Progressive Expansion Sampling of Quasi-Static Magnetic Fields in Unconfined Regions
- We present a fast and accurate measurement technique for quasi-static magnetic fields by employing a progressive sampling method in an unconfined input space. The proposed machine learning algorithm is tested against uniform sampling on printed circuit board test structures and a buck converter. We prove allocation of multiple, separated regions with predefined lateral field limits at MHz frequencies. The feasibility of equivalent magnetic dipole source modeling based on a small number of samples is demonstrated. Compared to uniform testing, progressive expansion sampling identifies contours of given field limits in less than 3% of the reference measurement time.
| Author: | Norbert SeligerORCID, Georg Faltlhauser |
|---|---|
| DOI: | https://doi.org/10.1109/TCPMT.2023.3283285 |
| Parent Title (English): | IEEE Transactions on Components, Packaging and Manufacturing Technology |
| Document Type: | Article (peer reviewed) |
| Language: | English |
| Publication Year: | 2023 |
| Release Date: | 2023/06/12 |
| Tag: | Gaussian Process Classifier; Machine Learning; MoM modeling; Near-Field Scanning; Progressive Expansion Sampling |
| Page Number: | 9 |
| Peer reviewed: | Ja |
| faculties / departments: | Fakultät für Ingenieurwissenschaften |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie |
