One of the benefits expected by digitalization of production environments is a tremendous increase in flexibility. The same also applies to other applications of the Internet of Things (IoT) such as environmental monitoring or home appliances Localization will become a key topic to reduce engineering efforts but also to give a sensor location awareness, i.e., to assign sensor data a context within a factory environment. This applies both to mobile devices such as AGVs or product identifiers as well as machinery and machine components, which will face the need to be (physically) reconfigured and moved frequently during their life-time.
This research project should investigate novel localization algorithms that can perform localization using directional antennas. Directional antennas offer advantages in suppressing multipath propagation and other disturbances in today’s localization schemes. The basic idea is to find a position estimate that matches the received RSS values at all directed antennas best. This can be done in an iterative algorithm, where initially the error between a set of sampling points in the most probable solution area compared to the measured RSS values is calculated. From the resulting set of error values a gradient field is calculated that can be followed to the global error minima used to further refine the localization. Additionally, by performing a clustering of nodes including quality parameters such as jitter or other variance metrics information is additionally weighted to achieve a higher localization precision.
**This project is partially co-funded by NÖ Forschungs- und Bildungsges.m.b.H. (NFB) within the programme science calls 2017 digitization (FTI 2017).
Details
| Duration | 01/01/2019 - 30/09/2021 |
|---|---|
| Funding | Bundesländer (inkl. deren Stiftungen und Einrichtungen) |
| Program |
nfb
|
| Department | |
| Principle investigator for the project (University for Continuing Education Krems) | Dipl.-Ing. Albert Treytl |
| Project members |
Publications
Gusenbauer, M.; Stanciu, S.; Kovacs, A.; Oezelt, H.; Fischbacher, J.; Zhao, P.; Woodcock, T. G.; Schrefl, T.; Stanciu S. (2024). Micromagnetic study of grain junctions in MnAl-C containing intergranular inclusions. Elsevier Journal of Magnetism and Magnetic Materials, Vol. 606: 172390
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2023). Nanoscale chemical segregation to twin interfaces in t -MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
Gusenbauer, M.; Oezelt, H.; Kovacs, A.; Fischbacher, J.; Zhao, P.; Woodcock, T.-G.; Schrefl, T. (2023). Magnetization reversal of large granular magnetic materials. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, Wien
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2022). Nanoscale chemical segregation to twin interfaces in t-MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
Lectures
Machine Learning assisted interface analysis in MnAl-C
MSE 2024, 25/09/2024
Machine Learning-Enhanced Modelling of Large Magnetic Systems
IGTE Symposium 2024, 18/09/2024
Triple junction modeling with carbide inclusions in MnAl-C
International Conference on Magnetism, 03/07/2024
Magnetization reversal of large granular magnetic materials
HMM 2023, 05/06/2023
Multiscaling strategies in computational magnet design
Going Green – CARE INNOVATION 2023, 11/05/2023
Machine learning as building block for macromagnetic simulations
CMAM 2022, 31/08/2022
Coercivity analysis of twin boundaries with demagnetization negligible models in arbitrary field direction
JEMS 2022, 26/07/2022
