• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.218-219
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• 2003

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.51-57
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• 2007

The expected busy period for the controllable M/G/1 queueing model operating under the triadic policy is derived by using the pseudo probability density function which is totally different from the actual probability density function. In order to justify the approach using the pseudo probability density function to derive the expected busy period for the triadic policy, well-known expected busy periods for the dyadic policies are derived from the obtained result as special cases.

• Journal of the Korean Society of Combustion
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• v.15 no.4
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• pp.15-21
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• 2010

The transported probability density function(PDF) model has been applied to simulate the turbulent nonpremixed piloted jet flame. To realistically account for the mixture fraction PDF informations on the turbulent non-premixed jet flame, the present Lagrangian PDF transport approach is based on the joint velocity-composition-turbulence frequency PDF formulation. The fluctuating velocity of stochastic fields is modeled by simplified Langevin model(SLM), turbulence frequency of stochastic fields is modeled by Jayesh-Pope model and effects of molecular diffusion are represented by the interaction by exchange with the mean (IEM) mixing model. To validate the present approach, the numerical results obtained by the joint velocity-composition-turbulence frequency PDF model are compared with experimental data in terms of the unconditional and conditional means of mixture fraction, temperature and species and PDFs.

• School Mathematics
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• v.4 no.4
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• pp.677-688
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• 2002

The context and intuitive understanding is very important in Statistics Education. Especially, there is a need to mitigate student's difficulty in studying probability density function. One of teaching method this concept is to using relative frequency histogram. But, as using this method, we should know several problems included in that. This study investigate problems in the method for teaching probability density function as gradual meaning of histogram. Also, as alternative approach, this thesis introduce the density curve concept. The application of four methods to teach the concept of the probability density function and analysis of the survey result is done in this research.

• Journal of the Korean Society of Combustion
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• v.22 no.3
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• pp.29-34
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• 2017

The multi-environment probability density function model has been applied to simulate a turbulent premixed flame in a swirl combustor. To realistically account for the unsteady flow motion inside the combustor, the formulations are derived for the large eddy simulation. The Flamelet generated manifolds is utilized to simplify a multi-dimensional composition space with reasonable accuracy. The sub grid scale mixing is modeled by the interaction by exchange with the mean mixing model. To validate the present approach, the simulation results are compared with experimental data in terms of mean velocity, temperature, and species mass fractions.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.349-359
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• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.107-114
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• 2013

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.319-326
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• 2004

Uncertainty is inevitably involved in rock slope engineering since the rock masses are formed by natural process and subsequently the geotechnical characteristics of rock masses cannot be exactly obtained. Therefore the reliability analysis method has been suggested to deal properly with uncertainty. The reliability analysis method can be divided into level I, II and III on the basis of the approach for consideration of random variable and probability density function of reliability function. The level II approach, which is focused in this study, assumes the probability density function of random variables as normal distribution and evaluates the probability of failure with statistical moments such as mean and standard deviation. This method has the advantage that can be used the problem which the Monte Carlo simulation approach cannot be applied since the complete information on the random variables are not available. In this study, the analysis results of level II reliability approach compared with the analysis results of level III approach to verify the appropriateness of the level II approach. In addition, the results are compared with the results of the deterministic analysis.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.443-455
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• 1999

A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.