is the first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. it is written by leading international researchers.

There are many books on advanced control for specialists, but not many present these topics for non-specialists. Assuming only a basic knowledge of automatic control and signals and systems, this second edition of Optimal and Robust Control offers a straightforward, self-contained handbook of advanced topics and tools in automatic control. The book deals with advanced automatic control techniques, paying particular attention to robustness-the ability to guarantee stability in the presence of uncertainty. It explains advanced techniques for handling uncertainty and optimizing the control loop. It also details analytical strategies for obtaining reduced order models. The authors then propose using the Linear Matrix Inequality (LMI) technique as a unifying tool to solve many types of advanced control problems. Topics covered in the book include, LQR and H approaches Kalman and singular value decomposition Open-loop balancing and reduced order models Closed-loop balancing Positive-real systems, bounded-real systems, and imaginary-negative systems Criteria for stability control Time-delay systems This easy-to-read text presents the essential theoretical background and provides numerous examples and MATLAB (R) exercises to help the reader efficiently acquire new skills. Written for electrical, electronic, computer science, space, and automation engineers interested in automatic control, this book can also be used for self-study of for a one-semester course in robust control. This fully renewed second edition of the book also includes new fundamental topics such as Lyapunov functions for stability, variational calculus, formulation in terms of optimization problems of matrix algebraic equations, negative-imaginary systems, and time-delay systems.

This book presents advances in control technologies for efficient operation of the brushless doubly-fed induction generator (BDFIG). For robust and low-cost operation of BDFIGs, it is required to keep high-quality output voltage and eliminate the speed/position encoder under different loads and operation conditions. Some advanced control technologies, from the authors' latest work on these topics, are presented to achieve this goal with simple and accurate texts, illustrations, and tables. The qualified outcomes obtained from this book assure the high-performance operation of BDFIGs and also give the readers a straight insight toward challenges in this research area in the future.

provides a detailed background to start working and doing research on mean-field-type control and game theory includes several numerical examples using a MatLab-based user-friendly toolbox provides analyzsis of mean-field-type control and game problems incorporating several stochastic processes, e.g., Brownian motions, Poisson jumps, and random coefficients includes several engineering applications in both continuous and discrete time, such as micro-grid energy storage, stirred tank reactor, mechanism design for evolutionary dynamics, multi-level building evacuation problem, and the COVID-19 propagation control

The author has spent approximately 50 years in the field of systems engineering. This Focus book provides a "looking back" at his 50-year run and the lessons he learned and would like to share with other engineers, so they can use these lessons in their day-to-day work in systems engineering and related fields. The book is written from a systems engineering perspective. It offers 50 lessons learned working for a variety of different companies, which can be used across many other engineering fields. The book will be of interested to students and engineers across many fields, as well as students and engineers working in business and management fields.

Thanks to rapid technological developments in terms of Computational Intelligence, smart tools have been playing active roles in daily life. It is clear that the 21st century has brought about many advantages in using high-level computation and communication solutions to deal with real-world problems; however, more technologies bring more changes to society. In this sense, the concept of smart cities has been a widely discussed topic in terms of society and Artificial Intelligence-oriented research efforts. The rise of smart cities is a transformation of both community and technology use habits, and there are many different research orientations to shape a better future. The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. As recently designed, advanced smart systems require intense use of complex computational solutions (i.e., Deep Learning, Big Data, IoT architectures), the mechanisms of these systems become 'black-box' to users. As this means that there is no clear clue about what is going on within these systems, anxieties regarding ensuring trustworthy tools also rise. In recent years, attempts have been made to solve this issue with the additional use of XAI methods to improve transparency levels. This book provides a timely, global reference source about cutting-edge research efforts to ensure the XAI factor in smart city-oriented developments. The book includes both positive and negative outcomes, as well as future insights and the societal and technical aspects of XAI-based smart city research efforts. This book contains nineteen contributions beginning with a presentation of the background of XAI techniques and sustainable smart-city applications. It then continues with chapters discussing XAI for Smart Healthcare, Smart Education, Smart Transportation, Smart Environment, Smart Urbanization and Governance, and Cyber Security for Smart Cities.

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.

Proposes an integrative approach combining Human Factors Expertise with Automotive Engineering Develops an in-depth case study of design a fuel-efficient driving intervention Offers an examination of an innovative study of feed-forward eco-driving advice based on current vehicle and road environment status Presents lessons and insights gained from the current work and can be applied across domains Utilizes a documented scientific and research lead approach to designing novel speed advisory and fuel use minimisation systems suitable for combustion vehicles, hybrids, and electric vehicles

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models? Build beginning-to-end workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data

Today's real-world problems and applications in sensory systems and target detection require efficient, comprehensive and fault-tolerant multi-sensor allocation. This book presents the theory and applications of novel methods developed for such sophisticated systems. It discusses the advances in multi-agent systems and AI along with collaborative control theory and tools. Further, it examines the formulation and development of an allocation framework for heterogeneous multi-sensor systems for various real-world problems that require sensors with different performances to allocate multiple tasks, with unknown a priori priorities that arrive at unknown locations at unknown time. It demonstrates how to decide which sensor to allocate to which tasks when and where. Lastly, it explains the reliability and availability issues of task allocation systems, and includes methods for their optimization. The presented methods are explained, measured, and evaluated by extensive simulations, and the results of these simulations are presented in this book. This book is an ideal resource for academics, researchers and graduate students as well as engineers and professionals and is relevant for various applications such as sensor network design, multi-agent systems, task allocation, target detection, and team formation.

Artificial Intelligence: Technologies, Applications, and Challenges is an invaluable resource for readers to explore the utilization of Artificial Intelligence, applications, challenges, and its underlying technologies in different applications areas. Using a series of present and future applications, such as indoor-outdoor securities, graphic signal processing, robotic surgery, image processing, character recognition, augmented reality, object detection and tracking, intelligent traffic monitoring, emergency department medical imaging, and many more, this publication will support readers to get deeper knowledge and implementing the tools of Artificial Intelligence. The book offers comprehensive coverage of the most essential topics, including: Rise of the machines and communications to IoT (3G, 5G). Tools and Technologies of Artificial Intelligence Real-time applications of artificial intelligence using machine learning and deep learning. Challenging Issues and Novel Solutions for realistic applications Mining and tracking of motion based object data image processing and analysis into the unified framework to understand both IoT and Artificial Intelligence-based applications. This book will be an ideal resource for IT professionals, researchers, under or post-graduate students, practitioners, and technology developers who are interested in gaining insight to the Artificial Intelligence with deep learning, IoT and machine learning, critical applications domains, technologies, and solutions to handle relevant challenges.

Provides insights from experienced academicians on the digitalization of education Discusses how to enhance the quality of education in higher education institutes Covers how to transform the traditional education to digital education Presents the latest tools being used to conduct contactless classes, assessment, tracking performance, providing feedback, and carrying out assignments and projects. Offers the ability to be able to learn the latest on virtual classrooms and laboratories.

This book focuses on computational and fractional analysis, two areas that are very important in their own right, and which are used in a broad variety of real-world applications. We start with the important Iyengar type inequalities and we continue with Choquet integral analytical inequalities, which are involved in major applications in economics. In turn, we address the local fractional derivatives of Riemann-Liouville type and related results including inequalities. We examine the case of low order Riemann-Liouville fractional derivatives and inequalities without initial conditions, together with related approximations. In the next section, we discuss quantitative complex approximation theory by operators and various important complex fractional inequalities. We also cover the conformable fractional approximation of Csiszar's well-known f-divergence, and present conformable fractional self-adjoint operator inequalities. We continue by investigating new local fractional M-derivatives that share all the basic properties of ordinary derivatives. In closing, we discuss the new complex multivariate Taylor formula with integral remainder. Sharing results that can be applied in various areas of pure and applied mathematics, the book offers a valuable resource for researchers and graduate students, and can be used to support seminars in related fields.

Intelligent control is a rapidly developing, complex, and challenging field of increasing practical importance and still greater potential. Its applications have a solid core in robotics and mechatronics but branch out into areas as diverse as process control, automotive industry, medical equipment, renewable energy and air conditioning.

"Intelligent Control with LabVIEW " is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEW, showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments.

This text delivers a thorough grounding in the main tools of intelligent control:

fuzzy logic systems, emulating human reasoning processes;

artificial neural networks, mimicking our ability to learn;

neuro-fuzzy systems for implementing controller adaptation;

evolutionary methods, using the ideas of genetic evolution for optimization; and

predictive methods, giving information about a system s future behavior.

"Intelligent Control with LabVIEW " facilitates the learning and teaching of its subject by:

extensive use of worked examples;

end-of-chapter problems with separate solutions; and

provision of intelligent control tools for LabVIEW .

Graduate and senior undergraduate students learning about intelligent control will find great help in this textbook s easy-to-read style and graphical programming approach. The book will be of material assistance to instructors interested in using LabVIEW -based teaching when designing a course in intelligent control. Researchers in intelligent systems who employ LabVIEW as a laboratory tool will also benefit from its dedicated support for intelligent control.

Whilst our outside world is modifying into a more complex and hybrid networked world, our most intimate dwelling, our home, is at risk of falling behind as for many it seems to have remained the same as it has been for many decades. This book explores what it means to have a home in such a networked world. It describes what architecture can, or perhaps should, contribute to enable a more participatory role for inhabitants. This forward-thinking book will try to answer the question - What is the role and position of technology in our most intimate locations both now and what could it be like in the future?

This book provides an overview of distributed control and distributed optimization theory, followed by specific details on industrial applications to smart grid systems. It discusses the fundamental analysis and design schemes for developing actual working smart grids and covers all aspects concerning the conventional and nonconventional methods of their use. Hybrid Intelligence for Smart Grid Systems provides an overview of a smart grid, along with its needs, benefits, challenges, and existing structure and describes the inverter topologies adopted for integrating renewable power, and provides an overview of its needs, benefits, challenges, and possible future technologies. This pioneering book is a must-read for researchers, engineering professionals, and students, giving them the tools needed to move from the concept of a smart grid to its actual design and implementation. Moreover, it will enable regulators, policymakers, and energy executives to understand the future of energy delivery systems towards safe, economical, high-quality power delivery in a dynamic and demanding environment.

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies

This book highlights all aspects of innovative 21st-century education technologies and skills which can enhance the teaching and learning process on a broader spectrum, based on best practices around the globe. It offers case studies on real problems involving higher education, it includes policies that need to be adaptable to the new environments such as the role of accreditation, online learning, MOOCs, and mobile-based learning. The book covers all aspects of the digital competencies of teachers to fulfill the required needs of 21st-century classrooms and uses a new pedagogical approach suitable for educational policies. Innovative Education Technologies for 21st Teaching and Learning is the first book that addresses the teaching and learning challenges and how those challenges can be mitigated by technology which educational institutions are facing due to the COVID-19 pandemic. This book is suitable for teachers, students, instructional and course designers, policymakers, and anyone interested in 21st-century education.

Discusses the challenges of automation and automated systems, when humans are left out of the loop, but need to intervene when necessary Provides human control and accepting that humans must handle the unexpected and describes methods to support this Based on recent accident analysis involving autonomous systems and helping to move our understanding forward Supports a more modern view on human errors in order to improve safety in the shipping/marine industry Describes human centered design as a fundamental issue to support the ability of sensemaking

Provides a structured look, at the unique characteristic for smart sensor networks to resolving the issues of many real-world applications in a broad range of areas such as Smart Healthcare, Engineering, Scientific Research, Social Media, Industrial Automation and more Offers a systematic look at the unique characteristics of AI based wireless sensor networks through their usage in a broad range of areas Delivers recent trends and core concepts in both analytics and application in smart sensor networks using AI Explores the development and application of AI and evolutionary computing as applied to wireless sensor networks Focuses on stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation result and an application-oriented approach.

Includes specific pedagogy used in engineering teaching Offers case studies and classroom practices used by engineering institutions Discusses innovative strategies used in lockdown days during COVID-19 pandemic Presents guidelines and comparisons on various national and international accreditation bodies Explores cost effective technologies and open source tools specifically used for low income educational institutions

Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.

Classical Feedback Control with Nonlinear Multi-Loop Systems describes the design of high-performance feedback control systems, emphasizing the frequency-domain approach widely used in practical engineering. It presents design methods for high-order nonlinear single- and multi-loop controllers with efficient analog and digital implementations. Bode integrals are employed to estimate the available system performance and to determine the ideal frequency responses that maximize the disturbance rejection and feedback bandwidth. Nonlinear dynamic compensators provide global stability and improve transient responses. This book serves as a unique text for an advanced course in control system engineering, and as a valuable reference for practicing engineers competing in today's industrial environment.

An electric machine is a device that converts mechanical energy into electrical energy or vice versa. It can take the form of an electric generator, electric motor, or transformer. Electric generators produce virtually all electric power we use all over the world. Electric machine blends the three major areas of electrical engineering: power, control and power electronics. This book presents the relation of power quantities for the machine as the current, voltage power flow, power losses, and efficiency. This book will provide a good understanding of the behavior and its drive, beginning with the study of salient features of electrical dc and ac machines.

Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction and estimation, exclusive to power system protection issues Presents introduction to enhanced electricity system analysis using decision-making tools Covers AI Applications in different protective relaying functions Discusses Issues and challenges in the protection of transmission and distribution systems Includes dedicated chapter on case studies, and applications