LEADER 01489cam2-2200433---450- 001 990005950940203316 005 20140606124807.0 010 $a978-88-916-0221-3 035 $a000595094 035 $aUSA01000595094 035 $a(ALEPH)000595094USA01 035 $a000595094 100 $a20140605d2014----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aFormulario generale dell'edilizia$emodulistica, note esplicative, riferimenti giuridici per: progettazioni edilizie ...$fMario Di Nicola 205 $a7. ed. aggiornata a: Legge 27 febbraio 2014, n. 15 ... 210 $aSantarcangelo di Romagna$cMaggioli$d2014 215 $a854 p.$d24 cm$eCD-ROM 225 2 $aEdilizia & Urbanistica$v298 410 0$1001000335969$aEdilizia & Urbanistica$v, 298 454 1$12001 461 1$1001-------$12001 606 0 $aPianificazione urbanistica$xFormulari$2BNCF 676 $a346.45045 700 1$aDI NICOLA,$bMario$0287383 801 0$aIT$bsalbc$gISBD 912 $a990005950940203316 951 $aXXIV.3. Coll. 11/ 4$b80009 G.$cXXIV.3. Coll. 11/$d00354858 959 $aBK 969 $aGIU 979 $aFIORELLA$b90$c20140605$lUSA01$h1127 979 $aFIORELLA$b90$c20140605$lUSA01$h1131 979 $aFIORELLA$b90$c20140605$lUSA01$h1320 979 $aFIORELLA$b90$c20140606$lUSA01$h1247 979 $aFIORELLA$b90$c20140606$lUSA01$h1248 996 $aFormulario generale dell'edilizia$9831519 997 $aUNISA LEADER 05165nam 22006495 450 001 9910502972903321 005 20251113180725.0 010 $a3-030-77939-4 024 7 $a10.1007/978-3-030-77939-9 035 $a(CKB)5360000000049950 035 $a(MiAaPQ)EBC6739112 035 $a(Au-PeEL)EBL6739112 035 $a(OCoLC)1273410337 035 $a(PPN)258296704 035 $a(DE-He213)978-3-030-77939-9 035 $a(EXLCZ)995360000000049950 100 $a20211001d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for Unmanned Systems /$fedited by Anis Koubaa, Ahmad Taher Azar 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (731 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v984 311 08$a3-030-77938-6 327 $aDeep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review -- Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment -- Reactive Obstacle Avoidance Method for a UAV -- Guaranteed Performances for Learning-Based Control Systems using Robust Control Theory -- A cascaded deep Neural Network for Position Estimation of Industrial Robots -- Managing Deep Learning Uncertainty for Autonomous Systems -- Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning -- Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids -- Reinforcement learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster -- Image-Based Identification of Animal Breeds Using Deep Learning. 330 $aThis 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. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v984 606 $aControl engineering 606 $aRobotics 606 $aAutomation 606 $aComputational intelligence 606 $aEngineering$xData processing 606 $aControl, Robotics, Automation 606 $aComputational Intelligence 606 $aData Engineering 615 0$aControl engineering. 615 0$aRobotics. 615 0$aAutomation. 615 0$aComputational intelligence. 615 0$aEngineering$xData processing. 615 14$aControl, Robotics, Automation. 615 24$aComputational Intelligence. 615 24$aData Engineering. 676 $a006.31 702 $aKouba?a$b Anis 702 $aAzar$b Ahmad Taher 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910502972903321 996 $aDeep learning for unmanned systems$92894694 997 $aUNINA