Ying Sun – författare

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.

Synthesizes the connections between the East Asian Summer Monsoon, the Indian Summer Monsoon and the Asian Summer Monsoon system Includes subsections on holistic characteristics, sub-seasonal and interannual variability, teleconnection patterns, and projections of future change Connects current theory and practice on Asian Monsoon forecasting, providing researchers with new skills and information to use in climate and weather forecasting

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.

Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

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. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization.

This book adopts the methods of field investigation and case study, systematically summarizes the development course of Nanjiang County in Sichuan Province since China’s reform and opening-up, and comprehensively sorts out its main experiences of promoting green development and rural governance.