| spelling |
oapen-20.500.12657-232932024-03-22T19:23:41Z Machine Learning for Cyber Physical Systems Beyerer, Jürgen Kühnert, Christian Niggemann, Oliver Engineering Computational intelligence Computer organization Electrical engineering Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence 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. 2020-03-18 13:36:15 2020-04-01T09:10:28Z 2020-04-01T09:10:28Z 2019 book 1006862 http://library.oapen.org/handle/20.500.12657/23293 eng Technologien für die intelligente Automation application/pdf n/a 1006862.pdf https://www.springer.com/9783662584859 Springer Nature 10.1007/978-3-662-58485-9 10.1007/978-3-662-58485-9 6c6992af-b843-4f46-859c-f6e9998e40d5 136 Berlin, Heidelberg open access
|
| description |
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.
|
