Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Stefanie Schwaar"'
A major challenge in a time-dependent reliability analysis is to find a good balance between computational effort, accuracy, and comprehension of the analysis. In this contribution, computational efforts are reduced, and comprehension is increased by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3326e2b6591d20f60b8821cc082896a
https://publica.fraunhofer.de/handle/publica/268862
https://publica.fraunhofer.de/handle/publica/268862
Publikováno v:
Computational Statistics & Data Analysis. 167:107375
As an example for the fast calculation of distributional parameters of Gaussian processes, a new Monte Carlo algorithm for the computation of quantiles of the supremum norm of weighted Brownian bridges is proposed. As it is known, the corresponding d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::074d4e3d0c2b53904c8bcceb44139016
https://doi.org/10.1016/j.csda.2021.107375
https://doi.org/10.1016/j.csda.2021.107375
Publikováno v:
Proceedings of the 29th European Safety and Reliability Conference (ESREL).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef863ccda520fc3d37e496bfffd910b9
https://doi.org/10.3850/978-981-11-2724-3_0142-cd
https://doi.org/10.3850/978-981-11-2724-3_0142-cd
Autor:
Stefanie Schwaar, Sergei Mitrofanov, Tatiana Karaseva, Vitalii Makogin, Claudia Redenbach, Denis Dresvyanskiy, Evgeny Spodarev
The paper considers the problem of anomaly detection in 3D images of fibre materials. The spatial Stochastic Expectation Maximisation algorithm and Adaptive Weights Clustering are applied to solve this problem. The initial 3D grey scale image was div
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d57481c27de9b06a6cc83b07e15a1fb3
Autor:
Denis Dresvyanskiy, Tatiana Karaseva, Sergei Mitrofanov, Claudia Redenbach, Stefanie Schwaar, Vitalii Makogin, Evgeny Spodarev
Publikováno v:
IOP Conference Series: Materials Science & Engineering; Jun2019, Vol. 537 Issue 2, p1-1, 1p
Publikováno v:
Scopus-Elsevier
Although variational autoencoders (VAE) are successfully used to obtain meaningful low-dimensional representations for high-dimensional data, the characterization of critical points of the loss function for general observation models is not fully und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4f41f6e8a1fbdd3381fbb638d415cf5b
http://www.scopus.com/inward/record.url?eid=2-s2.0-85116988861&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85116988861&partnerID=MN8TOARS