Aleksandra Thamm
Aleksandra Thamm, M. Eng.
Researcher
Martensstr. 3
91058 Erlangen
Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)
Martensstr. 3
91058 Erlangen
- Phone number: +49 9131 85-27826
- Fax number: +49 9131 85-27270
- Email: aleksandra.thamm@fau.de
- Website: https://lme.tf.fau.de/person/athamm/
Office hours
Each week Mo,Academic CV
- Since 08/2020:
Ph.D. Student at Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg - 10/2018 – 04/2020
M.Eng. Electronic and Mechatronic Systems, Technische Hochschule Nürnberg - 10/2013 – 06/2018
B.Eng. Mechatronics/Precision Engineering, Technische Hochschule Nürnberg
Working Experience
Siemens AG, Erlangen
- Since 04/2020
Ph.D. Student
DI CS MC ID TI - 05/2019 – 02/2020
Master’s Thesis Student
DI CS SD EH MC&PMA 3
Certa Systems GmbH, Cadolzburg
- 04/2018 – 02/2019
Working Student - 10/2017 – 03/2018
Bachelor’s Thesis Student - 03/2017 – 08/2017
Working Student - 09/2016 – 02/2017
Intern
Technische Hochschule, Nürnberg
- 04/2018 – 07/2019
Teaching Assistant - 08/2015 – 05/2017
E-Learning Tutor
Publications
2024
Journal Articles
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Deep learning based drill wear segmentation and analysis of the wear progress
In: International Journal on Interactive Design and Manufacturing (2024)
ISSN: 1955-2513
DOI: 10.1007/s12008-024-02045-0
BibTeX: Download
2023
Journal Articles
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Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach
In: Materials 16 (2023), p. 7001
ISSN: 1996-1944
DOI: 10.3390/ma16217001
BibTeX: Download
2022
Journal Articles
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An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes
In: Biomedical Physics and Engineering Express (2022)
ISSN: 2057-1976
DOI: 10.1088/2057-1976/ac9415
BibTeX: Download
Conference Contributions
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Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 (Singapore, September 18, 2022 - September 22, 2022)
DOI: 10.1007/978-3-031-16437-8_61
URL: https://link.springer.com/chapter/10.1007/978-3-031-16437-8_61
BibTeX: Download
2021
Conference Contributions
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Failure and Risk Analysis based on Maintenance Reports of Machines Components in Manufacturing Industry
XIII. International Conference on the Theory of Machines and Mechanisms - TMM2020 (Liberec, September 7, 2021 - September 9, 2021)
DOI: 10.1007/978-3-030-83594-1_29
BibTeX: Download
Lectures
No matching records found.
Students Theses
| Student | Title | Type | Status |
|---|---|---|---|
| Lukas Rosteck | AI-based Anomaly Detection in Process Signals for Condition Monitoring of Industrial Machines | MA thesis | finished |
| Peter Herbst | Unsupervised Contextual Anomaly Detection in Frequency Converter Data | MA thesis | finished |
| Sebastian Müller | Detection of localized necking in Hydraulic Bulge Tests using Deep Learning Methods | MA thesis | finished |
| Benjamin Geißler | Interpolation of ARAMIS Grids and Analysis of Numerical Stability on Deep Learning Methods | Project | finished |
| Amit Kumar Sharma | Prediction of Steam Turbine Blade Vibration Amplitudes using Machine Learning Methods | MA thesis | finished |
| Melanie Kienberger | Predictive Maintenance for SINAMICs Frequency Converter | MA thesis | finished |