• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.82.1-82
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• 2002

$\textbullet$ Nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter incorporating numbers of validated measurements $\textbullet$ Probability density function of the NDS $\textbullet$ Simulation results in a clutter environment to verify the performances $\textbullet$ Sensitivity analysis for the unknown spatial clutter density

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.100-107
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• 1999

This paper describes a probabilistic remaining life assessment program for the creep crack growth. The probabilistic life assessment program is developed to increase the reliability of life assessment. The probabilistic life assessment involves some uncertainties, such as, initial crack size, material properties, and loading condition, and a triangle distribution function is used for random variable generation. The resulting information provides the engineer with an assessment of the probability of structural failure as a function of operating time given the uncertainties in the input data. This study forms basis of the probabilistic life assessment technique and will be extended to other damage mechanisms.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.192-200
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• 1994

The methodology of a simple probabilistic fatigue crack under random loading is proposed. Using the crack closure concept, the crack opening stress is assumed to be constant during random loading. The loading history was analyzed to determine the probability density functions, probability distribution functions and other related parameters for the probabilistic fatigue crack growth analysis. Fatigue crack growth using the exisiting available data was predicted by the proposed probabilistic analysis and compared with experimental data.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.325-328
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• 1996

In this paper, we proposed an estimation method of a posterior probability and PDF(Probability density function) using a feed forward neural network and code books of VQ(vector quantization). In this study, We estimates a posterior probability and probability density function, which compose a new parameter with well-known Mel cepstrum and verificate the performance for the five vowels taking from syllables by NN(neural network) and PNN(probabilistic neural network). In case of new parameter, showed the best result by probabilistic neural network and recognition rates are average 83.02%.

• Tunnel and Underground Space
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• v.6 no.1
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• pp.30-38
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• 1996

A two dimensional analysis program for groundwater flow in fractured network was developed to analyze the influence of discontinuity characteristics on groundwater flow. This program involves the generation of discontinuities and also connectivity analysis. The discontinuities were generated by the probabilistic density function(P.D.F.) reflecting the characteristics of discontinuities. And the fracture network model was completed through the connectivity analysis. This program also involves the analysis of groundwater flow through the discontinuity network. The result of numerical experiment shows that the equivalent hydraulic conductivity increased and became closer to isotropic as the density and trace length increased. And hydraulic head decreased along the fracture zone because of much water-flow. The grouting increased the groundwater head around cavern. An analysis of groundwater flow through discontinuity network was performed around underground oil storage cavern which is now under construction. The probabilistic density functions(P.D.F) were obtained from the investigation of the discontinuity trace map. When the anisotropic hydraulic conductivity is used, the flow rate into the cavern was below the acceptable value to maintain the hydraulic containment. But when the isotropic hydraulic conductivity is used, the flow rate was above the acceptable value.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.639-644
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• 2007

The accurate peak response estimation of a seismically excited structure with frictional damping system(FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated that by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In the case that on earthquake load is defined with probabilistic characteristics, the corresponding response of the structure with FDS has probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake loads generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Finally, coefficients of the proposed PDF is obtained by regression analysis of the statistical distribution of the time history responses. Finally, the correlation between PDFs and statistical response distribution is presented.

• Composites Research
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• v.26 no.1
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• pp.29-35
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• 2013

Composite sandwich structure can improve the specific bending stiffness significantly and save the weight nearly 30 percent compared with the composite laminates. However, it has more inherent uncertainties of the material property caused by manufacturing process than metals. Therefore, the reliability-based probabilistic design approach is required. In this paper, the PMS(Probabilistic Margin of Safety) is calculated for the simplified fuselage structure made of composite sandwich to provide the probabilistic reasonable evidence that the classical design method based on the safety factor cannot ensure the structural safety. In this phase, the probability density function estimated by CMCS(Crude Monte-Carlo Simulation) is used. Furthermore, the RBDO(Reliability-Based Design Optimization) under the probabilistic constraint are performed, and the RBDO-MPDF(RBDO by Moving Probability Density Function) is proposed for an efficient computation. The examined results in this paper can be helpful for advanced design techniques to ensure the reliability of structures under the uncertainty and computationally inexpensive RBDO methods.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.