1 to 7 of 7 Results
Apr 2, 2025 - DFG SPP 2422
Baum, Sebastian; Heinzelmann, Pascal, 2025, "Deep Drawing and Cutting Simulations Dataset", https://doi.org/10.18419/DARUS-4801, DaRUS, V1, UNF:6:kR8BEJgV33K792gINT4dbA== [fileUNF]
The benchmark dataset was generated through a comprehensive simulation study of the deep drawing process for DP600 sheet metal, incorporating variations in geometry, material properties, and process parameters. The simulations were based on the deep drawing of modified quadratic cups with a length of 210 mm and a drawing depth of 30 mm. Three disti... |
Mar 27, 2025
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Aug 12, 2024 - Robot Dog Go1 Edu
Kamm, Simon; Eißen, Dominik; Jazdi, Nasser; Weyrich, Michael, 2024, "Floor Type Detection Dataset", https://doi.org/10.18419/DARUS-4353, DaRUS, V2
Dataset for Floor Type Detection of the Robot Dog Unitree Go1 Edu. Details can be found in the seperate report. !!Privacy Statement!! This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us and we will immed... |
Jul 15, 2024
Datasets created with the Robot Dog Go1 Edu |
Jan 23, 2024 - SynDAB
Müller, Manuel; Hagmanns, Raphael; Petereit, Janko; Egloffstein, Thomas; Weyrich, Lucas; Ebert, Christof; Weyrich, Michael, 2024, "SynDAB Dataset", https://doi.org/10.18419/DARUS-3758, DaRUS, V1
The dataset comprises a collection of images captured from the perspective of an excavator. The images depict a landfill with various environmental contexts. Specifically, various type of barrels are contained. Moreover, the dataset contains the simulated LiDAR data of these scenes. |
Dec 26, 2023
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Apr 1, 2022
Maschler, Benjamin; Iliev, Angel; Pham, Thi Thu Huong; Weyrich, Michael, 2022, "Stuttgart Open Relay Degradation Dataset (SOReDD)", https://doi.org/10.18419/DARUS-2785, DaRUS, V1
Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning algorithm accordingly. Industrial transfer learning offers to lower the effort of such adaptation by allowing the utilization of previously acquired knowledge in solving... |