• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.379-394
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• 2022

Shallow footings usually belonged to the category of thick plate structures. For accurate analysis of thick plates, the contribution of out-of-plane components of the stress tensor should be considered in the formulation. Most of the available shallow footing models are based on the classical plate theories, which usually neglect the effects of the out-of-plane stresses. In this study, a mixed-field plate finite element model (FEM) is developed for the analysis of shallow footings rested on soil foundations. In addition to displacement field variables, the out-of-plane components of the stress tensor are also assumed as a priori unknown variables. For modeling the interaction effect of the soil under and outside of the shallow footings, the modified Vlasov theory is used. The tensionless nature of the supporting soil foundation is taken into account by adopting an incremental, iterative procedure. The equality requirement of displacements at the interface between the shallow footing and soil is fulfilled using the penalty approach. For validation of the present mixed FEM, the obtained results are compared with the results of 3D FEM and previous results published in the literature. The comparisons show the present mixed FEM is an efficient and accurate tool for solving the problems of shallow footings rested on subsoil.

• 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.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.27-38
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• 2018

Purpose: A large number of attributes with mixed levels are often considered in the conjoint analysis. In the cases where attributes have two or three levels, we research on the efficient design of survey questionnaire to estimate all the main effect and two factor interaction effects with a reasonable size of it. Methods: To reduce the number of questions in a questionnaire, the balanced incomplete block mixed level factorial design with minimum aberration was proposed by Lim and Chung (2016). Based on the number of questions and that of the respondents in that design, D-optimality criterion is adopted to find efficient designs where the main effect and two factor interaction effects are estimated. Results: The list of the number of questions and that of the respondents in efficient designs for survey questionnaire are recommended based on the D-efficiency of each design and the proposed selection criteria for the number of both questions and the respondents. By analyzing all the respondents survey data generated by the simulation study, we find the proper model. Conclusion: The proposed methods of designing survey questionnaires seem to perform well in the sense that how often the proper model is found in a simulation study where all the respondents survey data are generated by the simulation model.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.8
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• Journal of Environmental Science International
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• v.3 no.2
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• pp.129-140
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• 1994

An intermediate atmosphere-ocean coupled model appropnate for the study of El Nino has been developed. The model is not only economic to use but also contains several most important physical processes. The geometrical effects which were not confided in the previous intermediate model study of Ahn (1990), are included in the model for more realistic simulation of the event. The results show that the individual models respond appropriately to the given boundary conditions. At the same time, in the coupled model experiment, ENSO-like oceanic and atmospheric anomalies are also well simulated under an external triggering similar to the initiation forcing of ENSO. It is expected that this type of model can be effectively used for the. study and simulation of El Nido. More improvement of modeling may be Possible after inclusion of subsequent processes such as inclusion of ocean mixed layer dynamics.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.33-39
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• 2018

Purpose: Accelerated degradation tests can be effective in assessing product reliability when degradation leading to failure can be observed. This article proposes an accelerated degradation test model for highly reliable solid state drives (SSDs). Methods: We suggest a nonlinear mixed-effects (NLME) model to degradation data for SSDs. A Monte Carlo simulation is used to estimate lifetime distribution in accelerated degradation testing data. This simulation is performed by generating random samples from the assumed NLME model. Conclusion: We apply the proposed method to degradation data collected from SSDs. The derived power model is shown to be much better at fitting the degradation data than other existing models. Finally, the Monte Carlo simulation based on the NLME model provides reasonable results in lifetime estimation.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.289-294
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• 2015

Linear mixed models are commonly used in the clinical pharmaceutical studies to analyze repeated measures such as the crossover study data of bioequivalence studies. In these models, random effects describe the correlation between repeated outcomes and variance-covariance matrix explain within-subject variabilities. Bioequivalence analysis verifies whether a 90% confidence interval for geometric mean ratio of Cmax and AUC between reference drug and test drug is included in the bioequivalence margin [0.8, 1.25] performed using linear mixed models with period, sequence and treatment effects as fixed and sequence nested subject effects as random. A Levofloxacin study is referred to for an example of real data analysis.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.2
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• pp.251-257
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• 2008

Food compost, having a higher organic contents than soil, could be an alternative material to prevent the proliferation of heavy metals contamination in soil. In this study we used a convection-dispersion local equilibrium sorption model(CDE) and a two-site non-equilibrium sorption model to find the effects on the adsorption and transportation of Pb by mixing food compost with soil and we also tried to find the effect of velocity and concentration of the injected solution on the characteristics of Pb. We measured Pb concentrations in injection-liquid and in effluent, and then applied them to CXTFIT program. As a result of column experiments, some parameters(D, R, ${\beta}$, ${\omega}$) used in two-site non-equilibrium adsorption model were obtained. Characteristics of Pb adsorption and transport were analyzed using the parameters(D, R, ${\beta}$, ${\omega}$) obtained from the CXTFIT program, We could know that mixed soil with food compost showed a higher adsorption capacity from the retardation factor(R) calculated from the breakthrough curve(BTCs) of Pb. Rs of soil and mixed soil are 20.45, 37.45 respectively, indicating that the adsorption and the transportation characteristics could be accessed quantitatively by using of two-site non-equilibrium adsorption model.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1169-1178
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• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• Journal of Microbiology and Biotechnology
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• v.18 no.6
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• pp.1130-1135
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• 2008

The effect of pH on anaerobic hydrogen production was investigated under various pH conditions ranging from pH 3 to 10. When the modified Gompertz equation was applied to the statistical analysis of the experimental data, the hydrogen production potential and specific hydrogen production rate at pH 5 were 1,182 ml and 112.5 ml/g biomass-h, respectively. In this experiment, the maximum theoretical hydrogen conversion ratio was 22.56%. The Haldane equation model was used to find the optimum pH for hydrogen production and the maximum specific hydrogen production rate. The optimum pH predicted by this model is 5.5 and the maximum specific hydrogen production rate is 119.6 ml/g VSS-h. These data fit well with the experimented data($r^2=0.98$).