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Titlebook: Smart Cities, Green Technologies, and Intelligent Transport Systems; 10th International C Cornel Klein,Matthias Jarke,Oleg Gusikhin Confere

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發(fā)表于 2025-3-21 17:14:11 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Smart Cities, Green Technologies, and Intelligent Transport Systems
副標題10th International C
編輯Cornel Klein,Matthias Jarke,Oleg Gusikhin
視頻videohttp://file.papertrans.cn/869/868658/868658.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Smart Cities, Green Technologies, and Intelligent Transport Systems; 10th International C Cornel Klein,Matthias Jarke,Oleg Gusikhin Confere
描述?This book includes extended and revised selected papers from the 10th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2021, and 7th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2021, held as virtual event, in April 28–30, 2021. The conference was held virtually due to the COVID-19 crisis..The 22 full papers included in this book were carefully reviewed and selected from 140 submissions. The papers present research on advances and applications in the fields of smart cities, electric vehicles, sustainable computing and communications, energy aware systems and technologies, intelligent vehicle technologies, intelligent transport systems and infrastructure, connected vehicles..
出版日期Conference proceedings 2022
關鍵詞Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-17098-0
isbn_softcover978-3-031-17097-3
isbn_ebook978-3-031-17098-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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Identifying Requirements to Model a Data Lifecycle in Smart City Frameworksentified with aid of a data taxonomy. Furthermore, five smart city frameworks will be analyzed using requirements identified in this study as a reference, as well as a new illustrative use case that uses sensitive information will be presented.
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Building Rich Interior Hazard Maps for?Public Safetyreover, we used two of the scanned buildings as a case study to illustrate our process and show detailed evaluation results. Our results show that the deep neural network . with transfer learning and hard-negative mining performs well in labeling public-safety objects in our image dataset, especiall
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A Scalable Approach to?Vocation and?Fleet Identification for?Heavy-Duty Vehiclesdicate that both vocation and fleet identification are possible with a high level of accuracy. The macro average precision and recall of the SVM vocation classifier are approximately 85%. This result was achieved despite the fact that each vocation consisted of multiple fleets. The macro average pre
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A New Traffic Sign Detection Technique Using Two-Stage Convolutional Neural Networksed out the videos acquired from highway, suburb and urban scenarios. The experimental results obtained using Faster R-CNN for detection combined with VGG for classification have demonstrated its superior performance compared to YOLOv3 and Mask R-CNN.
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