---
_id: '55159'
abstract:
- lang: eng
text: "We introduce a method based on Gaussian process regression to identify discrete
variational principles from observed solutions of a field theory. The method is
based on the data-based identification of a discrete Lagrangian density. It is
a geometric machine learning technique in the sense that the variational structure
of the true field theory is reflected in the data-driven model by design. We provide
a rigorous convergence statement of the method. The proof circumvents challenges
posed by the ambiguity of discrete Lagrangian densities in the inverse problem
of variational calculus.\r\nMoreover, our method can be used to quantify model
uncertainty in the equations of motions and any linear observable of the discrete
field theory. This is illustrated on the example of the discrete wave equation
and Schrödinger equation.\r\nThe article constitutes an extension of our previous
article arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians
for variational dynamics from an ode setting to the setting of discrete pdes."
author:
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
citation:
ama: Offen C. Machine learning of discrete field theories with guaranteed convergence
and uncertainty quantification.
apa: Offen, C. (n.d.). Machine learning of discrete field theories with guaranteed
convergence and uncertainty quantification.
bibtex: '@article{Offen, title={Machine learning of discrete field theories with
guaranteed convergence and uncertainty quantification}, author={Offen, Christian}
}'
chicago: Offen, Christian. “Machine Learning of Discrete Field Theories with Guaranteed
Convergence and Uncertainty Quantification,” n.d.
ieee: C. Offen, “Machine learning of discrete field theories with guaranteed convergence
and uncertainty quantification.” .
mla: Offen, Christian. Machine Learning of Discrete Field Theories with Guaranteed
Convergence and Uncertainty Quantification.
short: C. Offen, (n.d.).
date_created: 2024-07-10T13:43:50Z
date_updated: 2024-08-12T13:43:32Z
ddc:
- '510'
department:
- _id: '636'
external_id:
arxiv:
- '2407.07642'
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2024-07-10T13:39:32Z
date_updated: 2024-07-10T13:39:32Z
description: |-
We introduce a method based on Gaussian process regression to identify discrete
variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design.
We provide a rigorous convergence statement of the method.
The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus.
Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory.
This is illustrated on the example of the discrete wave equation and Schrödinger equation.
The article constitutes an extension of our previous article for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes.
file_id: '55160'
file_name: L_Collocation.pdf
file_size: 4569314
relation: main_file
title: Machine learning of discrete field theories with guaranteed convergence and
uncertainty quantification
file_date_updated: 2024-07-10T13:39:32Z
has_accepted_license: '1'
keyword:
- System identification
- inverse problem of variational calculus
- Gaussian process
- Lagrangian learning
- physics informed machine learning
- geometry aware learning
language:
- iso: eng
oa: '1'
page: '28'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication_status: submitted
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/Christian-Offen/Lagrangian_GP_PDE
status: public
title: Machine learning of discrete field theories with guaranteed convergence and
uncertainty quantification
type: preprint
user_id: '85279'
year: '2024'
...
---
_id: '58491'
abstract:
- lang: eng
text: Similar to bulk metal forming, clinch joining is characterised by
large plastic deformations and a variety of different 3D stress states, including
severe compression. However, inherent to plastic forming is the nucleation and
growth of defects, whose detrimental effects on the material behaviour can be
described by continuum damage models and eventually lead to material failure.
As the damage evolution strongly depends on the stress state, a stress-state-dependent
model is utilised to correctly track the accumulation. To formulate and parameterise
this model, besides classical experiments, so-called modified punch tests are
also integrated herein to enhance the calibration of the failure model by capturing
a larger range of stress states and metal-forming-specific loading conditions.
Moreover, when highly ductile materials are considered, such as the dual-phase
steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong
necking and localisation might occur prior to fracture. This can alter the stress
state and affect the actual strain at failure. This influence is captured by coupling
plasticity and damage to incorporate the damage-induced softening effect. Its
relative importance is shown by conducting inverse parameter identifications to
determine damage and failure parameters for both mentioned ductile metals based
on up to 12 different experiments.
article_number: '157'
author:
- first_name: Johannes
full_name: Friedlein, Johannes
last_name: Friedlein
- first_name: Max
full_name: Böhnke, Max
last_name: Böhnke
- first_name: Malte
full_name: Schlichter, Malte
last_name: Schlichter
- first_name: Mathias
full_name: Bobbert, Mathias
last_name: Bobbert
- first_name: Gerson
full_name: Meschut, Gerson
last_name: Meschut
- first_name: Julia
full_name: Mergheim, Julia
last_name: Mergheim
- first_name: Paul
full_name: Steinmann, Paul
last_name: Steinmann
citation:
ama: Friedlein J, Böhnke M, Schlichter M, et al. Material Parameter Identification
for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch
Joining. Journal of Manufacturing and Materials Processing. 2024;8(4).
doi:10.3390/jmmp8040157
apa: Friedlein, J., Böhnke, M., Schlichter, M., Bobbert, M., Meschut, G., Mergheim,
J., & Steinmann, P. (2024). Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining. Journal of Manufacturing
and Materials Processing, 8(4), Article 157. https://doi.org/10.3390/jmmp8040157
bibtex: '@article{Friedlein_Böhnke_Schlichter_Bobbert_Meschut_Mergheim_Steinmann_2024,
title={Material Parameter Identification for a Stress-State-Dependent Ductile
Damage and Failure Model Applied to Clinch Joining}, volume={8}, DOI={10.3390/jmmp8040157},
number={4157}, journal={Journal of Manufacturing and Materials Processing}, publisher={MDPI
AG}, author={Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert,
Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}, year={2024}
}'
chicago: Friedlein, Johannes, Max Böhnke, Malte Schlichter, Mathias Bobbert, Gerson
Meschut, Julia Mergheim, and Paul Steinmann. “Material Parameter Identification
for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch
Joining.” Journal of Manufacturing and Materials Processing 8, no. 4 (2024).
https://doi.org/10.3390/jmmp8040157.
ieee: 'J. Friedlein et al., “Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining,” Journal of Manufacturing
and Materials Processing, vol. 8, no. 4, Art. no. 157, 2024, doi: 10.3390/jmmp8040157.'
mla: Friedlein, Johannes, et al. “Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining.” Journal of Manufacturing
and Materials Processing, vol. 8, no. 4, 157, MDPI AG, 2024, doi:10.3390/jmmp8040157.
short: J. Friedlein, M. Böhnke, M. Schlichter, M. Bobbert, G. Meschut, J. Mergheim,
P. Steinmann, Journal of Manufacturing and Materials Processing 8 (2024).
date_created: 2025-01-31T16:59:13Z
date_updated: 2025-01-31T17:03:34Z
doi: 10.3390/jmmp8040157
intvolume: ' 8'
issue: '4'
keyword:
- ductile damage
- stress-state dependency
- failure
- parameter identification
- punch test
- clinching
language:
- iso: eng
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '131'
name: 'TRR 285 - A: TRR 285 - Project Area A'
- _id: '139'
name: 'TRR 285 – A05: TRR 285 - Subproject A05'
publication: Journal of Manufacturing and Materials Processing
publication_identifier:
issn:
- 2504-4494
publication_status: published
publisher: MDPI AG
status: public
title: Material Parameter Identification for a Stress-State-Dependent Ductile Damage
and Failure Model Applied to Clinch Joining
type: journal_article
user_id: '84990'
volume: 8
year: '2024'
...
---
_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: '42163'
abstract:
- lang: eng
text: 'The article shows how to learn models of dynamical systems from data which
are governed by an unknown variational PDE. Rather than employing reduction techniques,
we learn a discrete field theory governed by a discrete Lagrangian density $L_d$
that is modelled as a neural network. Careful regularisation of the loss function
for training $L_d$ is necessary to obtain a field theory that is suitable for
numerical computations: we derive a regularisation term which optimises the solvability
of the discrete Euler--Lagrange equations. Secondly, we develop a method to find
solutions to machine learned discrete field theories which constitute travelling
waves of the underlying continuous PDE.'
author:
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
citation:
ama: 'Offen C, Ober-Blöbaum S. Learning discrete Lagrangians for variational PDEs
from data and detection of travelling waves. In: Nielsen F, Barbaresco F, eds.
Geometric Science of Information. Vol 14071. Lecture Notes in Computer
Science (LNCS). Springer, Cham.; 2023:569-579. doi:10.1007/978-3-031-38271-0_57'
apa: Offen, C., & Ober-Blöbaum, S. (2023). Learning discrete Lagrangians for
variational PDEs from data and detection of travelling waves. In F. Nielsen &
F. Barbaresco (Eds.), Geometric Science of Information (Vol. 14071, pp.
569–579). Springer, Cham. https://doi.org/10.1007/978-3-031-38271-0_57
bibtex: '@inproceedings{Offen_Ober-Blöbaum_2023, series={Lecture Notes in Computer
Science (LNCS)}, title={Learning discrete Lagrangians for variational PDEs from
data and detection of travelling waves}, volume={14071}, DOI={10.1007/978-3-031-38271-0_57},
booktitle={Geometric Science of Information}, publisher={Springer, Cham.}, author={Offen,
Christian and Ober-Blöbaum, Sina}, editor={Nielsen, F and Barbaresco, F}, year={2023},
pages={569–579}, collection={Lecture Notes in Computer Science (LNCS)} }'
chicago: Offen, Christian, and Sina Ober-Blöbaum. “Learning Discrete Lagrangians
for Variational PDEs from Data and Detection of Travelling Waves.” In Geometric
Science of Information, edited by F Nielsen and F Barbaresco, 14071:569–79.
Lecture Notes in Computer Science (LNCS). Springer, Cham., 2023. https://doi.org/10.1007/978-3-031-38271-0_57.
ieee: 'C. Offen and S. Ober-Blöbaum, “Learning discrete Lagrangians for variational
PDEs from data and detection of travelling waves,” in Geometric Science of
Information, Saint-Malo, Palais du Grand Large, France, 2023, vol. 14071,
pp. 569–579, doi: 10.1007/978-3-031-38271-0_57.'
mla: Offen, Christian, and Sina Ober-Blöbaum. “Learning Discrete Lagrangians for
Variational PDEs from Data and Detection of Travelling Waves.” Geometric Science
of Information, edited by F Nielsen and F Barbaresco, vol. 14071, Springer,
Cham., 2023, pp. 569–79, doi:10.1007/978-3-031-38271-0_57.
short: 'C. Offen, S. Ober-Blöbaum, in: F. Nielsen, F. Barbaresco (Eds.), Geometric
Science of Information, Springer, Cham., 2023, pp. 569–579.'
conference:
end_date: 2023-09-01
location: Saint-Malo, Palais du Grand Large, France
name: ' GSI''23 6th International Conference on Geometric Science of Information'
start_date: 2023-08-30
date_created: 2023-02-16T11:32:48Z
date_updated: 2024-08-12T13:46:29Z
ddc:
- '510'
department:
- _id: '636'
doi: 10.1007/978-3-031-38271-0_57
editor:
- first_name: F
full_name: Nielsen, F
last_name: Nielsen
- first_name: F
full_name: Barbaresco, F
last_name: Barbaresco
external_id:
arxiv:
- '2302.08232 '
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2023-08-02T12:04:17Z
date_updated: 2023-08-02T12:04:17Z
description: |-
The article shows how to learn models of dynamical systems
from data which are governed by an unknown variational PDE. Rather
than employing reduction techniques, we learn a discrete field theory
governed by a discrete Lagrangian density Ld that is modelled as a neural network. Careful regularisation of the loss function for training Ld is
necessary to obtain a field theory that is suitable for numerical computations: we derive a regularisation term which optimises the solvability of
the discrete Euler–Lagrange equations. Secondly, we develop a method to
find solutions to machine learned discrete field theories which constitute
travelling waves of the underlying continuous PDE.
file_id: '46273'
file_name: LDensityLearning.pdf
file_size: 1938962
relation: main_file
title: Learning discrete Lagrangians for variational PDEs from data and detection
of travelling waves
file_date_updated: 2023-08-02T12:04:17Z
has_accepted_license: '1'
intvolume: ' 14071'
keyword:
- System identification
- discrete Lagrangians
- travelling waves
language:
- iso: eng
oa: '1'
page: 569-579
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Geometric Science of Information
publication_identifier:
eisbn:
- 978-3-031-38271-0
publication_status: published
publisher: Springer, Cham.
quality_controlled: '1'
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/Christian-Offen/LagrangianDensityML
series_title: Lecture Notes in Computer Science (LNCS)
status: public
title: Learning discrete Lagrangians for variational PDEs from data and detection
of travelling waves
type: conference
user_id: '85279'
volume: 14071
year: '2023'
...
---
_id: '34211'
abstract:
- lang: eng
text: "Nowadays, clinching is a widely used joining technique, where sheets are
joined by pure deformation to create an interlock without the need for auxiliary
parts. This leads to advantages such as reduced joining time and manufacturing\r\ncosts.
On the other hand, the joint strength solely relies on directed material deformation,
which renders an accurate material modelling essential to reliably predict the
joint forming. The formation of the joint locally involves large plastic strains
and possibly complex non-proportional loading paths, as typical of many metal
forming applications. Consequently, a finite plasticity formulation is utilised
incorporating a Chaboche–Rousselier kinematic hardening law to capture the Bauschinger
effect. Material parameters are identified from tension–compression tests on miniature
spec-\r\nimens for the dual-phase steel HCT590X. The resulting material model
is implemented in LS-Dyna to study the locally diverse loading paths and give
a quantitative statement on the importance of kinematic hardening for clinching.
It turns out that the Bauschinger effect mainly affects the springback of the
sheets and has a smaller effect on the joint forming itself."
author:
- first_name: Johannes
full_name: Friedlein, Johannes
last_name: Friedlein
- first_name: Julia
full_name: Mergheim, Julia
last_name: Mergheim
- first_name: Paul
full_name: Steinmann, Paul
last_name: Steinmann
citation:
ama: 'Friedlein J, Mergheim J, Steinmann P. Influence of Kinematic Hardening on
Clinch Joining of Dual-Phase Steel HCT590X Sheet Metal. In: The Minerals, Metals
& Materials Series. Springer International Publishing; 2022. doi:10.1007/978-3-031-06212-4_31'
apa: Friedlein, J., Mergheim, J., & Steinmann, P. (2022). Influence of Kinematic
Hardening on Clinch Joining of Dual-Phase Steel HCT590X Sheet Metal. In The
Minerals, Metals & Materials Series. Springer International Publishing.
https://doi.org/10.1007/978-3-031-06212-4_31
bibtex: '@inbook{Friedlein_Mergheim_Steinmann_2022, place={Cham}, title={Influence
of Kinematic Hardening on Clinch Joining of Dual-Phase Steel HCT590X Sheet Metal},
DOI={10.1007/978-3-031-06212-4_31},
booktitle={The Minerals, Metals & Materials Series}, publisher={Springer
International Publishing}, author={Friedlein, Johannes and Mergheim, Julia and
Steinmann, Paul}, year={2022} }'
chicago: 'Friedlein, Johannes, Julia Mergheim, and Paul Steinmann. “Influence of
Kinematic Hardening on Clinch Joining of Dual-Phase Steel HCT590X Sheet Metal.”
In The Minerals, Metals & Materials Series. Cham: Springer International
Publishing, 2022. https://doi.org/10.1007/978-3-031-06212-4_31.'
ieee: 'J. Friedlein, J. Mergheim, and P. Steinmann, “Influence of Kinematic Hardening
on Clinch Joining of Dual-Phase Steel HCT590X Sheet Metal,” in The Minerals,
Metals & Materials Series, Cham: Springer International Publishing,
2022.'
mla: Friedlein, Johannes, et al. “Influence of Kinematic Hardening on Clinch Joining
of Dual-Phase Steel HCT590X Sheet Metal.” The Minerals, Metals & Materials
Series, Springer International Publishing, 2022, doi:10.1007/978-3-031-06212-4_31.
short: 'J. Friedlein, J. Mergheim, P. Steinmann, in: The Minerals, Metals &
Materials Series, Springer International Publishing, Cham, 2022.'
date_created: 2022-12-05T21:01:29Z
date_updated: 2022-12-05T21:05:52Z
doi: 10.1007/978-3-031-06212-4_31
keyword:
- Clinching
- Material modelling
- Kinematic hardening
- Parameter identification
- Bauschinger effect
language:
- iso: eng
place: Cham
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285'
- _id: '131'
name: 'TRR 285 - A: TRR 285 - Project Area A'
- _id: '139'
name: 'TRR 285 – A05: TRR 285 - Subproject A05'
publication: The Minerals, Metals & Materials Series
publication_identifier:
isbn:
- '9783031062117'
- '9783031062124'
issn:
- 2367-1181
- 2367-1696
publication_status: published
publisher: Springer International Publishing
status: public
title: Influence of Kinematic Hardening on Clinch Joining of Dual-Phase Steel HCT590X
Sheet Metal
type: book_chapter
user_id: '7850'
year: '2022'
...
---
_id: '36339'
abstract:
- lang: eng
text: Al-Li-based alloys are an attractive material for aircraft and aerospace applications.
Preparation of these alloys by twin-roll casting (TRC), which combines rapid metal
solidification and subsequent plastic reduction in a single processing step, could
improve the properties of the alloys compared to materials prepared by conventional
direct-chill casting. A commonly used approach for identifying primary phases
is a chemical analysis by energy dispersive spectroscopy (EDS). More accurate
results can be achieved by combining the method with diffraction analysis. This
process can be considerably simplified in microscopes equipped with automated
crystal orientation and phase mapping (ACOM-TEM). Al-Cu-Li-Mg-Zr alloy was prepared
by twin-roll casting. A combination of TEM and STEM images with chemical analysis
by EDS and ACOM-TEM was used to obtain complex information about phases of boundary
primary particles. The efficiency of the individual methods for the phase identification
in TRC Al-Li-based alloys is discussed.
author:
- first_name: Lucia
full_name: BAJTOŠOVÁ, Lucia
last_name: BAJTOŠOVÁ
- first_name: Olexandr
full_name: Grydin, Olexandr
id: '43822'
last_name: Grydin
- first_name: Mykhailo
full_name: STOLBCHENKO, Mykhailo
last_name: STOLBCHENKO
- first_name: Mirko
full_name: Schaper, Mirko
id: '43720'
last_name: Schaper
- first_name: Barbora
full_name: KŘIVSKÁ, Barbora
last_name: KŘIVSKÁ
- first_name: Rostislav
full_name: KRÁLÍK, Rostislav
last_name: KRÁLÍK
- first_name: Michaela
full_name: ŠLAPÁKOVÁ, Michaela
last_name: ŠLAPÁKOVÁ
- first_name: Miroslav
full_name: CIESLAR, Miroslav
last_name: CIESLAR
citation:
ama: 'BAJTOŠOVÁ L, Grydin O, STOLBCHENKO M, et al. Phase identification in twin-roll
cast Al-Li alloys. In: METAL 2022 Conference Proeedings. TANGER Ltd.; 2022.
doi:10.37904/metal.2022.4437'
apa: BAJTOŠOVÁ, L., Grydin, O., STOLBCHENKO, M., Schaper, M., KŘIVSKÁ, B., KRÁLÍK,
R., ŠLAPÁKOVÁ, M., & CIESLAR, M. (2022). Phase identification in twin-roll
cast Al-Li alloys. METAL 2022 Conference Proeedings. Metal 2022, Brno.
https://doi.org/10.37904/metal.2022.4437
bibtex: '@inproceedings{BAJTOŠOVÁ_Grydin_STOLBCHENKO_Schaper_KŘIVSKÁ_KRÁLÍK_ŠLAPÁKOVÁ_CIESLAR_2022,
title={Phase identification in twin-roll cast Al-Li alloys}, DOI={10.37904/metal.2022.4437},
booktitle={METAL 2022 Conference Proeedings}, publisher={TANGER Ltd.}, author={BAJTOŠOVÁ,
Lucia and Grydin, Olexandr and STOLBCHENKO, Mykhailo and Schaper, Mirko and KŘIVSKÁ,
Barbora and KRÁLÍK, Rostislav and ŠLAPÁKOVÁ, Michaela and CIESLAR, Miroslav},
year={2022} }'
chicago: BAJTOŠOVÁ, Lucia, Olexandr Grydin, Mykhailo STOLBCHENKO, Mirko Schaper,
Barbora KŘIVSKÁ, Rostislav KRÁLÍK, Michaela ŠLAPÁKOVÁ, and Miroslav CIESLAR. “Phase
Identification in Twin-Roll Cast Al-Li Alloys.” In METAL 2022 Conference Proeedings.
TANGER Ltd., 2022. https://doi.org/10.37904/metal.2022.4437.
ieee: 'L. BAJTOŠOVÁ et al., “Phase identification in twin-roll cast Al-Li
alloys,” presented at the Metal 2022, Brno, 2022, doi: 10.37904/metal.2022.4437.'
mla: BAJTOŠOVÁ, Lucia, et al. “Phase Identification in Twin-Roll Cast Al-Li Alloys.”
METAL 2022 Conference Proeedings, TANGER Ltd., 2022, doi:10.37904/metal.2022.4437.
short: 'L. BAJTOŠOVÁ, O. Grydin, M. STOLBCHENKO, M. Schaper, B. KŘIVSKÁ, R. KRÁLÍK,
M. ŠLAPÁKOVÁ, M. CIESLAR, in: METAL 2022 Conference Proeedings, TANGER Ltd., 2022.'
conference:
end_date: 2022-05-19
location: Brno
name: Metal 2022
start_date: 2022-05-18
date_created: 2023-01-12T09:42:02Z
date_updated: 2023-04-27T16:35:42Z
department:
- _id: '158'
- _id: '321'
doi: 10.37904/metal.2022.4437
keyword:
- Al-Cu-Li-M-Zr-Fe alloy
- twin-roll casting
- phase identification
- ACOM-TEM
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.confer.cz/metal/2022/4437-phase-identification-in-twin-roll-cast-al-li-alloys
oa: '1'
publication: METAL 2022 Conference Proeedings
publication_identifier:
issn:
- 2694-9296
publication_status: published
publisher: TANGER Ltd.
quality_controlled: '1'
status: public
title: Phase identification in twin-roll cast Al-Li alloys
type: conference
user_id: '43720'
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: '9992'
abstract:
- lang: eng
text: State-of-the-art industrial compact high power electronic packages require
copper-copper interconnections with larger cross sections made by ultrasonic bonding.
In comparison to aluminium-copper, copper-copper interconnections require increased
normal forces and ultrasonic power, which might lead to substrate damage due to
increased mechanical stresses. One option to raise friction energy without increasing
vibration amplitude between wire and substrate or bonding force is the use of
two-dimensional vibration. The first part of this contribution reports on the
development of a novel bonding system that executes two-dimensional vibrations
of a tool-tip to bond a nail- like pin onto a copper substrate. Since intermetallic
bonds only form properly when surfaces are clean, oxide free and activated, the
geometries of tool-tip and pin were optimised using finite element analysis. To
maximize the area of the bonded annulus the distribution of normal pressure was
optimized by varying the convexity of the bottom side of the pin. Second, a statistical
model obtained from an experimental parameter study shows the influence of different
bonding parameters on the bond result. To find bonding parameters with the minimum
number of tests, the experiments have been planned using a D-optimal experimental
design approach.
author:
- first_name: Collin
full_name: Dymel, Collin
id: '66833'
last_name: Dymel
- first_name: Paul
full_name: Eichwald, Paul
last_name: Eichwald
- first_name: Reinhard
full_name: Schemmel, Reinhard
id: '28647'
last_name: Schemmel
- first_name: Tobias
full_name: Hemsel, Tobias
id: '210'
last_name: Hemsel
- first_name: Michael
full_name: Brökelmann, Michael
last_name: Brökelmann
- first_name: Matthias
full_name: Hunstig, Matthias
last_name: Hunstig
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Dymel C, Eichwald P, Schemmel R, et al. Numerical and statistical investigation
of weld formation in a novel two-dimensional copper-copper bonding process. In:
(Proceedings of 7th Electronics System-Integration Technology Conference, Dresden,
Germany). ; 2018:1-6.'
apa: Dymel, C., Eichwald, P., Schemmel, R., Hemsel, T., Brökelmann, M., Hunstig,
M., & Sextro, W. (2018). Numerical and statistical investigation of weld formation
in a novel two-dimensional copper-copper bonding process. In (Proceedings of
7th Electronics System-Integration Technology Conference, Dresden, Germany)
(pp. 1–6).
bibtex: '@inproceedings{Dymel_Eichwald_Schemmel_Hemsel_Brökelmann_Hunstig_Sextro_2018,
title={Numerical and statistical investigation of weld formation in a novel two-dimensional
copper-copper bonding process}, booktitle={(Proceedings of 7th Electronics System-Integration
Technology Conference, Dresden, Germany)}, author={Dymel, Collin and Eichwald,
Paul and Schemmel, Reinhard and Hemsel, Tobias and Brökelmann, Michael and Hunstig,
Matthias and Sextro, Walter}, year={2018}, pages={1–6} }'
chicago: Dymel, Collin, Paul Eichwald, Reinhard Schemmel, Tobias Hemsel, Michael
Brökelmann, Matthias Hunstig, and Walter Sextro. “Numerical and Statistical Investigation
of Weld Formation in a Novel Two-Dimensional Copper-Copper Bonding Process.” In
(Proceedings of 7th Electronics System-Integration Technology Conference, Dresden,
Germany), 1–6, 2018.
ieee: C. Dymel et al., “Numerical and statistical investigation of weld formation
in a novel two-dimensional copper-copper bonding process,” in (Proceedings
of 7th Electronics System-Integration Technology Conference, Dresden, Germany),
2018, pp. 1–6.
mla: Dymel, Collin, et al. “Numerical and Statistical Investigation of Weld Formation
in a Novel Two-Dimensional Copper-Copper Bonding Process.” (Proceedings of
7th Electronics System-Integration Technology Conference, Dresden, Germany),
2018, pp. 1–6.
short: 'C. Dymel, P. Eichwald, R. Schemmel, T. Hemsel, M. Brökelmann, M. Hunstig,
W. Sextro, in: (Proceedings of 7th Electronics System-Integration Technology Conference,
Dresden, Germany), 2018, pp. 1–6.'
date_created: 2019-05-27T10:18:10Z
date_updated: 2020-05-07T05:33:56Z
department:
- _id: '151'
keyword:
- ultrasonic wire-bonding
- bond-tool design
- parameter identification
- statistical engineering
language:
- iso: eng
page: 1-6
project:
- _id: '93'
grant_number: MP-1-1-015
name: Hochleistungsbonden in energieeffizienten Leistungshalbleitermodulen
publication: (Proceedings of 7th Electronics System-Integration Technology Conference,
Dresden, Germany)
quality_controlled: '1'
status: public
title: Numerical and statistical investigation of weld formation in a novel two-dimensional
copper-copper bonding process
type: conference
user_id: '210'
year: '2018'
...
---
_id: '13222'
abstract:
- lang: eng
text: When performing measurements, the effects of the measurement system itself
on the measured data generally must be eliminated. Consequently, those effects,
i.e. the system’s dynamic behavior, need to be known. For the piezo-composite
transducers in an ultrasonic transmission line, a model based approach is used
to describe their dynamic behavior and take into account its dependence on the
environment temperature and the acoustic impedance of the target medium. Temperature-dependent
model parameters are presented, which are obtained by performing a multiplepart
identification process on the transducer model, based on electrical impedance
measurements [1]. The identification process uses an inverse approach for optimizing
a subset of the model parameters. Additionally, algorithmic differentiation methods
are used to determine accurate derivatives. In a final optimization step, impedance
measurements taken at different temperatures are used to determine the temperature
dependencies of the model parameters. These can then be used to assess the plausibility
of the identification results. Additionally, the parameters can be expressed as
polynomials in the temperature to take different operating conditions into account.
author:
- first_name: Manuel
full_name: Webersen, Manuel
id: '11289'
last_name: Webersen
orcid: 0000-0001-6411-4232
- first_name: Fabian
full_name: Bause, Fabian
last_name: Bause
- first_name: Jens
full_name: Rautenberg, Jens
last_name: Rautenberg
- first_name: Bernd
full_name: Henning, Bernd
id: '213'
last_name: Henning
citation:
ama: 'Webersen M, Bause F, Rautenberg J, Henning B. Identification of temperature-dependent
model parameters of ultrasonic piezo-composite transducers. In: AMA Service GmbH,
ed. AMA Conferences 2015. ; 2015:195-200.'
apa: Webersen, M., Bause, F., Rautenberg, J., & Henning, B. (2015). Identification
of temperature-dependent model parameters of ultrasonic piezo-composite transducers.
In AMA Service GmbH (Ed.), AMA Conferences 2015 (pp. 195–200). Nürnberg.
bibtex: '@inproceedings{Webersen_Bause_Rautenberg_Henning_2015, title={Identification
of temperature-dependent model parameters of ultrasonic piezo-composite transducers},
booktitle={AMA Conferences 2015}, author={Webersen, Manuel and Bause, Fabian and
Rautenberg, Jens and Henning, Bernd}, editor={AMA Service GmbHEditor}, year={2015},
pages={195–200} }'
chicago: Webersen, Manuel, Fabian Bause, Jens Rautenberg, and Bernd Henning. “Identification
of Temperature-Dependent Model Parameters of Ultrasonic Piezo-Composite Transducers.”
In AMA Conferences 2015, edited by AMA Service GmbH, 195–200, 2015.
ieee: M. Webersen, F. Bause, J. Rautenberg, and B. Henning, “Identification of temperature-dependent
model parameters of ultrasonic piezo-composite transducers,” in AMA Conferences
2015, Nürnberg, 2015, pp. 195–200.
mla: Webersen, Manuel, et al. “Identification of Temperature-Dependent Model Parameters
of Ultrasonic Piezo-Composite Transducers.” AMA Conferences 2015, edited
by AMA Service GmbH, 2015, pp. 195–200.
short: 'M. Webersen, F. Bause, J. Rautenberg, B. Henning, in: AMA Service GmbH (Ed.),
AMA Conferences 2015, 2015, pp. 195–200.'
conference:
end_date: 2015-05-21
location: Nürnberg
name: SENSOR 2015
start_date: 2015-05-19
corporate_editor:
- AMA Service GmbH
date_created: 2019-09-13T13:21:38Z
date_updated: 2022-01-06T06:51:31Z
department:
- _id: '49'
keyword:
- piezo-composite
- transducer
- temperature dependency
- identification
- plausibility
language:
- iso: eng
page: 195-200
publication: AMA Conferences 2015
status: public
title: Identification of temperature-dependent model parameters of ultrasonic piezo-composite
transducers
type: conference
user_id: '11289'
year: '2015'
...
---
_id: '9876'
abstract:
- lang: eng
text: Piezoelectric inertia motors use the inertia of a body to drive it by means
of a friction contact in a series of small steps. It has been shown previously
in theoretical investigations that higher velocities and smoother movements can
be obtained if these steps do not contain phases of stiction (''stick-slip`` operation),
but use sliding friction only (''slip-slip`` operation). One very promising driving
option for such motors is the superposition of multiple sinusoidal signals or
harmonics. In this contribution, the theoretical results are validated experimentally.
In this context, a quick and reliable identification process for parameters describing
the friction contact is proposed. Additionally, the force generation potential
of inertia motors is investigated theoretically and experimentally. The experimental
results confirm the theoretical result that for a given maximum frequency, a signal
with a high fundamental frequency and consisting of two superposed sine waves
leads to the highest velocity and the smoothest motion, while the maximum motor
force is obtained with signals containing more harmonics. These results are of
fundamental importance for the further development of high-velocity piezoelectric
inertia motors.
author:
- first_name: Matthias
full_name: Hunstig, Matthias
last_name: Hunstig
- first_name: Tobias
full_name: Hemsel, Tobias
id: '210'
last_name: Hemsel
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Hunstig M, Hemsel T, Sextro W. High-velocity operation of piezoelectric inertia
motors: experimental validation. Archive of Applied Mechanics. 2014:1-9.
doi:10.1007/s00419-014-0940-0'
apa: 'Hunstig, M., Hemsel, T., & Sextro, W. (2014). High-velocity operation
of piezoelectric inertia motors: experimental validation. Archive of Applied
Mechanics, 1–9. https://doi.org/10.1007/s00419-014-0940-0'
bibtex: '@article{Hunstig_Hemsel_Sextro_2014, title={High-velocity operation of
piezoelectric inertia motors: experimental validation}, DOI={10.1007/s00419-014-0940-0},
journal={Archive of Applied Mechanics}, publisher={Springer Berlin Heidelberg},
author={Hunstig, Matthias and Hemsel, Tobias and Sextro, Walter}, year={2014},
pages={1–9} }'
chicago: 'Hunstig, Matthias, Tobias Hemsel, and Walter Sextro. “High-Velocity Operation
of Piezoelectric Inertia Motors: Experimental Validation.” Archive of Applied
Mechanics, 2014, 1–9. https://doi.org/10.1007/s00419-014-0940-0.'
ieee: 'M. Hunstig, T. Hemsel, and W. Sextro, “High-velocity operation of piezoelectric
inertia motors: experimental validation,” Archive of Applied Mechanics,
pp. 1–9, 2014.'
mla: 'Hunstig, Matthias, et al. “High-Velocity Operation of Piezoelectric Inertia
Motors: Experimental Validation.” Archive of Applied Mechanics, Springer
Berlin Heidelberg, 2014, pp. 1–9, doi:10.1007/s00419-014-0940-0.'
short: M. Hunstig, T. Hemsel, W. Sextro, Archive of Applied Mechanics (2014) 1–9.
date_created: 2019-05-20T13:08:08Z
date_updated: 2019-05-20T13:08:43Z
department:
- _id: '151'
doi: 10.1007/s00419-014-0940-0
keyword:
- Inertia motor
- High velocity
- Stick-slip motor
- Slip-slip operation
- Friction parameter identification
language:
- iso: eng
page: 1-9
publication: Archive of Applied Mechanics
publication_identifier:
issn:
- 0939-1533
publisher: Springer Berlin Heidelberg
status: public
title: 'High-velocity operation of piezoelectric inertia motors: experimental validation'
type: journal_article
user_id: '55222'
year: '2014'
...
---
_id: '11816'
abstract:
- lang: eng
text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters
of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the
resulting Expectation Maximization (EM) algorithm delivers virtually biasfree
and efficient estimates, and we discuss its convergence properties. We also discuss
optimal classification in the presence of censored data. Censored data are frequently
encountered in wireless LAN positioning systems based on the fingerprinting method
employing signal strength measurements, due to the limited sensitivity of the
portable devices. Experiments both on simulated and real-world data demonstrate
the effectiveness of the proposed algorithms.
author:
- first_name: Manh Kha
full_name: Hoang, Manh Kha
last_name: Hoang
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
Gaussian data with application to WiFi indoor positioning. In: 38th International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:3721-3725.
doi:10.1109/ICASSP.2013.6638353'
apa: Hoang, M. K., & Haeb-Umbach, R. (2013). Parameter estimation and classification
of censored Gaussian data with application to WiFi indoor positioning. In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
(pp. 3721–3725). https://doi.org/10.1109/ICASSP.2013.6638353
bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
classification of censored Gaussian data with application to WiFi indoor positioning},
DOI={10.1109/ICASSP.2013.6638353},
booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
pages={3721–3725} }'
chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
3721–25, 2013. https://doi.org/10.1109/ICASSP.2013.6638353.
ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
censored Gaussian data with application to WiFi indoor positioning,” in 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–3725.
mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–25, doi:10.1109/ICASSP.2013.6638353.
short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- Gaussian processes
- Global Positioning System
- convergence
- expectation-maximisation algorithm
- fingerprint identification
- indoor radio
- signal classification
- wireless LAN
- EM algorithm
- ML estimation
- WiFi indoor positioning
- censored Gaussian data classification
- clipped data
- convergence properties
- expectation maximization algorithm
- fingerprinting method
- maximum likelihood estimation
- optimal classification
- parameters estimation
- portable devices sensitivity
- signal strength measurements
- wireless LAN positioning systems
- Convergence
- IEEE 802.11 Standards
- Maximum likelihood estimation
- Parameter estimation
- Position measurement
- Training
- Indoor positioning
- censored data
- expectation maximization
- signal strength
- wireless LAN
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...
---
_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'
...