Sheng Wang – författare

This book reports on the latest research and applications in the fields of sustainable manufacturing and remanufacturing, as well as process planning and optimization technologies. It introduces innovative algorithms, methodologies, industrial case studies and applications.It focuses on two topics: sustainable manufacturing for machining technologies and remanufacturing of waste electronic equipment, and various methods are covered for each one, including macro process planning, dynamic scheduling, selective disassembly planning and cloud-based disassembly planning. The experimental analysis provided for every method explains the benefits, as well as how they are sustainable for various real-world applications. Further, a theoretical analysis and algorithm design is presented for each, accompanied by the contributors’ relevant research, including:•         step-by-step guides; •         application scenarios; •         relevant literature surveys; •         implementation details and case studies; and •         critical reviews on the relevant technologies.This book is a valuable resource for researchers in sustainable manufacturing, remanufacturing and product lifecycle management communities, as well as practicing engineers and decision-makers in industry and all those interested in sustainable product development. It is also useful reading material for postgraduates and academics wanting to conduct relevant research, and a reference resource for manufacturing engineers developing innovative tools and methodologies.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress.  This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress.  This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

This book explores the hydrogen integration in energy systems from a whole system optimisation perspective. The book introduces new modelling, optimisation, and reliability evaluation approaches that support the net zero transition.Global warming has been witnessed in the past few decades—as caused by the emission of greenhouse gases, such as carbon dioxide, it not only affects the habitat of animals but also threatens the lives of mankind in modern societies. Around the world, resulting from the excessive consumption of fossil fuels, such as natural gas, the energy sector is the culprit behind carbon dioxide emissions and climate change. To achieve net zero ambitions, energy systems worldwide urgently call for cleaner energy sources. Green hydrogen, produced through power-to-gas facilities using surplus renewable energy generation, has emerged as a promising solution. However, because hydrogen shares distinguished properties with natural gas, many fundamental questions, including how to smoothly integrate hydrogen into the existing energy systems, how to operate such a new structured energy system, and what is the systematic impact, remain unclear. To answer these questions and bridge the research gap, this book aims to develop new modelling, optimization, and reliability evaluation approaches that support the transition of current energy systems to hydrogen-integrated energy systems (HI-ES).This book sets the theoretical foundations for developing more advanced control strategies and application scenarios for HI-ES, and inform policy makings through bespoke advice on hydrogen integration road maps.

This book explores the hydrogen integration in energy systems from a whole system optimisation perspective. The book introduces new modelling, optimisation, and reliability evaluation approaches that support the net zero transition.Global warming has been witnessed in the past few decades—as caused by the emission of greenhouse gases, such as carbon dioxide, it not only affects the habitat of animals but also threatens the lives of mankind in modern societies. Around the world, resulting from the excessive consumption of fossil fuels, such as natural gas, the energy sector is the culprit behind carbon dioxide emissions and climate change. To achieve net zero ambitions, energy systems worldwide urgently call for cleaner energy sources. Green hydrogen, produced through power-to-gas facilities using surplus renewable energy generation, has emerged as a promising solution. However, because hydrogen shares distinguished properties with natural gas, many fundamental questions, including how to smoothly integrate hydrogen into the existing energy systems, how to operate such a new structured energy system, and what is the systematic impact, remain unclear. To answer these questions and bridge the research gap, this book aims to develop new modelling, optimization, and reliability evaluation approaches that support the transition of current energy systems to hydrogen-integrated energy systems (HI-ES).This book sets the theoretical foundations for developing more advanced control strategies and application scenarios for HI-ES, and inform policy makings through bespoke advice on hydrogen integration road maps.

This book constitutes the refereed proceedings of the 16th International Workshop on Machine Learning in Medical Imaging, MLMI 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, on September 23, 2025.The 65 full papers included in this book were carefully reviewed and selected from 101 submissions. They focus on advanced scientific research covering topics such as deep learning, foundation models, generative learning, and statistical methods with their applications to computer-aided diagnosis, multi-modality fusion, image reconstruction, digital pathology, and large-scale data analytics.

This book constitutes the refereed proceedings of the 16th International Workshop on Machine Learning in Medical Imaging, MLMI 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, on September 23, 2025.The 65 full papers included in this book were carefully reviewed and selected from 101 submissions. They focus on advanced scientific research covering topics such as deep learning, foundation models, generative learning, and statistical methods with their applications to computer-aided diagnosis, multi-modality fusion, image reconstruction, digital pathology, and large-scale data analytics.

This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

This book reports on the latest research and applications in the fields of sustainable manufacturing and remanufacturing, as well as process planning and optimization technologies. It introduces innovative algorithms, methodologies, industrial case studies and applications.It focuses on two topics: sustainable manufacturing for machining technologies and remanufacturing of waste electronic equipment, and various methods are covered for each one, including macro process planning, dynamic scheduling, selective disassembly planning and cloud-based disassembly planning. The experimental analysis provided for every method explains the benefits, as well as how they are sustainable for various real-world applications. Further, a theoretical analysis and algorithm design is presented for each, accompanied by the contributors’ relevant research, including:•         step-by-step guides; •         application scenarios; •         relevant literature surveys; •         implementation details and case studies; and •         critical reviews on the relevant technologies.This book is a valuable resource for researchers in sustainable manufacturing, remanufacturing and product lifecycle management communities, as well as practicing engineers and decision-makers in industry and all those interested in sustainable product development. It is also useful reading material for postgraduates and academics wanting to conduct relevant research, and a reference resource for manufacturing engineers developing innovative tools and methodologies.

This book reports on the latest research and applications in the fields of sustainable manufacturing and remanufacturing, as well as process planning and optimization technologies. It introduces innovative algorithms, methodologies, industrial case studies and applications.It focuses on two topics: sustainable manufacturing for machining technologies and remanufacturing of waste electronic equipment, and various methods are covered for each one, including macro process planning, dynamic scheduling, selective disassembly planning and cloud-based disassembly planning. The experimental analysis provided for every method explains the benefits, as well as how they are sustainable for various real-world applications. Further, a theoretical analysis and algorithm design is presented for each, accompanied by the contributors’ relevant research, including:•         step-by-step guides; •         application scenarios; •         relevant literature surveys; •         implementation details and case studies; and •         critical reviews on the relevant technologies.This book is a valuable resource for researchers in sustainable manufacturing, remanufacturing and product lifecycle management communities, as well as practicing engineers and decision-makers in industry and all those interested in sustainable product development. It is also useful reading material for postgraduates and academics wanting to conduct relevant research, and a reference resource for manufacturing engineers developing innovative tools and methodologies.

This book focuses on the risk modeling, analysis and control of multi-energy systems considering cross-sectorial failure propagation. Both models and methods have been addressed with engineering practice. This is accomplished by doing a thorough investigation into the modeling of system physics and reliabilities in both long- and short-term phases. Different models and methods to evaluate the risk of multi-energy systems considering various disturbances, e.g., component failures, load uncertainties and extreme weather, are studied in detail.  Furthermore, several risk control strategies for multi-energy systems, such as long-term capacity planning and integrated demand response, are analyzed in this book, which is especially suited for readers interested in system risk management. The book can benefit researchers, engineers, and graduate students in the fields of electrical and electronic engineering, energy engineering, complex network and control engineering, etc.

This book focuses on the risk modeling, analysis and control of multi-energy systems considering cross-sectorial failure propagation. Both models and methods have been addressed with engineering practice. This is accomplished by doing a thorough investigation into the modeling of system physics and reliabilities in both long- and short-term phases. Different models and methods to evaluate the risk of multi-energy systems considering various disturbances, e.g., component failures, load uncertainties and extreme weather, are studied in detail.  Furthermore, several risk control strategies for multi-energy systems, such as long-term capacity planning and integrated demand response, are analyzed in this book, which is especially suited for readers interested in system risk management. The book can benefit researchers, engineers, and graduate students in the fields of electrical and electronic engineering, energy engineering, complex network and control engineering, etc.