• Journal of Preventive Medicine and Public Health
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• v.42 no.2
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• pp.135-142
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• 2009

Objectives : An appropriate sampling strategy for estimating an epidemiologic volume of diabetes has been evaluated through a simulation. Methods : We analyzed about 250 million medical insurance claims data submitted to the Health Insurance Review & Assessment Service with diabetes as principal or subsequent diagnoses, more than or equal to once per year, in 2003. The database was re-constructed to a 'patient-hospital profile' that had 3,676,164 cases, and then to a 'patient profile' that consisted of 2,412,082 observations. The patient profile data was then used to test the validity of a proposed sampling frame and methods of sampling to develop diabetic-related epidemiologic indices. Results : Simulation study showed that a use of a stratified two-stage cluster sampling design with a total sample size of 4,000 will provide an estimate of 57.04%(95% prediction range, 49.83 - 64.24%) for a treatment prescription rate of diabetes. The proposed sampling design consists, at first, stratifying the area of the nation into "metropolitan/city/county" and the types of hospital into "tertiary/secondary/primary/clinic" with a proportion of 5:10:10:75. Hospitals were then randomly selected within the strata as a primary sampling unit, followed by a random selection of patients within the hospitals as a secondly sampling unit. The difference between the estimate and the parameter value was projected to be less than 0.3%. Conclusions : The sampling scheme proposed will be applied to a subsequent nationwide field survey not only for estimating the epidemiologic volume of diabetes but also for assessing the present status of nationwide diabetes control.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.67-74
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• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.1-20
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• 2009

The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.33-43
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• 2001

To estimate the reliability of a large and complex network with a small variance, we propose two dynamic Monte Carlo sampling methods: the sequential minimal cut set (SMCS) and the sequential minimal path set (SMPS) methods. These methods do not require all minimal cut sets or path sets to be given in advance and do not simulate all arcs at each trial, which can decrease the valiance of network reliability. Based on the proposed methods, we develop the importance sampling estimators, the total hazard (or safety) estimator and the hazard (or safety) importance sampling estimator, and compare the performance of these simulation estimators. It is found that these estimators can significantly reduce the variance of the raw simulation estimator and the usual importance sampling estimator. Especially, the SMCS algorithm is very effective in case that the failure probabilities of arcs are low. On the contrary, the SMPS algorithm is effective in case that the success Probabilities of arcs are low.

• Journal of the Korean Institute of Telematics and Electronics
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• v.18 no.2
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• pp.22-26
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• 1981

Truncated expansions based upon the sampling theorem but containing only a few terms can be very useful for practical interpolations of band-limited or narrow-band random signals. The major goal of this work is to find and coiupare efficient and "statistically accurate" algorithms for the dynamic analysis of the ocean systems. The stalistical accuracy of truncated sampling interpolations is investicated, and one simple ocean systems, which yields a Runge-Kutta simulation algorithm of improved accuracy with very little increase in computation, is indicated.indicated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.3-147
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• 2001

We have proposed a digital control method, where the controlled variable is a joint angular velocity, of space robot manipulators using the transpose of Generalized Jacobian Matrix. The explicit relationship between the control law and the sampling period, however, is unknown because the controller gains include the sampling period implicitly. This paper presents a novel digital control method which explicitly describes the relation between the sampling period and the controller gains. Computer simulation of a 3-DOF planar space robot manipulator is peformed. Simulation result demonstrates the effctiveness of the proposed method.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.133-141
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• 2009

This study was conducted to support for determining an efficient sampling design for forest resources assessments in South Korea with respect to statistical efficiency. For this objective, different systematic sampling designs were simulated and compared based on an artificial forest population that had been built from field sample data and satellite data in Yang-Pyeong County, Korea. Using the k-NN technique, two thematic maps (growing stock and forest cover type per pixel unit) across the test area were generated; field data (n=191) and Landsat ETM+ were used as source data. Four sampling designs (systematic sampling, systematic sampling for post-stratification, systematic cluster sampling, and stratified systematic sampling) were employed as optimum sampling design candidates. In order to compute error variance, the Monte Carlo simulation was used (k=1,000). Then, sampling error and relative efficiency were compared. When the objective of an inventory was to obtain estimations for the entire population, systematic cluster sampling was superior to the other sampling designs. If its objective is to obtain estimations for each sub-population, post-stratification gave a better estimation. In order to successfully perform this procedure, it requires clear definitions of strata of interest per field observation unit for efficient stratification.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.57-69
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• 2007

Adaptive cluster sampling design is known as a sampling method for rare clustered population. Three modified adaptive cluster sampling designs are proposed. The adjusted Hansen-Hurwitz estimator and the Horvitz-Thompson estimator are considered. Efficiency issue of the proposed sampling designs is discussed in a Monte-Carlo simulation study.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.305-310
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• 1991

In industrial multivariable plants, it is often the case that the plant outputs are detected in a similar components not simultaneously but serially. In this paper, the problem of estimating the state vector of the plant based on the data obtained from such a detecting scheme is considered, and a special type of observer (referred to as a "multiple serial-sampling" type observer) which renews its internal states whenever a new group of data is obtained is proposed. It is proved that such an observer can be constructed for almost every sampling period if the plant is observable as a continuous-time multivariable system, and that the poles of the closed-loop system using the serial-sampling type observer consist of the poles of the observer and those of the state feedback system. The behaviors of the observer and the closed-loop system are studied by simulation. The results of simulation indicate that a multiple serial-sampling type observer can estimate the state of the plant more accurately than the ordinary type observers and improve the closed-loop performance, especially, in the existence of dectecting noise.ing noise.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.544-549
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• 1990

In industrial multivariable plants, it is often the case that the plant outputs are detected not simultaneously but serially. In this paper, the problem of estimating the state vector of the plant based on the data obtained from such a detecting scheme is considered, and a special type of observer (referred to as a "serial-sampling' type observer) which renews its internal states whenever a new data is obtained is proposed. It is proved that such an observer can be constructed for almost every sampling period if the plant is observable as a continuous-time multivariable system, and that the poles of the closed-loop system using the serial-sampling type observer consist of the poles of the observer and those of the state feedback system. The behaviors of the observer and the closed-loop system are studied by simulation. The results of simulation indicate that a serial-sampling type observer can estimate the state of the plant more accurately than the ordinary type observers and improve the closed-loop performance.ance.