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  • 1
    Keywords: Geotechnical engineering. ; Engineering design. ; Civil engineering. ; Geology. ; Geotechnical Engineering and Applied Earth Sciences. ; Engineering Design. ; Civil Engineering. ; Geology.
    Description / Table of Contents: Introduction -- Development of the Support System with Confined Concrete -- Mechanical Mechanism Test on the Basic Components of Confined Concrete Arches -- Calculation Theory of the Confined Concrete Arch -- Experimental Study on the Bearing Behavior of Confined Concrete Arches -- Engineering Practice of the Confined Concrete Support System in Soft Rock Roadways in the Sea Area -- Engineering Practice of Confined Concrete Support System in Deep Roadways with High Stress.
    Abstract: This book examines the field of surrounding rock control mechanisms and support technologies in underground engineering, and proposes a high-strength support system to address the complex conditions in underground engineering, such as high stress, extremely soft rocks, fault fracture zone and strong mining activity. It also comprehensively discusses the concept and bearing mechanisms of the supporting system, design calculation methods, field application and key construction technologies. The book describes the design and construction of a large-scale mechanical test system, independently developed by the author for high- strength confined concrete arches, which can also be used to define the mechanism of deformation and failure of confined concrete arches. Further, the book explores the application of the confined concrete support system in underground engineering with complex conditions, and its control effect on soft surrounding rock. The first international book presenting the theory and key technologies of high-strength, confined concrete support, it is a valuable reference resource for design, construction and supervision staff in the field of geotechnical engineering, as well as for teachers, students and researchers. .
    Type of Medium: Online Resource
    Pages: XVI, 183 p. 147 illus., 123 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9789811538445
    DDC: 624.151
    Language: English
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  • 2
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-12-20
    Description: With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
    Keywords: QA75.5-76.95 ; T58.5-58.64 ; metadata ; image classification ; sensitivity analysis ; ROI detection ; residual learning ; image alignment ; adaptive convolutional kernels ; Hough transform ; class imbalance ; land surface temperature ; inundation mapping ; multiscale representation ; object-based ; convolutional neural networks ; scene classification ; morphological profiles ; hyperedge weight estimation ; hyperparameter sparse representation ; semantic segmentation ; vehicle classification ; flood ; Landsat imagery ; target detection ; multi-sensor ; building damage detection ; optimized kernel minimum noise fraction (OKMNF) ; sea-land segmentation ; nonlinear classification ; land use ; SAR imagery ; anti-noise transfer network ; sub-pixel change detection ; Radon transform ; segmentation ; remote sensing image retrieval ; TensorFlow ; convolutional neural network ; particle swarm optimization ; optical sensors ; machine learning ; mixed pixel ; optical remotely sensed images ; object-based image analysis ; very high resolution images ; single stream optimization ; ship detection ; ice concentration ; online learning ; manifold ranking ; dictionary learning ; urban surface water extraction ; saliency detection ; spatial attraction model (SAM) ; quality assessment ; Fuzzy-GA decision making system ; land cover change ; multi-view canonical correlation analysis ensemble ; land cover ; semantic labeling ; sparse representation ; dimensionality expansion ; speckle filters ; hyperspectral imagery ; fully convolutional network ; infrared image ; Siamese neural network ; Random Forests (RF) ; feature matching ; color matching ; geostationary satellite remote sensing image ; change feature analysis ; road detection ; deep learning ; aerial images ; image segmentation ; aerial image ; multi-sensor image matching ; HJ-1A/B CCD ; endmember extraction ; high resolution ; multi-scale clustering ; heterogeneous domain adaptation ; hard classification ; regional land cover ; hypergraph learning ; automatic cluster number determination ; dilated convolution ; MSER ; semi-supervised learning ; gate ; Synthetic Aperture Radar (SAR) ; downscaling ; conditional random fields ; urban heat island ; hyperspectral image ; remote sensing image correction ; skip connection ; ISPRS ; spatial distribution ; geo-referencing ; Support Vector Machine (SVM) ; very high resolution (VHR) satellite image ; classification ; ensemble learning ; synthetic aperture radar ; conservation ; convolutional neural network (CNN) ; THEOS ; visible light and infrared integrated camera ; vehicle localization ; structured sparsity ; texture analysis ; DSFATN ; CNN ; image registration ; UAV ; unsupervised classification ; SVMs ; SAR image ; fuzzy neural network ; dimensionality reduction ; GeoEye-1 ; feature extraction ; sub-pixel ; energy distribution optimizing ; saliency analysis ; deep convolutional neural networks ; sparse and low-rank graph ; hyperspectral remote sensing ; tensor low-rank approximation ; optimal transport ; SELF ; spatiotemporal context learning ; Modest AdaBoost ; topic modelling ; multi-seasonal ; Segment-Tree Filtering ; locality information ; GF-4 PMS ; image fusion ; wavelet transform ; hashing ; machine learning techniques ; satellite images ; climate change ; road segmentation ; remote sensing ; tensor sparse decomposition ; Convolutional Neural Network (CNN) ; multi-task learning ; deep salient feature ; speckle ; canonical correlation weighted voting ; fully convolutional network (FCN) ; despeckling ; multispectral imagery ; ratio images ; linear spectral unmixing ; hyperspectral image classification ; multispectral images ; high resolution image ; multi-objective ; convolution neural network ; transfer learning ; 1-dimensional (1-D) ; threshold stability ; Landsat ; kernel method ; phase congruency ; subpixel mapping (SPM) ; tensor ; MODIS ; GSHHG database ; compressive sensing ; bic Book Industry Communication::U Computing & information technology::UY Computer science
    Language: English
    Format: application/octet-stream
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  • 3
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-14
    Description: With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
    Keywords: QA75.5-76.95 ; T58.5-58.64 ; metadata ; image classification ; sensitivity analysis ; ROI detection ; residual learning ; image alignment ; adaptive convolutional kernels ; Hough transform ; class imbalance ; land surface temperature ; inundation mapping ; multiscale representation ; object-based ; convolutional neural networks ; scene classification ; morphological profiles ; hyperedge weight estimation ; hyperparameter sparse representation ; semantic segmentation ; vehicle classification ; flood ; Landsat imagery ; target detection ; multi-sensor ; building damage detection ; optimized kernel minimum noise fraction (OKMNF) ; sea-land segmentation ; nonlinear classification ; land use ; SAR imagery ; anti-noise transfer network ; sub-pixel change detection ; Radon transform ; segmentation ; remote sensing image retrieval ; TensorFlow ; convolutional neural network ; particle swarm optimization ; optical sensors ; machine learning ; mixed pixel ; optical remotely sensed images ; object-based image analysis ; very high resolution images ; single stream optimization ; ship detection ; ice concentration ; online learning ; manifold ranking ; dictionary learning ; urban surface water extraction ; saliency detection ; spatial attraction model (SAM) ; quality assessment ; Fuzzy-GA decision making system ; land cover change ; multi-view canonical correlation analysis ensemble ; land cover ; semantic labeling ; sparse representation ; dimensionality expansion ; speckle filters ; hyperspectral imagery ; fully convolutional network ; infrared image ; Siamese neural network ; Random Forests (RF) ; feature matching ; color matching ; geostationary satellite remote sensing image ; change feature analysis ; road detection ; deep learning ; aerial images ; image segmentation ; aerial image ; multi-sensor image matching ; HJ-1A/B CCD ; endmember extraction ; high resolution ; multi-scale clustering ; heterogeneous domain adaptation ; hard classification ; regional land cover ; hypergraph learning ; automatic cluster number determination ; dilated convolution ; MSER ; semi-supervised learning ; gate ; Synthetic Aperture Radar (SAR) ; downscaling ; conditional random fields ; urban heat island ; hyperspectral image ; remote sensing image correction ; skip connection ; ISPRS ; spatial distribution ; geo-referencing ; Support Vector Machine (SVM) ; very high resolution (VHR) satellite image ; classification ; ensemble learning ; synthetic aperture radar ; conservation ; convolutional neural network (CNN) ; THEOS ; visible light and infrared integrated camera ; vehicle localization ; structured sparsity ; texture analysis ; DSFATN ; CNN ; image registration ; UAV ; unsupervised classification ; SVMs ; SAR image ; fuzzy neural network ; dimensionality reduction ; GeoEye-1 ; feature extraction ; sub-pixel ; energy distribution optimizing ; saliency analysis ; deep convolutional neural networks ; sparse and low-rank graph ; hyperspectral remote sensing ; tensor low-rank approximation ; optimal transport ; SELF ; spatiotemporal context learning ; Modest AdaBoost ; topic modelling ; multi-seasonal ; Segment-Tree Filtering ; locality information ; GF-4 PMS ; image fusion ; wavelet transform ; hashing ; machine learning techniques ; satellite images ; climate change ; road segmentation ; remote sensing ; tensor sparse decomposition ; Convolutional Neural Network (CNN) ; multi-task learning ; deep salient feature ; speckle ; canonical correlation weighted voting ; fully convolutional network (FCN) ; despeckling ; multispectral imagery ; ratio images ; linear spectral unmixing ; hyperspectral image classification ; multispectral images ; high resolution image ; multi-objective ; convolution neural network ; transfer learning ; 1-dimensional (1-D) ; threshold stability ; Landsat ; kernel method ; phase congruency ; subpixel mapping (SPM) ; tensor ; MODIS ; GSHHG database ; compressive sensing ; thema EDItEUR::U Computing and Information Technology::UY Computer science
    Language: English
    Format: application/octet-stream
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  • 4
    Publication Date: 2023-01-13
    Keywords: File content; File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 5
    ISSN: 1520-4995
    Source: ACS Legacy Archives
    Topics: Biology , Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of the American Chemical Society 103 (1981), S. 4634-4635 
    ISSN: 1520-5126
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of natural products 52 (1989), S. 948-951 
    ISSN: 1520-6025
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 8
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of natural products 56 (1993), S. 279-281 
    ISSN: 1520-6025
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of natural products 56 (1993), S. 637-642 
    ISSN: 1520-6025
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of the American Chemical Society 116 (1994), S. 5671-5673 
    ISSN: 1520-5126
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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