• Wind and Structures
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• 제19권5호
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• pp.467-480
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• 2014

The estimation of wind speed values used in codes and standards is an integral part of the wind load evaluation process. In a number of codes and standards, wind speeds outside of tropical cyclone prone regions are estimated using a single probability distribution developed from observed wind speed data, with no distinction made between the types of causal wind hazard (e.g., thunderstorm). Non-tropical cyclone wind hazards (i.e., thunderstorm, non-thunderstorm) have been shown to possess different probability distributions and estimation of non-tropical cyclone wind speeds based on a single probability distribution has been shown to underestimate wind speeds. Current treatment of non-tropical cyclone wind hazards in worldwide codes and standards is touched upon in this work. Meteorological data is available at a considerable number of United States (U.S.) stations that have information on wind speed as well as the type of causal wind hazard. In this paper, probability distributions are fit to distinct storm types (i.e., thunderstorm and non-thunderstorm) and the results of these distributions are compared to fitting a single probability distribution to all data regardless of storm type (i.e., co-mingled). Distributions fitted to data separated by storm type and co-mingled data will also be compared to a derived (i.e., "mixed") probability distribution considering multiple storm types independently. This paper will analyze two extreme value distributions (e.g., Gumbel, generalized Pareto). It is shown that mixed probability distribution, on average, is a more conservative measure for extreme wind speed estimation. Using a mixed distribution is especially conservative in situations where a given wind speed value for either storm type has a similar probability of occurrence, and/or when a less frequent storm type produces the highest overall wind speeds. U.S. areas prone to multiple non-tropical cyclone wind hazards are identified.

• 상하수도학회지
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• 제8권2호
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• pp.35-42
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• 1994

This study is to analyse probability distribution characteristics of water supply demand. Two cities located near Seoul were selected as study areas. In this study, two probalility distribution types were tested using the K-S(Kolmogorov-Smirnov) method. The K-S method was used to prove the goodness of the selected distribution type. And also, the goodness of maximum day demand to average day demand ratio which was obtained by field data was tested. Conclusions are as follows. 1.Bothl normal distribution type and lognormal distribution type are appropriate as the probalility distribution type for the water supply demand. 2. The probability distribution characteristics can be used to test the goodness of the maximum day to average day demand ratio.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1998년도 학술발표회 발표논문집
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• pp.338-343
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• 1998

This study is to select appropriate probability distributions for ten days evaporation data for the purpose of representing statistical characteristics of real evaporation data in Korea. Nine probability distribution functions were assumed to be underlying distributions for ten days evaporation data of 20 stations with the duration of 20 years. The parameter of each probability distribution function were estimated by the maximum likelihood approach, and appropriate probability distributions were selected from the goodness of fit test. Log Pearson type III model was selected as an appropriate probability distribution for ten days evaporation data in Korea.

• Journal of the Korean Data and Information Science Society
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• 제26권2호
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• pp.535-545
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• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• 한국농공학회지
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• 제24권1호
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• pp.53-62
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• 1982

This study was attempted to find best fitted distribution and the equations for probable maximum flow with the evaluation of parameters by the method of moment for the rat- ional design of hydraulic structures in the annual exceedance series. Six subwatersheds were selected as studying basins along Geum River basin. The results obtained through this study were analyzed and summarized as follows. 1. Fitted probability distribution was showed in the order of Three Parameter Lognorm al, Type 1 Extremal, Exponential, Pearson Type III, and Log Pearson Type I distribu- tion as the results of x$^2$ goodness of fit test. 2. Kolmogorov-Smirnov test showed in the order of Three Parameter Lognormal, Exp- onential' Pearson Type III, Log Pearson Type III and Type 1 Extremal distribution for the fitted probability distribution. 3. It can be concluded that Three parameter Lognormal distribution is a best fitted one among some other distributions out of respect for each both tests. An Exponential distribution was proposed as a suitable one by Chow, V.T. showeci lower fittness than that of Three Parameter Lognormal in Geum River basin. 5. Probable flood flow equations followins the return periods for each station were obt- ained by Three Parameter Lognormal distribution. 6. It is urgently essential that best fitted probability distribution should be established for the annual exceedance series in the main river systems of Korea.

• 산업경영시스템학회지
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• 제41권2호
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• pp.117-132
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• 2018

There always exist nonzero inspection errors whether inspectors are humans or automatic inspection machines. Inspection errors can be categorized by two types, type I error and type II error, and they can be regarded as either a constant or a random variable. Under the assumption that two types of random inspection errors are distributed with the "uniform" distribution on a half-open interval starting from zero, it was proved that inspectors overestimate any given fraction defective with the probability more than 50%, if and only if the given fraction defective is smaller than a critical value, which depends upon only the ratio of a type II error over a type I error. In addition, it was also proved that the probability of overestimation approaches one hundred percent as a given fraction defective approaches zero. If these critical phenomena hold true for any error distribution, then it might have great economic impact on commercial inspection plans due to the unfair overestimation and the recent trend of decreasing fraction defectives in industry. In this paper, we deal with the same overestimation problem, but assume a "symmetrical triangular" distribution expecting better results since our triangular distribution is closer to a normal distribution than the uniform distribution. It turns out that the overestimation phenomenon still holds true even for the triangular error distribution.

• 한국지반공학회논문집
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• 제24권1호
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• pp.119-130
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• 2008

얕은기초 설계시 이용되는 지반의 물리적 공학적 설계변수들은 고유변동성(inherent variability) 및 측정 오차(measurement error) 등과 같은 여러 가지 요인으로 인하여 불확실성이 필연적으로 내포되어 있다. 본 논문에서는 얕은기초 설계시 기존의 결정론적 방법에서 고려하지 못하는 지반 불확실성 등을 포함한 설계 파라미터의 분산을 고려할 수 있는 확률론적 방법의 신뢰성 설계 사례를 제시하였다. 극한지지력 산정시 확률변수(random variable)인 단위중량, 점착력 및 내부마찰각 그리고 침하량 산정시 탄성계수에 대하여 국내 지반에 대한 적정 확률분포 형태 및 통계적 특성치를 결정하였다. 각 확률변수별로 채택된 분포를 적용하여 지지력과 침하량에 관한 신뢰지수와 파괴확률을 결정하였으며, 목표 신뢰지수 또는 확률에 대한 기초폭을 결정하였다. 얕은기초의 신뢰성 설계에 있어서 정규분포의 일괄적 적용보다는 각 확률변수에 해당하는 적합한 분포형태를 적용, 분석하는 것이 바람직한 것으로 나타났다.

• 한국산업정보학회논문지
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• 제9권3호
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• pp.32-37
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• 2004

This paper investigates the mathematical model of multi-server retrial queueing system with the Batch Markovian Arrival Process (BMAP), the Phase type (PH) service distribution and the finite buffer. The sufficient condition for the steady state distribution existence and the algorithm for calculating this distribution are presented. Finally, a formula to solve loss probability in the case of complete admission discipline is derived.

• 한국농공학회지
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• 제34권3호
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• pp.43-52
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• 1992

Design low flows were derived from the decision of a best fitting probability distribution and of an optimum transformation method can be contributed to the planning of water utilization and management of various hydraulic structures during dry season in the main river systems in Korea. The results were analyzed and summarized as follows. 1.Basic statistics for the selected watersheds were calculated as one of means for the analysis of extremal distribution. 2.Parameters for the different frequency distributions were calculated by the method of moment. 3.Type m extremal distribution was confirmed as a best one among others for the frequency distribution of the low flows by x$^2$ goodness of fit test. 4.Formulas for the design low flows of the Type m extremal distribution with two and three parameters were dervied for the selected watersheds. 5.Design low flows for the Type m extremal distribution when a minimum drought is zero or larger than zero were derived for the selected watersheds, respectively. 6.Design low flows of the Type m extremal distribution with two parameters are appeared to be reasonable when a minimum drought approaches to zero and the observed low flows varied within a relating small range while those with three parameters are seemed to be consistent with the probability distribution of low flows when a minimum drought is larger than zero and the observed low flows showed a wide range.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).