• 제목/요약/키워드: Probability density estimation

검색결과 221건 처리시간 0.023초

확률밀도함수를 이용한 피로균열 발생수명 예측에 관한 연구 (A Study on the Prediction of Fatigue Life by use of Probability Density Function)

  • 김종호
    • Journal of Advanced Marine Engineering and Technology
    • /
    • 제23권4호
    • /
    • pp.453-461
    • /
    • 1999
  • The estimation of fatigue life at the design stage is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the structure and machinery compo-nents. In this study the practical procedure of prediction of fatigue life by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function is shown with a $135,000m^3$ LNG tank being used as an example. In particular the parameters of Weibull distribution taht determine the stress spectrum are dis-cussed. At the end some of uncertainties associated with fatigue life prediction are discussed. The main results obtained from this study are as follows: 1. The practical procedure of prediction of fatigue life by use of cumulative damage factors expressed in combination of probability density function and S-N data is proposed. 2. The calculated fatigue life is influenced by the shape parameter and stress block. The conser-vative fatigue design can be achieved when using higher value of shape parameter and the stress blocks divded into more stress blocks.

  • PDF

지표면의 시공간적 변화를 고려한 비점오염원 저감 저류지 최적용량산정 (Optimal Volume Estimation for Non-point Source Control Retention Considering Spatio-Temporal Variation of Land Surface)

  • 최대규;김진관;이재관;김상단
    • 한국물환경학회지
    • /
    • 제27권1호
    • /
    • pp.9-18
    • /
    • 2011
  • In this study the optimal volume for non-point source control retention is estimated considering spatio-temporal variation of land surface. The 3-parameter mixed exponential probability density function is used to represent the statistical properties of rainfall events, and NRCS-CN method is applied as rainfall-runoff transformation. The catchment drainage area is divided into individual $30m{\times}30m$ cells, and runoff curve number is estimated at each cell. Using the derived probability density function theory, the stormwater probability density function at each cell is derived from the rainfall probability density function and NRCS-CN rainfall-runoff transformation. Considering the antecedent soil moisture condition at each cell and the spatial variation of CN value at the whole catchment drainage area, the ensemble stormwater capture curve is established to estimate the optimal volume for an non-point source control retention. The comparison between spatio-temporally varied land surface and constant land surface is presented as a case study for a urban drainage area.

IMAGE DENOISING BASED ON MIXTURE DISTRIBUTIONS IN WAVELET DOMAIN

  • Bae, Byoung-Suk;Lee, Jong-In;Kang, Moon-Gi
    • 한국방송∙미디어공학회:학술대회논문집
    • /
    • 한국방송공학회 2009년도 IWAIT
    • /
    • pp.246-249
    • /
    • 2009
  • Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

  • PDF

Wakeby Distribution and the Maximum Likelihood Estimation Algorithm in Which Probability Density Function Is Not Explicitly Expressed

  • Park Jeong-Soo
    • Communications for Statistical Applications and Methods
    • /
    • 제12권2호
    • /
    • pp.443-451
    • /
    • 2005
  • The studied in this paper is a new algorithm for searching the maximum likelihood estimate(MLE) in which probability density function is not explicitly expressed. Newton-Raphson's root-finding routine and a nonlinear numerical optimization algorithm with constraint (so-called feasible sequential quadratic programming) are used. This algorithm is applied to the Wakeby distribution which is importantly used in hydrology and water resource research for analysis of extreme rainfall. The performance comparison between maximum likelihood estimates and method of L-moment estimates (L-ME) is studied by Monte-carlo simulation. The recommended methods are L-ME for up to 300 observations and MLE for over the sample size, respectively. Methods for speeding up the algorithm and for computing variances of estimates are discussed.

Notes on the Ratio and the Right-Tail Probability in a Log-Laplace Distribution

  • Woo, Jung-Soo
    • Journal of the Korean Data and Information Science Society
    • /
    • 제18권4호
    • /
    • pp.1171-1177
    • /
    • 2007
  • We consider estimation of the right-tail probability in a log-Laplace random variable, As we derive the density of ratio of two independent log-Laplace random variables, the k-th moment of the ratio is represented by a special mathematical function. and hence variance of the ratio can be represented by a psi-function.

  • PDF

Air-Data Estimation for Air-Breathing Hypersonic Vehicles

  • Kang, Bryan-Heejin
    • Transactions on Control, Automation and Systems Engineering
    • /
    • 제1권1호
    • /
    • pp.75-86
    • /
    • 1999
  • An air-data estimator for generic air-breathing hypersonic vehicles (AHSVs) is developed and demonstrated with an example vehicle configuration. The AHSV air-data estimation strategy emphasized improvement of the angle of attack estimate accuracy to a degree necessitated by the stringent operational requirements of the air-breathing propulsion. the resulting estimation problem involves highly nonlinear diffusion process (propagation); consequently, significant distortion of a posteriori conditional density is suspected. A simulation based statistical analysis tool is developed to characterize the nonlinear diffusion process. The statistical analysis results indicate that the diffusion process preserves the symmetry and unimodality of initial probability density shape state variables, and provide the basis for applicability of an Extended Kalman Filter (EKF). An EKF is designed for the AHSV air-data system and the air data estimation capabilities are demonstrated.

  • PDF

ATSC Digital Television Signal Detection with Spectral Correlation Density

  • Yoo, Do-Sik;Lim, Jongtae;Kang, Min-Hong
    • Journal of Communications and Networks
    • /
    • 제16권6호
    • /
    • pp.600-612
    • /
    • 2014
  • In this paper, we consider the problem of spectrum sensing for advanced television systems committee (ATSC) digital television (DTV) signal detection. To exploit the cyclostationarity of the ATSC DTV signals, we employ spectral correlation density (SCD) as the decision statistic and propose an optimal detection algorithm. The major difficulty is in obtaining the probability distribution functions of the SCD. To overcome the difficulty, we probabilistically model the pilot frequency location and employ Gaussian approximation for the SCD distribution. Then, we obtain a practically implementable detection algorithm that outperforms the industry leading systems by 2-3 dB. We also propose various techniques that greatly reduce the system complexity with performance degradation by only a few tenths of decibels. Finally, we show how robust the system is to the estimation errors of the noise power spectral density level and the probability distribution of the pilot frequency location.

Design wind speed prediction suitable for different parent sample distributions

  • Zhao, Lin;Hu, Xiaonong;Ge, Yaojun
    • Wind and Structures
    • /
    • 제33권6호
    • /
    • pp.423-435
    • /
    • 2021
  • Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

Important measure analysis of uncertainty parameters in bridge probabilistic seismic demands

  • Song, Shuai;Wu, Yuan H.;Wang, Shuai;Lei, Hong G.
    • Earthquakes and Structures
    • /
    • 제22권2호
    • /
    • pp.157-168
    • /
    • 2022
  • A moment-independent importance measure analysis approach was introduced to quantify the effects of structural uncertainty parameters on probabilistic seismic demands of simply supported girder bridges. Based on the probability distributions of main uncertainty parameters in bridges, conditional and unconditional bridge samples were constructed with Monte-Carlo sampling and analyzed in the OpenSees platform with a series of real seismic ground motion records. Conditional and unconditional probability density functions were developed using kernel density estimation with the results of nonlinear time history analysis of the bridge samples. Moment-independent importance measures of these uncertainty parameters were derived by numerical integrations with the conditional and unconditional probability density functions, and the uncertainty parameters were ranked in descending order of their importance. Different from Tornado diagram approach, the impacts of uncertainty parameters on the whole probability distributions of bridge seismic demands and the interactions of uncertainty parameters were considered simultaneously in the importance measure analysis approach. Results show that the interaction of uncertainty parameters had significant impacts on the seismic demand of components, and in some cases, it changed the most significant parameters for piers, bearings and abutments.

확률강우량 추정을 위한 확률분포함수의 매개변수 추정법에 대한 신뢰성 평가 (Reliability Evaluation of Parameter Estimation Methods of Probability Density Function for Estimating Probability Rainfalls)

  • 한정우;권현한;김태웅
    • 한국방재학회 논문집
    • /
    • 제9권6호
    • /
    • pp.143-151
    • /
    • 2009
  • 최근의 극한 수문사상은 홍수, 가뭄과 같은 심각한 재해를 발생시킨다. 많은 연구자들은 불확실한 미래의 확률강우량 및 유출량의 예측을 위해 많은 노력을 하고 있다. 본 연구에서는 불확실성이 낮은 확률강우량의 산정을 위하여 매개변수 추정법을 평가하였다. 인천, 강릉, 광주, 부산, 추풍령 관측소를 연구 대상 관측소로 선정하여 자료를 수집하였고, ARMA모형을 이용하여 합성강우자료를 구축하였다. 본 연구에서는 극치강우사상에 적합한 것으로 알려진 Gumbel 분포와 GEV 분포모형에 대한 매개변수를 최우도법과 베이지안 추론방법을 사용하여 추정하였으며, Bootstrap 방법을 이용하여 확률강우량의 신뢰구간 길이를 추정하였다. 매개변수 추정 방법별 산정된 확률강우량의 신뢰구간 길이를 비교함으로서 불확실성이 낮은 확률강우량을 산정할 수 있는 매개변수 추정방법을 선정하였다.