351 to 360 of 577 Results
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 |
Python Source Code - 2.4 KB -
MD5: 9b63a89131ad52136b92531a6b7f5e71
Pre-processing code to change the images into available shape and format |
Python Source Code - 7.0 KB -
MD5: 10e1540fe5afda49692b2437eb245903
Training code in order to train the model |
Python Source Code - 17.5 KB -
MD5: 3a13bef31e567313fc8ef766bc00d377
Customized image library in order to save and load the image data |
Plain Text - 2.1 KB -
MD5: d378b2f2e6e39b343cb17bd2522c6b7d
Detailed description of operation with codes |
Python Source Code - 3.8 KB -
MD5: c84b9fd76ff97e75a91ccde5cc1395e2
3D U-net model |
Sep 26, 2022 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2021, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of a pre-stressed monodisperse rubber and glass particle mixture with 50% volume rubber content", https://doi.org/10.18419/DARUS-2208, DaRUS, V2, UNF:6:Rf3VjhimtgZ3IVZt0sEaiw== [fileUNF]
This dataset contains two micro X-ray Computed Tomography (micro-XRCT) data sets from scans of the identical cylindrical sample (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The sample consists of monodisperse soft (rubber) and stiff (glass) particles mixture. Both particles have an identical diameter of 4 mm.... |