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Addressing the unprecedented international interest in China’s high-speed railways, this book adopts a global perspective to examine the success of the system and probes into its going-global strategy in the context of the “Belt and Road” initiative, providing readers around the world a better understanding of infrastructure construction under the “Belt and Road” plan, as well as the global vision of communication and mutual exchange and prosperity among the countries along the Belt and Road route. The previous American President, Barack Obama, once told President Xi Jinping that there were two things about China that he particularly admired: the high-speed railway system, and the mathematics education. "The Belt and Road, and the Global Strategy of China’s High-speed Rail" provides scholarly researchers and those generally interested in China's High-speed rail excellent insight into this impressive and rapid development. .

Economic conditions. Economic development --- Foreign trade. International trade --- Development aid. Development cooperation --- Developing countries: economic development problems --- Economics --- Transport. Traffic --- internationale economische organisaties --- internationale economische politiek --- economie --- ontwikkelingssamenwerking --- wereldeconomie --- transport --- internationale economie --- duurzame ontwikkeling --- China --- Asia

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This book is a collection of contributions by selected active researchers in the optical fiber fields highlighting the design, fabrication, and application of optical fibers and fiber systems and covering various topics such as microstructured optical fibers, polymer fibers, nonlinear effects, optical tweezers, and gyroscopic systems. The goal of the book is to provide an updated overview of the current research trends in the optical fiber fields, serving as a general reference for the recent development in optical fiber technologies, though inevitably many topics are not covered.

Optical fibers. --- Light guides (Optical fibers) --- Fiber optics --- Fibers --- Optical materials --- Optical wave guides --- Physical Sciences --- Engineering and Technology --- Optoelectronics --- Physics --- Optics and Lasers

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Intelligent Vibration Control in Civil Engineering Structures provides readers with an all-encompassing view of the theoretical studies, design methods, real-world implementations, and applications relevant to the topic The book focuses on design and property tests on different intelligent control devices, innovative control strategies, analysis examples for structures with intelligent control devices, and designs and tests for intelligent controllers. Focuses on the principles, methods, and applications of intelligent vibration control in civil engineering Covers intelligent control, including active and semi-active control Includes comprehensive contents, such as design and properties of different intelligent control devices, control strategies, and dynamic analysis, intelligent controller design, numerical examples, and experimental data

Civil engineering. --- Vibration. --- Cycles --- Mechanics --- Sound --- Engineering --- Public works

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Global urbanization has been happening for decades and will continue in the future, converting substantial amounts of natural land cover types into urban surface types. This human-caused global land cover change has induced environmental issues that can affect the quality of urban life, such as heat waves and air pollution. Accordingly, preventing and regulating these urban environmental problems has become a common concern for scientists and policy makers. To this end, obtaining spatially explicit information on the distribution and change of urban land covers may be the key to solving this problem. With global coverage, free access, and frequent observation intervals, medium spatial resolution multispectral satellite imagery has been one of the primary datasets used for mapping urban land covers at various geographical scales. However, distinguishing between spectrally and spatially heterogeneous urban surface types in these image data is challenging due to their limited spectral and spatial resolutions, thus limiting urban land cover mapping accuracy. For the same reason, all continental-scale land cover products developed from medium spatial resolution satellite imagery cannot characterize the spatial distribution of specific urban land covers, such as the urban green space. In this PhD dissertation, we tried to exploit the potential of medium spatial resolution multispectral satellite imagery in urban remote sensing by (i) improving the capability of the image data to distinguish between urban land covers spatially and spectrally and (ii) producing reliable maps of the spatial distribution of urban green spaces at the Europe continental scale.In Chapter-2, we tried to reduce the presence of mixed pixels in urban areas by increasing the spatial resolution of satellite images, thus improving the capability of the image data to distinguish between urban land covers spatially. In particular, given the spatial textures provided by the four Sentinel-2 10 m bands, we proposed an improved unmixing-based image fusion approach (UnFuSen2) to sharpen the six Sentinel-2 20 m bands to 10 m resolution. Compared to traditional unmixing-based image fusion methods, UnFuSen2 can self-adapt to the spectral variability of varying land covers and improve the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. In parallel, the objective of Chapter-3 was to improve the capability of satellite imagery to distinguish between urban surface covers spectrally. Given that Fisher Discriminant Analysis (FDA) can enhance the interclass spectral separability between samples of different classes and the spectral similarity of samples of the same category, Chapter-3 integrated FDA and Multiple Endmember Spectral Mixture Analysis (MESMA) (F-MESMA) for more accurate mapping of urban land cover. Our experiments demonstrated that compared to other state-of-art data transformation methods, the ratio of within- vs between-class spectral variability of urban land covers was most strongly reduced after applying the FDA. Consequently, F-MESMA consistently provided the most accurate impervious surface fraction estimates across five urban areas (RMSE F-MESMA = 0.13 vs. RMSE alternative approaches = [0.16-0.17]).Furthermore, in Chapter-4, we analyzed the individual and the combined effect of UnFuSen2, FDA, and multi-temporal observed image data on Sentinel-2-based urban land cover mapping accuracy. Our results demonstrated that the classification accuracy of UnFuSen2-processed single-date imagery, FDA-processed single-date imagery, and Sentinel-2 image time series (ITS) was higher than that of the original single-date Sentinel-2 imagery. The classification of the ITS that consists of UnFuSen2-processed single-date images showed the highest average Kappa coefficient (0.7225) compared to the classifications of other datasets.Finally, in Chapter-5, we used a machine learning-based subpixel classification approach to map urban green spaces across Europe from Landsat images in 1990, 2000, and 2015, filling a gap in the accurate extraction of urban green space information at a continental scale using medium spatial resolution multispectral satellite imagery. Our results showed that the modeled urban green area fractions yielded low RMSE values ranging from 0.09 to 0.16 across ten validation urban areas. Meanwhile, our modeled urban green space maps were validated to outperform other land cover products such as CORINE and the Urban Atlas. Based on the obtained urban green space maps, we found: (i) urban green spaces in Western European countries are more spatially concentrated, while those in Eastern and Southern Europe are relatively sparsely distributed; (ii) the green area in urban core areas (the urbanized areas before 1990) remained almost constant between 1990 and 2000 but started to increase noticeably between 2000 and 2015 throughout Europe; (iii) recent urban expansions (the urbanized areas after 1990) contain more urban green space than the increased urban green area in urban core areas from 1990 to 2015; (iv) the urban green area per capita has been increasing in Western, Eastern, and Northern Europe from 1990 to 2015, but has been declining in Southern Europe.

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Urbanization causes urban areas to expand continuously at the cost of other land cover areas such as urban green. This evolution is problematic for the quality of life in the city because of the ecosystem services that are provided by vegetation. Mitigation of the urban heat island effect by evapotranspiration and the creation of shadow, the provision of habitats for native species and health benefits are examples of these services. Remote sensing offers great techniques for monitoring the growth or decline of urban green spaces and plays an important role in sustainable policy making. In the context of urban green monitoring in the Brussels Capital Region, the usage of SAR and optical imagery has not been explored enough. Another knowledge gap is the importance of timing for the acquisition of the remote sensing data in order to optimally monitor urban green. We did an comparative analysis of the performance between SAR and optical imagery for the mapping of urban green. For this performance analysis, MLC, RF and SVM classifier were be applied. Next, the influence of timing of acquisition on the classification accuracy was investigated. Subsequently, the additional value of multi-temporal and multi-sensor data for the mapping of urban green was explored. Finally, the area of urban green in the Brussels Capital Region in the year 2020 was calculated. Landsat 8, Sentinel-1 and Sentinel-2 satellite imagery were used for this research. The OLI sensor of the Landsat 8 satellite can capture spectral information in 7 spectral bands at 30 m resolution while the MSI sensor of Sentinel-2 captures data in 12 spectral bands at 10, 20 and 60 m. Sentinel-1 has a dual-polarization C-SAR onboard which can produce images at any weather conditions and time of day. Firstly, a spectral separability analysis based on the JM distance and the InStability Index was performed. Both Landsat 8 and Sentinel-2 had a high chance of separating the classification classes from each other, while a lower separability between classes was observed for Sentinel-1. After validating the classifications, Sentinel-2 outperformed Landsat 8 slightly and Sentinel-1 significantly. The SVM classifier produced the most accurate results. Next, the influence of the timing of acquisition on the classification accuracy was investigated. Autumn was in most cases the best season for Landsat 8 imagery, spring for Sentinel-2 and summer for Sentinel-1. Although a small increase in accuracy was observed, the additional value for multi-sensor data was acknowledged based the almost guaranteed increase in accuracy when working with accessible and quality image data such as Sentinel-1 and Sentinel-2. In the case of multi-temporal data, there still an uncertainty about its additional value. An urban green area of 82.38 km² was calculated, which equals to 50.7% of the Brussels Capital Region. Several improvements or gaps regarding the research were identified such as the use of higher spatial resolution data, analyzing the influence of texture measures on the accuracy of the classifications and optimizing the time window for the development of multi-temporal image data.