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pro vyhledávání: '"Radermacher, Ansgar"'
Failure Mode, Effects and Criticality Analysis (FMECA) is one of the safety analysis methods recommended by most of the international standards. The classical FMECA is made in a form of a table filled in either manually or by using safety analysis to
Externí odkaz:
http://arxiv.org/abs/2403.16904
As autonomous systems increasingly rely on Deep Neural Networks (DNN) to implement the navigation pipeline functions, uncertainty estimation methods have become paramount for estimating confidence in DNN predictions. Bayesian Deep Learning (BDL) offe
Externí odkaz:
http://arxiv.org/abs/2301.05297
As autonomous systems increasingly rely on Deep Neural Networks (DNN) to implement the navigation pipeline functions, uncertainty estimation methods have become paramount for estimating confidence in DNN predictions. Bayesian Deep Learning (BDL) offe
Externí odkaz:
http://arxiv.org/abs/2206.01953
Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they
Externí odkaz:
http://arxiv.org/abs/2110.13729
A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks, different meth
Externí odkaz:
http://arxiv.org/abs/2006.15172
Autor:
Hussein, Mahmoud, Nouacer, Reda, Radermacher, Ansgar, Puccetti, Armand, Gaston, Christophe, Rapin, Nicolas
Publikováno v:
In Microprocessors and Microsystems October 2018 62:41-49
Publikováno v:
In Microprocessors and Microsystems July 2017 52:272-286
Publikováno v:
2022 18th European Dependable Computing Conference (EDCC).
As autonomous systems increasingly rely on Deep Neural Networks (DNN) to implement the navigation pipeline functions, uncertainty estimation methods have become paramount for estimating confidence in DNN predictions. Bayesian Deep Learning (BDL) offe
Autor:
Radermacher, Ansgar.
Techn. Hochsch., Diss., 2000--Aachen.
Externí odkaz:
http://sylvester.bth.rwth-aachen.de/dissertationen/2001/023/01_023.pdf
http://deposit.d-nb.de/cgi-bin/dokserv?idn=962023787
http://deposit.d-nb.de/cgi-bin/dokserv?idn=962023787
Publikováno v:
JRWRTC 2015: 9th Junior Researcher Workshop on Real-Time Computing
JRWRTC 2015: 9th Junior Researcher Workshop on Real-Time Computing, Nov 2015, Lille, France
JRWRTC 2015: 9th Junior Researcher Workshop on Real-Time Computing, Nov 2015, Lille, France
In conjunction with the 23rd International Conference on Real-Time and Network Systems (RTNS 2015); International audience; This paper presents an initial approach towards making AUTOSAR dynamic starting from the application layer down to the operati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::27fd74acdc250447c8db60534f075781
https://hal.inria.fr/hal-01416872
https://hal.inria.fr/hal-01416872