Lei Huang – författare

Kriging can be used to determine optimal unbiased predictions for regionalized variables and has been shown to be a powerful tool in slope reliability analysis for reliability-based design. This is the first book to systematically cover the basic theory and applications of the method in slope reliability assessment. The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging application methods are introduced, which enhance computational accuracy and reduce model errors. These include optimization algorithms for spatial parameters in Kriging, adaptive modeling of spatial correlation structures, efficient sampling methods based on Monte Carlo simulation, quantitative analysis of slope failure risks, and reliability analysis methods for unreinforced and reinforced slopes based on conditional random fields. Several case studies are presented to illustrate the practical application and implementation procedures, bridging theory, and practical engineering.Kriging in Slope Reliability Analysis particularly suits consulting engineers, researchers, and postgraduate students.

Kriging can be used to determine optimal unbiased predictions for regionalized variables and has been shown to be a powerful tool in slope reliability analysis for reliability-based design. This is the first book to systematically cover the basic theory and applications of the method in slope reliability assessment. The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging application methods are introduced, which enhance computational accuracy and reduce model errors. These include optimization algorithms for spatial parameters in Kriging, adaptive modeling of spatial correlation structures, efficient sampling methods based on Monte Carlo simulation, quantitative analysis of slope failure risks, and reliability analysis methods for unreinforced and reinforced slopes based on conditional random fields. Several case studies are presented to illustrate the practical application and implementation procedures, bridging theory, and practical engineering.Kriging in Slope Reliability Analysis particularly suits consulting engineers, researchers, and postgraduate students.

This work presents a review of the state-of-the-art techniques for maintaining QoS support for multimedia services over wireless networks. Several novel ideas and algorithms on integrated connection- and packet-level QoS, dynamic radio resource management, and multimedia QoS-aware services are described. Special emphasis is given to the following: mathematical models for analysing connection-level and packet-level QoS; radio resource management schemes for TDMA and CDMA systems; QoS-aware call admission control and seamless handoff schemes; dynamic call admission control for CBR and VBR traffic; and Markov decision process and linear programming techniques for optimal call admission control policy design. The book should be of interest to research scientists and graduate students working in the areas of wireless networks and QoS issues for multimedia traffic and related areas.

Due to the great success and enormous impact of IP networks, In­ ternet access (such as sending and receiving e-mails) and web brows­ ing have become the ruling paradigm for next generation wireless systems. On the other hand, great technological and commercial success of services and applications is being witnessed in mobile wire­ less communications with examples of cellular, pes voice telephony and wireless LANs. The service paradigm has thus shifted from the conventional voice service to seamlessly integrated high quality mul­ timedia transmission over broadband wireless mobile networks. The multimedia content may include data, voice, audio, image, video and so on. With availability of more powerful portable devices, such as PDA, portable computer and cellular phone, coupled with the easier access to the core network (using a mobile device), the number of mobile users and the demand for multimedia-based applications is increasing rapidly. As a result, there is an urgent need for a sys­ tem that supports heterogeneous multimedia services and provides seamless access to the desired resources via wireless connections. Therefore, the convergence of multimedia communication and wireless mobile networking technologies into the next generation wireless multimedia (WMM) networks with the vision of "anytime, anywhere, anyform" information system is the certain trend in the foreseeable future. However, successful combination of these two technologies presents many challenges such as available spectral bandwidth, energy efficiency, seamless end-to-end communication, robustness, security, etc.

?This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures.

​This book presents and surveys normalization techniques with a deep analysis in training deep neural networks.  In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks.  Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures.  The author provides guidelines for elaborating, understanding, and applying normalization methods.  This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks.  The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.

This book surveys normalization techniques with a deep analysis in training deep neural networks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs. This Second Edition builds upon the original material with the addition of more recent proposed methods and expanded technical details for new normalization methods and network architectures tailored to specific tasks.