TU Bergakademie Freiberg Data Publications
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Data publications from research of Freiberg University of Mining and Technology.
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- ItemOpen AccessDynamic Image Analysis: Python Notebooks, Particle Datasets, and Simulation Results(Technische Universität Bergakademie Freiberg, 2024-08-20) Buchwald, ThomasThis submission serves as a validation dataset for the simulation results of 2D imaging methods from 3D particle meshes. The submission contains dynamic image analysis data that can be used for validation of simulation results, Python notebooks for extraction of particles and calculation of particle characteristics, and the final results in as pickled Pandas DataFrames and CSV files. This dataset extends to previous submission: https://doi.org/10.25532/OPARA-479 and https://doi.org/10.25532/OPARA-587, which contain the 3D particle dataset and the simulation algorithm notebooks.
- ItemOpen Access2D Imaging Simulation from 3D Particle Data: Extended Python Notebooks, Simulation Results(Technische Universität Bergakademie Freiberg, 2024-07-30) Buchwald, Thomas2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains two distinct parts: Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. This dataset extends a previous one (https://doi.org/10.25532/OPARA-479) by an additional method of particle surface area determination and a larger dataset of randomly oriented projections – ten instead of the previous three. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
- ItemOpen AccessColloidal probe atomic force microscopy measurements on pristine gas bubbles, agglomerates and particle-laden gas bubbles(Technische Universität Bergakademie Freiberg, 2024-07-08) Nicklas, JanThis dataset contains experimental Atomic Force Microscopy (AFM) force-displacement measurements of poorly wetted alumina particles (colloidal probes) < 15 µm interacting with pristine gas bubbles, agglomerates and particle-laden gas bubbles (hetero-coagulates) in an aqueous environment. The data was generated in the context of the Collaborative Research Center CRC 920 (Project ID 169148856 – subproject B04) that is funded by the German Research Foundation (DFG). The data set is of interest for researches that study the agglomeration or hetero-coagulation in liquid environment, and it is expected to be of particular interest to those that are concerned with the simulation and modeling of these processes (for example by Discrete Element Method). The AFM raw data was baseline corrected to increase the accessibility of the data sets to those less familiar with the AFM. A detailed interpretation of the AFM data in this repository is found in https://doi.org/10.1021/acs.langmuir.3c01781 and https://doi.org/10.1016/j.colsurfa.2024.134660.
- ItemOpen Access2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results(Technische Universität Bergakademie Freiberg, 2024-05-14) Buchwald, Thomas; Ditscherlein, Ralf2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.
- ItemOpen AccessSupplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"(Technische Universität Bergakademie Freiberg, 2024-04-26) Daus, SarahThis dataset contains the supplementary data of the open-access publication "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation". It provides all the necessary data and scripts to replicate the results of the study and to further use the synthetic data generation to train one's own deep learning image segmentation model. The original scripts have been slightly modified to make them more suitable for general use, e.g. parameters have been passed to an argument parser instead of hard-coding them whenever possible. Abstract of Paper: Monitoring particle properties directly in the process using in-situ microscopy can provide valuable input to control loops, improve process understanding and facilitate process optimization. However, obtaining reliable information from these images remains a challenge, especially for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper presents a new approach to generate training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed on irregularly shaped alumina particles. An instance segmentation model trained on data generated using the proposed approach successfully detected and characterized particles, even at high solids concentrations.