• 한국안전학회지
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• 제25권3호
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• pp.112-117
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• 2010

This study presents a methodology to evaluate the driving safety of vehicles against localized high wind on the roads over the valleys or along the coasts. Risk level for vehicle accident is derived from the side slip caused by cross wind, and then safety criteria based on reliability for driving stability are defined. The level of safety is classified according to probability of exceeding against wind speed using the concept of extreme value statistics. To attain the safety level of vehicle on bridges, numerical simulations using Computational Fluid Dynamics(CFD) are performed. Based on this result, risk reduction and quality improvement is expected through analysis for each alternative in bridges design, construction and operation & maintenance stage with proposed process

• Wind and Structures
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• 제9권3호
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• pp.179-191
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• 2006

Parent wind data are often acknowledged to fit a Weibull probability distribution, implying that wind epoch maxima should fall into the domain of attraction of the Gumbel (Type I) extreme value distribution. However, observations of wind epoch maxima are not fitted well by this distribution and a Generalised Extreme Value (GEV) analysis leading to a Type III fit empirically appears to be better. Thus there is an apparent paradox. The reasons why advocates of the GEV approach seem to prefer it are briefly summarised. This paper gives a detailed analysis of the errors involved when the GEV is fitted to epoch maxima of Weibull origin. It is shown that the results in terms of the shape parameter are an artefact of these errors. The errors are unavoidable with the present sample sizes. If proper significance tests are applied, then the null hypothesis of a Type I fit, as predicted by theory, will almost always be retained. The GEV leads to an unacceptable ambiguity in defining design loads. For these reasons, it is concluded that the GEV approach does not seem to be a sensible option.

• 신재생에너지
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• 제20권2호
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• pp.2-16
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• 2024

Various loads induced by marine environmental conditions, such as waves, currents, and wind, are crucial for the operation and viability of offshore wind power (OWP) systems. In particular, waves have a significant impact on the stress and fatigue load of offshore structures, and highly reliable design parameters should be derived through extreme value analysis (EVA) techniques. In this study, extreme wave analyses were conducted with various Weibull distribution models to determine the reliable design parameters of an OWP system suitable for the Ulsan area. Forty-three years of long-term hindcast data generated by a numerical wave model were adopted as the analyses data, and the least-squares method was used to estimate the parameters of the distribution function for EVA. The inverse first-order reliability method was employed as the EVA technique. The obtained results were compared among themselves under the assumption that the marginal probability distributions were 2p, 3p, and exponentiated Weibull distributions.

• 응용통계연구
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• 제28권2호
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• pp.137-149
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• 2015

본 논문에서는 비정상 극치 강수 자료에 대해 계층적 베이지안 모형을 적용하여 시간에 따른 모수의 변화를 추정하며, 미래 확률 강수량에 대한 극단값 분포를 예측하고 더 나아가 반환기간에 대한 경향과 예측 값을 얻고자 한다. 이전의 고전적 통계 방법을 통한 강수 자료의 모수 추정연구의 경우, 자료의 정상성 가정 하에 고정된 모수를 추정하는 방법으로, 최근 나타난 비정상 강수 사상과 같이 강수량이 가지는 분포의 모수적 변화가 예상되는 경우 해석상 문제가 발생한다. 이러한 문제점을 해결하기 위해 모형의 관심모수에 시간에 따른 자기 상관 선형 회귀 함수를 적합한 계층적 베이지안 모형을 고려한다. 제안된 모형의 효율성을 확인하기 위해서 1973년부터 2011년까지 39년 동안의 우리나라 여러지역의 기상 관측소에서 관측된 일일 강우량 자료가 사용하여 대표적인 극단값 분포인 Generalized Extreme Value(GEV) 분포에 적합시키고, 계층적 베이지안 모형을 이용하여 이들 분포의 모수들에 자기상관 시간모형을 소개한 후 우리나라 여러지역에 대한 반환기간에 대한 시간에 따른 경향을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• 상하수도학회지
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• 제30권4호
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• pp.369-379
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• 2016

It was confirmed that the extreme value distribution model applies to probability of exceeding more than once a day monthly the facility capacities using data of daily maximum inflow rate for 7 wastewater treatment plant. The result of applying the extreme value model, A, D, E wastewater treatment plant has a problem compared to B, C, F, G wastewater treatment plant. but all the wastewater treatment plant has a problem except C, F wastewater treatment plant based 80% of facility capacity. In conclusion, if you make a standard in statistical aspects probability exceeding more than once a day monthly can be 'exceed day is less than a few times annually' or 'probability of exceeding more than once a day monthly is less than what percent'.

• Smart Structures and Systems
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• 제21권5호
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• pp.549-559
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• 2018

The dynamic behaviors of the bridge structures have great effects on the comfortability and safety of running high-speed trains, which can also reflect the structural degradation. This paper aims to reveal the characteristics of the dynamic behaviors induced by train loadings for a combined highway and railway bridge. Monitoring-based analysis of the acceleration and dynamic displacement of the bridge girder is carried out. The effects of train loadings on the vertical acceleration of the bridge girder are analyzed; the spatial variability of the train-induced lateral girder displacement is studied; and statistical analysis has been performed for the daily extreme values of the train-induced girder deflections. It is revealed that there are great time and spatial variabilities for the acceleration induced by train loadings for the combined highway and railway cable-stayed bridge. The daily extreme values of the train-induced girder deflections can be well fitted by the general extreme value distribution.

• Ocean Systems Engineering
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• 제4권4호
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• pp.293-308
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• 2014

Unlike the traditional displacement type vessels, the high speed planing crafts are supported by the lift forces which are highly non-linear. This non-linear phenomenon causes their motions in an irregular seaway to be non-Gaussian. In general, it may not be possible to express the probability distribution of such processes by an analytical formula. Also the process might not be stationary or ergodic in which case the statistical behavior of the motion to be constantly changing with time. Therefore the extreme values of such a process can no longer be calculated using the analytical formulae applicable to Gaussian processes. Since closed form analytical solutions do not exist, recourse is taken to fitting a distribution to the data and estimating the statistical properties of the process from this fitted probability distribution. The peaks over threshold analysis and fitting of the Generalized Pareto Distribution are explored in this paper as an alternative to Weibull, Generalized Gamma and Rayleigh distributions in predicting the short term extreme value of a random process.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.734-737
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• 2004

When the margin level is set relatively low, margin violation probability increases and the default probability of the futures market rises. On the other hand, if the margin level is set high, the margin violation probability decreases, but the futures market becomes less attractive to hedgers as the investor's opportunity cost increases. In this paper, we investigate whether the movement of KOSPI200(Korea Composite Stock Price Index 200) futures daily prices can be modeled with the extreme value theory. Base on this investigation, we examine the validity of the margin level set by the extreme value theory. Computational results are presented to compare the extreme value distribution and the empirical distribution of margin violation in KOSPI200. Some observations and implications drawn from the computational experiment are also discussed.

• 상하수도학회지
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• 제26권2호
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• pp.321-329
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• 2012

In this study, statistical analysis under both stationary and non-stationary climate was conducted for rainfall data measured in Seoul. Generalised Extreme Value (GEV) distribution and Gumbel distribution were used for the analysis. Rainfall changes under the non-stationary climate were estimated by applying time variable (t) to location parameter (${\xi}$). Rainfall depths calculated in non-stationary climate increased by 1.1 to 6.2mm and 1.0 to 4.6mm for the GEV distribution and gumbel distribution respectively from those stationary forms. Changes in annual maximum rainfall were estimated with rate of change in the location parameter (${\xi}1{\cdot}t$), and temporal changes of return period were predicted. This was also available for re-evaluating the current sewer design return period. Design criteria of sewer system was newly suggested considering life expectance of the system as well as temporal changes in the return period.