The Asian Summer Monsoon: Characteristics, Variability, Teleconnections and Projection focuses on the connections between the Indian Summer and East Asian Summer Monsoons, also including the South China Sea Summer Monsoon. While these systems have profound differences, their interactions have significant impacts on the climatic regimes in the region and throughout the world. In summer, the ASM engine pumps moisture transported across thousands of miles from the Indian and Pacific Oceans to the monsoon regions, producing heavy rains over south and east Asia and its adjacent marginal seas. This book reviews the different subsystems and their impact, providing guidance to enhance prediction models.

The Asian Summer Monsoon: Characteristics, Variability, Teleconnections and Projection

Science & Math

Sun, Ying

Liu, Yunyun

Liang, Ping

Earth Sciences

Elsevier

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications

Engineering & Transportation

Harrou, Fouzi

Hering, Amanda S.

Madakyaru, Muddu

Dairi, abdelkader

Engineering

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems.

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning

Zeroual, Abdelhafid

Hittawe, Mohamad Mazen

Transportation

This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers.

Multi-agent Optimization: Cetraro, Italy 2014 (C.I.M.E. Foundation Subseries)

Pang, Jong-Shi

Nedić, Angelia

Scutari, Gesualdo

Reference

Springer

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Books by Sun, Ying