Articles liés à Deep Learning for Unmanned Systems
Synopsis
This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.
In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN).
The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.
- The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)
- The book chapters present various techniques of deep learning for robotic applications.
- The book chapters contain a good literature survey with a long list of references.
- The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.
- The book chapters are lucidly illustrated with numerical examples and simulations.
- The book chapters discuss details of applications and future research areas.
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Deep Learning for Unmanned Systems
Edité par
Springer International Publishing, 2021
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
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Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
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Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 37970242/1
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Deep Learning for Unmanned Systems.
Edité par
Cham, Springer International Publishing : Imprint: Springer., 2021
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
Ancien ou d'occasion
Couverture rigide
Edition originale
Vendeur : Universitätsbuchhandlung Herta Hold GmbH, Berlin, Allemagne
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1st ed. 2021. VIII, 732 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Computational Intelligence, 984. Sprache: Englisch. N° de réf. du vendeur 7823BB
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Deep Learning for Unmanned Systems
Edité par
Springer International Publishing, 2021
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Investigates the latest Deep Learning applications in theoretical and practical fields of for any unmanned system, robot, drone, underwater, etc.Includes selected and extended high-quality papers related to application of Deep Learning for Unmanne. N° de réf. du vendeur 469419018
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Deep Learning for Unmanned Systems
Edité par
Springer International Publishing, 2021
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications.The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas. N° de réf. du vendeur 9783030779382
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Deep Learning for Unmanned Systems
Edité par
Springer International Publishing Okt 2021, 2021
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications.The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas. 740 pp. Englisch. N° de réf. du vendeur 9783030779382
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Deep Learning for Unmanned Systems
ISBN 10 : 3030779386
ISBN 13 : 9783030779382
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN).The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications.The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 740 pp. Englisch. N° de réf. du vendeur 9783030779382
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