LEADER 01272nam 2200337Ia 450 001 996385392903316 005 20221108080629.0 035 $a(CKB)1000000000603889 035 $a(EEBO)2240962080 035 $a(OCoLC)12259110 035 $a(EXLCZ)991000000000603889 100 $a19850712d1699 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aSeveral instances of the wrongs and oppressions by Q's and R's, suffered by the sailers of the English navy from the beginning of the late war$b[electronic resource] $emost humbly presented to the fountain of justice, the Parliament of England 210 $aLondon $cPrinted by G. Croom ...$d1699 215 $a[4] p 300 $aCaption title. 300 $aSigned at end: Gerrald Byrne, a lover of the king and country. 300 $aImprint from colophon. 300 $aReproduction of original in Huntington Library. 330 $aeebo-0113 700 $aByrne$b Gerrald$01001297 801 0$bEAA 801 1$bEAA 801 2$bm/c 801 2$bWaOLN 906 $aBOOK 912 $a996385392903316 996 $aSeveral instances of the wrongs and oppressions by Q's and R's, suffered by the sailers of the English navy from the beginning of the late war$92363716 997 $aUNISA LEADER 02037nam 2200577Ia 450 001 9910778616103321 005 20230421033231.0 010 $a1-280-19642-4 010 $a9786610196425 010 $a0-309-55622-8 010 $a0-585-02521-5 035 $a(CKB)110986584753394 035 $a(OCoLC)427404692 035 $a(CaPaEBR)ebrary10062911 035 $a(SSID)ssj0000242046 035 $a(PQKBManifestationID)11188134 035 $a(PQKBTitleCode)TC0000242046 035 $a(PQKBWorkID)10301240 035 $a(PQKB)10318810 035 $a(MiAaPQ)EBC3376993 035 $a(Au-PeEL)EBL3376993 035 $a(CaPaEBR)ebr10062911 035 $a(CaONFJC)MIL19642 035 $a(OCoLC)940510389 035 $a(EXLCZ)99110986584753394 100 $a19940428d1994 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aScience and judgment in risk assessment$b[electronic resource] /$fCommittee on Risk Assessment of Hazardous Air Pollutants, Board on Environmental Studies and Toxicology, Commission on Life Sciences, National Research Council 210 $aWashington, D.C. $cNational Academy Press$d1994 215 $a1 online resource (667 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-309-07490-8 311 $a0-309-04894-X 320 $aIncludes bibliographical references and index. 606 $aAir$xPollution$xToxicology$zUnited States$xStatistical methods 606 $aHealth risk assessment$xStatistical methods 615 0$aAir$xPollution$xToxicology$xStatistical methods. 615 0$aHealth risk assessment$xStatistical methods. 676 $a363.73/92/0973 712 02$aNational Research Council (U.S.).$bCommittee on Risk Assessment of Hazardous Air Pollutants. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778616103321 996 $aScience and judgment in risk assessment$93768999 997 $aUNINA LEADER 05745nam 22006735 450 001 9910866569003321 005 20250808085414.0 010 $a3-031-47062-1 024 7 $a10.1007/978-3-031-47062-2 035 $a(CKB)32323149400041 035 $a(MiAaPQ)EBC31502925 035 $a(Au-PeEL)EBL31502925 035 $a(DE-He213)978-3-031-47062-2 035 $a(EXLCZ)9932323149400041 100 $a20240620d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Cyber-Physical Systems $eSelected papers from the International Conference ML4CPS 2023 /$fedited by Oliver Niggemann, Jürgen Beyerer, Maria Krantz, Christian Kühnert 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (130 pages) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v18 311 08$a3-031-47061-3 327 $aCausal Structure Learning using PCMCI+ and Path Constraints from Wavelet-based Soft Interventions -- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining -- Using ML-based Models in Simulation of CPPSs: A Case Study of Smart Meter Production -- Deploying machine learning in high pressure resin transfer molding and part post processing: a case study -- Development of a Robotic Bin Picking Approach based on Reinforcement Learning -- Control Reconfiguration of CPS via Online Identification using Sparse Regression (SINDYc) -- Using Forest Structures for Passive Automata Learning -- Domain Knowledge Injection Guidance for Predictive Maintenance -- Towards a systematic approach for Prescriptive Analytics use cases in smart factories -- Development of a standardized data acquisition prototype for heterogeneous sensor environments as a basis for ML applications in pultrusion -- A Digital Twin Design for conveyor belts predictive maintenance -- Augmenting explainable data-driven models in energy systems: A Python framework for feature engineering. 330 $aThis 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. The Editors Prof. Dr. Oliver Niggemann held the professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany) from 2008 to 2019 and was also deputy head of the Fraunhofer IOSB-INA until 2019. In 2019, he took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut Schmidt University in Hamburg. His research at the Institute for Automation Technology is in the field of artificial intelligence and machine learning for cyber-physical systems. Prof. Dr.-Ing. Jürgen Beyerer is a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Research interests include automated visual inspection, signal and image processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation. Dr. Maria Krantz is a Postdoc at the Helmut Schmidt University in Hamburg. Her main research interests are causality in Cyber-Physical Systems and applications of diagnosis algorithms in production systems. Dr. Christian Kühnert is senior scientist at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data analytics for cyber-physical systems. 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v18 606 $aCooperating objects (Computer systems) 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial intelligence 606 $aNeural networks (Computer science) 606 $aCyber-Physical Systems 606 $aComputer Engineering and Networks 606 $aArtificial Intelligence 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aCooperating objects (Computer systems) 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aNeural networks (Computer science) 615 14$aCyber-Physical Systems. 615 24$aComputer Engineering and Networks. 615 24$aArtificial Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a621.38 700 $aNiggemann$b Oliver$01356466 701 $aBeyerer$b Ju?rgen$01029954 701 $aKrantz$b Maria$01746862 701 $aKuhnert$b Christian$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910866569003321 996 $aMachine Learning for Cyber-Physical Systems$94178610 997 $aUNINA