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of 227
pro vyhledávání: '"Meisen, Tobias"'
This technical report outlines our method for generating a synthetic dataset for semantic segmentation using a latent diffusion model. Our approach eliminates the need for additional models specifically trained on segmentation data and is part of our
Externí odkaz:
http://arxiv.org/abs/2406.17541
Autor:
de Puiseau, Constantin Waubert, Dörpelkus, Christian, Peters, Jannik, Tercan, Hasan, Meisen, Tobias
Learned construction heuristics for scheduling problems have become increasingly competitive with established solvers and heuristics in recent years. In particular, significant improvements have been observed in solution approaches using deep reinfor
Externí odkaz:
http://arxiv.org/abs/2406.07325
Autor:
Hahn, Yannik, Maack, Robert, Buchholz, Guido, Purrio, Marion, Angerhausen, Matthias, Tercan, Hasan, Meisen, Tobias
The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding (GMAW). The
Externí odkaz:
http://arxiv.org/abs/2310.12632
Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, nevertheless, insufficient results for others. From this single-strategy perspe
Externí odkaz:
http://arxiv.org/abs/2305.10192
Autor:
de Puiseau, Constantin Waubert, Peters, Jannik, Dörpelkus, Christian, Tercan, Hasan, Meisen, Tobias
Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carri
Externí odkaz:
http://arxiv.org/abs/2301.04182
In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote RL both in research and real-world applications, frameworks are required which
Externí odkaz:
http://arxiv.org/abs/2212.00906
The heterogeneity of data poses a great challenge when data from different sources is to be merged for one application. Solutions for this are offered, for example, by ontology-based data management (OBDM). A challenge of OBDM is the automatic creati
Externí odkaz:
http://arxiv.org/abs/2005.06916
The demand for more transparency of decision-making processes of deep reinforcement learning agents is greater than ever, due to their increased use in safety critical and ethically challenging domains such as autonomous driving. In this empirical st
Externí odkaz:
http://arxiv.org/abs/2004.03237
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics. We a
Externí odkaz:
http://arxiv.org/abs/2004.01254
Publikováno v:
In Journal of Energy Storage 15 November 2023 72 Part A