• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1269-1276
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• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.2
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• pp.67-73
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• 1985

In Digital Terrain Model(DTM), more accurate data acquisition method is of importance. This paper has the purpose of accuracy analysis of progressive sampling method, one of data acquisition method. Especially, The following in accuracy analysis are compared and analyzed. -Comparison and analysis for position error between the digital contour lines using digital terrain model and the conventional contour lines using A-10 Plotter. -Analysis for height error of interpolation points according to application of progressive sampling method. For above numerical tests, Computer Program related to auto-carto of contour lines was made up. As a result of tests, threshold and sampling criterion have close of mutual relation to accuracy. Particularly, it was found that auto-carto of contour lines-threshold of 1.0 m and standard criterion-almost concurred in conventional contour lines.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.53-63
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• 2013

This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of Animal Science and Technology
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• v.44 no.4
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• pp.383-390
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• 2002

The data were consisted of 1,262 records for carcass traits observed at Hanwoo steers from 1998 to 2001 at Namwon and Deakwanryung branch of National Livestock Research Institute, Rural Development Administration. Pedigrees of young bulls were traced back to search for magnifying inbreeding. Genetic parameters for carcass traits with Gibbs sampling in a threshold animal model were compared to estimates with REML algorithm in linear model. As the results, most of bulls were not inbred and sire pedigree group was non-inbred population. However, most of the bulls fell in some relationship with each other. Heritability estimates as fully posterior means by Gibbs samplers in threshold model were higher than those by REML in linear model. Furthermore, these estimates in threshold model using GS showed higher estimates than estimates using tested young bulls in previous study and same model. Heritability estimate by GS for marbling score was 0.74 and genetic correlation estimate between marbling score and body weight at slaughter was –0.44. Further study for correlation of breeding values between REML algorithm in linear model and Gibbs sampling algorithm in threshold model was needed.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.564-573
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• 2014

Economic optimization of cumene manufacturing process to produce cumene from benzene and propylene was studied. The chosen objective function was the operational profit per year that subtracted capital cost, utility cost, and reactants cost from product revenue and other benefit. The number of design variables of the optimization are 6. Matlab connected to and controlled Unisim Design to calculate operational profit with the given design variables. As the first step of the optimization, design variable points was sampled and operational profit was calculated by using Unisim Design. By using the sampled data, the estimation model to calculate the operational profit was constructed, and the optimization was performed on the estimation model. This study compared second order polynomial and support vector regression as the estimation method. As the sampling method, central composite design was compared with Hammersley sequence sampling. The optimization results showed that support vector regression and Hammersley sequence sampling were superior than second order polynomial and central composite design, respectively. The optimized operational profit was 17.96 MM$ per year, which was 12% higher than 16.04 MM$ of base case.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1103-1118
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• 2009

When we wish to estimate the mean or total of a finite population, the numbering of the population units is of importance. In this paper, we have proposed two methods for estimating the mean or total of a population having a linear trend, for the case when the reciprocal of the sampling fraction is an even number and the sample size is an odd number. The first method involves drawing a sample by using a method which is a generalization of Singh et al's (1968) modified systematic sampling, and using interpolation in determining the estimator. The second method involves selecting a sample by modified systematic sampling, and estimating the population parameters by the regression estimation method. Under the criterion of the expected mean square error based on Cochran's (1946) infinite superpopulation model, the proposed methods have been compared with existing methods. We have also made a comparison between the two proposed methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.114-122
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• 2011

We propose an automatic localization technique based on Uniform Arc Length Sampling (UALS) of 360 degree range sensor data. The proposed method samples 3D points from dense a point-cloud which is acquired by the sensor, registers the sampled points to a digital surface model(DSM) in real-time, and determines the location of an Unmanned Ground Vehicle(UGV). To reduce the sampling and registration time of a sequence of dense range data, 3D range points are sampled uniformly in terms of ground sample distance. Using the proposed method, we can reduce the number of 3D points while maintaining their uniformity over range data. We compare the registration speed and accuracy of the proposed method with a conventional sample method. Through several experiments by changing the number of sampling points, we analyze the speed and accuracy of the proposed method.

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.6
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• pp.23-30
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• 2010

A detailed circuit level model requires a small sampling time to represent high frequency switching behaviors with proper resolution. The small sampling time leads a large execution time to obtain the system analysis results. As the alternative of the detailed circuit model, an averaged PWM switch model was proposed for the rapid system level analysis. There exists a voltage distortion between the reference and output voltage because of non-ideal switching characteristics, such as the dead-time, diode forward voltage drop and conduction resistance. This paper analyzed causes and effects of the voltage distortion. The average inverter model, which reflecting this voltage distortion, is developed for the rapid and accurate analysis of automotive electric drive system in MATLAB/Simulink environment. The rapidity and accuracy of the proposed inverter model is proved through comparison between simulation and experiment.