Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

RENEWABLE INTEGRATED POWER SYSTEM STABILITY AND CONTROL Discover new challenges and hot topics in the field of penetrated power grids in this brand-new interdisciplinary resource Renewable Integrated Power System Stability and Control delivers a comprehensive exploration of penetrated grid dynamic analysis and new trends in power system modeling and dynamic equivalencing. The book summarizes long-term academic research outcomes and contributions and exploits the authors’ extensive practical experiences in power system dynamics and stability to offer readers an insightful analysis of modern power grid infrastructure. In addition to the basic principles of penetrated power system modeling, model reduction, and model derivation, the book discusses inertia challenge requirements and control levels, as well as recent advances in visualization of virtual synchronous generators and their associated effects on system performance. The physical constraints and engineering considerations of advanced control schemes are deliberated at length. Renewable Integrated Power System Stability and Control also considers robust and adaptive control strategies using real-time simulations and experimental studies. Readers will benefit from the inclusion of: A thorough introduction to power systems, including time horizon studies, structure, power generation options, energy storage systems, and microgrids An exploration of renewable integrated power grid modeling, including basic principles, host grid modeling, and grid-connected MG equivalent models A study of virtual inertia, including grid stability enhancement, simulations, and experimental results A discussion of renewable integrated power grid stability and control, including small signal stability assessment and the frequency point of view Perfect for engineers and operators in power grids, as well as academics studying the technology, Renewable Integrated Power System Stability and Control will also earn a place in the libraries of students in Electrical Engineering programs at the undergraduate and postgraduate levels who wish to improve their understanding of power system operation and control.

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

This book constitutes the refereed proceedings of the Third International Conference on Big Data and Security, ICBDS 2021, held in Shenzhen, China, in November 2021 The 46 revised full papers and 13 short papers were carefully reviewed and selected out of 221 submissions. The papers included in this volume are organized according to the topical sections on cybersecurity and privacy; big data; blockchain and internet of things, and artificial intelligence/ machine learning security.

The Internet of Things (IoT) and Big Data are hot topics in the world of intelligence operations and information gathering. This first-of-its-kind volume reveals the benefits of addressing these topics with the integration of Fusion of Information and Analytics Technologies (FIAT). The book explains how FIAT is materialized into decision support systems that are capable of supporting the prognosis, diagnosis, and prescriptive tasks within complex systems and organizations. This unique resource offers keen insight into how complex systems emerge from the interrelation of social and cognitive information, cyber and physical worlds, and the various models of decision-making and situational awareness. Practitioners also discover the central notions of analytics and information fusion. Moreover the book introduces propos such as integration through a FIAT computational model and applications at the systems level. This book concludes with a list of prospective research activities that can contribute towards the required FIAT integration for critical application domains such as: energy, health, transport and defense and security.

This book includes original, peer-reviewed research papers from the 2021 International Top-Level Forum on Engineering Science and Technology Development Strategy -- the 6th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2021), held in Nanjing, China, on August 14-22, 2021. The accepted papers cover the following topics: 1. Advanced power transmission technology 2. AC/DC hybrid power grid technology3. Power Internet of Things Technology and Application4. Operation, control and protection of smart grid5. Active distribution network technology6. Power electronic technology and application7. New technology of substation automation8. Energy storage technology and application9. Application of new technologies such as artificial intelligence, blockchain, and big data10. Application of Information and Communication Technology11. Low-carbon energy planning and security12. Low-carbon operation of the power system13. Low-carbon energy comprehensive utilization technology14. Carbon trading and power market15. Carbon emission stream and carbon capture technology16. Energy saving and smart energy technology17. Analysis and evaluation of low-carbon efficiency of power system18. Carbon flow modelling in power system operationThe papers included in this proceeding share the latest research results and practical application examples on the methodologies and algorithms in these areas, which makes the book a valuable reference for researchers, engineers, and university students.

This enlightening volume examines core areas of development in electric power systems, emphasizing the pivotal contributions of women engineers to the industry’s evolution. The authors cover a broad spectrum of key topics, including generation technologies, transmission and distribution progress, environmental challenges, worldwide electrification, and workforce issues. Advances in conventional and renewable energy technologies, in parallel with growing environmental concerns, and in conjunction with the aging of both the infrastructure itself and the workforce, have led to imposing and fascinating challenges for the engineers of tomorrow. This book documents the critical role of women engineers and their pioneering discoveries, relates their stories of success and struggle in their own words, and shares their perspectives on how these challenges will be addressed in the decades ahead.

Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. - Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery - Includes comprehensible, theoretical chapters written for large and diverse audiences - Provides a wealth of selected application to the topics included