• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.63-69
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• 1999

The research trend of the simulation optimization has been focused on exploring continuous decision variables. Yet, the research in discrete decision variable area has not been fully studied. A new research trend for optimizing discrete decision variables ha just appeared recently. This study, therefore, deals with a discrete simulation method to get the system evaluation criteria required for designing a complex probabilistic discrete event system and to search the effective and reliable alternatives to satisfy the objective values of the given system through a on-line, single run with the short time period. Finding the alternative, we construct an algorithm which changes values of decision variables and a design alternative by using the stopping algorithm which ends the simulation in a steady state of system. To avoid the loss of data while analyzing the acquired design alternative in the steady state, we provide background for estimation of an auto-regressive model and mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data through simulation with the short time period. In numerical experiment we applied the proposed algorithm to (s, S) inventory system problem with varying Δt value. In case of the (s, S) inventory system, we obtained good design alternative when Δt value is larger than 100.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.19-28
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• 2018

In this paper, we studied on the sampling of ocean meteorological data to analyze signature of naval ships. The newest ocean meteorological data, that was quality controled by the Korea Meteorological Administration(KMA), was collected. Outliers were removed from the data by setting the usable range of data. After that, the data size was reduced through the random sampling method, taking geopolitical significance and effective area of buoy, for probabilistic analysis. Moreover, the sample sizes were set at 100, 200, and 400 by considering the population size and a 95% confidence level. The final sample was obtained using the two-dimensional stratified sampling method based on highly correlated water temperature and air temperature. The sum of the squared errors and the confidence interval was calculated to compare the result of sampling. As a result, this study proposed reasonable sample size for infra­red signature analysis of naval ships.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Safety and Health at Work
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• v.10 no.1
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• pp.80-86
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• 2019

Background: Emigration of health-care workers is a problem within global health systems which affects many countries, including Peru. Several factors have caused health-care workers to emigrate, including burnout syndrome (BS). This study aims to identify the association between BS and its dimensions with the intention of physicians and nurses to emigrate from Peru in 2014. Methods: A cross-sectional study, based on a secondary analysis of the National Survey of Health Users (ENSUSALUD - 2014) was conducted. Sampling was probabilistic, considering the 24 departments of Peru. We include the questionnaire for physicians and nurses, accounting for 5062 workers. BS was measured by the Maslach Burnout Inventory-Human Services Survey. Adjusted odds ratio (OR) was calculated using multiple logistic regression. Results: Of the study population, 44.1% were physicians, 37.7% males, and 23.1% were working in Lima. It was found that 2.8% [95% confidence interval (CI): 2.19-3.45] of health-care workers had BS. The overall prevalence of intention to emigrate among health-care workers was 7.4% (95% CI: 6.36-8.40). Association was found between BS and intention to emigrate in Peruvian health-care workers (OR = 2.15; 95% CI: 1.05-4.40). Emotional exhaustion was the BS dimension most associated with intention to emigrate (OR = 1.80; 95% CI: 1.16-2.78). Conclusion: Physicians and nurses from Peru who suffered from BS were more likely to have intention to emigrate. Policies should be established to reduce BS as a strategy to control "brain drain" from health-care workers of Peru.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.3
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• pp.75-83
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• 1984

The probability of failure is used to analyze the reliability of three dimensional slope failure, instead of conventional factor of safety. The strength parameters are assumed to be normal variated and beta variated. These are interval estimated under the specified confidence level and maximum likelihood estimation. The pseudonormal and beta random variables are generated using the uniform probability transformation method according to central limit theorem and rejection method. By means of a Monte-Carlo Simulation, the probability of failure is defined as; $P_f=M/N$ N: Total number of trials M: Total number of failures Some of the conclusions derived. from the case study include; 1. Three dimensional factors of safety are generally much higher than 2-D factors of safety. However situations appear to exist where the 3-D factor of safety can be lower than the 2-D factor of safety. 2. The $F_3/F_2$ ratio appears to be quite sensitive to c and ${\phi}$ and to the shape of the 3-D shear surface and the slope but not to be to the unit weight of soil. 3. From the two models (normal, beta) considered for the distribution of the factor of safety, the beta distribution generally provides lager than normal distribution. 4. Results obtained using the beta and normal models are presented in a nomgraph relating slope height and slop angle to probability of failure.

• Journal of Educational Research in Mathematics
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• v.21 no.4
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• pp.327-344
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• 2011

This study is the first step for us toward improving high school students' capability of statistical inferences, such as obtaining and interpreting the confidence interval on the population mean that is currently learned in high school. We suggest 5 underlying concepts of 'discretion of contingency and inevitability', 'discretion of induction and deduction', 'likelihood principle', 'variability of a statistic' and 'statistical model', those are necessary to appreciate statistical inferences as a reliable arguing tools in spite of its occasional erroneous conclusions. We assume those 5 concepts above are to be gradually developing in their school periods and Korean mathematics textbooks of grades 1-12 were analyzed. Followings were found. For the right choice of solving methodology of the given problem, no elementary textbook but a few high school textbooks describe its difference between the contingent circumstance and the inevitable one. Formal definitions of population and sample are not introduced until high school grades, so that the developments of critical thoughts on the reliability of inductive reasoning could not be observed. On the contrary of it, strong emphasis lies on the calculation stuff of the sample data without any inference on the population prospective based upon the sample. Instead of the representative properties of a random sample, more emphasis lies on how to get a random sample. As a result of it, the fact that 'the random variability of the value of a statistic which is calculated from the sample ought to be inherited from the randomness of the sample' could neither be noticed nor be explained as well. No comparative descriptions on the statistical inferences against the mathematical(deductive) reasoning were found. Few explanations on the likelihood principle and its probabilistic applications in accordance with students' cognitive developmental growth were found. It was hard to find the explanation of a random variability of statistics and on the existence of its sampling distribution. It is worthwhile to explain it because, nevertheless obtaining the sampling distribution of a particular statistic, like a sample mean, is a very difficult job, mere noticing its existence may cause a drastic change of understanding in a statistical inference.