1 code implementation • 28 Feb 2024 • Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann, Fabian Mies
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space.
no code implementations • 12 Oct 2023 • Ansgar Steland
Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a data set has been collected under normal conditions.
no code implementations • 14 Feb 2023 • Ansgar Steland
Open-end as well as closed-end monitoring is studied under mild assumptions on the training sample and the observations of the monitoring period.
no code implementations • 6 Jan 2021 • Ansgar Steland, Bart E. Pieters
Results are discussed about supervised learning with such networks and regression methods in terms of consistency and bounds for the generalization and prediction error.
no code implementations • 13 Sep 2020 • Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland, Adalbert Wilhelm, Tim Friede
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion.
no code implementations • 22 May 2020 • Ansgar Steland
Supervised learning by extreme learning machines resp.
no code implementations • 26 Jul 2018 • Evgenii Sovetkin, Ansgar Steland
The next important step is the extraction of the solar cells of a PV module, for instance to pass them to a procedure to detect and analyze defects on their surface.
no code implementations • 18 Jun 2018 • Sergiu Deitsch, Claudia Buerhop-Lutz, Evgenii Sovetkin, Ansgar Steland, Andreas Maier, Florian Gallwitz, Christian Riess
Automated segmentation of cells is therefore a key step in automating the visual inspection workflow.
no code implementations • 28 Apr 2018 • Ansgar Steland
This problem especially arises when working with surrogate models, e. g. to enrich observed data by simulated data, where the surrogates $Y_n$'s are constructed to justify that they approximate the $ X_n $'s.