Publications

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

411 to 420 of 501 Results

post_random_forest.py Jul 7, 2021 - Fracture network segmentation Plain Text - 1.5 KB - MD5: 2c64aaca9f227470a4c9602988e8f9e8 The predictions of the Random forest method can be merged into the original size of image with the code.
processing.bsh Jul 7, 2021 - Fracture network segmentation Unknown - 2.0 KB - MD5: 4e3d9a1297a097e73d8e5433ca5449e3 Prediction generator of the random forest method. The input file path, save path and trained model have to be chosen.
readme.txt Jul 7, 2021 - Fracture network segmentation Plain Text - 2.6 KB - MD5: 1d388e58b5fd9037ef315ff386a6da77 This file contains instruction of how to operate the codes.
Sato.py Jul 7, 2021 - Fracture network segmentation Plain Text - 908 B - MD5: 14c9f9428402bc7a550102032725a288 This file contains the workflow of the Sato method
trained_model_U_net_27.hdf5 Jul 7, 2021 - Fracture network segmentation Hierarchical Data Format - 89.9 MB - MD5: f8caecb9a1ddb337df1df2b6be0d82ed Trained model of the U-net model. This file is required to reproduce the results.
unet_v2_mod.py Jul 7, 2021 - Fracture network segmentation Plain Text - 3.5 KB - MD5: c4dbef168efd4edca836958329652022 The code to create the U-net architecture
Publication: Detecting micro fractures with X-ray computed tomography(Universität Stuttgart) Jun 28, 2021 This dataverse includes the underlying data of the publication: Lee, D., Karadimitriou, N., Ruf, M., & Steeb, H. (2022). Detecting micro fractures: A comprehensive comparison of conventional and machine-learning based segmentation methods. Solid Earth (EGU). Submitted.
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle", https://doi.org/10.18419/DARUS-2023, DaRUS, V1, UNF:6:kJCrJ2myXSEklpfIfr7AfQ== [fileUNF] This data contains a Thermal Humid Mechanical Cycle (THMC) of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The THMC is divided into a preheating step, a programming step and a s...
thmc_SMP_0_70.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Thermal Humid Mechanical Cycle Tabular Data - 1.6 MB - 13 Variables, 24697 Observations - UNF:6:kJCrJ2myXSEklpfIfr7AfQ==
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis", https://doi.org/10.18419/DARUS-2021, DaRUS, V1, UNF:6:YC87IXxORCLw3+woYmYT3A== [fileUNF] This data contains iso-thermal and iso-humid shear frequency-sweep measurements of Shape Memory Polymers (SMP). The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). Frequency-sweeps were performed in a temperature...

post_random_forest.py