• Title/Summary/Keyword: Probability Distribution

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인천 송도지역 지반정수의 확률분포 추정 (Probability Distribution of Geotechnical Properties of Songdo area in Incheon)

  • 김동휘;김민태;고성권;박정규;이우진
    • 한국지반공학회:학술대회논문집
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    • 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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    • pp.1399-1406
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    • 2009
  • Probability distribution of geotechnical properties is very useful information and it is used for evaluating the geotechnical properties itself and calculating probability of failure. In this study, probability distribution of compression index, recompression index, and void ratio are evaluated, and analysis results show that all property distributions satisfy normal and log-normal distribution.

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제주도 지하수위 관측지점별 적정 확률분포형의 결정 (Determination of Proper Probability Distribution for Groundwater Monitoring Stations in Jeju Island)

  • 정일문;남우성;김민규;최지안;김기표;박윤석
    • 한국지하수토양환경학회지:지하수토양환경
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    • 제23권1호
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    • pp.41-53
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    • 2018
  • Comprehensive statistical analysis for the 127 groundwater monitoring stations in Jeju Island during 2005~2015 was carried out for the re-establishment of management groundwater level. Three probability distribution functions such as normal distibution, GEV (General Extreme Value) distribution, and Gumbel distribution were applied and the maximum likelihood method was used for parameter estimation of each distribution. AIC (Akaike information criterion) was calculated based on the estimated parameters to determine the proper probability distribution for all 127 stations. The results showed that normal distribution and Gumble distribution were found in 11 stations. Whereas GEV distribution were found in 105 stations, which covered most of groundwater monitoring stations. Therefore, confidence levels should be established in accord with the proper probability distribution when groundwater level management is determined.

Reliability Estimation of Buried Gas Pipelines in terms of Various Types of Random Variable Distribution

  • Lee Ouk Sub;Kim Dong Hyeok
    • Journal of Mechanical Science and Technology
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    • 제19권6호
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    • pp.1280-1289
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    • 2005
  • This paper presents the effects of corrosion environments of failure pressure model for buried pipelines on failure prediction by using a failure probability. The FORM (first order reliability method) is used in order to estimate the failure probability in the buried pipelines with corrosion defects. The effects of varying distribution types of random variables such as normal, lognormal and Weibull distributions on the failure probability of buried pipelines are systematically investigated. It is found that the failure probability for the MB31G model is larger than that for the B31G model. And the failure probability is estimated as the largest for the Weibull distribution and the smallest for the normal distribution. The effect of data scattering in corrosion environments on failure probability is also investigated and it is recognized that the scattering of wall thickness and yield strength of pipeline affects the failure probability significantly. The normalized margin is defined and estimated. Furthermore, the normalized margin is used to predict the failure probability using the fitting lines between failure probability and normalized margin.

Bayesian MCMC 및 Metropolis Hastings 알고리즘을 이용한 강우빈도분석에서 확률분포의 매개변수에 대한 불확실성 해석 (Uncertainty Analysis for Parameters of Probability Distribution in Rainfall Frequency Analysis by Bayesian MCMC and Metropolis Hastings Algorithm)

  • 서영민;박기범
    • 한국환경과학회지
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    • 제20권3호
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    • pp.329-340
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    • 2011
  • The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

System Realization for Video Surveillance with Interframe Probability Distribution Analysis

  • Kim, Ja-Hwan;Ryu, Kwang-Ryol;Hur, Chang-Woo;Sclabassi, Robert J.
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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    • pp.306-309
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    • 2008
  • A system realization for video surveillance with interframe probability distribution analysis is presented in this paper. The system design is based on a high performance DSP processor, video surveillance is implemented by analyzing interframe probability distribution for scanning objects in a restricted area and the video analysis algorithm is decided for forming a different image from the probability distribution of several frames compressed by the standardized JPEG. The algorithm processing time of D1($720{\times}480$) image per frame is 85ms.

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피로균열전파수명의 확률분포추정 프로그램 개발 (Development of Probability Distribution Estimation Program for Fatigue Crack Growth Lives)

  • 김선진;안석환;윤성환
    • Journal of Advanced Marine Engineering and Technology
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    • 제25권5호
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    • pp.1058-1064
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    • 2001
  • In this paper, the development of probability distribution estimation program for fatigue crack growth lives was summarize. The probability distribution estimation program of life was developed to increase the reliability of life estimation. In this study, it is considered that the cause of scatter in fatigue crack growth data is due to material inhomogeneity. The material resistance to fatigue crack growth is modelled as a spatial stochastic process, which varies randomly along the crack path. We developed the GUI program to estimate the probability distribution and reliability using the non-Gaussian stochastic process method. This program can be used for the reliability assessment.

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GPH 분포를 이용한 파산확률의 계산 (Computing Ruin Probability Using the GPH Distribution)

  • 윤복식
    • 한국경영과학회지
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    • 제40권3호
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    • pp.39-48
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    • 2015
  • Even though ruin probability is a fundamental value to determine the insurance premium and policy, the complexity involved in computing its exact value forced us resort to an approximate method. In this paper, we first present an exact method to compute ruin probability under the assumption that the claim size has a GPH distribution, Then, for the arbitrary claim size distribution, we provide a method computing ruin probability quite accurately by approximating the distribution as a GPH. The validity of the proposed method demonstrated by a numerical example. The GPH approach seems to be valid for heavy-tailed claims as well as usual light-tailed claims.

AZ31 마그네슘합금의 피로균열진전수명에 적합한 확률분포 평가 (Estimation of Probability Distribution Fit for Fatigue Crack Propagation Life of AZ31 Magnesium Alloy)

  • 최선순
    • 대한기계학회논문집A
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    • 제33권8호
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    • pp.707-719
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    • 2009
  • The variables relating to the fatigue behavior have uncertainty and are random. The fatigue crack propagation is, thus, stochastic in nature. In this study, fatigue experiments are performed on the specimen of the magnesium alloy AZ31. The data of the fatigue life are scattered even in the same experimental condition. It is necessary to determine the probability distribution of the fatigue crack propagation life for the reliability analysis as well as the design and maintenance of structural components. Therefore the statistics and the probability distribution for the fatigue crack propagation life are investigated and the best fit probability distribution of that is proposed in this paper.

Application of probabilistic method to determination of aerodynamic force coefficients on tall buildings

  • Yong Chul Kim;Shuyang Cao
    • Wind and Structures
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    • 제36권4호
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    • pp.249-261
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    • 2023
  • Aerodynamic force coefficients are generally prescribed by an ensemble average of ten and/or twenty 10-minute samples. However, this makes it difficult to identify the exact probability distribution and exceedance probability of the prescribed values. In this study, 12,600 10-minute samples on three tall buildings were measured, and the probability distributions were first identified and the aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (cumulative probabilities) of wind load were then evaluated. It was found that the probability distributions of the mean and fluctuating aerodynamic force coefficients followed a normal distribution. The ratios of aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (Cf,Non) to the ensemble average of 12,600 samples (Cf,Ens), which was defined as an adjusting factor (Cf,Non/Cf,Ens), were less than 2%. The effect of coefficient of variation of wind speed on the adjusting factor is larger than that of the annual non-exceedance probability of wind load. The non-exceedance probabilities of the aerodynamic force coefficient is between PC,nonex = 50% and 60% regardless of force components and aspect ratios. The adjusting factors from the Gumbel distribution were larger than those from the normal distribution.

와이블 분포함수를 이용한 하수관로 노후도 추정 (Estimation of sewer deterioration by Weibull distribution function)

  • 강병준;유순유;박규홍
    • 상하수도학회지
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    • 제34권4호
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    • pp.251-258
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    • 2020
  • Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.