• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.760-768
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• 2017

The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.3
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• pp.231-241
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• 1996

The effect of radiation and buoyancy on the thermophoresis phenomenon owing to the presence of highly absorbing, emitting particles (such as soot or pulverized coal) suspended in a two phase flow system was investigated numerically for a turbulent mixed convection flow. The analysis of conservation equations for a gas-particle flow system was performed on the basis of a two-fluid model from a continuum Eulerian viewpoint. The modified van Driest and Cebeci mixing length turbulence model was adopted in the anaylsis of turbulent flow. In addition, the P-1 approximation was used to evaluate the radiation heat transfer. As expected from the particle concentration and drift velocity distribution, the cumulative collection efficiency E (x) becomes larger when the buoyancy effect increases (i.e. higher Grashof number), while smaller as the radiation effect increases (i.e. higher optical thickness).

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.2
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• pp.97-106
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• 2024

This study proposes a practical adjustment equation for the Embrittlement Trend Curve (ETC) to effectively apply it to reactor pressure vessel (RPV) materials in individual nuclear power plants. Traditional ETC adjustment methods have limitations due to constraints in number of group-specific measurements and a lack of statistical foundations. To address these issues, KAERI applied a Markov Chain Monte Carlo (MCMC)-based mixed-effect model to the latest ETC model, ASTM E900-15. This approach quantitatively calculates the mean, standard deviation, and prediction intervals of the adjustment intercept by considering the grouping characteristics of surveillance data and uncertainties in unirradiated specimens. Although the KAERI model provides quantitative distributions of parameters and intercepts, it has challenges in practical applications due to computational complexity and low portability. In this study, a simplified equation was developed using the statistical calculations of the mixed-effect model, which retains the primary outcomes of the KAERI model while enhancing portability. This equation supports effective adjustments to the ASTM E900-15 ETC for nuclear power plants with diverse material properties and operational conditions. It enables reliable evaluations of RPV integrity using plant-specific surveillance data. The findings of this study are expected to improve the precision and practicality of ETCs.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.262-264
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• 2005

We propose a filter design method to suppress the effect of gyroscope mixed random errors at INS system level. It is based on the result that mixed random errors can be represented by a single equivalent ARMA model. At first step, the time difference of equivalent ARMA process is performed, which consider the characteristic of indirect feedback Kalman filter used in INS filter. Next, a state space conversion of time differenced ARMA model is achieved. If the order of AR is greater than that of MA, the controllable or observable canonical form is used. Otherwise, we introduce the state equation of which the state variable is composed of the ARMA model output and several step ahead predicts of that. At final step, a complete form state equation is presented. The simulation results shows that the proposed method gives less transient error and better convergence compared to the conventional filter which assume the mixed random errors as white noise.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.263-275
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• 2014

The present study used a hierarchical Bayesian approach was used to develop a mixed effect model to describe the transitional behavior of subjects in time nonhomogeneous Markov chains. The posterior distributions of model parameters were not in analytically tractable forms; subsequently, a Gibbs sampling method was used to draw samples from full conditional posterior distributions. The proposed model was implemented with real data.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.511-520
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• 2010

The Pharmacokinetic model is a complex nonlinear model with pharmacokinetic parameters that is some-times represented by a complex form of differential equations. A population pharmacokinetic model adds individual variability using the random effects to the pharmacokinetic model. It amounts to the nonlinear mixed effect model. This paper, reviews the population pharmacokinetic model from a statistical viewpoint; in addition, a population pharmacokinetic model is also applied to the real clinical data along with a review of the statistical meaning of this model.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.267-276
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• 2022

Modelling the energy release performance of energetic material combustion in closed systems is of fundamental importance for aerospace and defense application. In particular, to compensate for the disadvantage of the combustion of single energetic material and maximize the benefits, a method of combusting the mixed energetic materials is used. However, since complicated heat transfer occurs when the energetic material is combusted, it is difficult to theoretically predict the combustion performance. Here, we suggest a theoretical model to estimate the energy release performance of mixed energetic material based on the model for the combustion performance of single energetic material. To confirm the effect of parameters on the model, and to gain insights into the combustion characteristics of the energetic material, we studied parameter analysis on the reaction temperature and the characteristic time scales of energy generation and loss. To validate the model, model predictions for mixed energetic materials are compared to experimental results depending on the amount and type of energetic material. The comparison showed little difference in maximum pressure and the reliability of the model was validated. Finally, we hope that the suggested model can predict the energy release performance of single or mixed energetic material for various types of materials, as well as the energetic materials used for validation.