• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.118-130
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• 2008

Today, Numbers of "0" and "1" which are represented as a digital code have a role of communicating with various media, result in the development of IT technology. With the changes of the times, new media is created and various contents and media are formed. There is convergence between exiting contents and media at the same time and also is in the rapid progress of unification and collapse between the boundary of the data. Under these digital circumstance, the amount of many data which are created and disappeared is rapidly changed as much as the remarkable growth of the digital technology. The purpose of this thesis is to make a classified method of a effective data component related to data management, data recycling and data copyright in 3D animation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.233-237
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• 2010

The recent advance of sensor networks and ubiquitous techniques allow collecting and analyzing of the data which overcome the limitation imposed by time and space in real-time for making decisions. Also, analysis and prediction of collected data can support useful and necessary information to users. The collected data in sensor networks environment is the stream data which has continuous, unlimited and sequential properties. Because of the continuous, unlimited and large volume properties of stream data, managing stream data is difficult. And the stream data needs dynamic processing method because of the memory constraint and access limitation. Accordingly, we analyze correlation stream data using principal component analysis. And using result of analysis, it helps users for making decisions.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.7
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• pp.261-271
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• 1986

This paper describes a method for the state estimation considering current measurement component and detection of the bad data. The state values are estimated by weighted least square method in which measurement vector included bus injection current and line current. The bad data are detected using standardized variable of normal distribution and identified using sensitivity coefficients. When the bad data were occured by the bad measurement values. The results of the application to the model power system reveal the effectiveness of the presented algorithms.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.177-190
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• 1991

In this paper, we discuss and review various measures which have been presented for studying outliers. high-leverage points, and influential observations when principal component regression is adopted. We suggest several diagnostics measures when principal component regression is used. A numerical example is illustrated. Some individual data points may be flagged as outliers, high-leverage point, or influential points.

• Honam Mathematical Journal
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• v.37 no.1
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• pp.99-112
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• 2015

In this paper we consider the periodic Cauchy problem for the generalized two-component Hunter-Saxton system with analytic initial data and we prove a Cauchy-Kowalevski type theorem for the generalized two-component Hunter-Saxton system, that establishes the existence and uniqueness of real analytic solutions.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.164-168
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• 2003

Bayes theorem, suggested by the British Mathematician Bayes (18th century), enables the prior estimate of the probability of an event under the condition given by a specific This theorem has been frequently used to revise the failure probability of a component or system. 2-Stage Bayesian procedure was firstly published by Shultis et al. (1981) and Kaplan (1983), and was further developed based on the studies of Hora & Iman (1990) Papazpgolou et al., Porn(1993). For a small observed failure number (below 12), the estimated reliability of a system or component is not reliable. In the case in which the reliability data of the corresponding system or component can be found in a generic reliability reference book, however, a reliable estimation of the failure probability can be realized by using Bayes theorem, which jointly makes use of the observed data (specific data) and the data found in reference book (generic data).

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.165-175
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• 2008

Currently, software development methods have been advanced to service-oriented from component-oriented, to component-oriented from object-oriented. The component-oriented and service-oriented software development methods are analyzed by object-oriented UML model. So, the efficient analysis method for object-oriented UML model needs. In this paper, we suggest the analysis guideline and process based on event using Input Data-Process-Output Data Table for identifying use cases and classes efficiently. And the suggested method complements the problems depending the developer's perspective and experience.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.127-134
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• 2005

A suitable representation for the conceptual simplicity of the data in statistics and signal processing is essential for a subsequent analysis such as prediction, pattern recognition, and spatial analysis. Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.