Mengchu Zhou – författare

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications.

Features:

This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications.

Features:

This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

Focuses on how to use web service computing and service-based workflow technologies to develop timely, effective workflows for both business and scientific fields

Utilizing web computing and Service-Oriented Architecture (SOA), Business and Scientific Workflows: A Web Service–Oriented Approach focuses on how to design, analyze, and deploy web service–based workflows for both business and scientific applications in many areas of healthcare and biomedicine. It also discusses and presents the recent research and development results.

This informative reference features application scenarios that include healthcare and biomedical applications, such as personalized healthcare processing, DNA sequence data processing, and electrocardiogram wave analysis, and presents:

With workflow-driven service composition and reuse being a hot topic in both academia and industry, this book is ideal for researchers, engineers, scientists, professionals, and students who work on service computing, software engineering, business and scientific workflow management, the internet, and management information systems (MIS).

Focuses on how to use web service computing and service-based workflow technologies to develop timely, effective workflows for both business and scientific fields

Utilizing web computing and Service-Oriented Architecture (SOA), Business and Scientific Workflows: A Web Service–Oriented Approach focuses on how to design, analyze, and deploy web service–based workflows for both business and scientific applications in many areas of healthcare and biomedicine. It also discusses and presents the recent research and development results.

This informative reference features application scenarios that include healthcare and biomedical applications, such as personalized healthcare processing, DNA sequence data processing, and electrocardiogram wave analysis, and presents:

With workflow-driven service composition and reuse being a hot topic in both academia and industry, this book is ideal for researchers, engineers, scientists, professionals, and students who work on service computing, software engineering, business and scientific workflow management, the internet, and management information systems (MIS).

Learn more about energy efficiency in traditional and advanced manufacturing settings with this leading and authoritative resource

Sustainable Manufacturing Systems: An Energy Perspective delivers a comprehensive analysis of energy efficiency in sustainable manufacturing. The book presents manufacturing modeling methods and energy efficiency evaluation and improvement methods for different manufacturing systems. It allows industry professionals to understand the methodologies and techniques being embraced around the world that lead to advanced energy management.

The book offers readers a comprehensive and systematic theoretical foundation for novel manufacturing system modeling, analysis, and control. It concludes with a summary of the insights and applications contained within and a discussion of future research issues that have yet to be grappled with.

Sustainable Manufacturing Systems answers the questions that energy customers, managers, decision makers, and researchers have been asking about sustainable manufacturing. The book’s release coincides with recent and profound advances in smart grid applications and will serve as a practical tool to assist industrial engineers in furthering the green revolution. Readers will also benefit from:

Perfect for researchers, undergraduate students, and graduate students in engineering disciplines, especially for those majoring in industrial, mechanical, electrical, and environmental engineering, Sustainable Manufacturing Systems will also earn a place in the libraries of management and business students interested in manufacturing system cost performance and energy management.

Learn more about energy efficiency in traditional and advanced manufacturing settings with this leading and authoritative resource

Sustainable Manufacturing Systems: An Energy Perspective delivers a comprehensive analysis of energy efficiency in sustainable manufacturing. The book presents manufacturing modeling methods and energy efficiency evaluation and improvement methods for different manufacturing systems. It allows industry professionals to understand the methodologies and techniques being embraced around the world that lead to advanced energy management.

The book offers readers a comprehensive and systematic theoretical foundation for novel manufacturing system modeling, analysis, and control. It concludes with a summary of the insights and applications contained within and a discussion of future research issues that have yet to be grappled with.

Sustainable Manufacturing Systems answers the questions that energy customers, managers, decision makers, and researchers have been asking about sustainable manufacturing. The book’s release coincides with recent and profound advances in smart grid applications and will serve as a practical tool to assist industrial engineers in furthering the green revolution. Readers will also benefit from:

Perfect for researchers, undergraduate students, and graduate students in engineering disciplines, especially for those majoring in industrial, mechanical, electrical, and environmental engineering, Sustainable Manufacturing Systems will also earn a place in the libraries of management and business students interested in manufacturing system cost performance and energy management.

Presents strategies with reachability graph analysis for optimizing resource allocation systems

Supervisory Control and Scheduling of Resource Allocation Systems offers an important guide to Petri net (PN) models and methods for supervisory control and system scheduling of resource allocation systems (RASs). Resource allocation systems are common in automated manufacturing systems, project management systems, cloud data centers, and software engineering systems. The authors—two experts on the topic—present a definition, techniques, models, and state-of-the art applications of supervisory control and scheduling problems.

The book introduces the basic concepts and research background on resource allocation systems and Petri nets. The authors then focus on the deadlock-free supervisor synthesis for RASs using Petri nets. The book also investigates the heuristic scheduling of RASs based on timed Petri nets. Conclusions and open problems are provided in the last section of the book. 

This important book:

Written for researchers, engineers, scientists, and professionals in system planning and control, engineering, operation, and management, Supervisory Control and Scheduling of Resource Allocation Systems provides an essential guide to the supervisory control and scheduling of resource allocation systems (RASs) using Petri net reachability graphs, which allow for multiple resource acquisitions and flexible routings.

Presents strategies with reachability graph analysis for optimizing resource allocation systems

Supervisory Control and Scheduling of Resource Allocation Systems offers an important guide to Petri net (PN) models and methods for supervisory control and system scheduling of resource allocation systems (RASs). Resource allocation systems are common in automated manufacturing systems, project management systems, cloud data centers, and software engineering systems. The authors—two experts on the topic—present a definition, techniques, models, and state-of-the art applications of supervisory control and scheduling problems.

The book introduces the basic concepts and research background on resource allocation systems and Petri nets. The authors then focus on the deadlock-free supervisor synthesis for RASs using Petri nets. The book also investigates the heuristic scheduling of RASs based on timed Petri nets. Conclusions and open problems are provided in the last section of the book. 

This important book:

Written for researchers, engineers, scientists, and professionals in system planning and control, engineering, operation, and management, Supervisory Control and Scheduling of Resource Allocation Systems provides an essential guide to the supervisory control and scheduling of resource allocation systems (RASs) using Petri net reachability graphs, which allow for multiple resource acquisitions and flexible routings.

Petri nets are widely used in modeling, analysis, and control of discrete event systems arising from manufacturing, transportation, computer and communication networks, and web service systems. However, Petri net models for practical systems can be very large, making it difficult to apply such models to real-life problems.

System Modeling and Control with Resource-Oriented Petri Nets introduces a new resource-oriented Petri net (ROPN) model that was developed by the authors. Not only does it successfully reduce model size, but it also offers improvements that facilitate effective modeling, analysis, and control of automated and reconfigurable manufacturing systems.

Presenting the latest research in this novel approach, this cutting-edge volume provides proven theories and methodologies for implementing cost and time-saving improvements to contemporary manufacturing systems. It provides effective tools for deadlock avoidance—deadlock-free routing and deadlock-free scheduling. The authors supply simple and complex industrial manufacturing system examples to illustrate time-tested concepts, theories, and approaches for solving real-life application problems. Written in a clear and concise manner, the text covers applications to automated and reconfigurable manufacturing systems, automated guided vehicle (AGV) systems, semiconductor manufacturing systems, and flexible assembly systems.

Explaining complex concepts in a manner that is easy to understand, the authors provide the understanding and tools needed for more effective modeling, analysis, performance evaluation, control, and scheduling of engineering processes that will lead to more flexible and efficient manufacturing systems.

Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed

Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata.

As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application.

The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested.

Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as:

A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.

Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed

Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata.

As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application.

The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested.

Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as:

A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.

Comprehensive overview of recent research advancements in scheduling approaches for cloud edge computing systems

Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems offers an in-depth collection of advanced task scheduling algorithms designed specifically for diverse cloud-edge-device computing systems. After an introductory overview, a series of intelligent scheduling approaches are presented, each specifically designed for a particular scenario within cloud-edge-device computing systems.

The book then summarizes the authors’ research findings in recent years, delving into topics including resource management, latency and real-time requirements, load balancing, priority constraints, algorithm design, and performance evaluation. The book enables readers to achieve efficient allocation of computing, storage, and network resources to optimize resource utilization. Real-world applications of scheduling technologies in smart cities and traffic management, industrial automation and smart factories, and healthcare monitoring systems are given in a separate chapter.

Additional topics include:

Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems is an essential resource for researchers and practitioners in the field of IoT seeking to understand specific challenges and requirements associated with task scheduling in cloud-edge-device computing systems.