---
_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: '17244'
abstract:
- lang: eng
text: 'Robots interacting with humans need to understand actions and make use of
language in social interactions. Research on infant development has shown that
language helps the learner to structure visual observations of action. This acoustic
information typically in the form of narration overlaps with action sequences
and provides infants with a bottom-up guide to find structure within them. This
concept has been introduced as acoustic packaging by Hirsh-Pasek and Golinkoff.
We developed and integrated a prominence detection module in our acoustic packaging
system to detect semantically relevant information linguistically highlighted
by the tutor. Evaluation results on speech data from adult-infant interactions
show a significant agreement with human raters. Furthermore a first approach based
on acoustic packages which uses the prominence detection results to generate acoustic
feedback is presented. Index Terms: prominence, multimodal action segmentation,
human robot interaction, feedback'
author:
- first_name: Lars
full_name: Schillingmann, Lars
last_name: Schillingmann
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Christian
full_name: Munier, Christian
last_name: Munier
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
citation:
ama: 'Schillingmann L, Wagner P, Munier C, Wrede B, Rohlfing K. Using Prominence
Detection to Generate Acoustic Feedback in Tutoring Scenarios. In: Interspeech
2011 (12th Annual Conference of the International Speech Communication Association).
; 2011:3105-3108.'
apa: Schillingmann, L., Wagner, P., Munier, C., Wrede, B., & Rohlfing, K. (2011).
Using Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios.
Interspeech 2011 (12th Annual Conference of the International Speech Communication
Association), 3105–3108.
bibtex: '@inproceedings{Schillingmann_Wagner_Munier_Wrede_Rohlfing_2011, title={Using
Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios}, booktitle={Interspeech
2011 (12th Annual Conference of the International Speech Communication Association)},
author={Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede,
Britta and Rohlfing, Katharina}, year={2011}, pages={3105–3108} }'
chicago: Schillingmann, Lars, Petra Wagner, Christian Munier, Britta Wrede, and
Katharina Rohlfing. “Using Prominence Detection to Generate Acoustic Feedback
in Tutoring Scenarios.” In Interspeech 2011 (12th Annual Conference of the
International Speech Communication Association), 3105–8, 2011.
ieee: L. Schillingmann, P. Wagner, C. Munier, B. Wrede, and K. Rohlfing, “Using
Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios,” in
Interspeech 2011 (12th Annual Conference of the International Speech Communication
Association), 2011, pp. 3105–3108.
mla: Schillingmann, Lars, et al. “Using Prominence Detection to Generate Acoustic
Feedback in Tutoring Scenarios.” Interspeech 2011 (12th Annual Conference of
the International Speech Communication Association), 2011, pp. 3105–08.
short: 'L. Schillingmann, P. Wagner, C. Munier, B. Wrede, K. Rohlfing, in: Interspeech
2011 (12th Annual Conference of the International Speech Communication Association),
2011, pp. 3105–3108.'
date_created: 2020-06-24T13:02:10Z
date_updated: 2023-02-01T12:53:54Z
department:
- _id: '749'
keyword:
- Feedback
- Human Robot Interaction
- Prominence
- Multimodal Action Segmentation
language:
- iso: eng
page: 3105-3108
publication: Interspeech 2011 (12th Annual Conference of the International Speech
Communication Association)
status: public
title: Using Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios
type: conference
user_id: '14931'
year: '2011'
...
---
_id: '17245'
author:
- first_name: Lars
full_name: Schillingmann, Lars
last_name: Schillingmann
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Christian
full_name: Munier, Christian
last_name: Munier
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
citation:
ama: 'Schillingmann L, Wagner P, Munier C, Wrede B, Rohlfing K. Acoustic Packaging
and the Learning of Words. In: ; 2011. doi:10.3389/conf.fncom.2011.52.00020'
apa: Schillingmann, L., Wagner, P., Munier, C., Wrede, B., & Rohlfing, K. (2011).
Acoustic Packaging and the Learning of Words. https://doi.org/10.3389/conf.fncom.2011.52.00020
bibtex: '@inproceedings{Schillingmann_Wagner_Munier_Wrede_Rohlfing_2011, title={Acoustic
Packaging and the Learning of Words}, DOI={10.3389/conf.fncom.2011.52.00020},
author={Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede,
Britta and Rohlfing, Katharina}, year={2011} }'
chicago: Schillingmann, Lars, Petra Wagner, Christian Munier, Britta Wrede, and
Katharina Rohlfing. “Acoustic Packaging and the Learning of Words,” 2011. https://doi.org/10.3389/conf.fncom.2011.52.00020.
ieee: 'L. Schillingmann, P. Wagner, C. Munier, B. Wrede, and K. Rohlfing, “Acoustic
Packaging and the Learning of Words,” 2011, doi: 10.3389/conf.fncom.2011.52.00020.'
mla: Schillingmann, Lars, et al. Acoustic Packaging and the Learning of Words.
2011, doi:10.3389/conf.fncom.2011.52.00020.
short: 'L. Schillingmann, P. Wagner, C. Munier, B. Wrede, K. Rohlfing, in: 2011.'
date_created: 2020-06-24T13:02:11Z
date_updated: 2023-02-01T12:54:16Z
department:
- _id: '749'
doi: 10.3389/conf.fncom.2011.52.00020
keyword:
- Prominence
- Multimodal Action Segmentation
- Feedback
- Color Saliency
- Human Robot Interaction
language:
- iso: eng
publication_identifier:
issn:
- 1662-5188
status: public
title: Acoustic Packaging and the Learning of Words
type: conference
user_id: '14931'
year: '2011'
...
---
_id: '11892'
abstract:
- lang: eng
text: For an environment to be perceived as being smart, contextual information
has to be gathered to adapt the system's behavior and its interface towards the
user. Being a rich source of context information speech can be acquired unobtrusively
by microphone arrays and then processed to extract information about the user
and his environment. In this paper, a system for joint temporal segmentation,
speaker localization, and identification is presented, which is supported by face
identification from video data obtained from a steerable camera. Special attention
is paid to latency aspects and online processing capabilities, as they are important
for the application under investigation, namely ambient communication. It describes
the vision of terminal-less, session-less and multi-modal telecommunication with
remote partners, where the user can move freely within his home while the communication
follows him. The speaker diarization serves as a context source, which has been
integrated in a service-oriented middleware architecture and provided to the application
to select the most appropriate I/O device and to steer the camera towards the
speaker during ambient communication.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Schmalenstroeer J, Haeb-Umbach R. Online Diarization of Streaming Audio-Visual
Data for Smart Environments. IEEE Journal of Selected Topics in Signal Processing.
2010;4(5):845-856. doi:10.1109/JSTSP.2010.2050519
apa: Schmalenstroeer, J., & Haeb-Umbach, R. (2010). Online Diarization of Streaming
Audio-Visual Data for Smart Environments. IEEE Journal of Selected Topics in
Signal Processing, 4(5), 845–856. https://doi.org/10.1109/JSTSP.2010.2050519
bibtex: '@article{Schmalenstroeer_Haeb-Umbach_2010, title={Online Diarization of
Streaming Audio-Visual Data for Smart Environments}, volume={4}, DOI={10.1109/JSTSP.2010.2050519},
number={5}, journal={IEEE Journal of Selected Topics in Signal Processing}, author={Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2010}, pages={845–856} }'
chicago: 'Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Online Diarization
of Streaming Audio-Visual Data for Smart Environments.” IEEE Journal of Selected
Topics in Signal Processing 4, no. 5 (2010): 845–56. https://doi.org/10.1109/JSTSP.2010.2050519.'
ieee: 'J. Schmalenstroeer and R. Haeb-Umbach, “Online Diarization of Streaming Audio-Visual
Data for Smart Environments,” IEEE Journal of Selected Topics in Signal Processing,
vol. 4, no. 5, pp. 845–856, 2010, doi: 10.1109/JSTSP.2010.2050519.'
mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Online Diarization of Streaming
Audio-Visual Data for Smart Environments.” IEEE Journal of Selected Topics
in Signal Processing, vol. 4, no. 5, 2010, pp. 845–56, doi:10.1109/JSTSP.2010.2050519.
short: J. Schmalenstroeer, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal
Processing 4 (2010) 845–856.
date_created: 2019-07-12T05:30:16Z
date_updated: 2023-10-26T08:10:18Z
department:
- _id: '54'
doi: 10.1109/JSTSP.2010.2050519
intvolume: ' 4'
issue: '5'
keyword:
- audio streaming
- audio visual data streaming
- context information speech
- face identification
- face recognition
- image segmentation
- middleware
- multimodal telecommunication
- online diarization
- service oriented middleware architecture
- sessionless telecommunication
- software architecture
- speaker identification
- speaker localization
- speaker recognition
- steerable camera
- telecommunication computing
- temporal segmentation
- terminal-less telecommunication
- video streaming
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/ScHa10.pdf
oa: '1'
page: 845-856
publication: IEEE Journal of Selected Topics in Signal Processing
quality_controlled: '1'
status: public
title: Online Diarization of Streaming Audio-Visual Data for Smart Environments
type: journal_article
user_id: '460'
volume: 4
year: '2010'
...