@inproceedings{SeligerFaltlhauser2022, author = {Seliger, Norbert and Faltlhauser, Georg}, title = {Active Expansion Sampling of Magnetic Near-Fields in Unbounded Regions}, series = {Proceedings 26th IEEE Workshop on Signal and Power Integrity (IEEE SPI-2022)}, booktitle = {Proceedings 26th IEEE Workshop on Signal and Power Integrity (IEEE SPI-2022)}, publisher = {IEEE}, organization = {Labor f{\"u}r Leistungselektronik und EMV, TH Rosenheim}, pages = {4}, year = {2022}, language = {en} } @article{SeligerFaltlhauser2023, author = {Seliger, Norbert and Faltlhauser, Georg}, title = {Progressive Expansion Sampling of Quasi-Static Magnetic Fields in Unconfined Regions}, series = {IEEE Transactions on Components, Packaging and Manufacturing Technology}, journal = {IEEE Transactions on Components, Packaging and Manufacturing Technology}, doi = {10.1109/TCPMT.2023.3283285}, pages = {9}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{SeligerFaltlhauser2023, author = {Seliger, Norbert and Faltlhauser, Georg}, title = {Progressive Expansion Sampling of Quasi-Static Magnetic Fields for EMI Noise Detection and Equivalent Source Modeling}, series = {Proceedings EMC Europe 2023}, booktitle = {Proceedings EMC Europe 2023}, publisher = {IEEE}, address = {Krakov}, pages = {6}, year = {2023}, abstract = {We introduce a quick and accurate quasi-static magnetic field scanning technique by employing a progressive sampling method in an unconfined input space. The proposed machine learning algorithm is tested against uniform sampling on a printed circuit board test structure. We prove allocation of multiple, separated regions with predefined tangential field strengths 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 detects contours of given field limits in less than 3\% of the reference measurement time.}, language = {en} }