• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.67-71
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• 2003

In order to manage a cluster system that consists of a number servers, management aspects such as configuration management, fault management, performance management, and user management should be considered. Especially, it is necessary to monitor performances for performance and fault management. An agent in each server collects performance counters, status changes, and events occurred in normal or abnormal states. The data collected are delivered to a collector sorter and processed in a report tool for performance analysts and management decision in the cluster system point of view, by detecting fault state and tracing out resource usage, service response, and response, and states until failed. In this paper we propose an architecture modeling of a performance report tool for proactive cluster system management. Some results on a cluster system are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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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.

• Journal of Computing Science and Engineering
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• v.3 no.3
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• pp.143-164
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• 2009

This article surveys recent research in the area of language modeling (sometimes called statistical language modeling) approaches to information retrieval. Language modeling is a formal probabilistic retrieval framework with roots in speech recognition and natural language processing. The underlying assumption of language modeling is that human language generation is a random process; the goal is to model that process via a generative statistical model. In this article, we discuss current research in the application of language modeling to information retrieval, the role of semantics in the language modeling framework, cluster-based language models, use of language modeling for XML retrieval and future trends.

• Journal of the Korean Institute of Gas
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• v.7 no.1 s.18
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• pp.41-46
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• 2003

The Phase and temperature changes of large clusters formed in a free jet expansion of acetylene in 14atm and 233K has been studied. The cluster has been treated as a sphere composed of many shells. A mean diameter of 4.88 microns was obtained by modeling the experimental cooling curve of clusters based on evaporation and heat conduction theory.

• Journal of Microbiology and Biotechnology
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• v.20 no.2
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• pp.294-300
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• 2010

The Iro protein is a member of the HiPIP family with the [$Fe_4S_4$] cluster for electron transfer. Many reports proposed that the conserved aromatic residues might be responsible for the stability of the iron-sulfur cluster in HiPIP. In this study, Tyr10 was found to be a critical residue for the stability of the [$Fe_4S_4$] cluster, according to site-directed mutagenesis results. Tyr10, Phe26, and Phe48 were essential for the stability of the [$Fe_4S_4$] cluster under acidic condition. Trp44 was not involved in the stability of the [$Fe_4S_4$] cluster. Molecular structure modeling for the mutant Tyr10 proteins revealed that the aromatic group of Tyr10 may form a hydrophobic barrier to protect the [$Fe_4S_4$] cluster from solvent.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1041-1045
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• 2010

Clusters can provide scalable and flexible architectures for parallel computing servers and data centers. Their performance prediction has been a very challenging issue. Existing performance measurement methodologies are able to measure the performance of servers already constructed. Thus they cannot provide a way to predict the overall system performance in advance when designing the system at the initial phase or adding more nodes for more capacity. Therefore, the performance modeling and prediction methodology for large-scale clusters is highly required. In this paper, we suggest a methodology to predict the performance of large-scale clusters, which consists of measurement, modeling and prediction steps. We apply the methodology to a real cluster server and show its usefulness.

• Journal of Microbiology and Biotechnology
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• v.18 no.6
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• pp.1070-1075
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• 2008

IscA was proposed to be involved in the iron-sulfur cluster assembly encoded by the iscSUA operon, but the role of IscA in the iron-sulfur cluster assembly still remains controversial. In our previous study, the IscA from A. ferrooxidans was successfully expressed in Escherichia coli, and purified to be a [$Fe_4S_4$] -cluster-containing protein. Cys35, Cys99, and Cys101 were important residues in ligating with the [$Fe_4S_4$] cluster. In this study, Asp97 was found to be another ligand for the iron-sulfur cluster binding according to site-directed mutagenesis results. Molecular modeling for the IscA also showed that Asp97 was a strong ligand with the [$Fe_4S_4$] cluster, which was in good agreement with the experimental results. Thus, the [$Fe_4S_4$] cluster in IscA from A. ferrooxidans was ligated by three cysteine residues and one aspartic acid.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.8-15
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• 2011

In this paper, we propose a new structure modeling algorithm from 3D cloud points of terrestrial LADAR data. Terrestrial LIDAR data have various obstacles which make it difficult to apply conventional algorithms designed for air-borne LIDAR data. In the proposed algorithm, the field data are separated into several clusters by adopting the structure extraction method which uses color information and Hough transform. And cluster based Delaunay triangulation technique is sequentially applied to model the artificial structure. Each cluster has its own priority and it makes possible to determine whether a cluster needs to be considered not. The proposed algorithm not only minimizes the effects of noise data but also interactively controls the level of modeling by using cluster-based approach.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.1 s.343
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• pp.57-70
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• 2006

This paper descWireless Sensor Network consisting of a number of small sensors with transceiver and data processor is an effective means for gathering data in a variety of environments. The data collected by each sensor is transmitted to a processing center that use all reported data to estimate characteristics of the environment or detect an event. This process must be designed to conserve the limited energy resources of the sensor since neighboring sensors generally have the data of similar information. Therefore, clustering scheme which sends aggregated information to the processing center may save energy. Existing multi-hop cluster energy consumption modeling scheme can not estimate exact energy consumption of an individual sensor. In this paper, we propose a new cluster energy consumption model which modified existing problem. We can estimate more accurate total energy consumption according to the number of clusterheads by using Voronoi tessellation. Thus, we can realize an energy efficient cluster formation. Our modeling has an accuracy over $90\%$ when compared with simulation and has considerably superior than existing modeling scheme about $60\%.$ We also confirmed that energy consumption of the proposed modeling scheme is more accurate when the sensor density is increased.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.11
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• pp.1114-1121
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• 2014

Armed forces conducts war under volatile and unpredictable situation. Constructing communication system which ensures a victory is very important and difficult work. Traffic modeling has been conducted to derive WNW topology which meets operational requirements and capability under tactical environment. The result of study explains based on DTaQ's IER that company level cluster has 10~20% better packet receive rate than brigade level size.