• Journal of Advanced Marine Engineering and Technology
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• 제39권8호
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• pp.828-832
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• 2015

This study is conducted for the evaluation of corrosion and lifetime prediction of fire extinguishing pipelines in residential buildings. The fire extinguishing pipeline is made of carbon steel. Twenty-four samples were selected among all the fire extinguishing pipelines in a building; the selection was based on specimenspositions, pipeline diameters, and pipeline thickness. Analysis was conducted by using the results of visual inspection, electrochemical potentiodynamic anodic polarization test, pitting depth measurements, and extreme value statistics with the Gumbel distribution. The maximum pitting depth and remaining life were statistically predicted using extreme value statistics. During visual inspection, pitting corrosion was observed in several samples. In addition, extreme value statistics demonstrated that there were several pipelines that were very sensitive to pitting corrosion. However, the pitting corrosion was not critical in all the pipelines; thus, it was necessary to change only those pipelines that were severely corroded.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.857-865
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• 2013

집중 호우로 인한 피해가 증가하면서 다양한 기법들을 이용하여 강우량 예측에 대한 관심이 높아졌다. 최근에는 극단분포를 활용하여 강우량을 예측하려는 시도가 늘고 있다. 본 연구에서는 일반화 극단 분포를 활용하여 실제 서울시의 1973년부터 2010년까지 7월달의 사후예측분포를 생성하고, 수치적인 계산을 위해서 MCMC (Markov chain Monte Carlo)알고리즘을 활용하였다. 이 연구를 통해서 사후예측분포의 점추정값들을 비교하였고 2011년 7월달의 자료와 비교해 봤을 때 집중 호우의 확률이 증가한 것을 알 수 있었다.

• Wind and Structures
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• 제33권6호
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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.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• Journal of the Korean Data and Information Science Society
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• 제24권5호
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• pp.1101-1101
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• 2013

• 응용통계연구
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• 제23권2호
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• pp.219-234
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• 2010

시가총액에 따른 인덱스(INDEX) 투자를 했을 경우에, VaR(Value at Risk)을 종합주가지수(KOSPI)로부터 얻은 수익율의 극단 손실값들로부터 추정한다. 이를 위해, 극단값 이론 중 BM(Block Maxima) 모형을 적용하며, 극단 손실값들의 비독립적 발생을 고려하기 위하여, extremal index 역시 추정한다. 모형의 타당성을 알아보기 위해, 실패율방법을 이용한 사후검정 (back-testing) 을 실시한다. 사후검정을 통해, BM 모형을 적용한 VaR의 추정이 적절함을 알 수 있었다. 또한, 일반적으로 많이 사용되는 GARCH 모형을 이용한 VaR의 추정과 비교한다. 이를 통해, 오차가 t-분포를 따른다고 가정하는 경우, GARCH 모형을 이용한 VaR의 추정이 BM 모형을 이용한 경우와 사후 검정결과에 차이가 없음을 확인하였다. 그러나, GARCH 모형을 통한 VaR 추정은 추정시점근방의 극단 손실값들에 민감하게 반응하지만, BM 모형은 그렇지 않았다. 따라서, 현 시점으로부터 단기간동안의 손실위험은 GARCH 모형을 이용한 VaR의 추정값을 사용하는 것이 적절하며, 장기간동안의 손실위험은 BM 모형으로부터 얻은 VaR의 추정값을 사용하는 것이 적절하다.

• Structural Engineering and Mechanics
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• 제74권1호
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• pp.55-67
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• 2020

The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.

• 한국해양공학회지
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• 제28권5호
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• pp.396-403
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• 2014

This paper considered the prediction of the tension force in the design of a TLP tendon, particularly focusing on the springing problem. Springing is an important parameter that exerts a large tension in special cases. It is a nonlinear phenomenon and requires the 2nd-order wave loads to solve. In this paper, a new prediction method for springing and the resultant extreme tension on the tendon of a TLP is introduced. Using the 2nd-order response function computed using the commercial program WADAM, the probability density function of the 2nd-order tension is obtained from an eigenvalue analysis using a quadratic transfer function and sea spectra. A new method is then suggested to predict the extreme tension loads with respect to the number of occurrences. It is shown that the PDF suggested in this study properly predicts the extreme tension in comparison with the time histories of the 2nd-order tension. The expected tension force is larger than that from a linear analysis in the same time windows. This supports the use of the present method to predict the tension due to springing.

• Structural Monitoring and Maintenance
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• 제10권2호
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.