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pro vyhledávání: '"Jens Henriksson"'
Autor:
Jens Henriksson, Stig Ursing, Murat Erdogan, Fredrik Warg, Anders Thorsén, Johan Jaxing, Ola Örsmark, Mathias Örtenberg Toftås
Publikováno v:
Requirements Engineering: Foundation for Software Quality ISBN: 9783031297854
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b673b7651f01de88c8bf556de5d22286
https://doi.org/10.1007/978-3-031-29786-1_16
https://doi.org/10.1007/978-3-031-29786-1_16
Publikováno v:
SEAA
Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9928f46bb0781d3ab069462bac690189
http://arxiv.org/abs/2204.12402
http://arxiv.org/abs/2204.12402
Autor:
Jens Henriksson, Sankar Raman Sathyamoorthy, Christian Berger, Cristofer Englund, Lars Tornberg, Markus Borg
Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e74237aa2261aca343cf35bd99db6d7d
Publikováno v:
Software Impacts. 13:100352
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ba7d18a75d0591b688f346a975e3044
https://doi.org/10.1016/j.simpa.2022.100352
https://doi.org/10.1016/j.simpa.2022.100352
Autor:
Markus Borg, Jens Henriksson, Lars Tornberg, Cristofer Englund, Sankar Raman Sathyamoorthy, Christian Berger
Publikováno v:
SEAA
Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applications, robustness of such approaches is still an issue. A common chall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::078a4d764403c57e575f3cad449cd0ad
http://arxiv.org/abs/2103.15580
http://arxiv.org/abs/2103.15580
Publikováno v:
AITest
Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. In current practice, there are two major techniques of input stimulation. O
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07093da44d09fcc87e45b110db488a43
http://arxiv.org/abs/2002.06611
http://arxiv.org/abs/2002.06611
Autor:
Markus Borg, Jens Henriksson, Lars Tornberg, Cristofer Englund, Stig Ursing, Sankar Raman Sathyamoorthy, Christian Berger
Publikováno v:
AITest
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applications, their robustness needs to be systematically analyzed. A common cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d06f839fd12a534d1eda07cae90769c
https://doi.org/10.1109/aitest.2019.00-12
https://doi.org/10.1109/aitest.2019.00-12
Autor:
Jens Henriksson
In this piece, which draws on firsthand experience within the Swedish government throughout one of the most dramatic consolidation episodes of the post-WWII period, Jens Henriksson seeks to pinpoint, and to convey to fellow policymakers, what equatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______645::e8b8d28033a51a1a268a507f5da3fbc1
http://bruegel.org/wp-content/uploads/imported/publications/el_010607_budget.pdf
http://bruegel.org/wp-content/uploads/imported/publications/el_010607_budget.pdf