Jian Tang – författare

Low Reynolds number aerodynamics is important to a number of natural and man-made flyers. Birds, bats, and insects have been of interest to biologists for years, and active study in the aerospace engineering community, motivated by interest in micro air vehicles (MAVs), has been increasing rapidly. The focus of this book is the aerodynamics associated with fixed and flapping wings. The book considers both biological flyers and MAVs, including a summary of the scaling laws which relate the aerodynamics and flight characteristics to a flyer's sizing on the basis of simple geometric and dynamics analyses, structural flexibility, laminar-turbulent transition, airfoil shapes, and unsteady flapping wing aerodynamics. The interplay between flapping kinematics and key dimensionless parameters such as the Reynolds number, Strouhal number, and reduced frequency is highlighted. The various unsteady lift enhancement mechanisms are also addressed.

Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources.Introduces a novel deep and broad regression algorithm specifically designed for small sample industrial modeling. It covers Deep Forest Regression for Industrial Modeling, Broad Forest Regression for Industrial Modeling, and Fuzzy Forest Regression for Industrial ModelingDelves into recent results concerning the hot topic of deep and broad learning using non-neuron units for regression and the interpretability of fuzzy trees. These innovative methods are supported by the use of multi-dimensional benchmark data, providing solid confirmationOffers a real application case for industrial modeling by focusing on dioxin emission concentration. This case revolves around a strict controlled environment index of the municipal solid waste incineration (MSWI) process. The book provides offline modeling techniques such as improved deep forest regression and simplified deep forest regression

Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources. - Introduces a novel deep and broad regression algorithm specifically designed for small sample industrial modeling. It covers Deep Forest Regression for Industrial Modeling, Broad Forest Regression for Industrial Modeling, and Fuzzy Forest Regression for Industrial Modeling- Delves into recent results concerning the hot topic of deep and broad learning using non-neuron units for regression and the interpretability of fuzzy trees. These innovative methods are supported by the use of multi-dimensional benchmark data, providing solid confirmation- Offers a real application case for industrial modeling by focusing on dioxin emission concentration. This case revolves around a strict controlled environment index of the municipal solid waste incineration (MSWI) process. The book provides offline modeling techniques such as improved deep forest regression and simplified deep forest regression

Low Reynolds number aerodynamics is important to a number of natural and man-made flyers. Birds, bats, and insects have been of interest to biologists for years, and active study in the aerospace engineering community, motivated by interest in micro air vehicles (MAVs), has been increasing rapidly. The focus of this book is the aerodynamics associated with fixed and flapping wings. The book considers both biological flyers and MAVs, including a summary of the scaling laws which relate the aerodynamics and flight characteristics to a flyer's sizing on the basis of simple geometric and dynamics analyses, structural flexibility, laminar-turbulent transition, airfoil shapes, and unsteady flapping wing aerodynamics. The interplay between flapping kinematics and key dimensionless parameters such as the Reynolds number, Strouhal number, and reduced frequency is highlighted. The various unsteady lift enhancement mechanisms are also addressed.

This book by leading experts consolidates information on the numerical simulation and AI-driven modeling of multiple points of dioxin concentration in the municipal solid waste incineration (MSWI) process to provide readers with the skills to develop safer and more efficient waste management practices.The authors of Modeling of the Whole Lifecycle Dioxins for Municipal Solid Waste Incineration: Numerical Simulation and AI-Driven Method explore the complex interplay between municipal solid waste (MSW) management and environmental health, specifically focusing on the generation and production of dioxins during incineration. This comprehensive work amalgamates theoretical insights and practical applications, leveraging numerical simulations and AI techniques to offer innovative modeling solutions. It addresses the critical problem of dioxin generation and emissions from MSWI through a structured approach integrating exhaustive research data, case studies, and advanced computational methodologies, providing an integrated model accounting for various operational parameters and their effects on emissions. It both discusses the existing theoretical frameworks and empirical studies and applies advanced modeling techniques to yield practical insights. Readers will find clear representations of simulation processes alongside discussions on optimization and control mechanisms. This facilitates an understanding of the underlying mechanisms of dioxin formation, enabling targeted solutions that are informed by both experimental results and advanced simulation techniques. This ultimately furnishes practitioners with robust tools to better understand and mitigate the risks associated with dioxin emissions so they can contribute to safer and more efficient waste management practices.This is an essential resource for engineers, policy-makers, and researchers involved in sustainable waste management and environmental protection. In particular, environmental engineers, MSWI plant operators, and researchers will benefit from the integrated theoretical and practical approaches and discussions of real-world scenarios and applications.

This book by leading experts consolidates information on the numerical simulation and AI-driven modeling of multiple points of dioxin concentration in the municipal solid waste incineration (MSWI) process to provide readers with the skills to develop safer and more efficient waste management practices.The authors of Modeling of the Whole Lifecycle Dioxins for Municipal Solid Waste Incineration: Numerical Simulation and AI-Driven Method explore the complex interplay between municipal solid waste (MSW) management and environmental health, specifically focusing on the generation and production of dioxins during incineration. This comprehensive work amalgamates theoretical insights and practical applications, leveraging numerical simulations and AI techniques to offer innovative modeling solutions. It addresses the critical problem of dioxin generation and emissions from MSWI through a structured approach integrating exhaustive research data, case studies, and advanced computational methodologies, providing an integrated model accounting for various operational parameters and their effects on emissions. It both discusses the existing theoretical frameworks and empirical studies and applies advanced modeling techniques to yield practical insights. Readers will find clear representations of simulation processes alongside discussions on optimization and control mechanisms. This facilitates an understanding of the underlying mechanisms of dioxin formation, enabling targeted solutions that are informed by both experimental results and advanced simulation techniques. This ultimately furnishes practitioners with robust tools to better understand and mitigate the risks associated with dioxin emissions so they can contribute to safer and more efficient waste management practices.This is an essential resource for engineers, policy-makers, and researchers involved in sustainable waste management and environmental protection. In particular, environmental engineers, MSWI plant operators, and researchers will benefit from the integrated theoretical and practical approaches and discussions of real-world scenarios and applications.