• 한국항공운항학회:학술대회논문집
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• 2005.05a
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• pp.128-134
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• 2005

• Bulletin of the Korean Space Science Society
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• 2006.10a
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• pp.167-167
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• 2006

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.43-43
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• 2004

• Translational and Clinical Pharmacology
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• v.26 no.3
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• pp.142-142
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• 2018

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1076-1079
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time in. Today any data storage system can not satisfy these conditions, but holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System architecture without mechanical actuating part is possible, so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this paper, to correct errors of binary data stored in holographic digital data storage system, find cluster centers using clustering algorithm and reduce intensities of pixels around centers. We archive the procedure by two algorithms of C-mean and subtractive clustering, and compare the results of the two algorithms. By using proper clustering algorithm, the intensity profile of data page will be uniform and the better data storage system can be realized.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.83-87
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time in. Today any data storage system can not satisfy these conditions, but holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System architecture without mechanical actuating pare is possible, so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this paper, to correct errors of binary data stored in holographic digital data storage system, find cluster centers using clustering algorithm and reduce intensities of pixels around centers. We archive the procedure by two algorithms of C-mean and subtractive clustering, and compare the results of the two algorithms. By using proper clustering algorithm, the intensity profile of data page will be uniform and the better data storage system can be realized.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.621-628
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• 2005

This article addresses the problem of measuring a joint agreement between multiple raters and a standard set of responses. A new agreement measure based on Um's approach is proposed. The proposed agreement measure is used for multivariate interval responses. Comparison is made between the proposed measure and other corresponding agreement measures using hypothetical data set.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.705-712
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• 2005

Entropy is the basic concept of information theory. It is well defined for random varibles with known probability density function(pdf). For given data with unknown pdf, entropy should be estimated. Usually, estimation of entropy is based on the approximations. In this paper, we consider a kernel based approximation and compare it to the cumulant approximation method for several distributions. Monte carlo simulation for various sample size is conducted.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.370-375
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• 1998

The objectives of this study are to evaluate the applicability of SCS TR-20 model for small ungauged watershed, to show the behavior of the model with variation of topography in watershed, and to evaluate the storage effect of paddy field for flood flow. For this purpose, simulated data from the model were compared with the observed flood data at two sites (HS#3, HS#4) in Balan watershed. From the comparison between simulated and observed data, it was found that the model is applicable to this watershed.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.531-541
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• 2001

In this study, we compare the capabilities of the data mining tools of the most updated version objectively and provide the useful information in which enterprises and universities chose them. In particular, we compare the SAS/Enterprise Miner 3.0, SPSS/Clementine 5.2 and IBM/Intelligent Miner 6.1 which are well known and easily gotten.