• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.59-72
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• 1996

The purpose of this study is to make an improvement to the batch-means method, which is a procedure to construct a confidence interval(c.i.) for the steady-state process mean of a stationary simulation output process. In the batch-means method, the data in the output process are grouped into batches. The sequence of means of the data included in individual batches is called a batch-menas process and can be treated as an independently and identically distributed set of variables if each batch includes sufficiently large number of observations. The traditional batch-means method, therefore, uses a batch size as large as possible in order to. destroy the autocovariance remaining in the batch-means process. The c.i. prodedure developed and empirically tested in this study uses a small batch size which can be well fitted by a simple ARMA model, and then utilizes the dependence structure in the fitted model to correct for bias in the variance estimator of the sample mean.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.885-897
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• 2000

The use of an unweighted mean and of separate tests is part of the current practice for analyzing interlaboratory studies, and we hope to improve on this method. We fit, using maximum likelihood(ML), a rather intricate, multi-parameter measurement model with the material's true value as a latent variable in a situation where quite serviceable regression and ANOVA calculations have already been developed. The model fit leads to both a weighted estimate of he overall mean, and to tests for equality of means, slopes and variances. Maximum likelihood tests for difference among variances poses a challenge in that the likelihood can easily becoem unbounded. Thus the major objective become to provide a useful test of variance equality.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1275-1301
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• 2023

This paper introduced the Weibull Marshall-Olkin Power Lomax (WMOPL) distribution. The statistical aspects of the proposed model are presented, such as the quantiles function, moments, mean residual life and mean deviations, variance, skewness, kurtosis, and reliability measures like the residual life function, and stress-strength reliability. The parameters of the new model are estimated using six different methods, and simulation research is illustrated to compare the six estimation methods. In the end, two real data sets show that the Weibull Marshall-Olkin Power Lomax distribution is flexible and suitable for modeling data.

• Child Health Nursing Research
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• v.26 no.1
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• pp.35-46
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• 2020

Purpose: The purpose of this study was to test a model for intention to discontinuation drinking high caffeinated beverages among undergraduate students. This model was based on the Ajzen's theory of planned behavior and Becker's health belief model. Methods: Participants consisted of 201 undergraduate students. Data were collected by questionnaires from March 11 to May 24, 2019. Collected data were analyzed using SPSS/WIN 22.0, AMOS 22.0 program. Results: The assessment of the model indicated an acceptable fit (normed x²=1.65, goodness-of-fit index [GFI]=.83, adjusted GFI=.79, comparative fit index [CFI]=.92, standardized root mean square residual [SRMR]=.05, Tucker-Lewis index [TLI]=.91, normed fit index [NFI]=.87, root mean square error of approximation [RMSEA]=.07). Perceived behavior control, subjective norm, the subjective attitude was found to have a significant direct effect on the intention to discontinuation of drinking a high caffeinated beverage. The variances of this model explained 45.3% of the variance in intention to discontinuation of drinking a high caffeinated beverage. Conclusion: These results suggest that a need to increase awareness of adverse effects and potential risks of high caffeinated beverage consumption in undergraduate students. Besides, the university and government should provide education and campaigns to prevent excessive high-caffeinated beverage consumption.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.163-172
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• 1993

A probabilistic approach for evaluation of prediction of the strains using Lade's single surface constitutive model was employed, based on first-order approximate mean and variance. Several experiments such as isotropic compression and drained triaxial compression tests were conducted to examine the variabilities of soil parameters for Lade's model. By taking into account the results of the experimental data such as mean values and standard deviations of soil parameter's, a new probabilistic approach, which explains the uncertainty of computed strains, is applied. The magnitude of the COV for each parameter and the correlation coefficient between the two parameters can be effectively used for reducing the number of the parameters for the model. It is concluded that Lade's single surface constitutive model is surperior model for the prediction of the strain, because the COV of strains is under the "0.51".

• Fibers and Polymers
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• v.7 no.4
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• pp.424-431
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• 2006

Roll drafting, a mechanical operation attenuating fiber bundles to an appropriate thickness, is an important operation unit for manufacturing staple yams. It influences not only the linear density regularity of the slivers or staple yams that are produced, but also the quality of the textile product and the efficiency of the thereafter processes. In this research, the dynamic states of the fiber bundle in the roll drafting zone were analyzed by simulation, based on the mathematical model that describes the dynamic behavior of the flowing bundle. The state variables are the linear density and velocity of the fiber bundles and we simulated the dynamics states of the bundle flow, e.g., the profiles of the linear density and velocity in the draft zone for various values of the model parameters and boundary conditions, including the initial conditions to obtain their influence on the dynamic state. Results showed that the mean velocity profile of the fiber bundle was strongly influenced by draft ratio and process speed, while the input sliver linear density has hardly affected the process dynamics. Velocity variance of individual fibers that could be supposed to be a disturbing factor in drafting was also influenced by the process speed. But the major disturbance occurred due to the velocity slope discontinuity at the front roll, which was strongly influenced by the process speed. Thickness of input sliver didn't play any important role in the process dynamics.

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.214-216
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• 2005

A magnitude of generated harmonic currents along with the operation of traction has nonlinear characteristics. The harmonic currents generated along with the operating speed of electrical railroad traction is to analyze very difficulty. This paper therefore presents probabilistic approach for the harmonic currents evaluation about the operating speed of the arbitrary single traction. To use probabilistic method for railroad system, probability density function(PDF) using measuring data based on the realistic harmonic currents per operating speed is calculated. Mean and variance of harmonic currents of single traction also are obtained the PDF of the operating speed and electrical railroad traction model. Uncertainty of harmonic currents expects to results through mean and variance with PDF. The probability of harmonic currents generated with the operating of arbitrary many tractions is calculated by the convolution of functions. The harmonics of different number of tractions are systematically investigated. It is assessed by the total demand distortion(TDD) for the railroad system.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.855-862
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• 1998

This paper investigates the impact of the forecast error on performance of a reservoir system for hydropower production. Forecast error is measured as th Root Mean Square Error (RMSE) and parametrically varied within a Generalized Maintenance Of Variance Extension (GMOVE) procedure. A set of transition probabilities are calculated as a function of the RMSE of the GMOVE procedure and then incorporated into a Bayesian Stochastic Dynamic Programming model which derives monthly operating policies and assesses their performance. As a case study, the proposed methodology is applied to the Skagit Hydropower System (SHS) in Washington state. The results show that the system performance is a nonlinear function of RMSE and therefor suggested that continued improvements in the current forecast accuracy correspond to gradually greater increase in performance of the SHS.