Zhihua Zhang Knihy

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.

Focusing on advanced multivariate time series analysis, this book delves into theories, models, and algorithms crucial for understanding climate and environmental science. Key topics include Multivariate Time-Frequency Analysis, Artificial Neural Networks, and Stochastic Modeling. It also explores significant research advances related to global and regional climate change, the ecosystem, the carbon cycle, paleoclimate, and effective strategies for climate change mitigation. The work serves as a valuable resource for researchers and practitioners in the field.

Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. series analysisChaos and dynamical systemsApproximationInterpolationStatistical methodsNumerical methodsOptimizationData envelopment analysisRisk assessmentsLife cycle assessmentsIndex