Zobrazeno 1 - 10
of 279
pro vyhledávání: '"Niggemann, Oliver"'
Autor:
Niggemann, Oliver, Biswas, Gautam, Diedrich, Alexander, Ehrhardt, Jonas, Heesch, René, Widulle, Niklas
The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver, Canada, brought together researchers to discuss recent advances in
Externí odkaz:
http://arxiv.org/abs/2410.07245
Autor:
Vranješ, Daniel, Niggemann, Oliver
Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must
Externí odkaz:
http://arxiv.org/abs/2405.18077
In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI,
Externí odkaz:
http://arxiv.org/abs/2405.18580
Autor:
Rensmeyer, Tim, Niggemann, Oliver
Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging where it is necessary to assess the reliability of a neural network's pr
Externí odkaz:
http://arxiv.org/abs/2403.08609
Autor:
Grossmann, Willi, Eilermann, Sebastian, Rensmeyer, Tim, Liebert, Artur, Hohmann, Michael, Wittke, Christian, Niggemann, Oliver
Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated and described. Aside from that, many unknown uncertainties, such as exac
Externí odkaz:
http://arxiv.org/abs/2312.10996
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time series. Machine learning seems to be an obvious solution, which becomes
Externí odkaz:
http://arxiv.org/abs/2311.03413
One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionali
Externí odkaz:
http://arxiv.org/abs/2311.03414
Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables in a set of time series is presented. An autoencoder is trained as usu
Externí odkaz:
http://arxiv.org/abs/2308.10496
Autor:
Augustin, Jan Lukas, Niggemann, Oliver
Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framework that combines concepts of model-based diagnosis with deep graph stru
Externí odkaz:
http://arxiv.org/abs/2308.06961
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of these methods. Thus, this study evaluates the robustness and generalizat
Externí odkaz:
http://arxiv.org/abs/2306.07737