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

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

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 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

This book gathers peer-reviewed proceedings of the 3rd International Conference on Innovative Computing (IC 2020). This book aims to provide an open forum for discussing recent advances and emerging trends in information technology, science, and engineering. Themes within the scope of the conference include Communication Networks, Business Intelligence and Knowledge Management, Web Intelligence, and any related fields that depend on the development of information technology. The respective contributions presented here cover a wide range of topics, from databases and data mining, networking and communications, the web and Internet of Things, to embedded systems, soft computing, social network analysis, security and privacy, optical communication, and ubiquitous/pervasive computing. Readers such as students, researchers, and industry professionals in the fields of cloud computing, Internet of Things, machine learning, information security, multimedia systems, and information technology benefit from this comprehensive overview of the latest advances in information technology. The book can also benefit young investigators looking to start a new research program.

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.

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.

The third volume in the Integrated Vehicle Health Management (IVHM) series focuses on the technology that actually supports the implementation of IVHM in real-life situations. Edited by Ian K. Jennions, Director of the IVHM Center at Cranfield University, UK, this book was written collaboratively by twenty-seven authors from industry, academia and governmental research agencies. Topics include: -Sensors, instrumentation and signal processing -Fault detection and diagnostics -Prognostics and metrics -Architecture -Data Management -Vehicle level reasoning systems -System's design -Applications and disruptive technologies Integrated Vehicle Heath Management: The Technology follows two bestsellers, also published by SAE International, which cover the fundamentals aspects of this new body of knowledge (Integrated Vehicle Health Management: Perspectives on an Emerging Field), and the business justification needed so that investments in the technology make sense (Integrated Vehicle Health Management: Business Case Theory and Practice).

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.

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems. Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.