---
_id: '63498'
author:
- first_name: Wilhelm
full_name: Kirchgässner, Wilhelm
last_name: Kirchgässner
- first_name: Nikolas
full_name: Förster, Nikolas
last_name: Förster
- first_name: Till
full_name: Piepenbrock, Till
last_name: Piepenbrock
- first_name: Oliver
full_name: Schweins, Oliver
last_name: Schweins
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
citation:
ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE:
H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power
Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174'
apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid,
O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms
With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE
Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174'
bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE:
H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={10.1109/TPEL.2024.3488174},
number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner,
Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid,
Oliver}, year={2025}, pages={3326–3335} }'
chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins,
and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary
Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.”
IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.'
ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid,
“HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
Dilated Convolutional Neural Networks in Ferrite Cores,” IEEE Transactions
on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.'
mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation
for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in
Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2,
2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.'
short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid,
IEEE Transactions on Power Electronics 40 (2025) 3326–3335.
date_created: 2026-01-06T08:07:13Z
date_updated: 2026-01-06T08:08:01Z
department:
- _id: '52'
doi: 10.1109/TPEL.2024.3488174
intvolume: ' 40'
issue: '2'
keyword:
- Mathematical models
- Estimation
- Data models
- Convolutional neural networks
- Accuracy
- Magnetic hysteresis
- Magnetic cores
- Temperature measurement
- Magnetic domains
- Temperature distribution
- Convolutional neural network (CNN)
- machine learning (ML)
- magnetics
page: 3326-3335
publication: IEEE Transactions on Power Electronics
status: public
title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
Dilated Convolutional Neural Networks in Ferrite Cores'
type: journal_article
user_id: '83383'
volume: 40
year: '2025'
...
---
_id: '33510'
abstract:
- lang: eng
text: In the manufacture of real wood products, defects can quickly occur during
the production process. To quickly sort out these defects, a system is needed
that finds damage in the irregularly structured surfaces of the product. The difficulty
in this task is that each surface is visually different and no standard defects
can be defined. Thus, damage detection using correlation does not work, so this
paper will test different machine learning methods. To evaluate different machine
learning methods, a data set is needed. For this reason, the available samples
were recorded manually using a static fixed camera. Subsequently, the images were
divided into sub-images, which resulted in a relatively small data set. Next,
a convolutional neural network (CNN) was constructed to classify the images. However,
this approach did not lead to a generalized solution, so the dataset was hashed
using the a- and pHash. These hash values were then trained with a fully supervised
system that will later serve as a reference model, in the semi-supervised learning
procedures. To improve the supervised model and not have to label every data point,
semi-supervised learning methods are used in the following. For this purpose,
the CEAL method (wrapper method) is considered in the first and then the Π-Model
(intrinsically semi-supervised).
author:
- first_name: Tom
full_name: Sander, Tom
last_name: Sander
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
- first_name: Volker
full_name: Geneiß, Volker
last_name: Geneiß
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
citation:
ama: 'Sander T, Lange S, Hilleringmann U, Geneiß V, Hedayat C, Kuhn H. Detection
of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised
Learning Methods. In: 2022 Smart Systems Integration (SSI). IEEE; 2022.
doi:10.1109/ssi56489.2022.9901433'
apa: Sander, T., Lange, S., Hilleringmann, U., Geneiß, V., Hedayat, C., & Kuhn,
H. (2022). Detection of Defects on Irregularly Structured Surfaces using Supervised
and Semi-Supervised Learning Methods. 2022 Smart Systems Integration (SSI).
2022 Smart Systems Integration (SSI), Grenoble, France. https://doi.org/10.1109/ssi56489.2022.9901433
bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneiß_Hedayat_Kuhn_2022, place={Grenoble,
France}, title={Detection of Defects on Irregularly Structured Surfaces using
Supervised and Semi-Supervised Learning Methods}, DOI={10.1109/ssi56489.2022.9901433},
booktitle={2022 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Sander,
Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat,
Christian and Kuhn, Harald}, year={2022} }'
chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneiß, Christian
Hedayat, and Harald Kuhn. “Detection of Defects on Irregularly Structured Surfaces
Using Supervised and Semi-Supervised Learning Methods.” In 2022 Smart Systems
Integration (SSI). Grenoble, France: IEEE, 2022. https://doi.org/10.1109/ssi56489.2022.9901433.'
ieee: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, and H. Kuhn,
“Detection of Defects on Irregularly Structured Surfaces using Supervised and
Semi-Supervised Learning Methods,” presented at the 2022 Smart Systems Integration
(SSI), Grenoble, France, 2022, doi: 10.1109/ssi56489.2022.9901433.'
mla: Sander, Tom, et al. “Detection of Defects on Irregularly Structured Surfaces
Using Supervised and Semi-Supervised Learning Methods.” 2022 Smart Systems
Integration (SSI), IEEE, 2022, doi:10.1109/ssi56489.2022.9901433.
short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneiß, C. Hedayat, H. Kuhn, in:
2022 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2022.'
conference:
end_date: 2022-04-28
location: Grenoble, France
name: 2022 Smart Systems Integration (SSI)
start_date: 2022-04-27
date_created: 2022-10-04T11:35:55Z
date_updated: 2022-10-04T11:37:39Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/ssi56489.2022.9901433
keyword:
- Machine Learning
- CNN
- Hashing
- semi-supervised learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9901433
place: Grenoble, France
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2022 Smart Systems Integration (SSI)
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregularly Structured Surfaces using Supervised and
Semi-Supervised Learning Methods
type: conference
user_id: '38240'
year: '2022'
...