• Membrane Journal
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• v.10 no.1
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• pp.19-29
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• 2000

To estimatc mutual diffusion coefficient for the analysis of mass transfer phenomena in polymer/solvent system, two models are proposed and the equations are derived. The estimates of mutual diffusion coefficients are obtained by two models suggested in this work and compared with and experimental data and Vrentas-Duda's. Vrentas-Duda's self diffusion coefficient was used for the mutual diffusion coefficient. Derivative of chemical potential on solvent was derived and used using original UNIFAC-FV and modified UNIFAC-FV. However, Vrentas-Duda's equation for mutual diffusion coefficient contains Flory-Huggins parameter x. For the derivative of chemical potential term, Vrentas-Duda assumed that parameter x was constant and independent of temperatures and concentrations The assumption is one of shortcoming in vrentas-Duda's mutual diffusion coefficient. New methods proposed in this work do not have such assumptions and simplifications. For the solvent of cyclohexane, n-pentane, and n-hexane in PIB(polyisolbutylene) and PMS-BR (poly(p-methylstyrene-co-isobutylene), new methods well correlate the experimental data at various temperatures and concentrations, and predicted the experimental data much better than Vrentas-Duda's for the PIB/toluene system. It is shown that new methods are excellent tools for correlating mutual diffusion coefficient data in polymer/solvent system over wide ranges of temperature and concentration without any assumptions or simplifications.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.305-312
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• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.401-408
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• 2013

Soil moisture is a very important variable in various area of hydrological processes. We predict the soil moisture using a support vector regression. The model is trained and tested using the soil moisture data observed in five sites in the Yongdam dam basin. With respect to soil moisture data of of four sites-Jucheon, Bugui, Sangieon and Ahncheon which are used to train the model, the correlation coefficient between the esimtates and the observed values is about 0.976. As the result of the application to Cheoncheon2 for validating the model, the correlation coefficient between the estimates and the observed values of soil moisture is about 0.835. We compare those results with those of artificial neural network models.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.487-490
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• 2002

In this paper, we report a novel way to evaluate the anticlinic interlayer coupling coefficient U between smectic layers of an antiferroelectric liquid crystal, by utilizing a small field-induced perturbation of the molecular orientation. U was found to exhibit an unusual "S-shaped" dependence on temperature, with values ranging between $0.4{\times}10^4$ and $0.4{\times}10^{-1}$ erg $cm^{-3}$ over a 10$^{\circ}C$ temperature range below smectic A-smectic $C_A$ phase transition temperature. The results are good agreement with estimates for U based upon the threshold field for the onset of solitary waves, and provide strong supporting the low-field regime for the single Fourier component model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.2
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• pp.239-246
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• 2003

In the PCA Load Contour Method, the biaxial bending design coefficient of columns(${\beta}$) is based on the equivalent rectangular stress block (RSB). And coefficient of ${\beta}$ estimates the reinforcement index to be a influencing parameter on biaxial moment strength of RC columns without considering the arbitrary condition of bar arrangement. The experimental results of high strength concrete (HSC) columns subjected to combined axial load and biaxial bending moment were compared to the analysis results of RSB method. As result, the accuracy of RSB method is still acceptable for HSC columns and, as the reinforcement is placed densely in each corner of column section, the ${\beta}$ is decreased.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1592-1596
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• 2007

Accuracy of flood forecasting is an important non-structural measure on the flood control and mitigation. Hence, combination of horologic model with real time error correction became an important issue. It is one of the efficient ways to improve the forecasting precision. In this work, an approach based on Kalman Filter (KF) is proposed to continuously revise state estimates to promote the accuracy of flood forecasting results. The case study refers to the Wi River in Korea, with the flood forecasting results of Xinanjiang model. Compared to the results, the corrected results based on the Kalman filter are more accurate. It proved that this method can take good effect on hydrologic forecasting of Wi River, Korea, although there are also flood peak discharge and flood reach time biases. The average determined coefficient and the peak discharge are quite improved, with the determined coefficient exceeding 0.95 for every year.

• Water for future
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• v.27 no.1
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• pp.133-140
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• 1994

The augmenting hydrologic data using a correlation procedue has been used to improve the estimates of the mean and variance at the site of interest with short record when one or more nearby sites with longer records are available. The variance of the unbiased maximum likelihood estimator of ${{\sigma}_v}^2$ derived by Moran based on the multivariate normal distribution is modified into the form of Matalas and jacobs for the bivariate normal distribution to get the critical minimum values of the multiple correlation coefficient which give the improvement for estimation the variance at the site of interest. Those values are tabulated for various lengths of records and the number of sites.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.1
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• pp.13-23
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• 2006

In 1998, Food and Agriculture Organization addressed that FAO Modified Penman method possibly over-estimates consumptive use of water comparing to the measured reference crop evapotranspiration (PET) and Penman-Monteith method can be better choice for accurate PET estimation. Nevertheless it is still difficult to find research efforts about paddy rice crop coefficient for Penman-Monteith method. This study aims to estimate paddy rice crop coefficients for Penman-Monteith and FAO modified Penman methods in the manner of comparing two equations. To estimate the crop coefficients, measured evapotranspiration data during 1982-1986 and 1995-1997 were used. The average Penman-Monteith crop coefficients ranged from 0.78 to 1.58 for translated paddy rice and from 0.87 to 1.74 for flood-direct seeded paddy rice. The average FAO Modified Penman crop coefficients ranged from 0.65 to 1.35 for translated paddy rice and from 0.70 to 1.58 for flood-direct seeded paddy rice.

• Journal of Environmental Science International
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• v.11 no.7
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• pp.679-686
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• 2002

Turbulence greatly influence on atmospheric flow field. In the atmosphere, turbulence is represented as turbulent diffusion coefficients. To estimate turbulent diffusion coefficients in previous studies, it has been used constants or 2-level method which divides surface layer and Ekman layer. In this study, it was introduced Smagorinsky method which estimates turbulent diffusion coefficient not to divide the layer but to continue in vertical direction. We simulated 3-D flow model and TKE equation applied turbulent diffusion coefficients using two methods, respectively. Then we showed the values of TKE and the condition of each term to TKE. The results of Smagorinsky method were reasonable. But the results of 2-level method were not reasonable. Therefor, it had better use Smagorinsky method to estimate turbulent diffusion coefficients. We are expected that if it is developed better TKE equation and model with study of computational method in several turbulent diffusion coefficients for reasonably turbulent diffusion, we will able to predict precise wind field and movements of air pollutants.