Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areas―including sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in the existing literature. Examining emerging real-world problems, this book summarizes recent research developments in problems with unideal and uncertain frameworks. It presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. Deriving thorough results under general conditions, this reference book: <ul>
<li>Corrects several popular but incorrect results in this area with thorough mathematical ideas</li> <li>Provides advanced mathematical methods, which lead to more general and significant results</li> <li>Presents updated systematic developments in both multisensor decision and estimation fusion, which cannot be seen in other existing books</li> <li>Includes numerous computer experiments that support every theoretical result</li> </ul> The book applies recently developed convex optimization theory and high efficient algorithms in estimation fusion, which opens a very attractive research subject on minimizing Euclidean error estimation for uncertain dynamic systems. Supplying powerful and advanced mathematical treatment of the fundamental problems, it will help to greatly broaden prospective applications of such developments in practice.

Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods

Zhou, Jie

Zhu, Yunmin

Shen, Xiaojing

Song, Enbin

Luo, Yingting

Computers & Technology

CRC-Press

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks.Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Liu, Zhiyuan

Computer Science

Morgan Claypool

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Springer

Involving several areas of geological engineering, geotechnical engineering and tunnel engineering, this book describes the soft soil deformation characteristics and dynamic responses induced by subway vibration load. Based on field monitoring and laboratory testing data, with both comprehensive micro-and macroanalysis, the authors present dynamic characteristics and deformation settlement of saturated soft clay surrounding subway tunnels using dynamic and static methodology. Mechanism of deformation, failure in microstructure of soft clay soil, dynamic response, macro deformation and settlement are all discussed and analyzed thoroughly and systematically. Some of the research findings in this book have been widely applied by large subway companies and will have broader application prospects in future. All the above make this book a valuable reference not only for technical engineers working in subway design or construction but also for advanced graduate students. Prof. Yiqun Tang works at the Department of Geotechnical Engineering, Tongji University, Shanghai, China.

Dynamic Response and Deformation Characteristic of Saturated Soft Clay under Subway Vehicle Loading (Springer Environmental Science and Engineering)

Engineering & Transportation

Tang, Yiqun

Ren, Xingwei

Yang, Qi

Transportation

New Concept English self-study Counselling Manual (Volume 2)

Commercial Press Pub. Date :2004-08-01

商业建筑设计(第2版)

机械工业出版社

Integrating information from several areas of engineering geology, hydrogeology, geotechnical engineering, this book addresses the general field of groundwater from an engineering perspective. It covers geological engineering as well as hydrogeological and environmental geological problems caused by groundwater engineering. It includes 10 chapters, i.e., basic groundwater theory, parameter calculation in hydrogeology, prevention of geological problem caused by groundwater, construction dewatering, wellpoint dewatering methods, dewatering wells and drilling, groundwater dewatering in foundation-pit engineering, groundwater engineering in bedrock areas, numerical simulation in groundwater engineering, groundwater corrosion on concrete and steel. Based on up-to-date literature, it describes recent developments and presents several case studies with examples and problems. It is an essential reference source for industrial and academic researchers working in the groundwater field and can also serve as lecture-based course material providing fundamental information and practical tools for both senior undergraduate and postgraduate students in fields of geology engineering, hydrogeology, geotechnical engineering or to conduct related research.

Groundwater Engineering (Springer Natural Hazards)

Science & Math

Yang, Ping

Yan, Jingjing

Zhou, Nianqing

Earth Sciences

Integrating information from several areas of engineering geology, hydrogeology, geotechnical engineering, this book addresses the general field of groundwater from an engineering perspective. It covers geological engineering as well as hydrogeological and environmental geological problems caused by groundwater engineering. It includes 10 chapters, i.e., basic groundwater theory, parameter calculation in hydrogeology, prevention of geological problem caused by groundwater, construction dewatering, wellpoint dewatering methods, dewatering wells and drilling, groundwater dewatering in foundation-pit engineering, groundwater engineering in bedrock areas, numerical simulation in groundwater engineering, groundwater corrosion on concrete and steel. Based on up-to-date literature, it describes recent developments and presents several case studies with examples and problems. It is an essential reference source for industrial and academic researchers working in the groundwater field and can also serve as lecture-based course material providing fundamental information and practical tools for both senior undergraduate and postgraduate students in fields of geology engineering, hydrogeology, geotechnical engineering or to conduct related research.

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