This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh's seminal paper on "fuzzy sets," published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh's paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years' anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments of Fuzzy Logic-based systems.

This book collects papers from the 8th Conference on Non-Integer Order Calculus and Its Applications that have been held on September 20-21, 2016 in Zakopane, Poland. The preceding two conferences were held in Szczecin, Poland in 2015, and in Opole, Poland, in 2014. This conference provides a platform for academic exchange on the theory and application of fractional calculus between domestic and international universities, research institutes, corporate experts and scholars. The Proceedings of the 8th Conference on Non-Integer Order Calculus and Its Applications 2016 brings together rigorously reviewed contributions from leading international experts. The included papers cover novel various important aspects of mathematical foundations of fractional calculus, modeling and control of fractional systems as well as controllability, detectability, observability and stability problems for this systems.

Model based predictive control (MBPC) is arguably the most important approach to the advance control of complex interacting industrial processes. Unique among modern theories, MBPC can handle real-time state and actuator constraints in a natural way, enabling plants to maximize their profits. In addition, the wide range of model structures, prediction horizons, and optimization criteria allows for tailor-made MBPC applications--whether they be for high-speed machine tools or large-scale industrial processes. This timely edited volume, based on a conference held at Oxford University and devoted exclusively to MBPC, brings the field up to date with the latest theoretical and practical advances. Topics include how MBPC is expanding to include variants in the basic themes (such as new prediction and optimization approaches, nonlinear models, and two-dimensional problems), general stabilization ideas for constrained plant, and unsolved problems in MBPC. This excellent volume is the introduction to the theory, current applications, and hot research areas in MBPC that students and professionals in control systems have been waiting for.

This book statistically confirms that complexity and changing technologies that affect the way operators interact within the systems of the nuclear facili-ties exacerbate the severity of incidents caused by human error and details the application of the systems engineering process to reduce human error given industries' rapidly advancing technology. Technology, Human Performance, and Nuclear Facilities: A Systems Engineering Approach to Reduce Human Error provides a basic understand-ing of Human Error/ P erformance and its relation to industrial operations and advancing technologies incorporated into facilities. The book discusses the context surrounding the complexity of changing technologies at nuclear facili-ties and the potential worsening of problems caused by human error when technology advancements concerning operator interaction with control sys-tems are implemented. It presents how to reduce human error propensity given the incorporation of advanced technology and covers ways to reduce human error using the systems engineering process. Also offered are several concepts related to the operator's involvement in the systems engineering process and the human performance integration with system operational requirements and system testing, evaluation, and validation, and the procedures and training development in the systems engineering process. This book presents empirical evidence for the importance of human performance management in the con-text of nuclear facilities and offers practical recommendations for the improve-ment of this function. Systems engineers, plant/ design engineers, the nuclear industry, plant operations management, and those involved in industrial and nuclear safety will find something of interest in this book.

This book is focused on the development of Phasor Measurement Units (PMUs) as a tool to analyse and control power systems. The book develops a nonlinear system-wide approach to control using PMU signals and provides numerous examples of different power systems to demonstrate the robustness of the approach in comparison to heuristic optimization. Some of the applicable controls include: * Excitation systems;* Wind power;* Static VAR compensators;* High evoltage DC; and* Inverter dynamics. For the operation of transmission and distribution systems, the book explains the dynamics of power systems and explores how well-established tools such as energy-based control and Kalman filters can address many of the existing and developing issues in their operation. By providing a thorough guide to PMUs, this book enables readers to fully understand the potential benefits their implementation can bring.

Striking a balance between state-of-the-art research and practical applications, this book provides a forum for contributions that cover the main research challenges in the cyclic modeling, development, and validation of concurrently acting distributed production systems: systems that employ multi-assortment production in large quantities, are characterized by gradual changes in their product mix, and exclusively manufacture products in a cyclic manner. Similar issues also arise in computer systems, e.g., embedded systems. Cyclic optimization problems that occur in them are unique and under-researched, but are attracting new interest primarily due to their great practical importance and the difficulty involved in obtaining efficient algorithms for solving specific cases with real constraints arising from manufacturing practice. Addressing these and other topics, the book will be of great interest to researchers in computer science, operations management, and production control, as well as practicing managers and engineers.

Exploring the interrelations between generalized metric spaces, lattice-ordered groups, and order statistics, the book contains a new algebraic approach to Signal Processing Theory. It describes mathematical concepts and results important in the development, analysis, and optimization of signal processing algorithms intended for various applications. The book offers a solution of large-scale Signal Processing Theory problems of increasing both signal processing efficiency under prior uncertainty conditions and signal processing rate that is provided by multiplication-free signal processing algorithms based on lattice-ordered group operations. From simple basic relationships to computer simulation, the text covers a wide range of new mathematical techniques essential for understanding the proposed signal processing algorithms developed for solving the following problems: signal parameter and spectral estimation, signal filtering, detection, classification, and resolution; array signal processing; demultiplexing and demodulation in multi-channel communication systems and multi-station networks; wavelet analysis of 1D/ 2D signals. Along with discussing mathematical aspects, each chapter presents examples illustrating operation of signal processing algorithms developed for various applications. The book helps readers understand relations between known classic and obtained results as well as recent research trends in Signal Processing Theory and its applications, providing all necessary mathematical background concerning lattice-ordered groups to prepare readers for independent work in the marked directions including more advanced research and development.

Industrial revolutions have impacted both, manufacturing and service. From the steam engine to digital automated production, the industrial revolutions have conduced significant changes in operations and supply chain management (SCM) processes. Swift changes in manufacturing and service systems have led to phenomenal improvements in productivity. The fast-paced environment brings new challenges and opportunities for the companies that are associated with the adaptation to the new concepts such as Internet of Things (IoT) and Cyber Physical Systems, artificial intelligence (AI), robotics, cyber security, data analytics, block chain and cloud technology. These emerging technologies facilitated and expedited the birth of Logistics 4.0. Industrial Revolution 4.0 initiatives in SCM has attracted stakeholders' attentions due to it is ability to empower using a set of technologies together that helps to execute more efficient production and distribution systems. This initiative has been called Logistics 4.0 of the fourth Industrial Revolution in SCM due to its high potential. Connecting entities, machines, physical items and enterprise resources to each other by using sensors, devices and the internet along the supply chains are the main attributes of Logistics 4.0. IoT enables customers to make more suitable and valuable decisions due to the data-driven structure of the Industry 4.0 paradigm. Besides that, the system's ability of gathering and analyzing information about the environment at any given time and adapting itself to the rapid changes add significant value to the SCM processes. In this peer-reviewed book, experts from all over the world, in the field present a conceptual framework for Logistics 4.0 and provide examples for usage of Industry 4.0 tools in SCM. This book is a work that will be beneficial for both practitioners and students and academicians, as it covers the theoretical framework, on the one hand, and includes examples of practice and real world.

Sheet and film processes include coating, papermaking, metal rolling, and polymer film extrusion. Products produced by these processes include paper, bumper stickers, plastic bags, windshield safety glass, and sheet metal. The total capitalization of industries that rely on these processes is well over $ 500 billion worldwide. These processes are notorious for being difficult to control.The goal of this book is to present the theoretical background and practical techniques for the identification and control of sheet and film processes. It is explained why many existing industrial control systems perform poorly for sheet and film processes. Identification and control algorithms are described and illustrated which provide consistent and reliable product quality. These algorithms include an experimental design technique that ensures that informative data are collected during input-output experimentation, model identification techniques that produce a process model and an estimate of its accuracy, and control techniques that take into account actuator constraints as well as robustness to model uncertainties.The algorithms covered in this book are truly the state of the art. Variations on some of the algorithms have been implemented on industrial sheet and film processes. Other algorithms are in various stages of implementation. All of the algorithms have been applied to realistic simulation models constructed from industrial plant data; many of these studies are included in this book.

This book explores the outcomes on flow control research activities carried out within the framework of two EU-funded projects focused on training-through-research of Marie Sklodowska-Curie doctoral students. The main goal of the projects described in this monograph is to assess the potential of the passive- and active-flow control methods for reduction of fuel consumption by a helicopter. The research scope encompasses the fields of structural dynamics, fluid flow dynamics, and actuators with control. Research featured in this volume demonstrates an experimental and numerical approach with a strong emphasis on the verification and validation of numerical models. The book is ideal for engineers, students, and researchers interested in the multidisciplinary field of flow control.

Enhanced e-book includes videos Many books have been written on modelling, simulation and control of four-wheeled vehicles (cars, in particular). However, due to the very specific and different dynamics of two-wheeled vehicles, it is very difficult to reuse previous knowledge gained on cars for two-wheeled vehicles. Modelling, Simulation and Control of Two-Wheeled Vehicles presents all of the unique features of two-wheeled vehicles, comprehensively covering the main methods, tools and approaches to address the modelling, simulation and control design issues. With contributions from leading researchers, this book also offers a perspective on the future trends in the field, outlining the challenges and the industrial and academic development scenarios. Extensive reference to real-world problems and experimental tests is also included throughout. Key features: * The first book to cover all aspects of two-wheeled vehicle dynamics and control * Collates cutting-edge research from leading international researchers in the field * Covers motorcycle control a subject gaining more and more attention both from an academic and an industrial viewpoint * Covers modelling, simulation and control, areas that are integrated in two-wheeled vehicles, and therefore must be considered together in order to gain an insight into this very specific field of research * Presents analysis of experimental data and reports on the results obtained on instrumented vehicles. Modelling, Simulation and Control of Two-Wheeled Vehicles is a comprehensive reference for those in academia who are interested in the state of the art of two-wheeled vehicles, and is also a useful source of information for industrial practitioners.

This introduction to the theory of feedback control systems covers the whole range of topics in control theory, unifying them in a single volume. Although the material is essentially mathematical, there is minimal emphasis on technicalities that are not absolutely essential for understanding control systems. Much of this second edition has been rewritten to take account of recent developments in control theory and how it is understood. A structural framework for the book is provided by three traditional topics in the applied mathematics of control theory: scalar, ordinary, and linear dynamic equations with constant coefficients; state space and optimal control theory in the form of dynamic programming; and elementary probability theory. Successful features have been retained from the first edition, including the uniform treatment of both continuous-time and discrete-time systems, the inclusion of a wide range of topics, and the provision of problems with answers, making it ideal in format for students and researchers in electrical and mechanical engineering, mathematical economics, mathematical biology, physiology, applied mathematics, and operational research.

The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

This book presents a comprehensive analysis and modelling of demand, capacity, quality of services, economics, and sustainability of the air transport system and its main components - - airports, airlines, and ATC/ATM (Air Traffic Control/Management). Airports consist of the airside and landside area characterized by their capacities for handling demand such as aircraft, air passengers, and air freight/cargo shipments. Regarding spatial configuration, airlines generally operate hub-and-spoke (conventional or legacy airlines) and point-to-point (LCCs - Low Cost Carriers) air route networks. Their fleets consisting of different aircraft types provide transport capacity for serving demand including air passengers and freight/cargo shipments. The ATC/ATM includes the controlled airspace, traffic management and control facilities and equipment on the ground, space, and on board aircraft, and the ATC Controllers). They all provide capacity to handle demand consisting of the flights between origin and destination airports carried out by airline aircraft. The outcome from the interrelationships between demand and capacity at these components materializes as the quality of services. At airports and airlines this is generally expressed by congestion and delays of aircraft, air passengers, and freight/cargo shipments. At ATC/ATM, this is expressed by delays, horizontal and vertical in-efficiency, and safety of flights. Economics of each component relate to its revenues, costs, and profits from handling demand, i.e., providing services of given quality. The sustainability of air transport system has become increasingly important issue for many internal and external actors/stakeholders involved to deal with. This has implied increasing the system's overall social-economic effects/benefits while reducing or maintaining constant impacts/costs on the environment and society at both global and regional/local scale under conditions of continuous medium- to long term growth.

The book provides future research directions in IoT and image processing based Energy, Industry, and Healthcare domain and explores the different applications of its associated technologies. However, the Internet of Things and image processing is a very big field with a lot of subfields, which are very important such as Smart Homes to improve our daily life, Smart Cities to improve the citizens' life, Smart Towns to recover the livability and traditions, Smart Earth to protect our world, and Industrial Internet of Things to create safer and easier jobs. This book considers very important research areas in Energy, Industry, and Healthcare domain with IoT and image processing applications.The aim of the book to highlights future directions of optimization methods in various engineering and science applications in various IoT and image processing applications. Emphasis is given to deep learning and similar models of neural network-based learning techniques employed in solving optimization problems of different engineering and science applications. The role of AI in mechatronics is also highlighted using suitable optimization methods. This book considers very important research areas in Energy, Industry, and Healthcare. It addresses major issues and challenges in Energy, Industry, and Healthcare and solutions proposed for IoT-enabled cellular/computer networks, routing/communication protocols, surveillances applications, secured data management, and positioning approaches. It focuses mainly on smart and context-aware implementations. Key sailing Features: The impact of the proposed book is to provide a major area of concern to develop a foundation for the implementation process of new image processing and IoT devices based on Energy, Industry, and Healthcare related technology. The researchers working on image processing and IoT devices can correlate their work with other requirements of advanced technology in Energy, Industry, and Healthcare domain. To make aware of the latest technology like AI and Machine learning in Energy, Industry, and Healthcare related technology. Useful for the researcher to explore new things like Security, cryptography, and privacy in Energy, Industry, and Healthcare related technology. People who want to start in Energy, Industry, and Healthcare related technology with image processing and IoT world.

Mathematical modeling is a powerful craft that requires practice. The more practice the better one will become in executing the art. The authors wrote this book to develop the craft of mathematical modeling and to foster a desire for lifelong learning, habits of mind and develop competent and confident problem solvers and decision makers for the 21st century. This book offers a problem-solving approach. The authors introduce a problem to help motivate the learning of a particular mathematical modeling topic. The problem provides the issue or what is needed to solve using an appropriate modeling technique. Then principles are applied to the problem and present the steps in obtaining an appropriate model to solve the problem. Modeling Change and Uncertainty: Covers both linear and nonlinear models of discrete dynamical systems. Introduces statistics and probability modeling. Introduces critical statistical concepts to handle univariate and multivariate data. Establishes a foundation in probability modeling. Uses ordinary differential equations (ODEs) to develop a more robust solution to problems. Uses linear programming and machine learning to support decision making. Introduces the reality of uncertainty and randomness that is all around us. Discusses the use of linear programing to solve common problems in modern industry. Discusses he power and limitations of simulations. Introduces the methods and formulas used in businesses and financial organizations. Introduces valuable techniques using Excel, MAPLE, and R. Mathematical modeling offers a framework for decision makers in all fields. This framework consists of four key components: the formulation process, the solution process, interpretation of the solution in the context of the actual problem, and sensitivity analysis. Modeling Change and Uncertainty will be of interest to mathematics departments offering advanced mathematical modeling courses focused on decision making or discrete mathematical modeling and by undergraduate, graduate students and practitioners looking for an opportunity to develop, practice, and apply the craft of mathematical modeling. Table of Contents 1. Perfect Partners: Combining Models of Change and Uncertainty with Technology 2. Modeling Change: Discrete Dynamical Systems (DDS) and Modeling Systems of DDS 3. Statistical and Probabilistic Models 4. Modeling with Probability 5. Differential Equations 6. Forecasting with Linear Programming and Machine Learning 7. Stochastic Models and Markov Chains 8. Linear Programming 9. Simulation of Queueing Models 10. Modeling of Financial Analysis 11. Reliability Models 12. Machine Learning and Unconstrained Optimal Process Dr. William P. Fox is currently a visiting professor of Computational Operations Research at the College of William and Mary. He is an emeritus professor in the Department of Defense Analysis at the Naval Postgraduate School and teaches a three-course sequence in mathematical modeling for decision making. He received his Ph.D. in Industrial Engineering from Clemson University. He has taught at the United States Military Academy for twelve years until retiring and at Francis Marion University where he was the chair of mathematics for eight years. He has many publications and scholarly activities including twenty plus books and one hundred and fifty journal articles. Colonel (R) Robert E. Burks, Jr., Ph.D. is an Associate Professor in the Defense Analysis Department of the Naval Postgraduate School (NPS) and the Director of the NPS' Wargaming Center. He holds a Ph.D. in Operations Research from the Air Force Institute of Technology. He is a retired logistics Army Colonel with more than thirty years of military experience in leadership, advanced analytics, decision modeling, and logistics operations who served as an Army Operations Research analyst at the Naval Postgraduate School, TRADOC Analysis Center, United States Military Academy, and the United States Army Recruiting Command. Other book by William P. Fox and Robert E. Burks: Advanced Mathematical Modeling with Technology, 2021, CRC Press. Other books by William P. Fox from CRC Press: Mathematical Modeling in the Age of the Pandemic, 2021, CRC Press. Advanced Problem Solving Using Maple: Applied Mathematics, Operations Research, Business Analytics, and Decision Analysis (w/William Bauldry), 2020, CRC Press. Mathematical Modeling with Excel (w/Brian Albright), 2020, CRC Press. Nonlinear Optimization: Models and Applications, 2020, CRC Press. Advanced Problem Solving with Maple: A First Course (w/William Bauldry), 2019. CRC Press. Mathematical Modeling for Business Analytics, 2018, CRC Press.

This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sufficient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations. Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective Systematically introduces the complex sampling concept, methods, and application for the control and state estimation Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

Within this carefully presented monograph, the authors extend the universal phenomenon of synchronization from finite-dimensional dynamical systems of ordinary differential equations (ODEs) to infinite-dimensional dynamical systems of partial differential equations (PDEs). By combining synchronization with controllability, they introduce the study of synchronization to the field of control and add new perspectives to the investigation of synchronization for systems of PDEs. With a focus on synchronization for a coupled system of wave equations, the text is divided into three parts corresponding to Dirichlet, Neumann, and coupled Robin boundary controls. Each part is then subdivided into chapters detailing exact boundary synchronization and approximate boundary synchronization, respectively. The core intention is to give artificial intervention to the evolution of state variables through appropriate boundary controls for realizing the synchronization in a finite time, creating a novel viewpoint into the investigation of synchronization for systems of partial differential equations, and revealing some essentially dissimilar characteristics from systems of ordinary differential equations. Primarily aimed at researchers and graduate students of applied mathematics and applied sciences, this text will particularly appeal to those interested in applied PDEs and control theory for distributed parameter systems.

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

The thesis provides different opportunities and ideas to face some current challenges in the electricity systems. It focuses on the effective and efficient integration of distributed low carbon technologies in the grid of the future. Planning and operation problems for different clean solutions, such as market bidding strategies for intermittent energy producers, demand side management algorithms for smart buildings, and electrical storage options for network operators, are studied for facilitating the integration of renewable energy sources in the power system chain.

Provides a comprehensive guide about how to use machine vision for Industry 4.0 applications like analysis of images for automated inspections, object detection, object tracking etc. Includes case studies of Robotics Internet of Things with its current and future applications in Healthcare, Agriculture, Transportation, etc. It highlights the inclusion of impaired people in industry, like intelligent assistant that helps deaf-mute people to transmit instructions and warnings in a manufacturing process. It examines the significant technological advancements in machine vision for industrial Internet of things and explores the commercial benefits using the real world applications from healthcare to transportation. Provides a conceptual framework of Machine vision for the various Industrial applications. Addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and post-graduate students, researchers, and anyone else interested in the trends, development, and opportunities for the Machine Vision for Industry 4.0 applications.

This book discusses IoT in healthcare and how it enables interoperability, machine-to-machine communication, information exchange, and data movement. It also covers how healthcare service delivery automates patient care with the help of mobility solutions, new technologies, and next-gen healthcare facilities with challenges faced and suggested solutions prescribed. Reinvention of Health Applications with IoT: Challenges and Solutions presents the latest applications of IoT in healthcare along with challenges and solutions. It looks at a comparison of advanced technologies such as Deep Learning, Machine Learning, and AI and explores the ways they can be applied to sensed data to improve prediction and decision-making in smart health services. It focuses on society 5.0 technologies and illustrates how they can improve society and the transformation of IoT in healthcare facilities to support patient independence. Case studies are included for applications such as smart eyewear, smart jackets, and smart beds. The book will also go into detail on wearable technologies and how they can communicate patient information to doctors in medical emergencies. The target audiences for this edited volume is researchers, practitioners, students, as well as key stakeholders involved in and working on healthcare engineering solutions.

Provides the basic concepts of process mining techniques for pattern recognition for readers to analyze, predict, forecast, and enhance the workflow of processes Covers the entire spectrum of process mining from process discovery to operational support Discusses several process mining techniques in the context of data science and big data Contains real-life applications and case studies related to process mining theories and practices Includes detailed examples, figures, and tables for easy understanding of concepts discussed

The book presents a significant expansion in depth and breadth of the previous edition. It includes substantially more numerical illustrations and copious supporting MATLAB code that the reader can use to replicate illustrations or build his or her own. The code is deliberately written to be as simple as possible and easy to edit. The book is an excellent starting point for any researcher to gain a solid grounding in MPC concepts and algorithms before moving into application or more advanced research topics. Sample problems for readers are embedded throughout the chapters, and in-text questions are designed for readers to demonstrate an understanding of concepts through numerical simulation.