• ETRI Journal
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• v.29 no.6
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• pp.829-831
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• 2007

This letter proposes an analytical model to characterize medium utilization in IEEE 802.11e operating in HCCA-EDCA mixed mode (HEMM). In contrast to existing works which model the backoff process in individual stations, we consider the channel occupancy pattern. Additionally, our work considers the operation of HEMM, which is not widely documented. We show that the proposed model accurately characterizes medium utilization with no more than 5% error.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.227-277
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• 2000

This paper presents an application of Mixed-Sensitivity H$_{\infty}$ control of a flexible manipulator. Firstly the detail model transfer function is derived from system identification. The objective is to position the free end of the beam with model including uncertainties and disturbance. we derive multiplicative uncertainties based on frequency response from difference between detail model and reduced model for designing controller. Finally we compare simulation results with experimental results.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.403-414
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• 2000

A diagnostic tool for testing homogeneity for random effects is proposed in unbalanced linear mixed model based on score statistic. The finite sample behavior of the test statistic is examined using Monte Carlo experiments examine the chi-square approximation of the test statistic under the null hypothesis.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.153-161
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• 2009

We consider a logistic model with random effects as the superpopulation for estimating the small area pro-portions. The best linear unbiased predictor under linear mired model is popular in small area estimation. We use this type of estimator under logistic mixed motel for the small area proportions, on which the estimation of mean squared error is also discussed. Two kinds of estimation methods, the parametric bootstrap and the linear approximation will be compared through a Monte Carlo study in the respects of the normality assumption on the random effects distribution and also the magnitude of sample sizes on the approximation.

• Korean Management Science Review
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• v.27 no.2
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• pp.55-66
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• 2010

In Korea, deap-sea fishing industry plays an important role in a food industry. However, it is in a difficult situation because of the more competitive business environment. Therefore, there is a need to restructure the deap-sea fishing industry by scraping superannuated ships. This paper is designed to present scrap programs for deap-sea fishing industry of Korea. We performed ratio analysis to evaluate financial performance of fishing companies and then applied a mixed integer programming (MIP) model to identify optimal schedule for scraping. The results of the financial ratio analysis indicates that it is legible to provide governmental aid to Atlantic trawl, Northern Pacific trawl, and Indian ocean trawl with minimum required rate of return (MRR) of 3%, and the Atlantic strip fishing industry is qualified to receive the governmental aid with MRR value of 5%. Furthermore, by applying the MIP model to develop scrap planning, we demonstrate how our model can be used to restructure the deap-sea fishing industry of Korea.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.301-311
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• 2020

Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.39-55
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• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.604-609
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• 2003

Turbulent temperature field in a channel with wall injection has been investigated using dynamic mixed model(DMM). This flow is pertinent to internal flows inside the hybrid rocket motors. In general, the results obtained with DMM are in better agreement with DNS results compared to those of dynamic Smagorinsky model(DSM). Such favorable features of DMM are attributed to the fact that it explicitly calculates the modified Leonard stress term which takes care of the local interaction between resolved and SGS stresses and only models the remaining cross and SGS Reynolds stress terms.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.122-127
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• 1998

This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

• 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.