Zhihua Zhang Libros

Focusing on innovative frame theory and its application in data science, this book delves into spatial-scale feature extraction, network dynamics, and data-driven environmental predictions. It highlights the importance of these techniques in advancing multi-channel data mining systems, crucial for achieving the United Nations' Sustainable Development Goals. Drawing from two decades of research, it offers advanced methodologies beneficial for scientists, professionals, and graduate students in data science, applied mathematics, environmental science, and geoscience.

Focusing on the application of Boolean control networks (BCNs) in biological systems, this thesis presents methods for reconstructibility analysis essential for state observers in control theory. It details a two-step design process for Luenberger-like observers that provide accurate state estimates when a BCN is reconstructible. The research further extends to unknown input observers, distributed observers, and reduced-order observers, thoroughly evaluating their performance. Additionally, it includes methods for output tracking control and fault diagnosis, validated through numerical examples.

With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network. This book is a concise and self-contained work with enormous　amount　of　information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.