Min Wu – författare

This fifth volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in image and video compression and multimedia.With this reference source you will:- Quickly grasp a new area of research- Understand the underlying principles of a topic and its application- Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved- Quick tutorial reviews of important and emerging topics of research in Image and Video Compression and Multimedia- Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge- Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

The digital information revolution has brought about profound changes in our society and our life. New devices and powerful software have made it possible for consumers worldwide to create, manipulate, share, and enjoy the multimedia information. Internet and wireless networks offer ubiquitous channels to deliver and to exchange multimedia information for such pur­ poses as remote collaboration, distant learning, and entertainment. With all these advances in multimedia coding and communication technologies over the past decade, the major hurdle for allowing much broader access of multimedia assets and deployment of multimedia services no longer lies with bandwidth-related issues, but with how to make sure that content is used for its intended purpose by its intended recipients. The core issue then be­ comes the development of secure management of content usage and delivery across communication networks. Data hiding and digital watermarking are promising new technologies for multimedia information protection and rights management. Secondary data can be embedded imperceptibly in digital multimedia signals for a variety of applications, including ownership protection, authentication, access con­ trol, and annotation. Data hiding can also be used to send side information in multimedia communication for providing additional functionalities or for enhancing performance. The extraction of the embedded data mayor may not need knowledge of the original host media data. In addition to im­ perceptibility, robustness against moderate processing such as compression is also an important consideration.

The digital information revolution has brought about profound changes in our society and our life. New devices and powerful software have made it possible for consumers worldwide to create, manipulate, share, and enjoy the multimedia information. Internet and wireless networks offer ubiquitous channels to deliver and to exchange multimedia information for such pur­ poses as remote collaboration, distant learning, and entertainment. With all these advances in multimedia coding and communication technologies over the past decade, the major hurdle for allowing much broader access of multimedia assets and deployment of multimedia services no longer lies with bandwidth-related issues, but with how to make sure that content is used for its intended purpose by its intended recipients. The core issue then be­ comes the development of secure management of content usage and delivery across communication networks. Data hiding and digital watermarking are promising new technologies for multimedia information protection and rights management. Secondary data can be embedded imperceptibly in digital multimedia signals for a variety of applications, including ownership protection, authentication, access con­ trol, and annotation. Data hiding can also be used to send side information in multimedia communication for providing additional functionalities or for enhancing performance. The extraction of the embedded data mayor may not need knowledge of the original host media data. In addition to im­ perceptibility, robustness against moderate processing such as compression is also an important consideration.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

The four novel Dynamic Event-Triggered Mechanisms (DETMs) in this book are for complex networked systems that optimize resource utilization while preserving system performance. These include hybrid dynamic variables-dependent ETMs, hybrid adjusting variables-dependent ETMs, bounded dynamic variable/time-varying threshold-dependent ETMs, and terminal constraint set-dependent mixed time/event-triggered mechanisms. It explores applications across fault diagnosis in networked systems, finite-time state estimation for complex dynamical networks, and MPC implementation. Presents a systematic study on dynamic event-triggered mechanisms Reviews comprehensive research results on dynamic event-triggered fault diagnosis, state estimation, and MPC Explores how these theories can be applied to the practical engineering problem of load frequency control in power systems Discusses complex factors including cyber-attacks, multiple timescales, jumping system parameters, hard constraints, polytopic uncertainties, nonlinearities, and limited network/computing resources Includes numerous simulations and examples to validate the theoretical results including single-link rigid robot model systems, motor systems, and power systems. This book is aimed at graduate students and researchers in control systems, computer sciences, and signal processing.

The four novel Dynamic Event-Triggered Mechanisms (DETMs) in this book are for complex networked systems that optimize resource utilization while preserving system performance. These include hybrid dynamic variables-dependent ETMs, hybrid adjusting variables-dependent ETMs, bounded dynamic variable/time-varying threshold-dependent ETMs, and terminal constraint set-dependent mixed time/event-triggered mechanisms. It explores applications across fault diagnosis in networked systems, finite-time state estimation for complex dynamical networks, and MPC implementation. Presents a systematic study on dynamic event-triggered mechanisms Reviews comprehensive research results on dynamic event-triggered fault diagnosis, state estimation, and MPC Explores how these theories can be applied to the practical engineering problem of load frequency control in power systems Discusses complex factors including cyber-attacks, multiple timescales, jumping system parameters, hard constraints, polytopic uncertainties, nonlinearities, and limited network/computing resources Includes numerous simulations and examples to validate the theoretical results including single-link rigid robot model systems, motor systems, and power systems. This book is aimed at graduate students and researchers in control systems, computer sciences, and signal processing.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.

The four novel Dynamic Event-Triggered Mechanisms (DETMs) in this book are for complex networked systems that optimize resource utilization while preserving system performance. These include hybrid dynamic variables-dependent ETMs, hybrid adjusting variables-dependent ETMs, bounded dynamic variable/time-varying threshold-dependent ETMs, and terminal constraint set-dependent mixed time/event-triggered mechanisms. It explores applications across fault diagnosis in networked systems, finite-time state estimation for complex dynamical networks, and MPC implementation.Presents a systematic study on dynamic event-triggered mechanismsReviews comprehensive research results on dynamic event-triggered fault diagnosis, state estimation, and MPCExplores how these theories can be applied to the practical engineering problem of load frequency control in power systemsDiscusses complex factors including cyber-attacks, multiple timescales, jumping system parameters, hard constraints, polytopic uncertainties, nonlinearities, and limited network/computing resourcesIncludes numerous simulations and examples to validate the theoretical results including single-link rigid robot model systems, motor systems, and power systems.This book is aimed at graduate students and researchers in control systems, computer sciences, and signal processing.

The digital information revolution has brought about profound changes in our society and our life. New devices and powerful software have made it possible for consumers worldwide to create, manipulate, share, and enjoy the multimedia information. Internet and wireless networks offer ubiquitous channels to deliver and to exchange multimedia information for such pur­ poses as remote collaboration, distant learning, and entertainment. With all these advances in multimedia coding and communication technologies over the past decade, the major hurdle for allowing much broader access of multimedia assets and deployment of multimedia services no longer lies with bandwidth-related issues, but with how to make sure that content is used for its intended purpose by its intended recipients. The core issue then be­ comes the development of secure management of content usage and delivery across communication networks. Data hiding and digital watermarking are promising new technologies for multimedia information protection and rights management. Secondary data can be embedded imperceptibly in digital multimedia signals for a variety of applications, including ownership protection, authentication, access con­ trol, and annotation. Data hiding can also be used to send side information in multimedia communication for providing additional functionalities or for enhancing performance. The extraction of the embedded data mayor may not need knowledge of the original host media data. In addition to im­ perceptibility, robustness against moderate processing such as compression is also an important consideration.

Information Technology in Healthcare: What Professionals Need to Know introduces students in the healthcare, nursing, and allied health professions to information technology. The material narrows the reader's understanding of the general term data in order to explain primary components of information technology as it applies specifically to healthcare fields.The book describes key concepts in the new discipline of health informatics, particularly electronic medical records, which are now widely used in health care. The five sections of the books cover the four primary components of medical information. Parts I and III focus on databases, Part II discusses electronic medical records, Part IV is devoted to the network, and Part V addresses software engineering.Written with an eye to current uses of technology in the field and designed for today's healthcare professionals, Information Technology in Healthcare is an excellent text for courses in healthcare administration, and undergraduate nursing and allied health programs.

This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the development needs of emotional robots and emotional human–robot interaction (HRI) systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.

This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the development needs of emotional robots and emotional human–robot interaction (HRI) systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.

This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the development needs of emotional robots and emotional human–robot interaction (HRI) systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.

This book discusses the developments in the advanced control and intelligent automation for complex systems completed over the last two decades, including the progress in advanced control theory and method, intelligent control and decision-making of complex metallurgical processes, intelligent systems and machine learning, intelligent robot systems design and control, and prediction and control technology for renewable energy. With the depth and breadth of coverage of this book, it serves as a useful reference for engineers in the field of automation and complex process control and graduate students interested in advanced control theory and computational intelligence as well as their applications to the complex industrial processes. This book offers an up-to-date overview of this active research area. It provides readers with the state-of-the-art methods for advanced control and intelligent automation for complex systems

This book discusses the developments in the advanced control and intelligent automation for complex systems completed over the last two decades, including the progress in advanced control theory and method, intelligent control and decision-making of complex metallurgical processes, intelligent systems and machine learning, intelligent robot systems design and control, and prediction and control technology for renewable energy. With the depth and breadth of coverage of this book, it serves as a useful reference for engineers in the field of automation and complex process control and graduate students interested in advanced control theory and computational intelligence as well as their applications to the complex industrial processes. This book offers an up-to-date overview of this active research area. It provides readers with the state-of-the-art methods for advanced control and intelligent automation for complex systems

This book explains how artificial intelligence (AI) works and helps readers adopt, use, and evaluate AI applications in public health. It serves as an introduction to AI literacy for beginners without a prerequisite for a math or information technology background. The conceptual frameworks for AI in public health also serve well for future agentic AI design.With the help of this textbook, instructors will be able to teach students the fundamental concepts and skills to identify unmet needs in public health and implement AI as part of their solutions. This book addresses evaluation issues for AI in public health that other books have not. The meta-AI homework with grading metrics provides metacognitive approaches for AI-human interactions.The book features 11 chapters organized in six parts:Part I introduces AI and explains why we need it in public health (AI literacy courses).Part II explains design/evaluation models of AI applications in public health.Part III discusses big data in public health, which is the foundation of AI applications (data awareness courses).Part IV focuses on case studies of usage of AI applications in public health (e.g., EquiRisk (Equity aware risk stratifications), PMCO-AI (Personalized Motivation, Capability and Opportunity empowered by AI), and PAPO (Policy Aware Personalized Opportunities))Part V concerns living with AI, including education of the public health workforce.Part VI discusses the future of AI in public health, including challenges and opportunities.Artificial Intelligence in Public Health is timely and essential reading for public health students and staff interested in improving their AI literacy and embracing this disruptive technology that could transform the field. It can be used as a core text for students in public health informatics BS/MS/PhD or certificate programs. It can also be used as a secondary text for students in BS/MS programs in schools of public health or students in health informatics/biomedical informatics programs with a public health concentration. Practitioners in public health agencies at different levels (federal, state, county, city, town, community) and researchers in public health informatics may also find the book useful for their work.

This book explains how artificial intelligence (AI) works and helps readers adopt, use, and evaluate AI applications in public health. It serves as an introduction to AI literacy for beginners without a prerequisite for a math or information technology background. The conceptual frameworks for AI in public health also serve well for future agentic AI design.With the help of this textbook, instructors will be able to teach students the fundamental concepts and skills to identify unmet needs in public health and implement AI as part of their solutions. This book addresses evaluation issues for AI in public health that other books have not. The meta-AI homework with grading metrics provides metacognitive approaches for AI-human interactions.The book features 11 chapters organized in six parts:Part I introduces AI and explains why we need it in public health (AI literacy courses).Part II explains design/evaluation models of AI applications in public health.Part III discusses big data in public health, which is the foundation of AI applications (data awareness courses).Part IV focuses on case studies of usage of AI applications in public health (e.g., EquiRisk (Equity aware risk stratifications), PMCO-AI (Personalized Motivation, Capability and Opportunity empowered by AI), and PAPO (Policy Aware Personalized Opportunities))Part V concerns living with AI, including education of the public health workforce.Part VI discusses the future of AI in public health, including challenges and opportunities.Artificial Intelligence in Public Health is timely and essential reading for public health students and staff interested in improving their AI literacy and embracing this disruptive technology that could transform the field. It can be used as a core text for students in public health informatics BS/MS/PhD or certificate programs. It can also be used as a secondary text for students in BS/MS programs in schools of public health or students in health informatics/biomedical informatics programs with a public health concentration. Practitioners in public health agencies at different levels (federal, state, county, city, town, community) and researchers in public health informatics may also find the book useful for their work.

Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.

This book discusses the intelligent optimization and control of complex metallurgical processes, including intelligent optimization and control of raw-material proportioning processes, coking process, and reheating furnaces; intelligent control of thermal state parameters in sintering processes; and intelligent decoupling control of gas collection and mixing-and-pressurization processes. The intelligent control and optimization methods presented were originally applied to complex metallurgical processes by the authors, and their effectiveness and their advantages have been theoretically proven and demonstrated practically. This book offers an up-to-date overview of this active research area, and provides readers with state-of-the-art methods for the control of complex metallurgical processes.