1167 research outputs found
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Optically accessible substrate carrier for a gravure printing research platform
No full textThis dataset contains technical drawings as well as 3D-models in STEP-format
(STEP - Standard for the Exchange of Product Model Data) for the main parts of
the optically accessible substrate carrier, which is used within the gravure
printing research platform developed by Pauline Rothmann-Brumm (2023) as part
of her [dissertation](https://doi.org/10.26083/tuprints-00026770). The gravure
printing research platform is an optimized, extended version of the machine
designed by [Schäfer (2020)](https://doi.org/10.25534/tuprints-00014204).
The optically accessible substrate carrier allows optical access to the
printing nip, which is the line of contact between the engraved printing
cylinder and the impression roller. This means that hydrodynamic pattern
formation phenomena during fluid transfer can be visualized _in situ_ using a
high speed camera. Exemplary high speed videos, recorded with the gravure
printing research platform, can be found in the
[GRAFI-r](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3842) and
[GRAFI-p](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3848)
datasets.
Further information can be found in the
[dissertation](https://doi.org/10.26083/tuprints-00026770) of Pauline
Rothmann-Brumm (2023) and in the provided README-file
Creation of regime maps for fluid splitting in gravure printing (MATLAB code)
No full textThis dataset contains MATLAB code for the creation of regime maps for gravure
printed patterns from the [HYPA-p](https://tudatalib.ulb.tu-
darmstadt.de/handle/tudatalib/3841) dataset. The regime maps show the location
of the three fluid splitting regimes, namely, point splitting, lamella
splitting and transition regime, in a map of tonal value of the printing form
over printing velocity.
The input for the code are the inference results of the trained convolutional
neural networks (CNNs), as provided [here](https://tudatalib.ulb.tu-
darmstadt.de/handle/tudatalib/3838). In this context, inference means
automated classification of unlabeled data.
Further information can be found in the dissertation of Pauline Rothmann-Brumm
(2023) and in the provided README-file
Lessons Learned from a Citizen Science Project for Natural Language Processing
No full textThis is the accompanying data for our paper "Lessons Learned from a Citizen Science Project for Natural Language Processing".
Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and at- tract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science
Pose Prediction for Mobile Ground Robots Evaluation Dataset
No full textThis dataset provides ground truth robot trajectories in rough terrain for the
evaluation of pose prediction approaches for mobile ground robots. It is
composed of six datasets in four different scenarios of the RoboCup Rescue
Robot League (RRL):
* Continuous Ramps: Series of double ramps
* Curb: Three 10 x 10 cm bars on flat ground
* Hurdles: Steps of varying heights
* Elevated Ramps: Boxes of varying heights with sloped tops
Four datasets were created in the Gazebo simulator and two were recorded on a
real robot platform in the DRZ Living Lab. Each dataset contains the ground
truth robot poses of a path through the arena. In Gazebo, the ground truth
poses are provided by the simulator. In the DRZ Living Lab, a high-performance
Qualisys optical motion capture system has been used.
The data has been recorded using the tracked robot "Asterix". It is a highly
mobile platform with main tracks and coupled flippers on the front and back
and a chassis footprint of 72 × 52 cm.
The data is provided as Bagfiles for ROS and is intended to be used with the
package [hector_pose_prediction_benchmark](https://github.com/tu-darmstadt-
ros-pkg/hector_pose_prediction_benchmark).
This dataset is published as part of the publication:
Oehler, Martin, et al. "Accurate Pose Prediction on Signed Distance Fields for
Mobile Ground Robots in Rough Terrain." 2023 IEEE International Symposium on
Safety, Security, and Rescue Robotics (SSRR). IEEE, 2023.
See the provided README for further information
Figure S3 Polarization curves GDE N modified PtC
No full textFigure S3: ORR polarization curves of the unmodified and N-doped Pt/C catalysts obtained in the GDE half-cell (data collected at room temperature in 2 M HClO4 in oxygen atmosphere) before and after thermodynamic and kinetic correction for MEA conditions (80 °C, 100 %RH). A) I/C ratio = 0.1 B) I/C ratio = 0.5 C) I/C ratio = 1.7
Example Circuits in Bristol Format and HyCC Circuit Format
No full textThis is a collection of circuits in the Bristol circuit format, see https://homes.esat.kuleuven.be/~nsmart/MPC/old-circuits.html and in HyCC binary format, see https://gitlab.com/securityengineering/HyCC. They were used inside FUSE for benchmarking and example applications. Inside the HyCC circuit examples folder, there is a folder called "compiled_to_fuseir" which contains already translated FUSE IR modules from HyCC circuit examples. The examples folder can be included into the top-level directory of the FUSE code-base to use the circuits inside the code, i.e. FUSE/examples is used inside the tests, benchmarks, and examples
Efficiently combining Machine Learning with OpenFOAM using SmartSim - Slides
Get PDFSlides: 18th OpenFOAM Workshop - Efficiently combining Machine Learning with OpenFOAM using SmartSim1.
Figure 1 ORR activity RDE, GDE + MEA IL modified Pt/C
No full textFigure 1: ORR activity of Pt/C catalyst modified with [BMIM][beti]. A) Mass specific ORR activity, depending on the amount of IL obtained in RDE measurements at room temperature in oxygen saturated 0.1 M HClO4 and 1600 rpm at a catalyst loading of 20 µgPt cm-2. B) ORR potentials at different current densities depending on the amount of IL obtained in GDE measurements at room temperature in oxygen atmosphere in 2 M HClO4 at a catalyst loading of 100 µgPt cm-2. C) ORR polarization curve of unmodified Pt/C and Pt/C modified with 15 wt.% of IL measured in a MEA at 80 °C and 100 %RH in oxygen atmosphere (1 atm) at a catalyst loading of 100 µgPt cm-2
One-stage gearbox top view rendering
No full textTop down rendering of a one-stage gearbox with split housin
Figure 2 Polarization curves GDE+MEA N modified PtC
No full textFigure 2: Mass specific ORR polarization curves of the unmodified and N-doped Pt/C catalysts obtained in MEA measurements at 80 °C and 100 %RH in oxygen atmosphere (1 atm) and in the GDE half-cell (data collected at room temperature in 2 M HClO4 in oxygen atmosphere and thereafter thermodynamically and kinetically corrected for MEA conditions). A) I/C ratio = 0.1 B) I/C ratio = 0.5 C) I/C ratio = 1.7