461 to 470 of 693 Results
Mar 6, 2023 - Data Analytics in Engineering
Lißner, Julian, 2023, "Microstructure feature engineering data", https://doi.org/10.18419/DARUS-3366, DaRUS, V1
The dataset contains image data of periodic microstructural representative volume elements (RVE), as well as the effective heat conductivity for multiple phase contrasts. Various features and feature descriptors (explained in the related publication) are provided, as well as the computation thereof. The features were used in a machine learning regr... |
Mar 6, 2023 -
Microstructure feature engineering data
Python Source Code - 2.1 KB -
MD5: 59a5a37eb3aa95980483fca18fbb4604
exemplary script to access the data |
Mar 6, 2023 -
Microstructure feature engineering data
Python Source Code - 14.6 KB -
MD5: 4643b4eac73889b050fb1059b7c5c6dd
loader object to use for easy data access |
Mar 6, 2023 -
Microstructure feature engineering data
Hierarchical Data Format - 199.9 MB -
MD5: 2526748b927fb0775b4cc1fa55945502
storage of the data |
Mar 6, 2023 -
Microstructure feature engineering data
Python Source Code - 17.7 KB -
MD5: f0adb59b30d0c7c87aa776d2716f96f4
scripts to compute all features |
Mar 6, 2023 -
Microstructure feature engineering data
Python Source Code - 4.1 KB -
MD5: ca6bfd9fae4c7d33b52bc70bc5b4a2bd
auxiliary (small) functions |
Feb 7, 2023 - Publication: Machine learning assists in increasing the time resolution of X-ray tomography
Lee, Dongwon; Steeb, Holger, 2023, "Image enhancement code: time-resolved tomograms of EICP application using 3D U-net", https://doi.org/10.18419/DARUS-2991, DaRUS, V1
This dataset contains the codes to reproduce the results of "Time resolved micro-XRCT dataset of Enzymatically Induced Calcite Precipitation (EICP) in sintered glass bead columns", cf. https://doi.org/10.18419/darus-2227. The code takes "low-dose" images as an input where the images contain many artifacts and noise as a trade-off of a fast data acq... |
7Z Archive - 417.9 MB -
MD5: ab58b9cb6a76de1e09c5baa790eada71
Data for training and examples for pre/post-processing |
Plain Text - 155 B -
MD5: 770701e71b665603a9e61292c7824135
An example of training log file (all the adopted parameters used during training are saved in this log) |
Python Source Code - 5.7 KB -
MD5: ee7d79e5e2f70aabaae7bae129627301
Post-processing code to create enhanced images |
