LEADER 01961oam 2200625M 450 001 9910715775003321 005 20191123062015.1 035 $a(CKB)5470000002514322 035 $a(OCoLC)1065762749 035 $a(OCoLC)995470000002514322 035 $a(EXLCZ)995470000002514322 100 $a20070221d1850 ua 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aChild, Farr & Co. (To accompany Bill H.R. No. 286.) May 16, 1850 210 1$a[Washington, D.C.] :$c[publisher not identified],$d1850. 215 $a1 online resource (1 page) 225 1 $aHouse report / 31st Congress, 1st session. House ;$vno. 329 225 1 $a[United States congressional serial set ] ;$v[serial no. 584] 300 $a"Child, Fair and Company" is the version of that appears most often in the Congressional Serial Set. 300 $aBatch processed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aFDLP item number not assigned. 517 $aChild, Farr & Co. 606 $aClaims 606 $aMalicious mischief 606 $aVandalism 606 $aDrawbacks 606 $aFires 606 $aHardware 606 $aSteamboats 606 $aTariff 606 $aMerchants 608 $aLegislative materials.$2lcgft 615 0$aClaims. 615 0$aMalicious mischief. 615 0$aVandalism. 615 0$aDrawbacks. 615 0$aFires. 615 0$aHardware. 615 0$aSteamboats. 615 0$aTariff. 615 0$aMerchants. 701 $aPhoenix$b Jonas Phillips$f1788-1859$pWhig (NY)$01387416 801 0$bWYU 801 1$bWYU 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCO 906 $aBOOK 912 $a9910715775003321 996 $aChild, Farr & Co. (To accompany Bill H.R. No. 286.) May 16, 1850$93530715 997 $aUNINA LEADER 05789nam 22006975 450 001 9910433248603321 005 20251230060512.0 010 $a3-662-62746-9 024 7 $a10.1007/978-3-662-62746-4 035 $a(CKB)4100000011679175 035 $a(DE-He213)978-3-662-62746-4 035 $a(MiAaPQ)EBC6436125 035 $a(Au-PeEL)EBL6436125 035 $a(OCoLC)1231609193 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/33766 035 $a(PPN)252511794 035 $a(ODN)ODN0010071703 035 $a(oapen)doab33766 035 $a(EXLCZ)994100000011679175 100 $a20201223d2021 u| 0 101 0 $aeng 135 $aurnn#---mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Cyber Physical Systems $eSelected papers from the International Conference ML4CPS 2020 /$fedited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann 205 $a1st ed. 2021. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer Vieweg,$d2021. 215 $a1 online resource (VII, 130 p. 42 illus., 25 illus. in color.) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v13 311 1 $a3-662-62745-0 327 $aPreface -- Energy Profile Prediction of Milling Processes Using Machine Learning Techniques -- Improvement of the prediction quality of electrical load profiles with artficial neural networks -- Detection and localization of an underwater docking station -- Deployment architecture for the local delivery of ML-Models to the industrial shop floor -- Deep Learning in Resource and Data Constrained Edge Computing Systems -- Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis -- Proposal for requirements on industrial AI solutions -- Information modeling and knowledge extraction for machine learning applications in industrial production systems -- Explanation Framework for Intrusion Detection -- Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning -- Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial ExampleAttacks -- First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems -- Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data. 330 $aThis open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS ? Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems. 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v13 606 $aCooperating objects (Computer systems) 606 $aTelecommunication 606 $aComputer engineering 606 $aComputer networks 606 $aCyber-Physical Systems 606 $aCommunications Engineering, Networks 606 $aComputer Engineering and Networks 615 0$aCooperating objects (Computer systems). 615 0$aTelecommunication. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aCyber-Physical Systems. 615 24$aCommunications Engineering, Networks. 615 24$aComputer Engineering and Networks. 676 $a621.38 686 $aCOM043000$aTEC007000$aTEC041000$2bisacsh 700 $aBeyerer$b Ju?rgen$4edt$01029954 702 $aBeyerer$b Ju?rgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaier$b Alexander$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNiggemann$b Oliver$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910433248603321 996 $aMachine Learning for Cyber Physical Systems$93018774 997 $aUNINA