---
_id: '63800'
abstract:
- lang: eng
text: In this contribution, we address the estimation of the frequency-dependent
elastic parameters of polymers in the ultrasound range, which is formulated as
an inverse problem. This inverse problem is implemented as a nonlinear regression-type
optimization problem, in which the simulation signals are fitted to the measurement
signals. These signals consist of displacement responses in waveguides, focusing
on hollow cylindrical geometries to enhance the simulation efficiency. To accelerate
the optimization and reduce the number of model evaluations and wait times, we
propose two novel methods. First, we introduce an adaptation of the Levenberg–Marquardt
method derived from a geometrical interpretation of the least-squares optimization
problem. Second, we introduce an improved objective function based on the autocorrelated
envelopes of the measurement and simulation signals. Given that this study primarily
relies on simulation data to quantify optimization convergence, we aggregate the
expected ranges of realistic material parameters and derive their distributions
to ensure the reproducibility of optimizations with proper measurements. We demonstrate
the effectiveness of our objective function modification and step adaptation for
various materials with isotropic material symmetry by comparing them with the
Broyden–Fletcher–Goldfarb–Shanno method. In all cases, our method reduces the
total number of model evaluations, thereby shortening the time to identify the
material parameters.
author:
- first_name: Dominik
full_name: Itner, Dominik
last_name: Itner
- first_name: Dmitrij
full_name: Dreiling, Dmitrij
id: '32616'
last_name: Dreiling
- first_name: Hauke
full_name: Gravenkamp, Hauke
last_name: Gravenkamp
- first_name: Bernd
full_name: Henning, Bernd
id: '213'
last_name: Henning
- first_name: Carolin
full_name: Birk, Carolin
last_name: Birk
citation:
ama: Itner D, Dreiling D, Gravenkamp H, Henning B, Birk C. A modified Levenberg–Marquardt
method for estimating the elastic material parameters of polymer waveguides using
residuals between autocorrelated frequency responses. Mechanical Systems and
Signal Processing. 2026;247:113904. doi:https://doi.org/10.1016/j.ymssp.2026.113904
apa: Itner, D., Dreiling, D., Gravenkamp, H., Henning, B., & Birk, C. (2026).
A modified Levenberg–Marquardt method for estimating the elastic material parameters
of polymer waveguides using residuals between autocorrelated frequency responses.
Mechanical Systems and Signal Processing, 247, 113904. https://doi.org/10.1016/j.ymssp.2026.113904
bibtex: '@article{Itner_Dreiling_Gravenkamp_Henning_Birk_2026, title={A modified
Levenberg–Marquardt method for estimating the elastic material parameters of polymer
waveguides using residuals between autocorrelated frequency responses}, volume={247},
DOI={https://doi.org/10.1016/j.ymssp.2026.113904},
journal={Mechanical Systems and Signal Processing}, author={Itner, Dominik and
Dreiling, Dmitrij and Gravenkamp, Hauke and Henning, Bernd and Birk, Carolin},
year={2026}, pages={113904} }'
chicago: 'Itner, Dominik, Dmitrij Dreiling, Hauke Gravenkamp, Bernd Henning, and
Carolin Birk. “A Modified Levenberg–Marquardt Method for Estimating the Elastic
Material Parameters of Polymer Waveguides Using Residuals between Autocorrelated
Frequency Responses.” Mechanical Systems and Signal Processing 247 (2026):
113904. https://doi.org/10.1016/j.ymssp.2026.113904.'
ieee: 'D. Itner, D. Dreiling, H. Gravenkamp, B. Henning, and C. Birk, “A modified
Levenberg–Marquardt method for estimating the elastic material parameters of polymer
waveguides using residuals between autocorrelated frequency responses,” Mechanical
Systems and Signal Processing, vol. 247, p. 113904, 2026, doi: https://doi.org/10.1016/j.ymssp.2026.113904.'
mla: Itner, Dominik, et al. “A Modified Levenberg–Marquardt Method for Estimating
the Elastic Material Parameters of Polymer Waveguides Using Residuals between
Autocorrelated Frequency Responses.” Mechanical Systems and Signal Processing,
vol. 247, 2026, p. 113904, doi:https://doi.org/10.1016/j.ymssp.2026.113904.
short: D. Itner, D. Dreiling, H. Gravenkamp, B. Henning, C. Birk, Mechanical Systems
and Signal Processing 247 (2026) 113904.
date_created: 2026-01-29T08:53:42Z
date_updated: 2026-02-02T12:44:47Z
department:
- _id: '49'
doi: https://doi.org/10.1016/j.ymssp.2026.113904
intvolume: ' 247'
keyword:
- Material parameter estimation
- Waveguide
- Nonlinear optimization
- Inverse problem
- Least squares
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.sciencedirect.com/science/article/pii/S0888327026000610/pdfft?md5=16e8493b44527f4ab0a6d13f634a01c3&pid=1-s2.0-S0888327026000610-main.pdf
oa: '1'
page: '113904'
project:
- _id: '89'
name: Vollständige Bestimmung der akustischen Materialparameter von Polymeren
publication: Mechanical Systems and Signal Processing
publication_identifier:
issn:
- 0888-3270
publication_status: published
status: public
title: A modified Levenberg–Marquardt method for estimating the elastic material parameters
of polymer waveguides using residuals between autocorrelated frequency responses
type: journal_article
user_id: '32616'
volume: 247
year: '2026'
...
---
_id: '59755'
abstract:
- lang: eng
text: "Due to the application of Artificial Intelligence (AI) in high-risk domains
like law or medicine,\r\ntrustworthy AI and trust in AI are of increasing scientific
and public relevance. A typical conception,\r\nfor example in the context of medical
diagnosis, is that a knowledgeable user receives AIgenerated\r\nclassification
as advice. Research to improve such interactions often aims to foster the\r\nuser’s
trust, which in turn should improve the combined human-AI performance. Given that
AI\r\nmodels can err, we argue that the possibility to critically review, thus
to distrust, an AI decision is\r\nan equally interesting target of research.\r\nWe
created two image classification scenarios in which the participants received
mock-up\r\nAI advice. The quality of the advice decreases for a phase of the experiment.
We studied the\r\ntask performance, trust and distrust of the participants, and
tested whether an instruction to\r\nremain skeptical and review each piece of
advice led to a better performance compared to a\r\nneutral condition. Our results
indicate that this instruction does not improve but rather worsens\r\nthe participants’
performance. Repeated single-item self-report of trust and distrust shows an\r\nincrease
in trust and a decrease in distrust after the drop in the AI’s classification
quality, with no\r\ndifference between the two instructions. Furthermore, via
a Bayesian Signal Detection Theory\r\nanalysis, we provide a procedure to assess
appropriate reliance in detail, by quantifying whether\r\nthe problems of under-
and over-reliance have been mitigated. We discuss implications of our\r\nresults
for the usage of disclaimers before interacting with AI, as prominently used in
current\r\nLLM-based chatbots, and for trust and distrust research."
article_type: original
author:
- first_name: Tobias Martin
full_name: Peters, Tobias Martin
id: '92810'
last_name: Peters
orcid: 0009-0008-5193-6243
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: 'Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when
receiving AI misclassifications? Frontiers in Psychology. 2025;16. doi:10.3389/fpsyg.2025.1574809'
apa: 'Peters, T. M., & Scharlau, I. (2025). Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications? Frontiers in Psychology,
16. https://doi.org/10.3389/fpsyg.2025.1574809'
bibtex: '@article{Peters_Scharlau_2025, title={Interacting with fallible AI: Is
distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={10.3389/fpsyg.2025.1574809},
journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau,
Ingrid}, year={2025} }'
chicago: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible
AI: Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in
Psychology 16 (2025). https://doi.org/10.3389/fpsyg.2025.1574809.'
ieee: 'T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust
helpful when receiving AI misclassifications?,” Frontiers in Psychology,
vol. 16, 2025, doi: 10.3389/fpsyg.2025.1574809.'
mla: 'Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI:
Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in Psychology,
vol. 16, 2025, doi:10.3389/fpsyg.2025.1574809.'
short: T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (2025).
date_created: 2025-05-02T09:22:39Z
date_updated: 2025-05-27T09:10:09Z
department:
- _id: '424'
- _id: '660'
doi: 10.3389/fpsyg.2025.1574809
intvolume: ' 16'
keyword:
- trust in AI
- trust
- distrust
- human-AI interaction
- Signal Detection Theory
- Bayesian parameter estimation
- image classification
language:
- iso: eng
project:
- _id: '124'
name: 'TRR 318 - C1: TRR 318 - Subproject C1 - Gesundes Misstrauen in Erklärungen'
publication: Frontiers in Psychology
publication_status: published
status: public
title: 'Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?'
type: journal_article
user_id: '92810'
volume: 16
year: '2025'
...
---
_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: '56940'
abstract:
- lang: ger
text: "Ziel dieser Arbeit ist die Entwicklung eines modellbasierten Beobachters
für eingangsaffine, nichtlineare Systeme, der trotz Modellungenauigkeiten eine
hohe Schätzgüte erzielt und zusätzlich eine parametrische, physikalisch interpretierbare
Darstellung dieser ermöglicht. Diese soll zur automatisierten Verbesserung des
Modells verwendet werden. Die vorliegende Arbeit analysiert sowohl Techniken der
hybriden Systemidentifikation wie physikalisch motivierte neuronale Netze, als
auch Methoden zur Kompensation von Modellungenauigkeiten im Beobachterentwurf.
Basierend auf der Analyse wird ein neuartiger, modellbasierter Beobachter entworfen,
der Systemzustände und Modellungenauigkeiten gleichzeitig schätzt und insbesondere
eine parametrische, physikalisch interpretierbare Darstellung der Ungenauigkeiten
erzielt. Diese besteht aus einer Linearkombination von physikalisch interpretierbaren
Funktionen, deren dazugehörige, dünnbesetzt modellierte Parameter mithilfe eines
augmentierten Zustands parallel zu den Systemzuständen geschätzt werden. Das Novum
dieser Arbeit stellt somit die echtzeitfähige Schätzung von Zuständen und Modellungenauigkeiten
in physikalisch-technischer Form dar, auf deren Grundlage ein Konzept zur automatisierten
Modelladaption umgesetzt wird. Die Applikation der neuartigen Methode ist in der
Situation auftretender Systemveränderungen besonders vorteilhaft, da diese zur
Laufzeit durch den augmentierten Beobachter\r\ngeschätzt und identifiziert werden
können. "
- lang: eng
text: "The aim of this thesis is the development of a model-based observer for input-affine,
nonlinear systems that achieves a high estimation quality despite model inaccuracies.
By additionally providing a parametric, physically interpretable representation
of the model inaccuracies, an automated improvement of the model should be enabled.
This thesis\r\nanalyzes techniques of hybrid system identification such as physics-guided
neural networks, as well as methods for compensating model inaccuracies within
the observer design. Based on this analysis, a novel model-based observer is designed,
which estimates states and model inaccuracies jointly and, in particular, obtains
a parametric, physically\r\ninterpretable representation of the inaccuracies.
This consists of a linear combination of physically interpretable functions, whose
associated parameters are modeled sparse and estimated in parallel to the system’s
states using an augmented state. The novelty of this thesis is thus the real-time
capability to jointly estimate states and model inaccuracies in a physical-technical
manner, on the basis of which an automated model adaption can be\r\ncarried out.
The application of the new methodology is particularly advantageous in the situation
of occurring system changes since these can be estimated and identified at run
time by the augmented observer."
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
citation:
ama: Götte R-S. Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit.
Vol 423.; 2024. doi:10.17619/UNIPB/1-2066
apa: Götte, R.-S. (2024). Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
(Vol. 423). https://doi.org/10.17619/UNIPB/1-2066
bibtex: '@book{Götte_2024, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title={Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption
unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit}, volume={423},
DOI={10.17619/UNIPB/1-2066},
author={Götte, Ricarda-Samantha}, year={2024}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten
zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. Vol. 423. Verlagsschriftenreihe des Heinz Nixdorf Instituts,
2024. https://doi.org/10.17619/UNIPB/1-2066.
ieee: R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit,
vol. 423. 2024.
mla: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten zur
automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. 2024, doi:10.17619/UNIPB/1-2066.
short: R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit,
2024.
date_created: 2024-11-07T11:43:05Z
date_updated: 2024-11-07T11:47:59Z
department:
- _id: '880'
- _id: '153'
doi: 10.17619/UNIPB/1-2066
intvolume: ' 423'
keyword:
- state estimation
- joint estimation
- sparsity
language:
- iso: ger
publication_identifier:
isbn:
- 978-3-947647-42-2
publication_status: published
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
supervisor:
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
- first_name: Ralf
full_name: Mikut, Ralf
last_name: Mikut
title: Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption
unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
type: dissertation
user_id: '43992'
volume: 423
year: '2024'
...
---
_id: '34171'
abstract:
- lang: eng
text: State estimation when only a partial model of a considered system is available
remains a major challenge in many engineering fields. This work proposes a joint,
square-root unscented Kalman filter to estimate states and model uncertainties
simultaneously by linear combinations of physics-motivated library functions.
Using a sparsity promoting approach, a selection of those linear combinations
is chosen and thus an interpretable model can be extracted. Results indicate a
small estimation error compared to a traditional square-root unscented Kalman
filter and exhibit the enhancement of physically meaningful models.
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. Estimating States and Model Uncertainties Jointly
by a Sparsity Promoting UKF. In: 12th IFAC Symposium on Nonlinear Control Systems
(NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015'
apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015},
number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS
2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90}
}'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” In 12th IFAC Symposium
on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015.
ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90,
doi: https://doi.org/10.1016/j.ifacol.2023.02.015.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on
Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90,
doi:https://doi.org/10.1016/j.ifacol.2023.02.015.
short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 2023, pp. 85–90.'
conference:
end_date: 2023-01-06
location: Canberra, Australien
name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022
start_date: 2023-01-04
date_created: 2022-12-01T07:17:00Z
date_updated: 2024-11-13T08:43:05Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2023.02.015
intvolume: ' 56'
issue: '1'
keyword:
- joint estimation
- unscented transform
- Kalman filter
- sparsity
- data-driven
- compressed sensing
language:
- iso: eng
page: 85-90
publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)
quality_controlled: '1'
status: public
title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '44326'
abstract:
- lang: eng
text: "Low-quality models that miss relevant dynamics lead to major challenges in
modelbased\r\nstate estimation. We address this issue by simultaneously estimating
the system’s states\r\nand its model inaccuracies by a square root unscented Kalman
filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector
of a linear combination containing suitable\r\nfunctions that approximate the
lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the
parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity
are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution.
However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe
regularized horseshoe distribution, a Gaussian that approximately features sparsity,
is\r\napplied instead. Results exhibit small estimation errors with model improvements
detected by\r\nan automated model reduction technique."
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. Approximating a Laplacian Prior for Joint State and
Model Estimation within an UKF. In: IFAC-PapersOnLine. Vol 56. ; 2023:869-874.'
apa: Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine,
56(2), 869–874.
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF}, volume={56}, number={2},
booktitle={IFAC-PapersOnLine}, author={Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2023}, pages={869–874} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF.” In IFAC-PapersOnLine,
56:869–74, 2023.
ieee: R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint
State and Model Estimation within an UKF,” in IFAC-PapersOnLine, Yokohama,
Japan, 2023, vol. 56, no. 2, pp. 869–874.
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF.” IFAC-PapersOnLine,
vol. 56, no. 2, 2023, pp. 869–74.
short: 'R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.'
conference:
end_date: 2023-07-14
location: Yokohama, Japan
name: 22nd IFAC World Congress
start_date: 2023-07-09
date_created: 2023-05-02T15:16:43Z
date_updated: 2024-11-13T08:42:37Z
department:
- _id: '153'
- _id: '880'
intvolume: ' 56'
issue: '2'
keyword:
- joint estimation
- unscented Kalman filter
- sparsity
- Laplacian prior
- regularized horseshoe
- principal component analysis
language:
- iso: eng
page: 869-874
publication: IFAC-PapersOnLine
quality_controlled: '1'
status: public
title: Approximating a Laplacian Prior for Joint State and Model Estimation within
an UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '33849'
abstract:
- lang: eng
text: Modern traffic control systems are key to cope with current and future traffic
challenges. In this paper information obtained from a microscopic traffic estimation
using various data sources is used to feed a new developed traffic control approach.
The presented method can control a traffic area with multiple traffic light systems
(TLS) reacting to individual road users and pedestrians. In contrast to widespread
green time extension techniques, this control selects the best phase sequence
by analyzing the current traffic state reconstructed in SUMO and its predicted
progress. To achieve this, the key aspect of the control strategy is to use Model
Predictive Control (MPC). In order to maintain realism for real world applications,
among other things, the traffic phase transitions are modelled in detail and integrated
within the prediction. For the efficiency, the approach incorporates a fuzzy logic
preselection of all phases reducing the computational effort. The evaluation itself
is able to be easily adjusted to focus on various objectives like low occupancies,
reducing waiting times and emissions, few number of phase transitions etc. determining
the best switching times for the selected phases. Exemplary traffic simulations
demonstrate the functionality of the MPC-based control and, in addition, some
aspects under development like the real-world communication network are also discussed.
author:
- first_name: Kevin
full_name: Malena, Kevin
id: '36303'
last_name: Malena
orcid: 0000-0003-1183-4679
- first_name: Christopher
full_name: Link, Christopher
id: '38249'
last_name: Link
- first_name: Leon
full_name: Bußemas, Leon
id: '51118'
last_name: Bußemas
- first_name: Sandra
full_name: Gausemeier, Sandra
id: '17793'
last_name: Gausemeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Malena K, Link C, Bußemas L, Gausemeier S, Trächtler A. Traffic Estimation
and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.
In: Klein C, Jarke M, Helfert M, Berns K, Gusikhin O, eds. Communications in
Computer and Information Science. Vol 1612. Communications in Computer and
Information Science. Springer International Publishing; 2022:232–254. doi:10.1007/978-3-031-17098-0_12'
apa: Malena, K., Link, C., Bußemas, L., Gausemeier, S., & Trächtler, A. (2022).
Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time
Traffic Environments. In C. Klein, M. Jarke, M. Helfert, K. Berns, & O. Gusikhin
(Eds.), Communications in Computer and Information Science (Vol. 1612,
pp. 232–254). Springer International Publishing. https://doi.org/10.1007/978-3-031-17098-0_12
bibtex: '@inbook{Malena_Link_Bußemas_Gausemeier_Trächtler_2022, place={Cham}, series={Communications
in Computer and Information Science}, title={Traffic Estimation and MPC-Based
Traffic Light System Control in Realistic Real-Time Traffic Environments}, volume={1612},
DOI={10.1007/978-3-031-17098-0_12},
booktitle={Communications in Computer and Information Science}, publisher={Springer
International Publishing}, author={Malena, Kevin and Link, Christopher and Bußemas,
Leon and Gausemeier, Sandra and Trächtler, Ansgar}, editor={Klein, Cornel and
Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, year={2022},
pages={232–254}, collection={Communications in Computer and Information Science}
}'
chicago: 'Malena, Kevin, Christopher Link, Leon Bußemas, Sandra Gausemeier, and
Ansgar Trächtler. “Traffic Estimation and MPC-Based Traffic Light System Control
in Realistic Real-Time Traffic Environments.” In Communications in Computer
and Information Science, edited by Cornel Klein, Mathias Jarke, Markus Helfert,
Karsten Berns, and Oleg Gusikhin, 1612:232–254. Communications in Computer and
Information Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-17098-0_12.'
ieee: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, and A. Trächtler, “Traffic
Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic
Environments,” in Communications in Computer and Information Science, vol.
1612, C. Klein, M. Jarke, M. Helfert, K. Berns, and O. Gusikhin, Eds. Cham: Springer
International Publishing, 2022, pp. 232–254.'
mla: Malena, Kevin, et al. “Traffic Estimation and MPC-Based Traffic Light System
Control in Realistic Real-Time Traffic Environments.” Communications in Computer
and Information Science, edited by Cornel Klein et al., vol. 1612, Springer
International Publishing, 2022, pp. 232–254, doi:10.1007/978-3-031-17098-0_12.
short: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, A. Trächtler, in: C. Klein,
M. Jarke, M. Helfert, K. Berns, O. Gusikhin (Eds.), Communications in Computer
and Information Science, Springer International Publishing, Cham, 2022, pp. 232–254.'
date_created: 2022-10-20T15:06:39Z
date_updated: 2026-01-26T08:49:52Z
department:
- _id: '153'
doi: 10.1007/978-3-031-17098-0_12
editor:
- first_name: Cornel
full_name: Klein, Cornel
last_name: Klein
- first_name: Mathias
full_name: Jarke, Mathias
last_name: Jarke
- first_name: Markus
full_name: Helfert, Markus
last_name: Helfert
- first_name: Karsten
full_name: Berns, Karsten
last_name: Berns
- first_name: Oleg
full_name: Gusikhin, Oleg
last_name: Gusikhin
intvolume: ' 1612'
keyword:
- Traffic control
- Traffic estimation
- Real-time
- MPC
- Fuzzy
- Isolated intersection
- Networked intersection
- Sensor fusion
language:
- iso: eng
page: 232–254
place: Cham
publication: Communications in Computer and Information Science
publication_identifier:
isbn:
- '9783031170973'
- '9783031170980'
issn:
- 1865-0929
- 1865-0937
publication_status: published
publisher: Springer International Publishing
quality_controlled: '1'
related_material:
record:
- id: '24159'
relation: continues
status: public
series_title: Communications in Computer and Information Science
status: public
title: Traffic Estimation and MPC-Based Traffic Light System Control in Realistic
Real-Time Traffic Environments
type: book_chapter
user_id: '552'
volume: 1612
year: '2022'
...
---
_id: '24551'
abstract:
- lang: eng
text: "Access to precise meteorological data is crucial to be able to plan and install
renewable energy systems \r\nsuch as solar power plants and wind farms. In case
of solar energy, knowledge of local irradiance and air temperature \r\nvalues
is very important. For this, various methods can be used such as installing local
weather stations or using \r\nmeteorological data from different organizations
such as Meteonorm or official Deutscher Wetterdienst (DWD). An \r\nalternative
is to use satellite reanalysis datasets provided by organizations like the National
Aeronautics and Space \r\nAdministration (NASA) and European Centre for Medium-Range
Weather Forecasts (ECMWF). In this paper the \r\n“Modern-Era Retrospective analysis
for Research and Applications” dataset version 2 (MERRA-2) will be presented,
\r\nand its performance will be evaluated by comparing it to locally measured
datasets provided by Meteonorm and DWD. \r\nThe analysis shows very high correlation
between MERRA-2 and local measurements (correlation coefficients of 0.99) \r\nfor
monthly global irradiance and air temperature values. The results prove the suitability
of MERRA-2 data for \r\napplications requiring long historical data. Moreover,
availability of MERRA-2 for the whole world with an acceptable \r\nresolution
makes it a very valuable dataset."
author:
- first_name: Arash
full_name: Khatibi, Arash
id: '43538'
last_name: Khatibi
- first_name: Stefan
full_name: Krauter, Stefan
id: '28836'
last_name: Krauter
orcid: 0000-0002-3594-260X
citation:
ama: 'Khatibi A, Krauter S. Comparison and Validation of Irradiance Data: Satellite
Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD).
In: Proceedings of the 38th European Photovoltaic Solar Energy Conference and
Exhibition (EUPVSEC 2021). ; 2021:1141-1147. doi:10.4229/EUPVSEC20212021-5BV.4.11'
apa: 'Khatibi, A., & Krauter, S. (2021). Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD). Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021), 1141–1147. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11'
bibtex: '@inproceedings{Khatibi_Krauter_2021, title={Comparison and Validation of
Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German
Weather Service (DWD)}, DOI={10.4229/EUPVSEC20212021-5BV.4.11},
booktitle={Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)}, author={Khatibi, Arash and Krauter, Stefan}, year={2021},
pages={1141–1147} }'
chicago: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD).” In Proceedings of the 38th European Photovoltaic Solar Energy
Conference and Exhibition (EUPVSEC 2021), 1141–47, 2021. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11.'
ieee: 'A. Khatibi and S. Krauter, “Comparison and Validation of Irradiance Data:
Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service
(DWD),” in Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147, doi: 10.4229/EUPVSEC20212021-5BV.4.11.'
mla: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD).” Proceedings of the 38th European Photovoltaic Solar Energy
Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–47, doi:10.4229/EUPVSEC20212021-5BV.4.11.'
short: 'A. Khatibi, S. Krauter, in: Proceedings of the 38th European Photovoltaic
Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147.'
conference:
end_date: 2021-09-10
name: 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC
2021)
start_date: 2021-09-06
date_created: 2021-09-16T10:20:41Z
date_updated: 2022-01-06T13:29:51Z
ddc:
- '550'
department:
- _id: '53'
doi: 10.4229/EUPVSEC20212021-5BV.4.11
file:
- access_level: closed
content_type: application/pdf
creator: krauter
date_created: 2022-01-06T13:26:47Z
date_updated: 2022-01-06T13:26:47Z
file_id: '29176'
file_name: Khatibi Krauter - MERRA 2 vs Meteonorm - EUPVSEC 2021.pdf
file_size: 2475972
relation: main_file
success: 1
file_date_updated: 2022-01-06T13:26:47Z
has_accepted_license: '1'
keyword:
- Energy potential estimation
- Photovoltaic
- Solar radiation
- Temperature measurement
- Satellite data
- Meteonorm
- MERRA-2
- DWD
language:
- iso: eng
page: 1141 - 1147
publication: Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)
publication_identifier:
isbn:
- 3-936338-78-7
publication_status: published
quality_controlled: '1'
status: public
title: 'Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset
MERRA-2 vs. Meteonorm and German Weather Service (DWD)'
type: conference
user_id: '28836'
year: '2021'
...
---
_id: '22724'
abstract:
- lang: eng
text: "\r\nPredictive Maintenance as a desirable maintenance strategy in industrial
applications relies on suitable condition monitoring solutions to reduce costs
and risks of the monitored technical systems. In general, those solutions utilize
model-based or data-driven methods to diagnose the current state or predict future
states of monitored technical systems. However, both methods have their advantages
and drawbacks. Combining both methods can improve uncertainty consideration and
accuracy. Different combination approaches of those hybrid methods exist to exploit
synergy effects. The choice of an appropriate approach depends on different requirements
and the goal behind the selection of a hybrid approach.\r\n\r\n \r\n\r\nIn this
work, the hybrid approach for estimating remaining useful lifetime takes potential
uncertainties into account. Therefore, a data-driven estimation of new measurements
is integrated within a model-based method. To consider uncertainties within the
system, a differentiation between different system behavior is realized throughout
diverse states of degradation.\r\n\r\nThe developed hybrid prediction approach
bases on a particle filtering method combined with a machine learning method,
to estimate the remaining useful lifetime of technical systems. Particle filtering
as a Monte Carlo simulation technique is suitable to map and propagate uncertainties.
Moreover, it is a state-of-the-art model-based method for predicting remaining
useful lifetime of technical systems. To integrate uncertainties a multi-model
particle filtering approach is employed. In general, resampling as a part of the
particle filtering approach has the potential to lead to an accurate prediction.
However, in the case where no future measurements are available, it may increase
the uncertainty of the prediction. By estimating new measurements, those uncertainties
are reduced within the data-driven part of the approach. Hence, both parts of
the hybrid approach strive to account for and reduce uncertainties.\r\n\r\n \r\n\r\nRubber-metal-elements
are employed as a use-case to evaluate the developed approach. Rubber-metal-elements,
which are used to isolate vibrations in various systems, such as railways, trucks
and wind turbines, show various uncertainties in their behavior and their degradation.
Those uncertainties are caused by diverse inner and outer factors, such as manufacturing
influences and operating conditions. By expert knowledge the influences are described,
analyzed and if possible reduced. However, the remaining uncertainties are considered
within the hybrid prediction method. Relative temperature is the selected measurand
to describe the element’s degradation. In lifetime tests, it is measured as the
difference between the element’s temperature and the ambient temperature. Thereby,
the influence of the ambient temperature on the element’s temperature is taken
into account. Those elements show three typical states of degradation that are
identified within the temperature measurements. Depending on the particular state
of degradation a new measurement is estimated within the hybrid approach to reduce
potential uncertainties.\r\n\r\nFinally, the performance of the developed hybrid
method is compared to a model-based method for estimating the remaining useful
lifetime of the same elements. Suitable performance indices are implemented to
underline the differences between the results."
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Sextro W. Hybrid Prediction Method for Remaining Useful Lifetime
Estimation Considering Uncertainties. In: Do P, King S, Fink Olga, eds. Proceedings
of the European Conference of the PHM Society 2021. Vol 6. ; 2021. doi:https://doi.org/10.36001/phme.2021.v6i1.2843
'
apa: Bender, A., & Sextro, W. (2021). Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties. In P. Do, S. King, & Olga
Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1). https://doi.org/10.36001/phme.2021.v6i1.2843
bibtex: '@inproceedings{Bender_Sextro_2021, title={Hybrid Prediction Method for
Remaining Useful Lifetime Estimation Considering Uncertainties}, volume={6}, DOI={https://doi.org/10.36001/phme.2021.v6i1.2843
}, number={1}, booktitle={Proceedings of the European Conference of the PHM
Society 2021}, author={Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and
King, Steve and Fink, Olga}, year={2021} }'
chicago: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties.” In Proceedings of the
European Conference of the PHM Society 2021, edited by Phuc Do, Steve King,
and Olga Fink, Vol. 6, 2021. https://doi.org/10.36001/phme.2021.v6i1.2843 .
ieee: 'A. Bender and W. Sextro, “Hybrid Prediction Method for Remaining Useful Lifetime
Estimation Considering Uncertainties,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, doi: https://doi.org/10.36001/phme.2021.v6i1.2843 .'
mla: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties.” Proceedings of the European
Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no.
1, 2021, doi:https://doi.org/10.36001/phme.2021.v6i1.2843
.
short: 'A. Bender, W. Sextro, in: P. Do, S. King, Olga Fink (Eds.), Proceedings
of the European Conference of the PHM Society 2021, 2021.'
conference:
end_date: 2021-07-02
name: 6th European Conference of Prognostics and Health Management
start_date: 2021-06-28
date_created: 2021-07-14T06:29:08Z
date_updated: 2023-09-22T07:19:48Z
department:
- _id: '151'
doi: 'https://doi.org/10.36001/phme.2021.v6i1.2843 '
editor:
- first_name: 'Phuc '
full_name: 'Do, Phuc '
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: ' Olga'
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- Hybrid prediction method
- Multi-model particle filtering
- Uncertainty quantification
- RUL estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.phmsociety.org/index.php/phme/article/view/2843
oa: '1'
publication: Proceedings of the European Conference of the PHM Society 2021
publication_identifier:
unknown:
- 978-1-936263-34-9
publication_status: published
quality_controlled: '1'
status: public
title: Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering
Uncertainties
type: conference
user_id: '54290'
volume: 6
year: '2021'
...
---
_id: '24159'
abstract:
- lang: eng
text: "The online fitting of a microscopic traffic simulation model to reconstruct
the current state of a real traffic\r\narea can be challenging depending on the
provided data. This paper presents a novel method based on limited\r\ndata from
sensors positioned at specific locations and guarantees a general accordance of
reality and\r\nsimulation in terms of multimodal road traffic counts and vehicle
speeds. In these considerations, the actual\r\npurpose of research is of particular
importance. Here, the research aims at improving the traffic flow by\r\ncontrolling
the Traffic Light Systems (TLS) of the examined area which is why the current
traffic state and\r\nthe route choices of individual road users are the matter
of interest. An integer optimization problem is derived\r\nto fit the current
simulation to the latest field measurements. The concept can be transferred to
any road traffic\r\nnetwork and results in an observation of the current multimodal
traffic state matching at the given sensor\r\nposition. First case studies show
promosing results in terms of deviations between reality and simulation."
author:
- first_name: Kevin
full_name: Malena, Kevin
id: '36303'
last_name: Malena
orcid: 0000-0003-1183-4679
- first_name: Christopher
full_name: Link, Christopher
id: '38249'
last_name: Link
- first_name: Sven
full_name: Mertin, Sven
id: '13195'
last_name: Mertin
- first_name: Sandra
full_name: Gausemeier, Sandra
id: '17793'
last_name: Gausemeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Malena K, Link C, Mertin S, Gausemeier S, Trächtler A. Online State Estimation
for Microscopic Traffic Simulations using Multiple Data Sources*. In: VEHITS
2021 Proceedings of the 7th International Conference on Vehicle Technology and
Intelligent Transport Systems. Vol 7. VEHITS 2021 Proceedings of the 7th International
Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS;
2021:386-395.'
apa: Malena, K., Link, C., Mertin, S., Gausemeier, S., & Trächtler, A. (2021).
Online State Estimation for Microscopic Traffic Simulations using Multiple Data
Sources*. VEHITS 2021 Proceedings of the 7th International Conference on Vehicle
Technology and Intelligent Transport Systems, 7, 386–395.
bibtex: '@inproceedings{Malena_Link_Mertin_Gausemeier_Trächtler_2021, place={Portugal},
series={VEHITS 2021 Proceedings of the 7th International Conference on Vehicle
Technology and Intelligent Transport Systems}, title={Online State Estimation
for Microscopic Traffic Simulations using Multiple Data Sources*}, volume={7},
booktitle={VEHITS 2021 Proceedings of the 7th International Conference on Vehicle
Technology and Intelligent Transport Systems}, publisher={SCITEPRESS}, author={Malena,
Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler,
Ansgar}, year={2021}, pages={386–395}, collection={VEHITS 2021 Proceedings of
the 7th International Conference on Vehicle Technology and Intelligent Transport
Systems} }'
chicago: 'Malena, Kevin, Christopher Link, Sven Mertin, Sandra Gausemeier, and Ansgar
Trächtler. “Online State Estimation for Microscopic Traffic Simulations Using
Multiple Data Sources*.” In VEHITS 2021 Proceedings of the 7th International
Conference on Vehicle Technology and Intelligent Transport Systems, 7:386–95.
VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology
and Intelligent Transport Systems. Portugal: SCITEPRESS, 2021.'
ieee: K. Malena, C. Link, S. Mertin, S. Gausemeier, and A. Trächtler, “Online State
Estimation for Microscopic Traffic Simulations using Multiple Data Sources*,”
in VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology
and Intelligent Transport Systems, Online Streaming, 2021, vol. 7, pp. 386–395.
mla: Malena, Kevin, et al. “Online State Estimation for Microscopic Traffic Simulations
Using Multiple Data Sources*.” VEHITS 2021 Proceedings of the 7th International
Conference on Vehicle Technology and Intelligent Transport Systems, vol. 7,
SCITEPRESS, 2021, pp. 386–95.
short: 'K. Malena, C. Link, S. Mertin, S. Gausemeier, A. Trächtler, in: VEHITS 2021
Proceedings of the 7th International Conference on Vehicle Technology and Intelligent
Transport Systems, SCITEPRESS, Portugal, 2021, pp. 386–395.'
conference:
end_date: 2021-04-30
location: Online Streaming
name: 7th International Conference on Vehicle Technology and Intelligent Transport
Systems
start_date: 2021-04-28
date_created: 2021-09-10T12:19:14Z
date_updated: 2026-01-26T08:49:53Z
department:
- _id: '153'
intvolume: ' 7'
keyword:
- Microscopic Traffic Simulation
- Online State Estimation
- Mixed Road Users
- Sensor Fusion
- Integer Programming
- Route Choice
- Vehicle2Infrastructure
language:
- iso: eng
main_file_link:
- url: https://www.scitepress.org/PublicationsDetail.aspx?ID=3xZWfOSENWk=&t=1
page: 386-395
place: Portugal
project:
- _id: '688'
name: Pilotprojekt "Schlosskreuzung"
publication: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle
Technology and Intelligent Transport Systems
publication_identifier:
isbn:
- 978-989-758-513-5
publication_status: published
publisher: SCITEPRESS
quality_controlled: '1'
related_material:
link:
- relation: confirmation
url: https://www.scitepress.org/PublicationsDetail.aspx?ID=3xZWfOSENWk=&t=1
record:
- id: '33849'
relation: is_continued_by
status: public
series_title: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle
Technology and Intelligent Transport Systems
status: public
title: Online State Estimation for Microscopic Traffic Simulations using Multiple
Data Sources*
type: conference
user_id: '36303'
volume: 7
year: '2021'
...
---
_id: '9978'
abstract:
- lang: eng
text: Piezoelectric transducers are used in a wide range of applications. Reliability
of these transducers is an important aspect in their application. Prognostics,
which involve continuous monitoring of the health of technical systems and using
this information to estimate the current health state and consequently predict
the remaining useful lifetime (RUL), can be used to increase the reliability,
safety, and availability of the transducers. This is achieved by utilizing the
health state and RUL predictions to adaptively control the usage of the components
or to schedule appropriate maintenance without interrupting operation. In this
work, a prognostic approach utilizing self-sensing, where electric signals of
a piezoelectric transducer are used as the condition monitoring data, is proposed.
The approach involves training machine learning algorithms to model the degradation
of the transducers through a health index and the use of the learned model to
estimate the health index of similar transducers. The current health index is
then used to estimate RUL of test components. The feasibility of the approach
is demonstrated using piezoelectric bimorphs and the results show that the method
is accurate in predicting the health index and RUL.
author:
- first_name: James Kuria
full_name: Kimotho, James Kuria
last_name: Kimotho
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
- first_name: Tobias
full_name: Hemsel, Tobias
id: '210'
last_name: Hemsel
citation:
ama: 'Kimotho JK, Sextro W, Hemsel T. Estimation of Remaining Useful Lifetime of
Piezoelectric Transducers Based on Self-Sensing. In: IEEE Transactions on Reliability.
; 2017:1-10. doi:10.1109/TR.2017.2710260'
apa: Kimotho, J. K., Sextro, W., & Hemsel, T. (2017). Estimation of Remaining
Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing. In IEEE
Transactions on Reliability (pp. 1–10). https://doi.org/10.1109/TR.2017.2710260
bibtex: '@inproceedings{Kimotho_Sextro_Hemsel_2017, title={Estimation of Remaining
Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing}, DOI={10.1109/TR.2017.2710260},
booktitle={IEEE Transactions on Reliability}, author={Kimotho, James Kuria and
Sextro, Walter and Hemsel, Tobias}, year={2017}, pages={1–10} }'
chicago: Kimotho, James Kuria, Walter Sextro, and Tobias Hemsel. “Estimation of
Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing.”
In IEEE Transactions on Reliability, 1–10, 2017. https://doi.org/10.1109/TR.2017.2710260.
ieee: J. K. Kimotho, W. Sextro, and T. Hemsel, “Estimation of Remaining Useful Lifetime
of Piezoelectric Transducers Based on Self-Sensing,” in IEEE Transactions on
Reliability, 2017, pp. 1–10.
mla: Kimotho, James Kuria, et al. “Estimation of Remaining Useful Lifetime of Piezoelectric
Transducers Based on Self-Sensing.” IEEE Transactions on Reliability, 2017,
pp. 1–10, doi:10.1109/TR.2017.2710260.
short: 'J.K. Kimotho, W. Sextro, T. Hemsel, in: IEEE Transactions on Reliability,
2017, pp. 1–10.'
date_created: 2019-05-27T09:41:06Z
date_updated: 2019-09-16T10:32:05Z
department:
- _id: '151'
doi: 10.1109/TR.2017.2710260
keyword:
- Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing
language:
- iso: eng
page: 1 - 10
publication: IEEE Transactions on Reliability
quality_controlled: '1'
status: public
title: Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based
on Self-Sensing
type: conference
user_id: '55222'
year: '2017'
...
---
_id: '9879'
abstract:
- lang: eng
text: Application of prognostics and health management (PHM) in the field of Proton
Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing
the reliability and availability of these systems. Though a lot of work is currently
being conducted to develop PHM systems for fuel cells, various challenges have
been encountered including the self-healing effect after characterization as well
as accelerated degradation due to dynamic loading, all which make RUL predictions
a difficult task. In this study, a prognostic approach based on adaptive particle
filter algorithm is proposed. The novelty of the proposed method lies in the introduction
of a self-healing factor after each characterization and the adaption of the degradation
model parameters to fit to the changing degradation trend. An ensemble of five
different state models based on weighted mean is then developed. The results show
that the method is effective in estimating the remaining useful life of PEM fuel
cells, with majority of the predictions falling within 5\% error. The method was
employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the
winner of the RUL category of the challenge.
author:
- first_name: 'James Kuria '
full_name: 'Kimotho, James Kuria '
last_name: Kimotho
- first_name: Tobias
full_name: Meyer, Tobias
last_name: Meyer
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter
with model parameter adaptation. In: Prognostics and Health Management (PHM),
2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406'
apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics
using particle filter with model parameter adaptation. In Prognostics and Health
Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics
using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406},
booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on},
author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014},
pages={1–6} }'
chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell
Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics
and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle
filter with model parameter adaptation,” in Prognostics and Health Management
(PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter
with Model Parameter Adaptation.” Prognostics and Health Management (PHM),
2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management
(PHM), 2014 IEEE Conference On, 2014, pp. 1–6.'
date_created: 2019-05-20T13:11:02Z
date_updated: 2019-05-20T13:12:27Z
department:
- _id: '151'
doi: 10.1109/ICPHM.2014.7036406
keyword:
- ageing
- particle filtering (numerical methods)
- proton exchange membrane fuel cells
- remaining life assessment
- PEM fuel cell prognostics
- PHM
- RUL predictions
- accelerated degradation
- adaptive particle filter algorithm
- dynamic loading
- model parameter adaptation
- prognostics and health management
- proton exchange membrane fuel cells
- remaining useful life estimation
- self-healing effect
- Adaptation models
- Data models
- Degradation
- Estimation
- Fuel cells
- Mathematical model
- Prognostics and health management
language:
- iso: eng
page: 1-6
publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on
status: public
title: PEM fuel cell prognostics using particle filter with model parameter adaptation
type: conference
user_id: '55222'
year: '2014'
...
---
_id: '11753'
abstract:
- lang: eng
text: This contribution describes a step-wise source counting algorithm to determine
the number of speakers in an offline scenario. Each speaker is identified by a
variational expectation maximization (VEM) algorithm for complex Watson mixture
models and therefore directly yields beamforming vectors for a subsequent speech
separation process. An observation selection criterion is proposed which improves
the robustness of the source counting in noise. The algorithm is compared to an
alternative VEM approach with Gaussian mixture models based on directions of arrival
and shown to deliver improved source counting accuracy. The article concludes
by extending the offline algorithm towards a low-latency online estimation of
the number of active sources from the streaming input data.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Dang Hai
full_name: Tran Vu, Dang Hai
last_name: Tran Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting
in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.
In: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014).
; 2014:213-217.'
apa: Drude, L., Chinaev, A., Tran Vu, D. H., & Haeb-Umbach, R. (2014). Towards
Online Source Counting in Speech Mixtures Applying a Variational EM for Complex
Watson Mixture Models. In 14th International Workshop on Acoustic Signal Enhancement
(IWAENC 2014) (pp. 213–217).
bibtex: '@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online
Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson
Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement
(IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai
and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }'
chicago: Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
“Towards Online Source Counting in Speech Mixtures Applying a Variational EM for
Complex Watson Mixture Models.” In 14th International Workshop on Acoustic
Signal Enhancement (IWAENC 2014), 213–17, 2014.
ieee: L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source
Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture
Models,” in 14th International Workshop on Acoustic Signal Enhancement (IWAENC
2014), 2014, pp. 213–217.
mla: Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying
a Variational EM for Complex Watson Mixture Models.” 14th International Workshop
on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–17.
short: 'L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International
Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.'
date_created: 2019-07-12T05:27:35Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- Accuracy
- Acoustics
- Estimation
- Mathematical model
- Soruce separation
- Speech
- Vectors
- Bayes methods
- Blind source separation
- Directional statistics
- Number of speakers
- Speaker diarization
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf
oa: '1'
page: 213-217
publication: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf
status: public
title: Towards Online Source Counting in Speech Mixtures Applying a Variational EM
for Complex Watson Mixture Models
type: conference
user_id: '44006'
year: '2014'
...
---
_id: '11716'
abstract:
- lang: eng
text: The accuracy of automatic speech recognition systems in noisy and reverberant
environments can be improved notably by exploiting the uncertainty of the estimated
speech features using so-called uncertainty-of-observation techniques. In this
paper, we introduce a new Bayesian decision rule that can serve as a mathematical
framework from which both known and new uncertainty-of-observation techniques
can be either derived or approximated. The new decision rule in its direct form
leads to the new significance decoding approach for Gaussian mixture models, which
results in better performance compared to standard uncertainty-of-observation
techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
full_name: Abdelaziz, Ahmed H.
last_name: Abdelaziz
- first_name: Steffen
full_name: Zeiler, Steffen
last_name: Zeiler
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984'
apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach,
R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal
Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).
https://doi.org/10.1109/ICASSP.2013.6638984
bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
significance decoding}, DOI={10.1109/ICASSP.2013.6638984},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
}'
chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984.
ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP),
2013 IEEE International Conference on, 2013, pp. 6827–6831.
mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984.
short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on
publication_identifier:
issn:
- 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11740'
abstract:
- lang: eng
text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
We assume the distortion to be white Gaussian noise of known mean and variance.
An approximate conjugate prior of the GMM parameters is derived allowing for a
computationally efficient implementation in a sequential estimation framework.
Simulations on artificially generated data demonstrate the superiority of the
proposed method compared to the Maximum Likelihood technique and to the ordinary
MAP approach, whose estimates are corrected by the known statistics of the distortion
in a straightforward manner.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
Mixture Model in the Presence of Noisy Observations. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:3352-3356.
doi:10.1109/ICASSP.2013.6638279'
apa: Chinaev, A., & Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)
(pp. 3352–3356). https://doi.org/10.1109/ICASSP.2013.6638279
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
DOI={10.1109/ICASSP.2013.6638279},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
pages={3352–3356} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 3352–56, 2013. https://doi.org/10.1109/ICASSP.2013.6638279.
ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
a Gaussian Mixture Model in the Presence of Noisy Observations,” in 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–3356.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations.” 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–56, doi:10.1109/ICASSP.2013.6638279.
short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
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/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...
---
_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: '11917'
abstract:
- lang: eng
text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich
exploits both temporal and spectral correlations of speech. To this end, the SPP
estimation is formulated as the posterior probability estimation of the states
of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
to decode the 2D-HMM which is based on the turbo principle. The experimental results
show that indeed the SPP estimates improve from iteration to iteration, and further
clearly outperform another state-of-the-art SPP estimation algorithm.
author:
- first_name: Dang Hai Tran
full_name: Vu, Dang Hai Tran
last_name: Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and
spectral correlations in speech presence probability estimation. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:863-867.
doi:10.1109/ICASSP.2013.6637771'
apa: Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation.
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771
bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
exploiting temporal and spectral correlations in speech presence probability estimation},
DOI={10.1109/ICASSP.2013.6637771},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013},
pages={863–867} }'
chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle
for Exploiting Temporal and Spectral Correlations in Speech Presence Probability
Estimation.” In 38th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2013), 863–67, 2013. https://doi.org/10.1109/ICASSP.2013.6637771.
ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation,”
in 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 2013, pp. 863–867.
mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for
Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.”
38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2013), 2013, pp. 863–67, doi:10.1109/ICASSP.2013.6637771.
short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.'
date_created: 2019-07-12T05:30:45Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637771
keyword:
- correlation methods
- estimation theory
- hidden Markov models
- iterative methods
- probability
- spectral analysis
- speech processing
- 2D HMM
- SPP estimates
- iterative algorithm
- posterior probability estimation
- spectral correlation
- speech presence probability estimation
- state-of-the-art SPP estimation algorithm
- temporal correlation
- turbo principle
- two-dimensional hidden Markov model
- Correlation
- Decoding
- Estimation
- Iterative decoding
- Noise
- Speech
- Vectors
language:
- iso: eng
page: 863-867
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
status: public
title: Using the turbo principle for exploiting temporal and spectral correlations
in speech presence probability estimation
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11745'
abstract:
- lang: eng
text: In this paper we present a novel noise power spectral density tracking algorithm
and its use in single-channel speech enhancement. It has the unique feature that
it is able to track the noise statistics even if speech is dominant in a given
time-frequency bin. As a consequence it can follow non-stationary noise superposed
by speech, even in the critical case of rising noise power. The algorithm requires
an initial estimate of the power spectrum of speech and is thus meant to be used
as a postprocessor to a first speech enhancement stage. An experimental comparison
with a state-of-the-art noise tracking algorithm demonstrates lower estimation
errors under low SNR conditions and smaller fluctuations of the estimated values,
resulting in improved speech quality as measured by PESQ scores.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Dang Hai
full_name: Tran Vu, Dang Hai
last_name: Tran Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral
Density Tracking by a MAP-based Postprocessor. In: 37th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2012). ; 2012.'
apa: Chinaev, A., Krueger, A., Tran Vu, D. H., & Haeb-Umbach, R. (2012). Improved
Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012).
bibtex: '@inproceedings{Chinaev_Krueger_Tran Vu_Haeb-Umbach_2012, title={Improved
Noise Power Spectral Density Tracking by a MAP-based Postprocessor}, booktitle={37th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},
author={Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach,
Reinhold}, year={2012} }'
chicago: Chinaev, Aleksej, Alexander Krueger, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
“Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.”
In 37th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2012), 2012.
ieee: A. Chinaev, A. Krueger, D. H. Tran Vu, and R. Haeb-Umbach, “Improved Noise
Power Spectral Density Tracking by a MAP-based Postprocessor,” in 37th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.
mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by
a MAP-Based Postprocessor.” 37th International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2012), 2012.
short: 'A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.'
date_created: 2019-07-12T05:27:26Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- MAP parameter estimation
- noise power estimation
- speech enhancement
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf
oa: '1'
publication: 37th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2012)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf
status: public
title: Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11845'
abstract:
- lang: eng
text: The paper proposes a modification of the standard maximum a posteriori (MAP)
method for the estimation of the parameters of a Gaussian process for cases where
the process is superposed by additive Gaussian observation errors of known variance.
Simulations on artificially generated data demonstrate the superiority of the
proposed method. While reducing to the ordinary MAP approach in the absence of
observation noise, the improvement becomes the more pronounced the larger the
variance of the observation noise. The method is further extended to track the
parameters in case of non-stationary Gaussian processes.
author:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Krueger A, Haeb-Umbach R. MAP-based estimation of the parameters of non-stationary
Gaussian processes from noisy observations. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2011). ; 2011:3596-3599.
doi:10.1109/ICASSP.2011.5946256'
apa: Krueger, A., & Haeb-Umbach, R. (2011). MAP-based estimation of the parameters
of non-stationary Gaussian processes from noisy observations. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 3596–3599).
https://doi.org/10.1109/ICASSP.2011.5946256
bibtex: '@inproceedings{Krueger_Haeb-Umbach_2011, title={MAP-based estimation of
the parameters of non-stationary Gaussian processes from noisy observations},
DOI={10.1109/ICASSP.2011.5946256},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011)}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2011},
pages={3596–3599} }'
chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of
the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.”
In IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011), 3596–99, 2011. https://doi.org/10.1109/ICASSP.2011.5946256.
ieee: A. Krueger and R. Haeb-Umbach, “MAP-based estimation of the parameters of
non-stationary Gaussian processes from noisy observations,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011,
pp. 3596–3599.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011),
2011, pp. 3596–99, doi:10.1109/ICASSP.2011.5946256.
short: 'A. Krueger, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599.'
date_created: 2019-07-12T05:29:22Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2011.5946256
keyword:
- Gaussian processes
- MAP-based estimation
- maximum a posteriori method
- maximum likelihood estimation
- nonstationary Gaussian processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/KrHa11.pdf
oa: '1'
page: 3596-3599
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011)
status: public
title: MAP-based estimation of the parameters of non-stationary Gaussian processes
from noisy observations
type: conference
user_id: '44006'
year: '2011'
...
---
_id: '11850'
abstract:
- lang: eng
text: In this paper, we present a novel blocking matrix and fixed beamformer design
for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure.
They are based on a new method for estimating the acoustical transfer function
ratios in the presence of stationary noise. The estimation method relies on solving
a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector
tracking utilizing the power iteration method is employed and shown to achieve
a high convergence speed. Simulation results demonstrate that the proposed beamformer
leads to better noise and interference reduction and reduced speech distortions
compared to other blocking matrix designs from the literature.
author:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Krueger A, Warsitz E, Haeb-Umbach R. Speech Enhancement With a GSC-Like Structure
Employing Eigenvector-Based Transfer Function Ratios Estimation. IEEE Transactions
on Audio, Speech, and Language Processing. 2011;19(1):206-219. doi:10.1109/TASL.2010.2047324
apa: Krueger, A., Warsitz, E., & Haeb-Umbach, R. (2011). Speech Enhancement
With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
Estimation. IEEE Transactions on Audio, Speech, and Language Processing,
19(1), 206–219. https://doi.org/10.1109/TASL.2010.2047324
bibtex: '@article{Krueger_Warsitz_Haeb-Umbach_2011, title={Speech Enhancement With
a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation},
volume={19}, DOI={10.1109/TASL.2010.2047324},
number={1}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Krueger, Alexander and Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2011},
pages={206–219} }'
chicago: 'Krueger, Alexander, Ernst Warsitz, and Reinhold Haeb-Umbach. “Speech Enhancement
With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
Estimation.” IEEE Transactions on Audio, Speech, and Language Processing
19, no. 1 (2011): 206–19. https://doi.org/10.1109/TASL.2010.2047324.'
ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like
Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 19, no. 1, pp.
206–219, 2011.
mla: Krueger, Alexander, et al. “Speech Enhancement With a GSC-Like Structure Employing
Eigenvector-Based Transfer Function Ratios Estimation.” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 19, no. 1, 2011, pp. 206–19,
doi:10.1109/TASL.2010.2047324.
short: A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
and Language Processing 19 (2011) 206–219.
date_created: 2019-07-12T05:29:28Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2047324
intvolume: ' 19'
issue: '1'
keyword:
- acoustical transfer function ratio
- adaptive eigenvector tracking
- array signal processing
- beamformer design
- blocking matrix
- eigenvalues and eigenfunctions
- eigenvector-based transfer function ratios estimation
- generalized sidelobe canceler
- interference reduction
- iterative methods
- power iteration method
- reduced speech distortions
- reverberant enclosure
- reverberation
- speech enhancement
- stationary noise
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/KrWaHa11.pdf
oa: '1'
page: 206-219
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer
Function Ratios Estimation
type: journal_article
user_id: '44006'
volume: 19
year: '2011'
...