Advances In Electromagnetics Empowered By Artificial Intelligence And Deep Learning
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Advances In Electromagnetics Empowered By Artificial Intelligence And Deep Learning
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Author : Sawyer D. Campbell
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-03
Advances In Electromagnetics Empowered By Artificial Intelligence And Deep Learning written by Sawyer D. Campbell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-03 with Technology & Engineering categories.
Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.
Theory And Computation Of Electromagnetic Fields In Layered Media
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Author : Vladimir Okhmatovski
language : en
Publisher: John Wiley & Sons
Release Date : 2024-04-23
Theory And Computation Of Electromagnetic Fields In Layered Media written by Vladimir Okhmatovski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-23 with Science categories.
Explore the algorithms and numerical methods used to compute electromagnetic fields in multi-layered media In Theory and Computation of Electromagnetic Fields in Layered Media, two distinguished electrical engineering researchers deliver a detailed and up-to-date overview of the theory and numerical methods used to determine electromagnetic fields in layered media. The book begins with an introduction to Maxwell’s equations, the fundamentals of electromagnetic theory, and concepts and definitions relating to Green’s function. It then moves on to solve canonical problems in vertical and horizontal dipole radiation, describe Method of Moments schemes, discuss integral equations governing electromagnetic fields, and explains the Michalski-Zheng theory of mixed-potential Green’s function representation in multi-layered media. Chapters on the evaluation of Sommerfeld integrals, procedures for far field evaluation, and the theory and application of hierarchical matrices are also included, along with: A thorough introduction to free-space Green’s functions, including the delta-function model for point charge and dipole current Comprehensive explorations of the traditional form of layered medium Green’s function in three dimensions Practical discussions of electro-quasi-static and magneto-quasi-static fields in layered media, including electrostatic fields in two and three dimensions In-depth examinations of the rational function fitting method, including direct spectra fitting with VECTFIT algorithms Perfect for scholars and students of electromagnetic analysis in layered media, Theory and Computation of Electromagnetic Fields in Layered Media will also earn a place in the libraries of CAD industry engineers and software developers working in the area of computational electromagnetics.
Deterministic And Stochastic Modeling In Computational Electromagnetics
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Author : Dragan Poljak
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-07
Deterministic And Stochastic Modeling In Computational Electromagnetics written by Dragan Poljak and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-07 with Science categories.
Deterministic and Stochastic Modeling in Computational Electromagnetics Help protect your network with this important reference work on cyber security Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges. Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic–stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models. Readers will also find: A range of specific examples demonstrating the efficiency of deterministic–stochastic modeling Computational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and more Introduction to fundamental principles in field theory to ground the discussion of computational modeling Deterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.
Machine Learning Applications In Electromagnetics And Antenna Array Processing
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Author : Manel Martínez-Ramón
language : en
Publisher: Artech House
Release Date : 2021-04-30
Machine Learning Applications In Electromagnetics And Antenna Array Processing written by Manel Martínez-Ramón and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with Technology & Engineering categories.
This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.
Convergence Of Deep Learning And Artificial Intelligence In Internet Of Things
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Author : Ajay Rana
language : en
Publisher: CRC Press
Release Date : 2022-12-27
Convergence Of Deep Learning And Artificial Intelligence In Internet Of Things written by Ajay Rana and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Computers categories.
This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. • Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things. • Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe. • Explores the importance of blockchain technology in the Internet of Things security issues. • Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy. • Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications. This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It further discusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.
Recent Advances In Internet Of Things And Machine Learning
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Author : Valentina E. Balas
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Recent Advances In Internet Of Things And Machine Learning written by Valentina E. Balas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.
The Application Of Machine Learning For Designing And Controlling Electromagnetic Fields
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Author : Dianjing Liu
language : en
Publisher:
Release Date : 2021
The Application Of Machine Learning For Designing And Controlling Electromagnetic Fields written by Dianjing Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Machine Learning is the study of computer algorithms that improve automatically through experience. In contrary to rule-based artificial intelligence which produces pre-defined outcomes based on manually coded rules, machine learning algorithms aimed at building models and making decisions based on the sampled data, and without explicitly programmed to do so. Recently, deep neural network-based machine learning algorithms achieved great success in many applications including image recognition, speech recognition, natural language understanding, etc, while their potentials in other domains are to be explored. In this thesis, we explore the application of deep-learning-based algorithms for designing and controlling electromagnetic fields. Firstly, we design the nano-scale structure of the optical medium to change its interaction with the electromagnetic field. This process is called inverse design and is a common problem in nanophotonics. Since an optical property can be achieved by more than one structure, the same design request can have multiple candidate solutions. This issue is called non-uniqueness and it fundamentally makes the direct training of an inverse design neural network hard to converge. We propose a deep-learning-based approach to overcome the non-uniqueness issue and train a neural network as an inverse design toolbox. Once the model is trained, it generates a design for input requests in a fraction of a second without needing any iterative optimization. Another application in photonics is the spontaneous development of the imaging system and the neural network. Typically in deep learning algorithms, the inputs to the neural networks are handcrafted representations of the data. For example, a fully connected neural network requires manually created feature vectors as the inputs. Compared with the fully connected network, the convolutional neural network can process the raw pixel values (i.e., the digital image) and therefore requires less feature engineering. However, these digital images are collected by sensory functions (usually a camera) which are also designed by human intelligence. Here we set up a reinforcement learning agent with the ability to develop a sensory function by itself. We show that although the agent does not have a functional visual sensor to observe the environment at the beginning, it is able to automatically develop parabolic imaging optics and detect a clear visual representation of the environment. Finally, we apply machine learning algorithms for the controlling of electromagnetic fields. A reinforcement learning agent controls the electromagnets to manipulate the spatial distribution of the magnetic field. We demonstrate that this field manipulation is able to levitate and control a magnetic object. The reinforcement learning agent develops the control strategy from experiences and under the guidance of the rewards. The trained agent shows good control skills, and is faster, and has less overshoot compared with the traditional PID controller.
Artificial Intelligence Internet Of Things Iot And Smart Materials For Energy Applications
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Author : Mohan Lal Kolhe
language : en
Publisher: CRC Press
Release Date : 2022-10-12
Artificial Intelligence Internet Of Things Iot And Smart Materials For Energy Applications written by Mohan Lal Kolhe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-12 with Computers categories.
This reference text offers the reader a comprehensive insight into recent research breakthroughs in blockchain, the Internet of Things (IoT), artificial intelligence and material structure and hybrid technologies in their integrated platform, while also emphasizing their sustainability aspects. The text begins by discussing recent advances in energy materials and energy conversion materials using machine learning, as well as recent advances in optoelectronic materials for solar energy applications. It covers important topics including advancements in electrolyte materials for solid oxide fuel cells, advancements in composite materials for Li-ion batteries, progression of materials for supercapacitor applications, and materials progression for thermochemical storage of low-temperature solar thermal energy systems. This book: Discusses advances in blockchain, the Internet of Things, artificial intelligence, material structure and hybrid technologies Covers intelligent techniques in materials progression for sensor development and energy material characterization using signal processing Examines the integration of phase change materials in construction for thermal energy regulation in new buildings Explores the current happenings in technology in conjunction with basic laws and mathematical models Connecting advances in engineering materials with the use of smart techniques including artificial intelligence, machine learning and Internet of Things (IoT) in a single volume, this text will be especially useful for graduate students, academic researchers and professionals in the fields of electrical engineering, electronics engineering, materials science, mechanical engineering and computer science.
Convergence Of Deep Learning And Internet Of Things
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Author : T. Kavitha
language : en
Publisher: Engineering Science Reference
Release Date : 2023
Convergence Of Deep Learning And Internet Of Things written by T. Kavitha and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Computers categories.
"For those interested in design and building intelligent Internet of Things, this research book offers solutions with the state-of-the-art and novel approaches for the IoT problems and challenges from a deep learning perspective"--
Machine Learning Applications For Intelligent Energy Management
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Author : Haris Doukas
language : en
Publisher:
Release Date : 2025-02-10
Machine Learning Applications For Intelligent Energy Management written by Haris Doukas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Business & Economics categories.
As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector. The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.