• International conference on construction engineering and project management
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• 2015.10a
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• pp.702-703
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• 2015

Currently, according to the climate change, serious damage by Typhoon has been occurred in the world. In this respect, the research on the damage prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, to address this issue, correlation analysis is conducted between various variables based on the data of hurricane from 2003 to 2012. The findings of this study can be utilized to develop for predicting the damage of hurricane on buildings.

• Journal of Korea Water Resources Association
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• v.46 no.10
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• pp.1029-1040
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• 2013

This study aims to develop a multivariate daily rainfall simulation model considering spatial coherence across watershed. The existing Hidden Markov Model (HMM) has been mainly applied to single site case so that the spatial coherences are not properly addressed. In this regard, HMM coupled with Chow-Liu Tree (CLT) that is designed to consider inter-dependences across rainfall networks was proposed. The proposed approach is applied to Han-River watershed where long-term and reliable hydrologic data is available, and a rigorous validation is finally conducted to verify the model's capability. It was found that the proposed model showed better performance in terms of reproducing daily rainfall statistics as well as seasonal rainfall statistics. Also, correlation matrix across stations for observation and simulation was compared and examined. It was confirmed that the spatial coherence was well reproduced via CLT-HMM model.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.543-547
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• 2002

In this paper, we introduce a more improved camera modeling method for linear pushbroom images than the method proposed by Orun and Natarajan(ON). ON model shows an accuracy of within 1 pixel if more than 10 ground control points(GCPs) are provided. In general, there is high correlation between platform position and attitude parameters but ON model ignores attitude variation in order to overcome such correlation. We propose a new method that obtains an optimal solution set of parameters without ignoring the attitude variation. We first assume that attitude parameters are constant and estimate platform position's. Then we estimate platform attitude parameters using the values of estimated position parameters. As a result, we can set up an accurate camera model for a linear pushbroom satellite scene. In particular, we can apply the camera model to its surrounding scenes because our model provide sufficient information on satellite's position and attitude not only for a single scene but also for a whole imaging segment. We tested on two images: one with a pixel size 6.6m$\times$6.6m acquired from EOC(Electro Optical Camera), and the other with a pixel size 10m$\times$l0m acquired from SPOT. Our camera model procedures were applied to the images and gave satisfying results. We had obtained the root mean square errors of 0.5 pixel and 0.3 pixel with 25 GCPs and 23 GCPs, respectively.

• Journal of Technology Innovation
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• v.15 no.2
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• pp.83-102
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• 2007

This study extends the existing method of Technology Forecasting with Data Envelopment Analysis (TFDEA) by incorporating a ranking method into the model so that we can reduce the required number of DMUs (Decision Making Units). TFDEA estimates technological rate of change with the set of observations identified by DEA(Data Envelopment Analysis) model. It uses an excessive number of efficient DMUs(Decision Making Units), when the number of inputs and outputs is large compare to the number of observations. Hence, we investigated the possibility of incorporating CCCA(Constrained Canonical Correlation Analysis) into TFDEA so that the ranking of DMUs can be made. Using the ranks developed by CCCA(Constrained Canonical Correlation Analysis), we could limit the number of efficient DMUs that are to be used in the technology forecasting process. The proposed hybrid model could establish technology frontiers with the efficient DMUs for each generation of technology with the help of CCCA that uses the common weights. We applied our hybrid model to forecast the technological progress of main battle tank in order to demonstrate its forecasting capability with practical application. It was found that our hybrid model generated statistically more reliable forecasting results than both TFDEA and the regression model.

• Journal of Environmental Science International
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• v.21 no.5
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• pp.613-622
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• 2012

A pilot-scale pulse-jet bagfilter was designed, built and tested for the effects of four operating conditions (filtration velocity, inlet dust concentration, pulse pressure, and pulse interval time) on the total system pressure drop, using coke dust from a steel mill factory. Two models were used to predict the total pressure drop according to the operating conditions. These model parameters were estimated from the 180 experimental data points. The empirical model (EM) with filtration velocity, areal density, inlet dust concentration, pulse interval time and pulse pressure shows the best correlation coefficient (R=0.971) between experimental data and model predictions. The empirical model was used as it showed higher correlation coefficient (R=0.971) compared to that of the Multivariate linear regression(MLR) (R=0.961). The minimum pulse pressure predicted by empirical model (EM) was 5kg/$cm^2$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.235-239
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• 2008

In this paper, we study a simulation model for cooperative MIMO (multiple-input multiple-output) channels and present a cooperative one-ring channel model which is extended from the geometrical one-ring and two-ring scattering models. Assuming that the source, the destination and the relay are surrounded by an infinite number of scatters, we derive a reference model for the cooperative one-ring channel and propose a simulation model based on the reference model provided in the paper. Then we show how modeling parameters of the simulation model are determined to match the correlation functions for the respective models. With numerical investigation, we also show that the correlation functions for the reference and the simulation are well matched.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.110-111
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• 2010

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.551-566
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• 2006

In this paper, we consider a Bayesian model selection for the intraclass correlation coefficient in familiar data. In particular, we compare two nested models such as the independence and intraclass models using the reference prior. A criterion for testing is the Bayesian Reference Criterion by Bernardo (1999) and the Intrinsic Bayes Factor by Berger and Pericchi (1996). We provide numerical examples using simulation data sets for illustration.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.695-705
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• 2003

In this article, we propose a regression and correlation analysis via dynamic graphs and implement them in Java Web Start. For the polynomial relations between dependent and independent variables, dynamic graphics are implemented for both polynomial regression and spline estimates for an instant model selection. The results include basic statistics. They are available both as a web-based service and an application.