---
_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: '62078'
abstract:
- lang: eng
text: 'Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior.
To capture this in simulations, intricate models with a variety of parameters
are typically used. The identification of values for such parameters is highly
challenging and requires in depth understanding of the model itself. Machine learning
(ML) is a promising approach for alleviating this challenge by directly predicting
parameters based on experimental results. So far, this works mostly for purely
artificial data. In this work, two approaches to generalize to experimental data
are investigated: a sequential approach, leveraging understanding of the constitutive
model and a direct, purely data driven approach. This is exemplary carried out
for a highly non-linear strain rate dependent constitutive model for the shear
behavior of FRP.The sequential model is found to work better on both artificial
and experimental data. It is capable of extracting well suited parameters from
the artificial data under realistic conditions. For the experimental data, the
model performance depends on the composition of the experimental curves, varying
between excellently suiting and reasonable predictions. Taking the expert knowledge
into account for ML-model training led to far better results than the purely data
driven approach. Robustifying the model predictions on experimental data promises
further improvement. '
author:
- first_name: Johannes
full_name: Gerritzen, Johannes
id: '105344'
last_name: Gerritzen
orcid: 0000-0002-0169-8602
- first_name: Andreas
full_name: Hornig, Andreas
last_name: Hornig
- first_name: Peter
full_name: Winkler, Peter
last_name: Winkler
- first_name: Maik
full_name: Gude, Maik
last_name: Gude
citation:
ama: 'Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning.
In: ECCM21 - Proceedings of the 21st European Conference on Composite Materials.
Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:10.60691/yj56-np80'
apa: Gerritzen, J., Hornig, A., Winkler, P., & Gude, M. (2024). Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning. ECCM21 - Proceedings of the 21st European Conference
on Composite Materials, 3, 1252–1259. https://doi.org/10.60691/yj56-np80
bibtex: '@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning}, volume={3}, DOI={10.60691/yj56-np80},
booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials},
publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen,
Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024},
pages={1252–1259} }'
chicago: Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct
Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive
Models Using Machine Learning.” In ECCM21 - Proceedings of the 21st European
Conference on Composite Materials, 3:1252–1259. European Society for Composite
Materials (ESCM), 2024. https://doi.org/10.60691/yj56-np80.
ieee: 'J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning,”
in ECCM21 - Proceedings of the 21st European Conference on Composite Materials,
2024, vol. 3, pp. 1252–1259, doi: 10.60691/yj56-np80.'
mla: Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear
Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 -
Proceedings of the 21st European Conference on Composite Materials, vol. 3,
European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80.
short: 'J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of
the 21st European Conference on Composite Materials, European Society for Composite
Materials (ESCM), 2024, pp. 1252–1259.'
date_created: 2025-11-04T12:47:06Z
date_updated: 2026-02-27T06:46:21Z
doi: 10.60691/yj56-np80
intvolume: ' 3'
keyword:
- Direct parameter identification
- Machine learning
- Convolutional neural networks
- Strain rate dependency
- Fiber reinforced plastics
- woven composites
- segmentation
- synthetic training data
- x-ray computed tomography
language:
- iso: eng
page: 1252–1259
project:
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '137'
name: TRR 285 - Subproject A03
- _id: '131'
name: TRR 285 - Project Area A
publication: ECCM21 - Proceedings of the 21st European Conference on Composite Materials
publication_identifier:
isbn:
- 978-2-912985-01-9
publisher: European Society for Composite Materials (ESCM)
status: public
title: Direct parameter identification for highly nonlinear strain rate dependent
constitutive models using machine learning
type: conference
user_id: '105344'
volume: 3
year: '2024'
...
---
_id: '47522'
abstract:
- lang: eng
text: Artificial benchmark functions are commonly used in optimization research
because of their ability to rapidly evaluate potential solutions, making them
a preferred substitute for real-world problems. However, these benchmark functions
have faced criticism for their limited resemblance to real-world problems. In
response, recent research has focused on automatically generating new benchmark
functions for areas where established test suites are inadequate. These approaches
have limitations, such as the difficulty of generating new benchmark functions
that exhibit exploratory landscape analysis (ELA) features beyond those of existing
benchmarks.The objective of this work is to develop a method for generating benchmark
functions for single-objective continuous optimization with user-specified structural
properties. Specifically, we aim to demonstrate a proof of concept for a method
that uses an ELA feature vector to specify these properties in advance. To achieve
this, we begin by generating a random sample of decision space variables and objective
values. We then adjust the objective values using CMA-ES until the corresponding
features of our new problem match the predefined ELA features within a specified
threshold. By iteratively transforming the landscape in this way, we ensure that
the resulting function exhibits the desired properties. To create the final function,
we use the resulting point cloud as training data for a simple neural network
that produces a function exhibiting the target ELA features. We demonstrate the
effectiveness of this approach by replicating the existing functions of the well-known
BBOB suite and creating new functions with ELA feature values that are not present
in BBOB.
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Konstantin
full_name: Dietrich, Konstantin
last_name: Dietrich
- first_name: Lennart
full_name: Schneider, Lennart
last_name: Schneider
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
citation:
ama: 'Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark
Functions Optimized for Exploratory Landscape Features. In: Proceedings of
the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA
’23. Association for Computing Machinery; 2023:129–139. doi:10.1145/3594805.3607136'
apa: Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke,
P., Trautmann, H., & Mersmann, O. (2023). Neural Networks as Black-Box Benchmark
Functions Optimized for Exploratory Landscape Features. Proceedings of the
17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139.
https://doi.org/10.1145/3594805.3607136
bibtex: '@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023,
place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box
Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={10.1145/3594805.3607136},
booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms}, publisher={Association for Computing Machinery}, author={Prager,
Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier,
Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann,
Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }'
chicago: 'Prager, Raphael Patrick, Konstantin Dietrich, Lennart Schneider, Lennart
Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, and Olaf Mersmann.
“Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape
Features.” In Proceedings of the 17th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 129–139. FOGA ’23. New York, NY, USA: Association for
Computing Machinery, 2023. https://doi.org/10.1145/3594805.3607136.'
ieee: 'R. P. Prager et al., “Neural Networks as Black-Box Benchmark Functions
Optimized for Exploratory Landscape Features,” in Proceedings of the 17th ACM/SIGEVO
Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139, doi: 10.1145/3594805.3607136.'
mla: Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions
Optimized for Exploratory Landscape Features.” Proceedings of the 17th ACM/SIGEVO
Conference on Foundations of Genetic Algorithms, Association for Computing
Machinery, 2023, pp. 129–139, doi:10.1145/3594805.3607136.
short: 'R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke,
H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on
Foundations of Genetic Algorithms, Association for Computing Machinery, New York,
NY, USA, 2023, pp. 129–139.'
date_created: 2023-09-27T15:43:17Z
date_updated: 2023-10-16T12:33:02Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3594805.3607136
keyword:
- Benchmarking
- Instance Generator
- Black-Box Continuous Optimization
- Exploratory Landscape Analysis
- Neural Networks
language:
- iso: eng
page: 129–139
place: New York, NY, USA
publication: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- '9798400702020'
publisher: Association for Computing Machinery
series_title: FOGA ’23
status: public
title: Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory
Landscape Features
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '52865'
abstract:
- lang: eng
text: This paper addresses new challenges of detecting campaigns in social media,
which emerged with the rise of Large Language Models (LLMs). LLMs particularly
challenge algorithms focused on the temporal analysis of topical clusters. Simple
similarity measures can no longer capture and map campaigns that were previously
broadly similar in content. Herein, we analyze whether the classification of messages
over time can be profitably used to rediscover poorly detectable campaigns at
the content level. Thus, we evaluate classical classifiers and a new method based
on siamese neural networks. Our results show that campaigns can be detected despite
the limited reliability of the classifiers as long as they are based on a large
amount of simultaneously spread artificial content.
author:
- first_name: Britta
full_name: Grimme, Britta
last_name: Grimme
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Hendrik
full_name: Winkelmann, Hendrik
last_name: Winkelmann
- first_name: Lucas
full_name: Stampe, Lucas
last_name: Stampe
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Grimme B, Pohl J, Winkelmann H, Stampe L, Grimme C. Lost in Transformation:
Rediscovering LLM-Generated Campaigns in Social Media. In: Disinformation in
Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings. Springer-Verlag;
2023:72–87. doi:10.1007/978-3-031-47896-3_6'
apa: 'Grimme, B., Pohl, J., Winkelmann, H., Stampe, L., & Grimme, C. (2023).
Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media.
Disinformation in Open Online Media: 5th Multidisciplinary International Symposium,
MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings,
72–87. https://doi.org/10.1007/978-3-031-47896-3_6'
bibtex: '@inproceedings{Grimme_Pohl_Winkelmann_Stampe_Grimme_2023, place={Berlin,
Heidelberg}, title={Lost in Transformation: Rediscovering LLM-Generated Campaigns
in Social Media}, DOI={10.1007/978-3-031-47896-3_6},
booktitle={Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings},
publisher={Springer-Verlag}, author={Grimme, Britta and Pohl, Janina and Winkelmann,
Hendrik and Stampe, Lucas and Grimme, Christian}, year={2023}, pages={72–87} }'
chicago: 'Grimme, Britta, Janina Pohl, Hendrik Winkelmann, Lucas Stampe, and Christian
Grimme. “Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social
Media.” In Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings,
72–87. Berlin, Heidelberg: Springer-Verlag, 2023. https://doi.org/10.1007/978-3-031-47896-3_6.'
ieee: 'B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, and C. Grimme, “Lost in Transformation:
Rediscovering LLM-Generated Campaigns in Social Media,” in Disinformation in
Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings, 2023, pp. 72–87,
doi: 10.1007/978-3-031-47896-3_6.'
mla: 'Grimme, Britta, et al. “Lost in Transformation: Rediscovering LLM-Generated
Campaigns in Social Media.” Disinformation in Open Online Media: 5th Multidisciplinary
International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22,
2023, Proceedings, Springer-Verlag, 2023, pp. 72–87, doi:10.1007/978-3-031-47896-3_6.'
short: 'B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, C. Grimme, in: Disinformation
in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings, Springer-Verlag,
Berlin, Heidelberg, 2023, pp. 72–87.'
date_created: 2024-03-25T14:38:01Z
date_updated: 2026-03-19T07:48:51Z
doi: 10.1007/978-3-031-47896-3_6
keyword:
- Social Media
- Campaign Detection
- Large Language Models
- Siamese Neural Networks
page: 72–87
place: Berlin, Heidelberg
publication: 'Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings'
publication_identifier:
isbn:
- 978-3-031-47895-6
publisher: Springer-Verlag
status: public
title: 'Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media'
type: conference
user_id: '103682'
year: '2023'
...
---
_id: '33957'
abstract:
- lang: eng
text: Manufacturing companies are challenged to make the increasingly complex work
processes equally manageable for all employees to prevent an impending loss of
competence. In this contribution, an intelligent assistance system is proposed
enabling employees to help themselves in the workplace and provide them with competence-related
support. This results in increasing the short- and long-term efficiency of problem
solving in companies.
author:
- first_name: Sahar
full_name: Deppe, Sahar
last_name: Deppe
- first_name: Lukas
full_name: Brandt, Lukas
last_name: Brandt
- first_name: Marc
full_name: Brünninghaus, Marc
last_name: Brünninghaus
- first_name: Jörg
full_name: Papenkordt, Jörg
id: '44648'
last_name: Papenkordt
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Gudrun
full_name: Tschirner-Vinke, Gudrun
last_name: Tschirner-Vinke
citation:
ama: Deppe S, Brandt L, Brünninghaus M, Papenkordt J, Heindorf S, Tschirner-Vinke
G. AI-Based Assistance System for Manufacturing. Published online 2022. doi:10.1109/ETFA52439.2022.9921520
apa: Deppe, S., Brandt, L., Brünninghaus, M., Papenkordt, J., Heindorf, S., &
Tschirner-Vinke, G. (2022). AI-Based Assistance System for Manufacturing.
ETFA, Stuttgart. https://doi.org/10.1109/ETFA52439.2022.9921520
bibtex: '@article{Deppe_Brandt_Brünninghaus_Papenkordt_Heindorf_Tschirner-Vinke_2022,
series={2022 IEEE 27th International Conference on Emerging Technologies and Factory
Automation (ETFA)}, title={AI-Based Assistance System for Manufacturing}, DOI={10.1109/ETFA52439.2022.9921520},
author={Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt,
Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}, year={2022}, collection={2022
IEEE 27th International Conference on Emerging Technologies and Factory Automation
(ETFA)} }'
chicago: Deppe, Sahar, Lukas Brandt, Marc Brünninghaus, Jörg Papenkordt, Stefan
Heindorf, and Gudrun Tschirner-Vinke. “AI-Based Assistance System for Manufacturing.”
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation
(ETFA), 2022. https://doi.org/10.1109/ETFA52439.2022.9921520.
ieee: 'S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, and G.
Tschirner-Vinke, “AI-Based Assistance System for Manufacturing.” 2022, doi: 10.1109/ETFA52439.2022.9921520.'
mla: Deppe, Sahar, et al. AI-Based Assistance System for Manufacturing. 2022,
doi:10.1109/ETFA52439.2022.9921520.
short: S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, G. Tschirner-Vinke,
(2022).
conference:
end_date: 2022-09-09
location: Stuttgart
name: ETFA
start_date: 2022-09-06
date_created: 2022-10-28T11:43:49Z
date_updated: 2023-11-23T08:07:51Z
department:
- _id: '178'
- _id: '574'
- _id: '184'
doi: 10.1109/ETFA52439.2022.9921520
keyword:
- Assistance system
- Knowledge graph
- Information retrieval
- Neural networks
- AR
language:
- iso: eng
project:
- _id: '409'
grant_number: 02L19C115
name: 'KIAM: KIAM: Kompetenzzentrum KI in der Arbeitswelt des industriellen Mittelstands
in OstWestfalenLippe'
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9921520
series_title: 2022 IEEE 27th International Conference on Emerging Technologies and
Factory Automation (ETFA)
status: public
title: AI-Based Assistance System for Manufacturing
type: conference
user_id: '44648'
year: '2022'
...
---
_id: '26539'
abstract:
- lang: eng
text: In control design most control strategies are model-based and require accurate
models to be applied successfully. Due to simplifications and the model-reality-gap
physics-derived models frequently exhibit deviations from real-world-systems.
Likewise, purely data-driven methods often do not generalise well enough and may
violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired
loss functions separately have shown promising results to conquer these drawbacks.
In this contribution we extend existing methods towards the identification of
non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN
and a physics-inspired loss term (-L) to successfully identify the system's dynamics,
while maintaining the consistency with physical laws. The proposed method is demonstrated
on two real-world nonlinear systems and outperforms existing techniques regarding
complexity and reliability.
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Composed Physics- and Data-driven System Identification
for Non-autonomous Systems in Control Engineering. In: 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC). ; 2022:67-76.
doi:10.1109/AIRC56195.2022.9836982'
apa: Götte, R.-S., & Timmermann, J. (2022). Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering. 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76. https://doi.org/10.1109/AIRC56195.2022.9836982
bibtex: '@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering}, DOI={10.1109/AIRC56195.2022.9836982},
booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022},
pages={67–76} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” In 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76, 2022. https://doi.org/10.1109/AIRC56195.2022.9836982.
ieee: 'R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System
Identification for Non-autonomous Systems in Control Engineering,” in 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), Cairo, Egypt, 2022, pp. 67–76, doi: 10.1109/AIRC56195.2022.9836982.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 2022, pp. 67–76, doi:10.1109/AIRC56195.2022.9836982.
short: 'R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.'
conference:
end_date: 2021-12-10
location: Cairo, Egypt
name: 3rd International Conference on Artificial Intelligence, Robotics and Control
start_date: 2021-12-08
date_created: 2021-10-19T14:47:17Z
date_updated: 2024-11-13T08:43:28Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836982
keyword:
- data-driven
- physics-based
- physics-informed
- neural networks
- system identification
- hybrid modelling
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.08148
oa: '1'
page: 67-76
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)
quality_controlled: '1'
status: public
title: Composed Physics- and Data-driven System Identification for Non-autonomous
Systems in Control Engineering
type: conference
user_id: '43992'
year: '2022'
...
---
_id: '31066'
abstract:
- lang: eng
text: 'While trade-offs between modeling effort and model accuracy remain a major
concern with system identification, resorting to data-driven methods often leads
to a complete disregard for physical plausibility. To address this issue, we propose
a physics-guided hybrid approach for modeling non-autonomous systems under control.
Starting from a traditional physics-based model, this is extended by a recurrent
neural network and trained using a sophisticated multi-objective strategy yielding
physically plausible models. While purely data-driven methods fail to produce
satisfying results, experiments conducted on real data reveal substantial accuracy
improvements by our approach compared to a physics-based model. '
author:
- first_name: Oliver
full_name: Schön, Oliver
last_name: Schön
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent
Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55.
; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282'
apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12),
19–24. https://doi.org/10.1016/j.ifacol.2022.07.282
bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55},
DOI={https://doi.org/10.1016/j.ifacol.2022.07.282},
number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems
(ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2022}, pages={19–24} }'
chicago: Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective
Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical
Systems.” In 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS
2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282.
ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in
14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022),
Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.'
mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks
for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24,
doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.'
conference:
end_date: 2022-07-01
location: Casablanca, Morocco
name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
start_date: 2022-06-29
date_created: 2022-05-05T06:22:55Z
date_updated: 2024-11-13T08:43:16Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2022.07.282
intvolume: ' 55'
issue: '12'
keyword:
- neural networks
- physics-guided
- data-driven
- multi-objective optimization
- system identification
- machine learning
- dynamical systems
language:
- iso: eng
page: 19-24
publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
quality_controlled: '1'
status: public
title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous
Dynamical Systems
type: conference
user_id: '43992'
volume: 55
year: '2022'
...
---
_id: '22480'
abstract:
- lang: eng
text: In this publication important aspects for the implementation of inductive
locating are explained. The miniaturized sensor platform called Sens-o-Spheres
is used as an application of this locating method. The sensor platform is applied
in bioreactors in order to obtain the environmental parameters, which makes a
localization by magnetic fields necessary. Since the properties of magnetic fields
in the localization area are very different from the wave characteristics, the
principle of inductive localization is investigated in this publication and explained
by using electrical equivalent circuit diagrams. Thereby, inductive localization
uses the coupling or the mutual inductivities between coils, which is noticeable
by an induced voltage. Therefore some properties and procedures are explained
to extract the location of Sens-o-Spheres or other industrial sensor platforms
from the couplings of the coils. One method calculates the location from an adapted
ratio calculation and the other method uses neural networks and stochastic filters
to obtain the results. In the end, these results are evaluated and compared.
author:
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Dominik
full_name: Schröder, Dominik
last_name: Schröder
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
citation:
ama: 'Lange S, Schröder D, Hedayat C, Kuhn H, Hilleringmann U. Development of Methods
for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms
in Bioprocesses. In: 22nd IEEE International Conference on Industrial Technology
(ICIT). Valencia, Spain : IEEE; 2021. doi:10.1109/icit46573.2021.9453609'
apa: 'Lange, S., Schröder, D., Hedayat, C., Kuhn, H., & Hilleringmann, U. (2021).
Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized
Sensor Platforms in Bioprocesses. In 22nd IEEE International Conference on
Industrial Technology (ICIT). Valencia, Spain : IEEE. https://doi.org/10.1109/icit46573.2021.9453609'
bibtex: '@inproceedings{Lange_Schröder_Hedayat_Kuhn_Hilleringmann_2021, place={
Valencia, Spain }, title={Development of Methods for Coil-Based Localization by
Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}, DOI={10.1109/icit46573.2021.9453609},
booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)},
publisher={IEEE}, author={Lange, Sven and Schröder, Dominik and Hedayat, Christian
and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021} }'
chicago: 'Lange, Sven, Dominik Schröder, Christian Hedayat, Harald Kuhn, and Ulrich
Hilleringmann. “Development of Methods for Coil-Based Localization by Magnetic
Fields of Miniaturized Sensor Platforms in Bioprocesses.” In 22nd IEEE International
Conference on Industrial Technology (ICIT). Valencia, Spain : IEEE, 2021.
https://doi.org/10.1109/icit46573.2021.9453609.'
ieee: S. Lange, D. Schröder, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Development
of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor
Platforms in Bioprocesses,” in 22nd IEEE International Conference on Industrial
Technology (ICIT), Valencia, Spain , 2021.
mla: Lange, Sven, et al. “Development of Methods for Coil-Based Localization by
Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” 22nd IEEE
International Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453609.
short: 'S. Lange, D. Schröder, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 22nd IEEE
International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain
, 2021.'
conference:
end_date: 2021-03-12
location: 'Valencia, Spain '
name: 22nd IEEE International Conference on Industrial Technology (ICIT)
start_date: 2021-03-10
date_created: 2021-06-20T23:25:54Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/icit46573.2021.9453609
keyword:
- Location awareness
- Coils
- Couplings
- Nonuniform electric fields
- Magnetic separation
- Neural networks
- Training data
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9453609
place: ' Valencia, Spain '
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: 22nd IEEE International Conference on Industrial Technology (ICIT)
publication_identifier:
isbn:
- '9781728157306'
publication_status: published
publisher: IEEE
status: public
title: Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized
Sensor Platforms in Bioprocesses
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '22481'
abstract:
- lang: eng
text: During the industrial processing of materials for the manufacture of new products,
surface defects can quickly occur. In order to achieve high quality without a
long time delay, it makes sense to inspect the work pieces so that defective work
pieces can be sorted out right at the beginning of the process. At the same time,
the evaluation unit should come close the perception of the human eye regarding
detection of defects in surfaces. Such defects often manifest themselves by a
deviation of the existing structure. The only restriction should be that only
matt surfaces should be considered here. Therefore in this work, different classification
and image processing algorithms are applied to surface data to identify possible
surface damages. For this purpose, the Gabor filter and the FST (Fused Structure
and Texture) features generated with it, as well as the salience metric are used
on the image processing side. On the classification side, however, deep neural
networks, Convolutional Neural Networks (CNN), and autoencoders are used to make
a decision. A distinction is also made between training using class labels and
without. It turns out later that the salience metric are best performed by CNN.
On the other hand, if there is no labeled training data available, a novelty classification
can easily be achieved by using autoencoders as well as the salience metric and
some filters.
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: Geneis, Volker
last_name: Geneis
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Franz-Barthold
full_name: Gockel, Franz-Barthold
last_name: Gockel
citation:
ama: 'Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction. In: 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE; 2021. doi:10.1109/icit46573.2021.9453646'
apa: 'Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H.,
& Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction. In 22nd IEEE International
Conference on Industrial Technology (ICIT). Valencia, Spain : IEEE. https://doi.org/10.1109/icit46573.2021.9453646'
bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021,
place={Valencia, Spain }, title={Detection of Defects on Irregular Structured
Surfaces by Image Processing Methods for Feature Extraction}, DOI={10.1109/icit46573.2021.9453646},
booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)},
publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich
and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold},
year={2021} }'
chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian
Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction.” In 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE, 2021. https://doi.org/10.1109/icit46573.2021.9453646.'
ieee: T. Sander et al., “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction,” in 22nd IEEE International
Conference on Industrial Technology (ICIT), Valencia, Spain , 2021.
mla: Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction.” 22nd IEEE International
Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453646.
short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B.
Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT),
IEEE, Valencia, Spain , 2021.'
conference:
end_date: 2021-03-12
location: 'Valencia, Spain '
name: 22nd IEEE International Conference on Industrial Technology (ICIT)
start_date: 2021-03-10
date_created: 2021-06-20T23:32:11Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/icit46573.2021.9453646
keyword:
- Image Processing
- Defect Detection
- wooden surfaces
- Machine Learning
- Neural Networks
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9453646
place: 'Valencia, Spain '
publication: 22nd IEEE International Conference on Industrial Technology (ICIT)
publication_identifier:
isbn:
- '9781728157306'
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregular Structured Surfaces by Image Processing Methods
for Feature Extraction
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '27381'
abstract:
- lang: eng
text: Graph neural networks (GNNs) have been successfully applied in many structured
data domains, with applications ranging from molecular property prediction to
the analysis of social networks. Motivated by the broad applicability of GNNs,
we propose the family of so-called RankGNNs, a combination of neural Learning
to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
between graphs, suggesting that one of them is preferred over the other. One practical
application of this problem is drug screening, where an expert wants to find the
most promising molecules in a large collection of drug candidates. We empirically
demonstrate that our proposed pair-wise RankGNN approach either significantly
outperforms or at least matches the ranking performance of the naive point-wise
baseline approach, in which the LtR problem is solved via GNN-based graph regression.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference
on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science.
Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5'
apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph
Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th
International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180).
Springer. https://doi.org/10.1007/978-3-030-88942-5
bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
DOI={10.1007/978-3-030-88942-5},
booktitle={Proceedings of The 24th International Conference on Discovery Science
(DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
Notes in Computer Science} }'
chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
Graph Neural Networks.” In Proceedings of The 24th International Conference
on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80.
Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.
ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
Networks,” in Proceedings of The 24th International Conference on Discovery
Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.'
mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
Neural Networks.” Proceedings of The 24th International Conference on Discovery
Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.
short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
pp. 166–180.'
conference:
end_date: 2021-10-13
location: Halifax, Canada
name: 24th International Conference on Discovery Science
start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
full_name: Soares, Carlos
last_name: Soares
- first_name: Luis
full_name: Torgo, Luis
last_name: Torgo
external_id:
arxiv:
- '2104.08869'
intvolume: ' 12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
(DS 2021)
publication_identifier:
isbn:
- '9783030889418'
- '9783030889425'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '19953'
abstract:
- lang: eng
text: Current GNN architectures use a vertex neighborhood aggregation scheme, which
limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman
(WL) graph isomorphism test. Here, we propose a novel graph convolution operator
that is based on the 2-dimensional WL test. We formally show that the resulting
2-WL-GNN architecture is more discriminative than existing GNN approaches. This
theoretical result is complemented by experimental studies using synthetic and
real data. On multiple common graph classification benchmarks, we demonstrate
that the proposed model is competitive with state-of-the-art graph kernels and
GNNs.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman
Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th
Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of
Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.'
apa: 'Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order
Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
(Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.'
bibtex: '@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand},
series={Proceedings of Machine Learning Research}, title={A Novel Higher-order
Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of
the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR},
author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin
Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings
of Machine Learning Research} }'
chicago: 'Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order
Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference
on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama,
129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR,
2020.'
ieee: C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman
Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine
Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.
mla: Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.”
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.
short: 'C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR,
Bangkok, Thailand, 2020, pp. 49–64.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-10-08T10:48:38Z
date_updated: 2022-01-06T06:54:17Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Sinno
full_name: Jialin Pan, Sinno
last_name: Jialin Pan
- first_name: Masashi
full_name: Sugiyama, Masashi
last_name: Sugiyama
external_id:
arxiv:
- '2007.00346'
file:
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:48Z
date_updated: 2020-10-08T11:21:00Z
file_id: '19954'
file_name: damke20.pdf
file_size: 771137
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:59Z
date_updated: 2020-10-08T11:24:29Z
file_id: '19955'
file_name: damke20-supp.pdf
file_size: 613163
relation: supplementary_material
file_date_updated: 2020-10-08T11:24:29Z
has_accepted_license: '1'
intvolume: ' 129'
keyword:
- graph neural networks
- Weisfeiler-Lehman test
- cycle detection
language:
- iso: eng
oa: '1'
page: 49-64
place: Bangkok, Thailand
publication: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
publication_status: published
publisher: PMLR
quality_controlled: '1'
series_title: Proceedings of Machine Learning Research
status: public
title: A Novel Higher-order Weisfeiler-Lehman Graph Convolution
type: conference
user_id: '48192'
volume: 129
year: '2020'
...
---
_id: '15488'
abstract:
- lang: eng
text: The continuous refinement of sensor technologies enables the manufacturing
industry to capture increasing amounts of data during the production process.
As processes take time to complete, sensors register large amounts of time-series-like
data for each product. In order to make this data usable, a feature extraction
is mandatory. In this work, we discuss and evaluate different network architectures,
input pre-processing and cost functions regarding, among other aspects, their
suitability for time series of different lengths.
author:
- first_name: Christian
full_name: Thiel, Christian
last_name: Thiel
- first_name: Carolin
full_name: Steidl, Carolin
last_name: Steidl
- first_name: Bernd
full_name: Henning, Bernd
id: '213'
last_name: Henning
citation:
ama: 'Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques
for varying-length time series from an industrial piercing press. In: AMA Service
GmbH, ed. 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019. Von-Münchhausen-Str.
49, 31515 Wunstorf; 2019. doi:10.5162/SENSOREN2019/P2.9'
apa: Thiel, C., Steidl, C., & Henning, B. (2019). P2.9 Comparison of deep feature
extraction techniques for varying-length time series from an industrial piercing
press. In AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme
2019. Von-Münchhausen-Str. 49, 31515 Wunstorf. https://doi.org/10.5162/SENSOREN2019/P2.9
bibtex: '@inproceedings{Thiel_Steidl_Henning_2019, place={Von-Münchhausen-Str. 49,
31515 Wunstorf}, title={P2.9 Comparison of deep feature extraction techniques
for varying-length time series from an industrial piercing press}, DOI={10.5162/SENSOREN2019/P2.9},
booktitle={20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}, author={Thiel,
Christian and Steidl, Carolin and Henning, Bernd}, editor={AMA Service GmbHEditor},
year={2019} }'
chicago: Thiel, Christian, Carolin Steidl, and Bernd Henning. “P2.9 Comparison of
Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial
Piercing Press.” In 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019,
edited by AMA Service GmbH. Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019. https://doi.org/10.5162/SENSOREN2019/P2.9.
ieee: C. Thiel, C. Steidl, and B. Henning, “P2.9 Comparison of deep feature extraction
techniques for varying-length time series from an industrial piercing press,”
in 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019, 2019.
mla: Thiel, Christian, et al. “P2.9 Comparison of Deep Feature Extraction Techniques
for Varying-Length Time Series from an Industrial Piercing Press.” 20. GMA/ITG-Fachtagung.
Sensoren Und Messsysteme 2019, edited by AMA Service GmbH, 2019, doi:10.5162/SENSOREN2019/P2.9.
short: 'C. Thiel, C. Steidl, B. Henning, in: AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung.
Sensoren Und Messsysteme 2019, Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019.'
corporate_editor:
- AMA Service GmbH
date_created: 2020-01-10T16:03:58Z
date_updated: 2022-01-06T06:52:27Z
department:
- _id: '49'
doi: 10.5162/SENSOREN2019/P2.9
keyword:
- Dynamic Time Warping
- Feature Extraction
- Masking
- Neural Networks
language:
- iso: eng
place: Von-Münchhausen-Str. 49, 31515 Wunstorf
publication: 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019
publication_identifier:
isbn:
- 978-3-9819376-0-2
status: public
title: P2.9 Comparison of deep feature extraction techniques for varying-length time
series from an industrial piercing press
type: conference
user_id: '11829'
year: '2019'
...