from the perspectives of triple helix model, we are able to verify that the inter-domain interactions between innovation actors, such as tri-lateral network, have been constantly increased in the Daedeok Innopolis. Based on our analysis, we identify some policy suggestions in order to strengthen the competitiveness of the Daedeok Innopolis as well as other innovation clusters in Korea. First, the network activities between innovation actors within innovation cluster should be strengthened based on the geographical accessibility. Second, private intermediate organizations should be established and their roles should be extended. Third, the entrepreneurial activities of universities within innovation cluster should be strengthened. In other words, the roles of universities within the Innopolis should be activated. Finally, the government should provide relevant policy supports to activate the interactions between innovation actors within innovation cluster.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Korean Institute of Interior Design Journal
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• no.3
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• pp.58-63
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• 1994

As following four steps, we can make a development about Model-Curriculum of Interior Architecture by an approach of occupational cluster in a College. Step. 1.Selecting of an occupational cluster in which students want to get a job. Step 2. Abstracting and analyzing about occupation , job and task of occupational cluster. Step.3. Selecting of courses by the systematization of occupation, job and task. Step.4. Selecting the goals of instruction in Interior Architecture. Through the above steps, the change of occupation has been promoted among occupational cluster by the development of curriculum based on the planning of Interior Architecture which has the widest range of job and task.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• ETRI Journal
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• v.43 no.3
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• pp.524-537
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• 2021

High-performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities of computing systems and has been used as a standard to compare the performance of computing systems since the early 1980s. In the initial system-design stage, it is critical to estimate the capabilities of a system quickly and accurately. However, the original HPL mathematical model based on a single core and single communication layer yields varying accuracy for modern processors and accelerators comprising large numbers of cores. To reduce the performance-estimation gap between the HPL model and an actual system, we propose a mathematical model for multi-communication layered HPL. The effectiveness of the proposed model is evaluated by applying it to a GPU cluster and well-known systems. The results reveal performance differences of 1.1% on a single GPU. The GPU cluster and well-known large system show 5.5% and 4.1% differences on average, respectively. Compared to the original HPL model, the proposed multi-communication layered HPL model provides performance estimates within a few seconds and a smaller error range from the processor/accelerator level to the large system level.