• 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 Construction Engineering and Project Management
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• v.4 no.4
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• pp.33-40
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• 2014

Maximizing the profitability and minimizing the duration of construction projects in extreme weather regions is a challenging objective that is essential for project success. An optimization model is presented herein for the time-profit trade-off analysis of construction projects under extreme weather conditions. The model generates optimal/near optimal schedules that maximize profit and minimize the duration of construction projects in extreme weather regions. The computations in the model are organized into: (1) a scheduling module that develops practical schedules for construction projects, (2) a profit module that computes project costs (direct, indirect, and total) and project profit, and (3) a multi-objective module that determines optimal/near optimal trade-offs between project duration and profit. One example is used to show the impact of extreme weather on construction time and profit. Another example is used to show the model's ability to generate optimal trade-offs between the time and profit of construction projects under extreme weather conditions.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.87-94
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• 2015

To compare changes in winter temperature over South Korea, 30-year average climate data and climate data of recent 10 years (2014~2014) such as mean temperature, daily maximum temperature and daily minimum temperature were analyzed. Also, we set analysis extreme cold waves frequency related to winter such as freezing days, snow days, days with temperature of below -5, and days with temperature of below -10. This process enabled the comparative analysis of winter temperature changes and extreme cold waves frequency related to winter. This study estimated that winter temperature has gradually increased throughout the last five decades, however, the frequency of extreme weather, such as cold waves has also increased.

• KIEE International Transactions on Power Engineering
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• v.5A no.3
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• pp.300-302
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• 2005

We employ a new technique to account for extreme values when using the generalized autoregressive conditionally heteroskedastic (GARCH) methodology to forecast day-ahead electricity prices in New York City.

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.1 s.24
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• pp.33-38
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• 2006

In marine salvage, extreme tide heights and tidal currents are necessary to anchor an accidental ship. In order to meet this requirement, a simple scheme was developed which yields the spatial informations on the extreme tide from the distribution of approximate highest astronomical tide heights using a relationship between extreme and highest astronomical tides at the standard port. This method is the inference method based on horizontally homogeneity of tide. This scheme was applied to estimate extreme tide heights and tidal currents in the Namhae (southern sea of Korea). The highest astronomical tide heights are computed by amplitude of four major constituents (M2, S2, K1, O1 tide). The estimated extreme tide heights are ranged from 70 to 260 cm for return period 50 years and from 80 to 270cm for return period 100 years, respectively. For return period 100 years, extreme tidal currents show value of 1.55 times as strong as those of normal state.

• Journal of Ocean Engineering and Technology
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• v.32 no.1
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• pp.21-27
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• 2018

The design and analysis of a quayside mooring system for safe mooring of Prelude FLNG under extreme environmental conditions were carried out. The design of the mooring system considered the yard operation conditions and maximum wind speed during a typhoon. In order to secure the mooring safety of Prelude FLNG under an extreme environment, a special steel structure was designed between the quay and Prelude FLNG to maintain the distance from the quay to a certain extent to avoid a collision with the inclined base. The mooring safety was also ensured by installing additional new parts on the quay. A mooring analysis and mooring safety review were performed with more rigorous modeling considering the nonlinearity of the mooring rope and fender. In order to secure additional safety of the mooring system under extreme environmental conditions, a safety assessment was conducted on the failures of the mooring components proposed in the marine mooring guidelines. Based on the results of the mooring analysis, it was confirmed that the Prelude FLNG can be safely moored even under the extreme conditions of typhoons, and a worst case scenario analysis verified that the mooring system design was robust enough. The proposed mooring analysis and design method will provide a basis for the safe mooring of ultra-large floating offshore structures of similar size in the future.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.947-958
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• 2014

Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.

• Journal of the Korean association of regional geographers
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• v.21 no.2
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• pp.394-410
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• 2015

This study aims to classify climate zones for extreme climate indices over the Republic of Korea. First, frequencies and magitudes of extreme high temperature, spatial distributions for extreme low temperature, and extreme precipitation are analysed. Frequencies of summer days in inland region show more than coastal region. In frequencies of frost days, the characteristics of altitude and longitude are appeared. Heavy precipitation days show many frequencies in the southern coastal region and Jeju island, but little in Gyeongsangbuk-do region. The classification of climate zone for extreme climate indices by principal component analysis and cluster analysis is conducted for the first half, second half of study period, and climatology period for 1981-2010. Summer days are classified according to latitude. In case of frost days, the eastern and the southern coastal region and Jeju island are classified as same region. Heavy precipitation days are classified according to longitude in south region of Gyeonggi-do and Gangwon-do. This study will help to prepare adaptation and mitigation system for climate change in wide range of fields.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.389-397
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both Gumbel distribution and trend analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.