The superstrong typhoon Lekima landed twice along the Yellow Sea of China in 2019;its wind characteristics and impact on bridges' buffeting performance are still unclear. Based on the measured wind and temperature...
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The superstrong typhoon Lekima landed twice along the Yellow Sea of China in 2019;its wind characteristics and impact on bridges' buffeting performance are still unclear. Based on the measured wind and temperature data of a long-span composite girder cable-stayed bridge along the Yellow Sea, this paper studies the wind characteristics and wind-temperature correlation of typhoon Lekima at the bridge site. First, the non-Gaussian and nonstationary tests of the typhoon are carried out at three stages, namely, before, during, and after the landing of the typhoon. Second, the turbulence intensity and gust factor at the three stages are statistically analyzed, and then the power spectra and the turbulence integral scale are compared by using four evaluation methods. Finally, the joint distribution model of the wind speed and structural temperature at the bottom of the composite girder is constructed, and their parameter estimation and the density functions of five copula functions are calculated, respectively. The results indicate that the wind speed has obvious Gaussian and nonstationary characteristics during the typhoon, while it has non-Gaussian and nonstationary characteristics before and after the typhoon. The gust factor is consistent in characterizing the turbulent characteristics of the fluctuating wind and linearly changes with the turbulence intensity. Besides, the power spectrum is consistent with four classical spectra in the low-frequency region before and during the typhoon, and the autocorrelation index method and power spectrum method are not suitable for calculating its turbulence integral scale. By comparing the square Euclidean distances, root mean square error, and the Kendall and Spearman rank correlation coefficients, the Frank-copula function has the best fitting accuracy for the correction between the wind speed and temperature among the five copula functions with a symmetric U-shaped distribution in thick-tails' location.
The Pareto distribution is commonly used to represent situations where a small portion of the population controls a disproportionately large share of resources, such as income or wealth distribution. Our study analyze...
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The Pareto distribution is commonly used to represent situations where a small portion of the population controls a disproportionately large share of resources, such as income or wealth distribution. Our study analyzed the Forbes Billionaire List from 2001 to 2023 by fitting it to a Pareto distribution using the Maximum Likelihood Estimation (MLE). Our results showed that the distribution parameter alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} consistently ranged from 1.0 to 1.5. When the distribution parameter alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} is less than 2, the underlying Pareto distribution has infinite variance, complicating the comparisons of deviations. To address this, we used Mean Absolute Deviation MAD (about median) as an alternative approach to estimate alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}. Using MAD resulted in a three times lower root-mean-square error than using MLE. We considered MAD-based kurtosis and skewness by analogy with quantile statistics. We derived new interpretations for these measures in terms of areas of appropriately folded cumulative distribution functions. We applied this innovative approach to the Forbes Billionaire dataset, focusing on various segments, including continents, gender, and industries. We examined historical trends and considered future predictions. Our findings suggest that
Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Ther...
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Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Therefore, using a mono-color stereo dual-camera system, we can integrate the lightness information of target monochrome images with the color information of guidance RGB images to accomplish image enhancement in a colorization manner. In this work, based on two assumptions, we introduce a novel probabilistic-concept guided colorization framework. First, adjacent contents with similar luminance are likely to have similar colors. By lightness matching, we can utilize colors of the matched pixels to estimate the target color value. Second, by matching multiple pixels from the guidance image, if more of these matched pixels have similar luminance values to the target one, we can estimate colors with more confidence. Based on the statisticaldistribution of multiple matching results, we retain the reliable color estimates as initial dense scribbles and then propagate them to the rest of the mono image. However, for a target pixel, the color information provided by its matching results is quite redundant. Hence, we introduce a patch sampling strategy to accelerate the colorization process. Based on the analysis of the posteriori probability distribution of the sampling results, we can use much fewer matches for color estimation and reliability assessment. To alleviate incorrect color propagation in the sparsely scribbled regions, we generate extra color seeds according to the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently and effectively restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.
The incorporation of recycled aggregates from construction and demolition waste into concrete represents a promising sustainability strategy within the construction sector. This article addresses this topic by introdu...
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The incorporation of recycled aggregates from construction and demolition waste into concrete represents a promising sustainability strategy within the construction sector. This article addresses this topic by introducing the development of a comprehensive database, which not only compiles valuable data on the main physical and mechanical properties of concrete with recycled aggregates but also includes specific insights derived from statistical distribution analysis. By integrating statistical distribution analysis, this article provides a nuanced understanding of the statistical variability in concrete properties when utilizing recycled aggregates. As a result, it helps reduce waste production, preserve natural resources, and address environmental concerns. The database’s accessibility and comprehensiveness are expected to foster research, knowledge dissemination, and the evolution of sustainable concrete technology. Ultimately, it contributes to the construction industry’s transformation towards a more environmentally responsible and resource-efficient future.
The algorithm RTI+ learns a Probabilistic Deterministic Real-Time Automaton (PDRTA) from unlabeled timed sequences. RTI+ is an efficient algorithm that runs in polynomial time and can be applied to a variety of real-w...
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ISBN:
(纸本)9789897582226
The algorithm RTI+ learns a Probabilistic Deterministic Real-Time Automaton (PDRTA) from unlabeled timed sequences. RTI+ is an efficient algorithm that runs in polynomial time and can be applied to a variety of real-world behavior identification problems. Nevertheless, we uncover a lack of accuracy in identifying the intervals (or time guards) of the PDRTA. This inaccuracy can lead to wrong predictions of timed sequences in the learned model. We show by example that segments in intervals that are not covered by training data are responsible for this effect. We call those segments gaps and name three types of gaps that can appear. Two of the types cause wrong predictions of sequences and should thus be removed from the model. Therefore, we introduce our novel Interval distributionanalysis (IDA) which utilizes statistical outlier detection to identify and remove gaps. In the context of ATM fraud detection, we show that IDA can improve the results of RTI+ in a real-world scenario.
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