Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Joschka Boedecker"'
Publikováno v:
Frontiers in Future Transportation, Vol 4 (2023)
Externí odkaz:
https://doaj.org/article/d370d6cae30b41839d638d3fdae51ee5
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 6, p e1011073 (2023)
Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment res
Externí odkaz:
https://doaj.org/article/627305547859459cb0f36d9f89bb7efe
Publikováno v:
Frontiers in Future Transportation, Vol 4 (2023)
Achieving feasible, smooth and efficient trajectories for autonomous vehicles which appropriately take into account the long-term future while planning, has been a long-standing challenge. Several approaches have been considered, roughly falling unde
Externí odkaz:
https://doaj.org/article/6621013391f345dd85d55b79a5c3a31b
Autor:
Ann-Kathrin Kiessner, Robin T. Schirrmeister, Lukas A.W. Gemein, Joschka Boedecker, Tonio Ball
Publikováno v:
NeuroImage: Clinical, Vol 39, Iss , Pp 103482- (2023)
Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains appr
Externí odkaz:
https://doaj.org/article/d2b5ef74b2a348ef88e2caef4fbe7c43
Autor:
Maria Kalweit, Ulrich A Walker, Axel Finckh, Rüdiger Müller, Gabriel Kalweit, Almut Scherer, Joschka Boedecker, Thomas Hügle
Publikováno v:
PLoS ONE, Vol 16, Iss 6, p e0252289 (2021)
BackgroundDeep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with h
Externí odkaz:
https://doaj.org/article/4fb44e422e2440d085807a0577cec8ba
Autor:
Lukas A.W. Gemein, Robin T. Schirrmeister, Patryk Chrabąszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
Publikováno v:
NeuroImage, Vol 220, Iss , Pp 117021- (2020)
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding
Externí odkaz:
https://doaj.org/article/d80c6bedfe70459f9cd1de2630408347
Autor:
Lukas D. J. Fiederer, Martin Völker, Robin T. Schirrmeister, Wolfram Burgard, Joschka Boedecker, Tonio Ball
Publikováno v:
Frontiers in Neurorobotics, Vol 13 (2019)
Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the question of how to continuously evaluate the quality of robotic behavior in a hybrid brain-c
Externí odkaz:
https://doaj.org/article/79699bee4a0f4ff7a60634e55f4a4c55
Autor:
Sreedhar S Kumar, Jan Wülfing, Samora Okujeni, Joschka Boedecker, Martin Riedmiller, Ulrich Egert
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 8, p e1005054 (2016)
Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulat
Externí odkaz:
https://doaj.org/article/e7297c58244742ab8049b0539d8d7e9e
Autor:
Gianluca Frison, Torsten Koller, Lilli Frison, Joschka Boedecker, Peter Engelmann, David Fischer, Sweetin Paul
Publikováno v:
IFAC-PapersOnLine. 53:17107-17112
While MPC is the state-of-the-art approach for building heating control with proven cost savings and improvement in energy flexibility, in practice, buildings are operated by simple rules-based controllers which are not able to accomplish an energy e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3db62b37bb02ee9470b590a5c44ed780
https://doi.org/10.1016/j.ifacol.2020.12.1652
https://doi.org/10.1016/j.ifacol.2020.12.1652
Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Although deep reinforcement learning algorithms have recently demonstrated impressive results in this context, they still req
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6a7a7e48e45b5501ed75135a3edb489
http://arxiv.org/abs/2110.03316
http://arxiv.org/abs/2110.03316
