• 한국환경과학회지
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• 제22권8호
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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.

• 대한전기학회논문지
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• 제45권2호
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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%.

• 한국해안해양공학회지
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• 제6권1호
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• pp.88-93
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• 1994

유한수심에서의 불규칙파에 적용할 수 있는 파고의 확률분포함수를 2가지 해석적 방법으로 유도하였다. 첫번째 방법으로 새로이 유도된 확률분포함수는 Rayleigh 확률분포함수에 대한 직교 다항식을 유도함으로써 급수형태로 표시된다. 유도된 확률밀도함수를 비정규성이 강한 천해에서 측정한 파랑자료와 비교하였다. 확률밀도함수가 자료의 막대그래프와 잘 일치하였으나, 확률밀도함수가 급수로 표시되어 있기 때문에 파고가 큰 부분에서 음의 확률값이 된다. 비록 음의 확률값의 크기가 작다 하더라도 파고의 극치분포함수를 구하기에 부적절하다고 판단된다. 두번째 방법은 최대 엔트로피 법(maximum entropy method)을 적용하여 파고 분포와 매우 잘 일치하며, 극치파고분포와 파고의 통계적인 특성 등을 추정하는 데 매우 유용함을 알 수 있다. 그러나 최대 엔트로피 법을 사용했을 경우, 비정규분포 특성을 나타내는 변위의 분포함수와 파고의 분포함수 사이의 함수관계를 구할 수 없었다.

• 산업경영시스템학회지
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• 제30권2호
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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.

• 대한금속재료학회지
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• 제46권9호
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• 한국지진공학회논문집
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• 제9권6호
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• pp.53-58
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• 2005

본 연구에서는 지진하중을 받는 교량의 거동을 확률밀도함수를 통하여 분석할 수 있는 기법을 개발하였다. 확률밀도함수의 전개는 추계론적 이론을 이용한 반해석적 방법을 통하여 구하였으며, 반해석적 방법은 교량운동방정식으로부터 상응하는 Fokker-Planck equation을 구한 후, path-integral solution을 유도하여 이를 수치적으로 해석함으로써 구할 수 있다. 교량거동의 확률밀도 함수전개로부터 교량거동의 확률적 특성을 파악하고 확률밀도함수의 범위로부터 교량응답거동의 포락선을 얻을 수 있으며 이를 이용하여 최대응답의 범위를 결정할 수 있다는 것을 밝혔다.

• 한국국방경영분석학회지
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• 제28권2호
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• pp.56-69
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• 2002

A technique of finding both probability density and distribution function for interkilling times is considered and demonstrated. An important result is that any arbitrary interfiring time random variables fit to this study, The interfiring renewal density function given a certain interfiring probability density function can be applied to obtain the corresponding interkilling renewal density function which helps us to estimate the expected number of killing events in a time period. The numerical inversion of Laplace transformation makes these possible and the results appear to be excellent. In case of ammunition supply is limited, an alternative way of getting the probability density function of time to the killing is investigated. The convolution technique may give us a means of settling for this new problem.

• 대한수학교육학회지:학교수학
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• 제4권4호
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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.

• Structural Engineering and Mechanics
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• 제86권3호
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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.

• 대한조선학회논문집
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• 제52권1호
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.