Machine Learning For Cyber Physical Systems
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Machine Learning For Cyber Physical Systems
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Author :
language : en
Publisher:
Release Date : 2018-05
Machine Learning For Cyber Physical Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05 with categories.
Cyber-Physical Systems (CPSs) represent an emerging research area that has attracted the attention of many researchers and are currently of interest in academia, industry, and government due to their potentially significant impact on society, environment, and economy. In general, CPS refers to the next generation of engineered systems that require tight integration of computing, communication, and control technologies to achieve stability, performance, reliability, robustness, and efficiency in dealing with physical systems of many application domains such as transportation, energy, medical, and defense. Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and a myriad of other engineered socio-technical systems, we must consider the safety of systems involving machine learning. Machine Learning for Cyber Physical Systems covers the latest trends and innovations in the field. It studies fundamental machine learning algorithms in supervised and unsupervised manners and examines new computing architecture for the development of next generation CPS. Important applications of CPS are also covered in this book. Particularly, regarding supervised machine learning algorithms, several generative learning and discriminative learning methods are proposed to improve learning performance. It has also been seen as the future of information technology which will transform how people interact with the physical world, just as the internet transformed how people interacted with each other. Toward intelligent CPS, it is necessary to incorporate computational intelligence into physical processes, adding new capacities to the systems such as safety, efficiency and productivity.
Machine Learning For Cyber Physical System Advances And Challenges
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Author : Janmenjoy Nayak
language : en
Publisher: Springer Nature
Release Date : 2024-04-11
Machine Learning For Cyber Physical System Advances And Challenges written by Janmenjoy Nayak and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-11 with Computers categories.
This book provides a comprehensive platform for learning the state-of-the-art machine learning algorithms for solving several cybersecurity issues. It is helpful in guiding for the implementation of smart machine learning solutions to detect various cybersecurity problems and make the users to understand in combating malware, detect spam, and fight financial fraud to mitigate cybercrimes. With an effective analysis of cyber-physical data, it consists of the solution for many real-life problems such as anomaly detection, IoT-based framework for security and control, manufacturing control system, fault detection, smart cities, risk assessment of cyber-physical systems, medical diagnosis, smart grid systems, biometric-based physical and cybersecurity systems using advance machine learning approach. Filling an important gap between machine learning and cybersecurity communities, it discusses topics covering a wide range of modern and practical advance machine learning techniques, frameworks, and development tools to enable readers to engage with the cutting-edge research across various aspects of cybersecurity.
Machine Learning For Cyber Physical Systems
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Author : Jürgen Beyerer
language : en
Publisher: Springer
Release Date : 2019-04-09
Machine Learning For Cyber Physical Systems written by Jürgen Beyerer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-09 with Computers categories.
The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Reinforcement Learning For Cyber Physical Systems
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Author : Chong Li
language : en
Publisher: CRC Press
Release Date : 2019-02-22
Reinforcement Learning For Cyber Physical Systems written by Chong Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-22 with Computers categories.
Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids. However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. Features Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.
Machine Learning For Cyber Physical Systems
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Author : Jürgen Beyerer
language : en
Publisher: Springer Nature
Release Date : 2020-12-23
Machine Learning For Cyber Physical Systems written by Jürgen Beyerer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Technology & Engineering categories.
This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Machine Learning For Cyber Physical Systems
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Author : Jürgen Beyerer
language : en
Publisher: Springer
Release Date : 2018-12-17
Machine Learning For Cyber Physical Systems written by Jürgen Beyerer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-17 with Technology & Engineering categories.
This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Machine Learning For Cyber Physical Systems
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Author : Oliver Niggemann
language : en
Publisher: Springer Nature
Release Date : 2024-06-20
Machine Learning For Cyber Physical Systems written by Oliver Niggemann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-20 with Technology & Engineering categories.
This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023. Cyber-physical systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. This is an open access book.
Machine Learning For Cyber Physical Systems Vol 9
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Author :
language : en
Publisher:
Release Date : 2019
Machine Learning For Cyber Physical Systems Vol 9 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Machine Learning For Cyber Physical Systems
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Author : Oliver Niggemann
language : en
Publisher: Springer
Release Date : 2016-02-19
Machine Learning For Cyber Physical Systems written by Oliver Niggemann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-19 with Technology & Engineering categories.
The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Big Data Analytics For Cyber Physical Systems
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Author : Guido Dartmann
language : en
Publisher: Elsevier
Release Date : 2019-07-15
Big Data Analytics For Cyber Physical Systems written by Guido Dartmann and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Law categories.
Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science. - Bridges the gap between IoT, CPS, and mathematical modelling - Features numerous use cases that discuss how concepts are applied in different domains and applications - Provides "best practices", "winning stories" and "real-world examples" to complement innovation - Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT