• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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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.

• Journal of Advanced Research in Ocean Engineering
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• v.4 no.4
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• pp.185-194
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• 2018

In this study, the extreme sloshing pressure was predicted using various statistical models: three-parameter Weibull distribution, generalized Pareto distribution, generalized extreme value distribution, and three-parameter log-logistic distribution. The estimation of sloshing impact pressure is important in design of liquid cargo tank in severe sea state. In order to get the extreme values of local impact pressures, a lot of model tests have been carried out and statistical analysis has been performed. Three-parameter Weibull distribution and generalized Pareto distribution are widely used as the statistical analysis method in sloshing phenomenon, but generalized extreme value distribution and three-parameter log-logistic distribution are added in this study. Additionally, statistical distributions are fitted to peak pressure data using three different parameter estimation methods. The data were obtained from a three-dimensional sloshing model text conducted at Seoul National University. The loading conditions were 20%, 50%, and 95% of tank height, and the analysis was performed based on the measured impact pressure on four significant panels with large sloshing impacts. These fittings were compared by observing probability of exceedance diagrams and probability plot correlation coefficient test for goodness-of-fit.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.4
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• pp.250-260
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• 1992

Two methods for computing extreme sea levels, which are the extreme probability method and the joint probability method, are examined at five different ports (Incheon, Cheju, Yeosu, Pusan, Mukho). The extreme probability mothod estimates the extreme sea levels from three different probability papers of Gumbel, Weibull and generalized extreme value(GEV) using the least square method, conventional moment method and probability weighted moment method. respectively. The results showed that the extreme sea levels estimated by the Gumbel paper or the least square method appeared higher than those calculated by other papers or methods. The extreme values estimated by the extreme probability method are approximately 5-10 cm lower than the values by the joint probability method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.231-242
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• 2009

The baseline distribution of a structure represents the statistical distribution of dynamic response feature from the healthy state of the structure. Generally, damage-sensitive dynamic response feature of a structure manifest themselves near the tail of a baseline statistical distribution. In this regard, some researchers have paid attention to extreme value distribution for modeling the tail of a baseline distribution. However, few researches have been conducted to theoretically understand the extreme value distribution from a perspective of statistical damage assessment. This study investigates the asymptotic convergence of domain of attraction in extreme value distribution through parameter estimation, which is needed for reliable statistical damage assessment. In particular, the asymptotic convergence of a domain of attraction is quantified with respect to the sample size out of which each extreme value is extracted. The effect of the sample size on false positive alarms in statistical damage assessment is quantitatively investigated as well. The validity of the proposed method is demonstrated through numerically simulated acceleration data on a two span continuous truss bridge.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.593-603
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• 2004

We consider the problem of estimating the scale parameter of the Weibull distribution based on multiply Type-II censored samples. We propose two estimators by using the approximate maximum likelihood estimation method for Weibull and extreme value distributions. The proposed estimators are compared in the sense of the mean squared error.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1093-1099
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• 2016

• Wind and Structures
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• v.1 no.1
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• pp.25-42
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• 1998

Improvements to the Gumbel method of extreme value analysis of wind data made over the last two decades are reviewed and illustrated using sample data for Jersey. A new procedure for extending the Gumbel method to include M-th highest annual extremes is shown to be less effective than the standard method, but leads to a method for calibrating peak-over-threshold methods against the standard Gumbel approach. Peak-over-threshold methods that include at least the 3rd highest annual extremes, specifically the modified Jensen and Franck method and the "Method of independent storms" are shown to give the best estimates of extremes from observations.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.45-50
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• 1996

This paper mainly describes the wind load assumption of 765kV transmission towers. We analyzed wind velocity data a meteorological observatories to get the wind velocity of 50 years return period by using Gumbel I type extreme value distribution. By multi-correlative regression analysis method, wind velocity at no observation site was obtained. Reference dynamics wind pressure map was obtained from above analysis and the wind pressure was classified as three regio in high temperature season.

• Wind and Structures
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• v.25 no.3
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• pp.261-282
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• 2017

The probabilistic information of directional extreme wind speeds is important for precisely estimating the design wind loads on structures. A new joint probability distribution model of directional extreme wind speeds is established based on observed wind-speed data using multivariate extreme value theory with the t-Copula function in the present study. At first, the theoretical deficiencies of the Gaussian-Copula and Gumbel-Copula models proposed by previous researchers for the joint probability distribution of directional extreme wind speeds are analysed. Then, the t-Copula model is adopted to solve this deficiency. Next, these three types of Copula models are discussed and evaluated with Spearman's rho, the parametric bootstrap test and the selection criteria based on the empirical Copula. Finally, the extreme wind speeds for a given return period are predicted by the t-Copula model with observed wind-speed records from several areas and the influence of dependence among directional extreme wind speeds on the predicted results is discussed.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.