---
_id: '61492'
abstract:
- lang: eng
text: "This paper deals with the development and results of a prediction framework
for traffic light control systems as well as the usage and benefits of such predictions
in green light optimal speed advisory (GLOSA) scenarios.\r\nVarious machine learning
methods like support vector machines, neural networks or reinforcement learning
were evaluated for their applicability in the prediction context and compared
based on their efficiency and most importantly accuracy. The resulting prediction
framework uses decision tree ensemble models combined with certain model knowledge
to forecast different control strategies. This method was chosen due to its best
performance in various test scenarios. Very high accuracy and fidelity were achieved
for standard control methods like fixed-time, time-of-day-based and 'ordinary'
traffic-based programs. Only for the more sophisticated model predictive control
which was tested lower accuracies were achieved.\r\nFor the upcoming GLOSA application
the penetration of equipped vehicles was varied for different traffic scenarios
and control strategies. Results showcase high potentials for enhancing urban mobility
and reducing environmental impact by lower emissions and waiting times. However,
it is also clear from the studies presented in this contribution that the coordination
of the control strategy with the GLOSA vehicles is of enormous importance."
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: 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, Gausemeier S, Trächtler A. ML-based Prediction Framework
for varying Traffic Signal Control Strategies and its GLOSA-application. In: 2025
IEEE 28th International Conference on Intelligent Transportation Systems (ITSC).
Vol 28. IEEE.'
apa: Malena, K., Link, C., Gausemeier, S., & Trächtler, A. (n.d.). ML-based
Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application.
2025 IEEE 28th International Conference on Intelligent Transportation Systems
(ITSC), 28.
bibtex: '@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction
Framework for varying Traffic Signal Control Strategies and its GLOSA-application},
volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent
Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link,
Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }'
chicago: Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler.
“ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and
Its GLOSA-Application.” In 2025 IEEE 28th International Conference on Intelligent
Transportation Systems (ITSC), Vol. 28. IEEE, n.d.
ieee: K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction
Framework for varying Traffic Signal Control Strategies and its GLOSA-application,”
in 2025 IEEE 28th International Conference on Intelligent Transportation Systems
(ITSC), Gold Coast (Australia), vol. 28.
mla: Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal
Control Strategies and Its GLOSA-Application.” 2025 IEEE 28th International
Conference on Intelligent Transportation Systems (ITSC), vol. 28, IEEE.
short: 'K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International
Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.'
conference:
end_date: 2025-11-21
location: Gold Coast (Australia)
name: 28th International Conference on Intelligent Transportation Systems (ITSC)
start_date: 2025-11-18
date_created: 2025-10-01T11:20:34Z
date_updated: 2026-01-26T08:50:37Z
department:
- _id: '153'
intvolume: ' 28'
keyword:
- ML
- Prediction
- Tree Ensembles
- GLOSA
language:
- iso: eng
publication: 2025 IEEE 28th International Conference on Intelligent Transportation
Systems (ITSC)
publication_status: accepted
publisher: IEEE
quality_controlled: '1'
status: public
title: ML-based Prediction Framework for varying Traffic Signal Control Strategies
and its GLOSA-application
type: conference
user_id: '36303'
volume: 28
year: '2026'
...
---
_id: '53793'
abstract:
- lang: eng
text: We utilize extreme learning machines for the prediction of partial differential
equations (PDEs). Our method splits the state space into multiple windows that
are predicted individually using a single model. Despite requiring only few data
points (in some cases, our method can learn from a single full-state snapshot),
it still achieves high accuracy and can predict the flow of PDEs over long time
horizons. Moreover, we show how additional symmetries can be exploited to increase
sample efficiency and to enforce equivariance.
author:
- first_name: Hans
full_name: Harder, Hans
id: '98879'
last_name: Harder
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme
Learning Machines.
apa: Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines.
bibtex: '@article{Harder_Peitz, title={Predicting PDEs Fast and Efficiently with
Equivariant Extreme Learning Machines}, author={Harder, Hans and Peitz, Sebastian}
}'
chicago: Harder, Hans, and Sebastian Peitz. “Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines,” n.d.
ieee: H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant
Extreme Learning Machines.” .
mla: Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently
with Equivariant Extreme Learning Machines.
short: H. Harder, S. Peitz, (n.d.).
date_created: 2024-04-30T08:43:14Z
date_updated: 2024-04-30T08:45:24Z
keyword:
- extreme learning machines
- partial differential equations
- data-driven prediction
- high-dimensional systems
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2404.18530
oa: '1'
publication_status: unpublished
status: public
title: Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines
type: preprint
user_id: '98879'
year: '2024'
...
---
_id: '55336'
abstract:
- lang: eng
text: "Predicting the remaining useful life of technical \r\nsystems has gained
significant attention in recent years due to \r\nincreasing demands for extending
the lifespan of degrading system \r\ncomponents. Therefore, already used systems
are retrofitted by \r\nintegrating sensors to monitor their performance and \r\nfunctionality,
enabling accurate diagnosis of their condition and \r\nprediction of their remaining
useful life. One of the main \r\nchallenges in this field is identified in the
missing data from the \r\ntime where the retrofitted system has already run but
without \r\nbeing monitored by sensors. In this paper, a novel approach for \r\nthe
combined diagnostics and prognostics of retrofitted systems is \r\nproposed. The
methodology aims to provide an accurate diagnosis \r\nof the system’s health state
and estimation of the remaining useful \r\nlife by a combination of a machine
learning and expert knowledge. \r\nTo evaluate the effectiveness of the proposed
methodology, a case \r\nstudy involving a retrofitted system in an industrial
setting is \r\nselected and applied. It is demonstrated that the approach \r\neffectively
diagnose the current system’s health state and \r\naccurately predict its remaining
useful life, thereby enabling \r\npredictive maintenance and decision-making.
Overall, our \r\nresearch contributes to advancing the field of condition \r\nmonitoring
for retrofitted systems by providing a comprehensive \r\nmethodology that addresses
the challenge of missing data."
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Aimiyekagbon OK, Sextro W. Diagnostics and Prognostics for Retrofitted
Systems: A Comprehensive Approach for Enhanced System Health Assessment. In: Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM). IEEE
Computer Society; 2024. doi:10.1109/PHM61473.2024.00038'
apa: 'Bender, A., Aimiyekagbon, O. K., & Sextro, W. (2024). Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment. Proceedings of the 2024 Prognostics and System Health Management
Conference (PHM). 2024 Prognostics and System Health Management Conference
(PHM), Stockholm, Schweden. https://doi.org/10.1109/PHM61473.2024.00038'
bibtex: '@inproceedings{Bender_Aimiyekagbon_Sextro_2024, title={Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment}, DOI={10.1109/PHM61473.2024.00038},
booktitle={Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)}, publisher={IEEE Computer Society}, author={Bender, Amelie and Aimiyekagbon,
Osarenren Kennedy and Sextro, Walter}, year={2024} }'
chicago: 'Bender, Amelie, Osarenren Kennedy Aimiyekagbon, and Walter Sextro. “Diagnostics
and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced
System Health Assessment.” In Proceedings of the 2024 Prognostics and System
Health Management Conference (PHM). IEEE Computer Society, 2024. https://doi.org/10.1109/PHM61473.2024.00038.'
ieee: 'A. Bender, O. K. Aimiyekagbon, and W. Sextro, “Diagnostics and Prognostics
for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment,”
presented at the 2024 Prognostics and System Health Management Conference (PHM),
Stockholm, Schweden, 2024, doi: 10.1109/PHM61473.2024.00038.'
mla: 'Bender, Amelie, et al. “Diagnostics and Prognostics for Retrofitted Systems:
A Comprehensive Approach for Enhanced System Health Assessment.” Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM), IEEE
Computer Society, 2024, doi:10.1109/PHM61473.2024.00038.'
short: 'A. Bender, O.K. Aimiyekagbon, W. Sextro, in: Proceedings of the 2024 Prognostics
and System Health Management Conference (PHM), IEEE Computer Society, 2024.'
conference:
end_date: 2024-05-31
location: Stockholm, Schweden
name: 2024 Prognostics and System Health Management Conference (PHM)
start_date: 2024-05-28
date_created: 2024-07-22T09:27:57Z
date_updated: 2024-07-22T09:29:26Z
department:
- _id: '151'
doi: 10.1109/PHM61473.2024.00038
keyword:
- retrofit
- diagnosis
- prognostics
- RUL prediction
- missing data
- ball bearings
language:
- iso: eng
publication: Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)
publication_identifier:
isbn:
- 979-8-3503-6058-5
publisher: IEEE Computer Society
quality_controlled: '1'
status: public
title: 'Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach
for Enhanced System Health Assessment'
type: conference
user_id: '54290'
year: '2024'
...
---
_id: '50479'
abstract:
- lang: eng
text: Verifying assertions is an essential part of creating and maintaining knowledge
graphs. Most often, this task cannot be carried out manually due to the sheer
size of modern knowledge graphs. Hence, automatic fact-checking approaches have
been proposed over the last decade. These approaches aim to compute automatically
whether a given assertion is correct or incorrect. However, most fact-checking
approaches are binary classifiers that fail to consider the volatility of some
assertions, i.e., the fact that such assertions are only valid at certain times
or for specific time intervals. Moreover, the few approaches able to predict when
an assertion was valid (i.e., time-point prediction approaches) rely on manual
feature engineering. This paper presents TEMPORALFC, a temporal fact-checking
approach that uses multiple sources of background knowledge to assess the veracity
and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets
and compare it to the state of the art in fact-checking and time-point prediction.
Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking
task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic
curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal
Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.
author:
- first_name: Umair
full_name: Qudus, Umair
last_name: Qudus
- first_name: Michael
full_name: Röder, Michael
last_name: Röder
- first_name: Sabrina
full_name: Kirrane, Sabrina
last_name: Kirrane
- first_name: Axel-Cyrille Ngonga
full_name: Ngomo, Axel-Cyrille Ngonga
last_name: Ngomo
citation:
ama: 'Qudus U, Röder M, Kirrane S, Ngomo A-CN. TemporalFC: A Temporal Fact Checking
Approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et al., eds.
The Semantic Web – ISWC 2023. Vol 14265. Lecture Notes in Computer Science.
Springer, Cham; 2023:465–483. doi:10.1007/978-3-031-47240-4_25'
apa: 'Qudus, U., Röder, M., Kirrane, S., & Ngomo, A.-C. N. (2023). TemporalFC:
A Temporal Fact Checking Approach over Knowledge Graphs. In T. R. Payne, V. Presutti,
G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &
J. Li (Eds.), The Semantic Web – ISWC 2023 (Vol. 14265, pp. 465–483). Springer,
Cham. https://doi.org/10.1007/978-3-031-47240-4_25'
bibtex: '@inproceedings{Qudus_Röder_Kirrane_Ngomo_2023, place={Cham}, series={ Lecture
Notes in Computer Science}, title={TemporalFC: A Temporal Fact Checking Approach
over Knowledge Graphs}, volume={14265}, DOI={10.1007/978-3-031-47240-4_25},
booktitle={The Semantic Web – ISWC 2023}, publisher={Springer, Cham}, author={Qudus,
Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga},
editor={R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón,
María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong
and Li, Juanzi}, year={2023}, pages={465–483}, collection={ Lecture Notes in Computer
Science} }'
chicago: 'Qudus, Umair, Michael Röder, Sabrina Kirrane, and Axel-Cyrille Ngonga
Ngomo. “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs.”
In The Semantic Web – ISWC 2023, edited by Terry R. Payne, Valentina Presutti,
Guilin Qi, María Poveda-Villalón, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi,
Gong Cheng, and Juanzi Li, 14265:465–483. Lecture Notes in Computer Science.
Cham: Springer, Cham, 2023. https://doi.org/10.1007/978-3-031-47240-4_25.'
ieee: 'U. Qudus, M. Röder, S. Kirrane, and A.-C. N. Ngomo, “TemporalFC: A Temporal
Fact Checking Approach over Knowledge Graphs,” in The Semantic Web – ISWC 2023,
Athens, Greece, 2023, vol. 14265, pp. 465–483, doi: 10.1007/978-3-031-47240-4_25.'
mla: 'Qudus, Umair, et al. “TemporalFC: A Temporal Fact Checking Approach over Knowledge
Graphs.” The Semantic Web – ISWC 2023, edited by Terry R. Payne et al.,
vol. 14265, Springer, Cham, 2023, pp. 465–483, doi:10.1007/978-3-031-47240-4_25.'
short: 'U. Qudus, M. Röder, S. Kirrane, A.-C.N. Ngomo, in: T. R. Payne, V. Presutti,
G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, J. Li
(Eds.), The Semantic Web – ISWC 2023, Springer, Cham, Cham, 2023, pp. 465–483.'
conference:
end_date: 2023-11-10
location: Athens, Greece
name: The Semantic Web – ISWC 2023
start_date: 2023-11-06
date_created: 2024-01-13T11:22:15Z
date_updated: 2024-01-13T11:48:28Z
ddc:
- '006'
department:
- _id: '34'
doi: 10.1007/978-3-031-47240-4_25
editor:
- first_name: Terry
full_name: R. Payne, Terry
last_name: R. Payne
- first_name: Valentina
full_name: Presutti, Valentina
last_name: Presutti
- first_name: Guilin
full_name: Qi, Guilin
last_name: Qi
- first_name: María
full_name: Poveda-Villalón, María
last_name: Poveda-Villalón
- first_name: Giorgos
full_name: Stoilos, Giorgos
last_name: Stoilos
- first_name: Laura
full_name: Hollink, Laura
last_name: Hollink
- first_name: Zoi
full_name: Kaoudi, Zoi
last_name: Kaoudi
- first_name: Gong
full_name: Cheng, Gong
last_name: Cheng
- first_name: Juanzi
full_name: Li, Juanzi
last_name: Li
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-01-13T11:25:48Z
date_updated: 2024-01-13T11:25:48Z
file_id: '50480'
file_name: ISWC 2023 TemporalFC-A Temporal Fact Checking approach over Knowledge
Graphs.pdf
file_size: 1944818
relation: main_file
success: 1
file_date_updated: 2024-01-13T11:25:48Z
has_accepted_license: '1'
intvolume: ' 14265'
jel:
- C
keyword:
- temporal fact checking · ensemble learning · transfer learning · time-point prediction
· temporal knowledge graphs
language:
- iso: eng
page: 465–483
place: Cham
project:
- _id: '410'
grant_number: '860801'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: The Semantic Web – ISWC 2023
publication_identifier:
isbn:
- '9783031472398'
- '9783031472404'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer, Cham
series_title: ' Lecture Notes in Computer Science'
status: public
title: 'TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs'
type: conference
user_id: '83392'
volume: 14265
year: '2023'
...
---
_id: '53801'
abstract:
- lang: eng
text: 'In this study, we evaluate the impact of gender-biased data from German-language
physician reviews on the fairness of fine-tuned language models. For two different
downstream tasks, we use data reported to be gender biased and aggregate it with
annotations. First, we propose a new approach to aspect-based sentiment analysis
that allows identifying, extracting, and classifying implicit and explicit aspect
phrases and their polarity within a single model. The second task we present is
grade prediction, where we predict the overall grade of a review on the basis
of the review text. For both tasks, we train numerous transformer models and evaluate
their performance. The aggregation of sensitive attributes, such as a physician’s
gender and migration background, with individual text reviews allows us to measure
the performance of the models with respect to these sensitive groups. These group-wise
performance measures act as extrinsic bias measures for our downstream tasks.
In addition, we translate several gender-specific templates of the intrinsic bias
metrics into the German language and evaluate our fine-tuned models. Based on
this set of tasks, fine-tuned models, and intrinsic and extrinsic bias measures,
we perform correlation analyses between intrinsic and extrinsic bias measures.
In terms of sensitive groups and effect sizes, our bias measure results show different
directions. Furthermore, correlations between measures of intrinsic and extrinsic
bias can be observed in different directions. This leads us to conclude that gender-biased
data does not inherently lead to biased models. Other variables, such as template
dependency for intrinsic measures and label distribution in the data, must be
taken into account as they strongly influence the metric results. Therefore, we
suggest that metrics and templates should be chosen according to the given task
and the biases to be assessed. '
article_number: '102235'
article_type: original
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Falk
full_name: Maoro, Falk
last_name: Maoro
- first_name: Michaela
full_name: Geierhos, Michaela
last_name: Geierhos
citation:
ama: 'Kersting J, Maoro F, Geierhos M. Towards comparable ratings: Exploring bias
in German physician reviews. Data & Knowledge Engineering. 2023;148.
doi:10.1016/j.datak.2023.102235'
apa: 'Kersting, J., Maoro, F., & Geierhos, M. (2023). Towards comparable ratings:
Exploring bias in German physician reviews. Data & Knowledge Engineering,
148, Article 102235. https://doi.org/10.1016/j.datak.2023.102235'
bibtex: '@article{Kersting_Maoro_Geierhos_2023, title={Towards comparable ratings:
Exploring bias in German physician reviews}, volume={148}, DOI={10.1016/j.datak.2023.102235},
number={102235}, journal={Data & Knowledge Engineering}, publisher={Elsevier},
author={Kersting, Joschka and Maoro, Falk and Geierhos, Michaela}, year={2023}
}'
chicago: 'Kersting, Joschka, Falk Maoro, and Michaela Geierhos. “Towards Comparable
Ratings: Exploring Bias in German Physician Reviews.” Data & Knowledge
Engineering 148 (2023). https://doi.org/10.1016/j.datak.2023.102235.'
ieee: 'J. Kersting, F. Maoro, and M. Geierhos, “Towards comparable ratings: Exploring
bias in German physician reviews,” Data & Knowledge Engineering, vol.
148, Art. no. 102235, 2023, doi: 10.1016/j.datak.2023.102235.'
mla: 'Kersting, Joschka, et al. “Towards Comparable Ratings: Exploring Bias in German
Physician Reviews.” Data & Knowledge Engineering, vol. 148, 102235,
Elsevier, 2023, doi:10.1016/j.datak.2023.102235.'
short: J. Kersting, F. Maoro, M. Geierhos, Data & Knowledge Engineering 148
(2023).
date_created: 2024-04-30T12:30:56Z
date_updated: 2024-04-30T12:41:14Z
ddc:
- '004'
department:
- _id: '579'
doi: 10.1016/j.datak.2023.102235
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2024-04-30T12:34:35Z
date_updated: 2024-04-30T12:34:35Z
file_id: '53802'
file_name: Kersting 2023.pdf
file_size: 1381398
relation: main_file
success: 1
file_date_updated: 2024-04-30T12:34:35Z
funded_apc: '1'
has_accepted_license: '1'
intvolume: ' 148'
keyword:
- Language model fairness
- Aspect phrase classification
- Grade prediction
- Physician reviews
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.1016/j.datak.2023.102235 '
oa: '1'
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '9'
grant_number: '160364472'
name: 'SFB 901 - B1: SFB 901 - Parametrisierte Servicespezifikation (Subproject
B1)'
publication: Data & Knowledge Engineering
publication_identifier:
issn:
- 0169-023X
publication_status: published
publisher: Elsevier
status: public
title: 'Towards comparable ratings: Exploring bias in German physician reviews'
type: journal_article
user_id: '58701'
volume: 148
year: '2023'
...
---
_id: '41971'
abstract:
- lang: ger
text: "Ultraschall-Drahtbonden ist eine Standardtechnologie im Bereich der Aufbau-
und Verbindungstechnik von Leistungshalbleitermodulen. Um Prozessschritte und
damit wertvolle Zeit zu sparen, sollen die Kupferdickdrähte für die Leistungshalbleiter
auch für die Kontaktierung von eingespritzten Anschlusssteckern im Modulrahmen
verwendet werden. Das Kontaktierungsverfahren mit diesen Drähten auf Steckern
in dünnwandigen Kunststoffrahmen führt häufig zu unzureichender Bondqualität.
In dieser Arbeit wird das Bonden von Anschlusssteckern experimentell und anhand
von Simulationen untersucht, um die Prozessstabilität zu steigern.\r\n\r\nZunächst
wurden Experimente auf Untergründen mit hoher Steifigkeit durchgeführt, um Störgrößen
von Untergrundeigenschaften zu verringern. Die gewonnenen Erkenntnisse erlaubten
die Entwicklung eines Simulationsmodells für die Vorhersage der Bondqualität.
Dieses basiert auf einer flächenaufgelösten Reibarbeitsbestimmung im Fügebereich
unter Berücksichtigung des Ultraschallerweichungseffektes und der hierdurch entstehenden
hohen Drahtverformung.\r\n\r\nExperimente an den Anschlusssteckern im Modulrahmen
zeigten eine verringerte Relativverschiebung zwischen Draht und Stecker, was zu
einer deutlichen Verringerung der Reibarbeit führt. Außerdem wurden verminderte
Schwingamplituden des Bondwerkzeugs nachgewiesen. Dies führt zu einer weiteren
Reduktion der Reibarbeit. Beide Effekte wurden mithilfe eines Mehrmassenschwingers
modelliert. Die gewonnenen Erkenntnisse und die erstellten Simulationsmodelle
ermöglichen die Entwicklung von Klemmvorrichtungen, welche die identifizierten
Störgrößen gezielt kompensieren und so ein verlässliches Bonden der Anschlussstecker
im gleichen Prozessschritt ermöglichen, in dem auch die Leistungshalbleiter kontaktiert
werden."
author:
- first_name: Simon
full_name: Althoff, Simon
last_name: Althoff
citation:
ama: Althoff S. Predicting the Bond Quality of Heavy Copper Wire Bonds Using
a Friction Model Approach. Vol 15. Shaker; 2023.
apa: Althoff, S. (2023). Predicting the Bond Quality of Heavy Copper Wire Bonds
using a Friction Model Approach (Vol. 15). Shaker.
bibtex: '@book{Althoff_2023, series={Schriften des Lehrstuhls für Dynamik und Mechatronik},
title={Predicting the Bond Quality of Heavy Copper Wire Bonds using a Friction
Model Approach}, volume={15}, publisher={Shaker}, author={Althoff, Simon}, year={2023},
collection={Schriften des Lehrstuhls für Dynamik und Mechatronik} }'
chicago: Althoff, Simon. Predicting the Bond Quality of Heavy Copper Wire Bonds
Using a Friction Model Approach. Vol. 15. Schriften Des Lehrstuhls Für Dynamik
Und Mechatronik. Shaker, 2023.
ieee: S. Althoff, Predicting the Bond Quality of Heavy Copper Wire Bonds using
a Friction Model Approach, vol. 15. Shaker, 2023.
mla: Althoff, Simon. Predicting the Bond Quality of Heavy Copper Wire Bonds Using
a Friction Model Approach. Shaker, 2023.
short: S. Althoff, Predicting the Bond Quality of Heavy Copper Wire Bonds Using
a Friction Model Approach, Shaker, 2023.
date_created: 2023-02-10T13:05:19Z
date_updated: 2023-02-10T13:05:42Z
department:
- _id: '151'
extern: '1'
intvolume: ' 15'
keyword:
- heavy copper bonding
- wire bonding
- quality prediction
- friction model
- point-contact-element
language:
- iso: eng
main_file_link:
- url: https://katalog.ub.uni-paderborn.de/local/r/9925085762506463?sr[q,any]=Simon%20Althoff
page: '192'
publication_identifier:
isbn:
- 978-3-8440-8903-5
publication_status: published
publisher: Shaker
related_material:
link:
- relation: confirmation
url: https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-8903-5&search=yes
series_title: Schriften des Lehrstuhls für Dynamik und Mechatronik
status: public
supervisor:
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
title: Predicting the Bond Quality of Heavy Copper Wire Bonds using a Friction Model
Approach
type: dissertation
user_id: '55222'
volume: 15
year: '2023'
...
---
_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: '21542'
abstract:
- lang: eng
text: Using near-field (NF) scan data to predict the far-field (FF) behaviour of
radiating electronic systems represents a novel method to accompany the whole
RF design process. This approach involves so-called Huygens' box as an efficient
radiation model inside an electromagnetic (EM) simulation tool and then transforms
the scanned NF measured data into the FF. For this, the basic idea of the Huygens'box
principle and the NF-to-FF transformation are briefly presented. The NF is measured
on the Huygens' box around a device under test using anNF scanner, recording the
magnitude and phase of the site-related magnetic and electric components. A comparison
between a fullwave simulation and the measurement results shows a good similarity
in both the NF and the simulated and transformed FF.Thus, this method is applicable
to predict the FF behaviour of any electronic system by measuring the NF. With
this knowledge, the RF design can be improved due to allowing a significant reduction
of EM compatibility failure at the end of the development flow. In addition, the
very efficient FF radiation model can be used for detailed investigations in various
environments and the impact of such an equivalent radiation source on other electronic
systems can be assessed.
author:
- first_name: Dominik
full_name: Schröder, Dominik
last_name: Schröder
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Christian
full_name: Hangmann, Christian
last_name: Hangmann
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
citation:
ama: 'Schröder D, Lange S, Hangmann C, Hedayat C. Far-field prediction combining
simulations with near-field measurements for EMI assessment of PCBs. In: Tensorial
Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis.
1st ed. Croyton, UK: The Institution of Engineering and Technology (IET); 2020:315-346
(32). doi:10.1049/pbcs072e_ch14'
apa: 'Schröder, D., Lange, S., Hangmann, C., & Hedayat, C. (2020). Far-field
prediction combining simulations with near-field measurements for EMI assessment
of PCBs. In Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
and EMC Analysis (1st ed., pp. 315-346 (32)). Croyton, UK: The Institution
of Engineering and Technology (IET). https://doi.org/10.1049/pbcs072e_ch14'
bibtex: '@inbook{Schröder_Lange_Hangmann_Hedayat_2020, place={Croyton, UK}, edition={1},
title={Far-field prediction combining simulations with near-field measurements
for EMI assessment of PCBs}, DOI={10.1049/pbcs072e_ch14},
booktitle={Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
and EMC Analysis}, publisher={ The Institution of Engineering and Technology (IET)},
author={Schröder, Dominik and Lange, Sven and Hangmann, Christian and Hedayat,
Christian}, year={2020}, pages={315-346 (32)} }'
chicago: 'Schröder, Dominik, Sven Lange, Christian Hangmann, and Christian Hedayat.
“Far-Field Prediction Combining Simulations with near-Field Measurements for EMI
Assessment of PCBs.” In Tensorial Analysis of Networks (TAN) Modelling for
PCB Signal Integrity and EMC Analysis, 1st ed., 315-346 (32). Croyton, UK: The
Institution of Engineering and Technology (IET), 2020. https://doi.org/10.1049/pbcs072e_ch14.'
ieee: 'D. Schröder, S. Lange, C. Hangmann, and C. Hedayat, “Far-field prediction
combining simulations with near-field measurements for EMI assessment of PCBs,”
in Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
and EMC Analysis, 1st ed., Croyton, UK: The Institution of Engineering and
Technology (IET), 2020, pp. 315-346 (32).'
mla: Schröder, Dominik, et al. “Far-Field Prediction Combining Simulations with
near-Field Measurements for EMI Assessment of PCBs.” Tensorial Analysis of
Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis, 1st ed., The
Institution of Engineering and Technology (IET), 2020, pp. 315-346 (32), doi:10.1049/pbcs072e_ch14.
short: 'D. Schröder, S. Lange, C. Hangmann, C. Hedayat, in: Tensorial Analysis
of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis, 1st ed., The
Institution of Engineering and Technology (IET), Croyton, UK, 2020, pp. 315-346
(32).'
date_created: 2021-03-18T13:49:49Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '485'
doi: 10.1049/pbcs072e_ch14
edition: '1'
keyword:
- Huygens' box
- NF-to-FF transformation
- efficient FF radiation model
- FF behaviour
- EMI assessment
- PCB
- near-field measurements
- efficient radiation model
- far-field behaviour
- RF design process
- far-field prediction
- Huygens'box principle
- fullwave simulation
- electronic system radiation
- equivalent radiation source
- electromagnetic simulation tool
- near-field scan data
- EM compatibility failure reduction
language:
- iso: eng
main_file_link:
- url: https://digital-library.theiet.org/content/books/10.1049/pbcs072e_ch14
page: 315-346 (32)
place: Croyton, UK
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
and EMC Analysis
publication_identifier:
isbn:
- '9781839530494'
- '9781839530500'
publication_status: published
publisher: ' The Institution of Engineering and Technology (IET)'
related_material:
record:
- id: '21542'
relation: other
status: public
status: public
title: Far-field prediction combining simulations with near-field measurements for
EMI assessment of PCBs
type: book_chapter
user_id: '38240'
year: '2020'
...
---
_id: '17810'
abstract:
- lang: eng
text: In all fields, the significance of a reliable and accurate predictive model
is almost unquantifiable. With deep domain knowledge, models derived from first
principles typically outperforms other models in terms of reliability and accuracy.
When it may become a cumbersome or an unachievable task to build or validate such
models of complex (non-linear) systems, machine learning techniques are employed
to build predictive models. However, the accuracy of such techniques is not only
dependent on the hyper-parameters of the chosen algorithm, but also on the amount
and quality of data. This paper investigates the application of classical time
series forecasting approaches for the reliable prognostics of technical systems,
where black box machine learning techniques might not successfully be employed
given insufficient amount of data and where first principles models are infeasible
due to lack of domain specific data. Forecasting by analogy, forecasting by analytical
function fitting, an exponential smoothing forecasting method and the long short-term
memory (LSTM) are evaluated and compared against the ground truth data. As a case
study, the methods are applied to predict future crack lengths of riveted aluminium
plates under cyclic loading. The performance of the predictive models is evaluated
based on error metrics leading to a proposal of when to apply which forecasting
approach.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- 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: 'Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data. In: PHM Society European Conference. Vol 5. ;
2020.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2020). Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data. PHM Society European Conference,
5(1).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM
Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2020} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation
of Time Series Forecasting Approaches for the Reliable Crack Length Prediction
of Riveted Aluminium Plates given Insufficient Data.” In PHM Society European
Conference, Vol. 5, 2020.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data,” in PHM Society European Conference, 2020, vol.
5, no. 1.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting
Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates
given Insufficient Data.” PHM Society European Conference, vol. 5, no.
1, 2020.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference,
2020.'
date_created: 2020-08-11T13:32:40Z
date_updated: 2023-09-22T09:13:16Z
department:
- _id: '151'
intvolume: ' 5'
issue: '1'
keyword:
- PHM 2019
- crack propagation
- forecasting
- unevenly spaced time series
- step ahead prediction
- short time series
language:
- iso: eng
publication: PHM Society European Conference
quality_controlled: '1'
status: public
title: Evaluation of time series forecasting approaches for the reliable crack length
prediction of riveted aluminium plates given insufficient data
type: conference
user_id: '9557'
volume: 5
year: '2020'
...
---
_id: '13460'
abstract:
- lang: eng
text: 'Remaining useful lifetime (RUL) predictions as part of a condition monitoring
system are focused in more and more research and industrial applications. To establish
an efficient and precise estimate of the RUL of a technical product, different uncertainties have to be handled. To minimize the uncertainties of the RUL estimation, a reliable
and accurate prognostic approach as well as a good failure threshold are important.
Regarding the failure threshold, most often an expert sets a fixed failure threshold. However, neither the a priori known failure threshold nor a fixedthreshold
value are feasible in every application. Especially in the case of varying characteristics
of the monitored system, an adaptive failure threshold is of great importance
concerning the accuracy of the RUL estimation. Rubber-metal-elements, which are
used in a wide range of applications for vibration and sound isolation, are mon-itored
by thermocouples to allow for lifetime predictions. Therefore, the element’s state
is described by its temper-ature during its service life. Aiming to establish
accurate RUL predictions of a rubber-metal-element, uncertainties due to nonlinear
material characteristics and changing operational conditions have to be considered.
Consequently, different temperature-based failure threshold definitions are implemented
and compared within a particle filtering approach. '
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Lennart
full_name: Schinke, Lennart
last_name: Schinke
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Schinke L, Sextro W. Remaining useful lifetime prediction based
on adaptive failure thresholds. In: Beer M, Zio E, eds. Proceedings of the
29th European Safety and Reliability Conference (ESREL2019). ; 2019:1262-1269.'
apa: Bender, A., Schinke, L., & Sextro, W. (2019). Remaining useful lifetime
prediction based on adaptive failure thresholds. In M. Beer & E. Zio (Eds.),
Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)
(Issue 29, pp. 1262–1269).
bibtex: '@inproceedings{Bender_Schinke_Sextro_2019, title={Remaining useful lifetime
prediction based on adaptive failure thresholds}, number={29}, booktitle={Proceedings
of the 29th European Safety and Reliability Conference (ESREL2019)}, author={Bender,
Amelie and Schinke, Lennart and Sextro, Walter}, editor={Beer, Michael and Zio,
Enrico}, year={2019}, pages={1262–1269} }'
chicago: Bender, Amelie, Lennart Schinke, and Walter Sextro. “Remaining Useful Lifetime
Prediction Based on Adaptive Failure Thresholds.” In Proceedings of the 29th
European Safety and Reliability Conference (ESREL2019), edited by Michael
Beer and Enrico Zio, 1262–69, 2019.
ieee: A. Bender, L. Schinke, and W. Sextro, “Remaining useful lifetime prediction
based on adaptive failure thresholds,” in Proceedings of the 29th European
Safety and Reliability Conference (ESREL2019), Hannover, 2019, no. 29, pp.
1262–1269.
mla: Bender, Amelie, et al. “Remaining Useful Lifetime Prediction Based on Adaptive
Failure Thresholds.” Proceedings of the 29th European Safety and Reliability
Conference (ESREL2019), edited by Michael Beer and Enrico Zio, no. 29, 2019,
pp. 1262–69.
short: 'A. Bender, L. Schinke, W. Sextro, in: M. Beer, E. Zio (Eds.), Proceedings
of the 29th European Safety and Reliability Conference (ESREL2019), 2019, pp.
1262–1269.'
conference:
end_date: 2019.09.26
location: Hannover
name: '29th European Safety and Reliability Conference '
start_date: 2019.09.22
date_created: 2019-09-30T08:49:19Z
date_updated: 2023-09-22T07:31:53Z
department:
- _id: '151'
editor:
- first_name: Michael
full_name: Beer, Michael
last_name: Beer
- first_name: Enrico
full_name: Zio, Enrico
last_name: Zio
issue: '29'
keyword:
- RUL prediction
- adaptive threshold
- prognostics
- condition monitoring
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://rpsonline.com.sg/proceedings/9789811127243/html/0445.xml
oa: '1'
page: 1262-1269
publication: Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)
publication_identifier:
isbn:
- 978-981-11-2724-3
publication_status: published
quality_controlled: '1'
status: public
title: Remaining useful lifetime prediction based on adaptive failure thresholds
type: conference
user_id: '54290'
year: '2019'
...
---
_id: '20868'
abstract:
- lang: eng
text: 'This study proposes a simple theoretical framework that allows for assessing
financial distress up to five years in advance. We jointly model financial distress
by using two of its key driving factors: declining cash-generating ability and
insufficient liquidity reserves. The model is based on stochastic processes and
incorporates firm-level and industry-sector developments. A large-scale empirical
implementation for US-listed firms over the period of 1980-2010 shows important
improvements in the discriminatory accuracy and demonstrates incremental information
content beyond state-of-the-art accounting and market-based prediction models.
Consequently, this study might provide important ex ante warning signals for investors,
regulators and practitioners.'
author:
- first_name: Sönke
full_name: Sievers, Sönke
id: '46447'
last_name: Sievers
- first_name: Jan
full_name: Klobucnik, Jan
last_name: Klobucnik
- first_name: David
full_name: Miersch, David
last_name: Miersch
citation:
ama: 'Sievers S, Klobucnik J, Miersch D. Predicting Early Warning Signals of
Financial Distress: Theory and Empirical Evidence.; 2017. doi:10.2139/ssrn.2237757'
apa: 'Sievers, S., Klobucnik, J., & Miersch, D. (2017). Predicting Early
Warning Signals of Financial Distress: Theory and Empirical Evidence. https://doi.org/10.2139/ssrn.2237757'
bibtex: '@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence}, DOI={10.2139/ssrn.2237757},
author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }'
chicago: 'Sievers, Sönke, Jan Klobucnik, and David Miersch. Predicting Early
Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017.
https://doi.org/10.2139/ssrn.2237757.'
ieee: 'S. Sievers, J. Klobucnik, and D. Miersch, Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence. 2017.'
mla: 'Sievers, Sönke, et al. Predicting Early Warning Signals of Financial Distress:
Theory and Empirical Evidence. 2017, doi:10.2139/ssrn.2237757.'
short: 'S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of
Financial Distress: Theory and Empirical Evidence, 2017.'
date_created: 2021-01-05T11:44:45Z
date_updated: 2022-01-06T06:54:41Z
department:
- _id: '275'
doi: 10.2139/ssrn.2237757
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757
page: '84'
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
Evidence'
type: working_paper
user_id: '46447'
year: '2017'
...
---
_id: '5199'
abstract:
- lang: eng
text: 'This study proposes a simple theoretical framework that allows for assessing
financial distress up to five years in advance. We jointly model financial distress
by using two of its key driving factors: declining cash-generating ability and
insufficient liquidity reserves. The model is based on stochastic processes and
incorporates firm-level and industry-sector developments. A large-scale empirical
implementation for US-listed firms over the period of 1980-2010 shows important
improvements in the discriminatory accuracy and demonstrates incremental information
content beyond state-of-the-art accounting and market-based prediction models.
Consequently, this study might provide important ex ante warning signals for investors,
regulators and practitioners. '
author:
- first_name: Jan
full_name: Klobucnik, Jan
last_name: Klobucnik
- first_name: David
full_name: Miersch, David
last_name: Miersch
- first_name: Sönke
full_name: Sievers, Sönke
last_name: Sievers
citation:
ama: 'Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial
Distress: Theory and Empirical Evidence. SSRN Electronic Journal. 2017.'
apa: 'Klobucnik, J., Miersch, D., & Sievers, S. (2017). Predicting Early Warning
Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic
Journal.'
bibtex: '@article{Klobucnik_Miersch_Sievers_2017, title={Predicting Early Warning
Signals of Financial Distress: Theory and Empirical Evidence}, journal={SSRN Electronic
Journal}, author={Klobucnik, Jan and Miersch, David and Sievers, Sönke}, year={2017}
}'
chicago: 'Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning
Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic
Journal, 2017.'
ieee: 'J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence,” SSRN Electronic Journal,
2017.'
mla: 'Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress:
Theory and Empirical Evidence.” SSRN Electronic Journal, 2017.'
short: J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).
date_created: 2018-10-31T12:19:42Z
date_updated: 2022-01-06T07:01:43Z
department:
- _id: '275'
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
Evidence'
type: journal_article
user_id: '64756'
year: '2017'
...
---
_id: '9889'
abstract:
- lang: eng
text: A measurement method is presented that combines the advantages of the multisine
measurement technique with a prediction method for peak bending behavior. This
combination allows the analysis of the dynamic behavior of mechanical structures
at distinctly reduced measurement durations and has the advantage of reducing
high excitation impacts on the structure under test.
author:
- first_name: Christian
full_name: Sprock, Christian
last_name: Sprock
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Sprock C, Sextro W. Time-efficient dynamic analysis of structures exhibiting
nonlinear peak bending. In: Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International. ; 2014:320-324. doi:10.1109/I2MTC.2014.6860760'
apa: Sprock, C., & Sextro, W. (2014). Time-efficient dynamic analysis of structures
exhibiting nonlinear peak bending. In Instrumentation and Measurement Technology
Conference (I2MTC) Proceedings, 2014 IEEE International (pp. 320–324). https://doi.org/10.1109/I2MTC.2014.6860760
bibtex: '@inproceedings{Sprock_Sextro_2014, title={Time-efficient dynamic analysis
of structures exhibiting nonlinear peak bending}, DOI={10.1109/I2MTC.2014.6860760},
booktitle={Instrumentation and Measurement Technology Conference (I2MTC) Proceedings,
2014 IEEE International}, author={Sprock, Christian and Sextro, Walter}, year={2014},
pages={320–324} }'
chicago: Sprock, Christian, and Walter Sextro. “Time-Efficient Dynamic Analysis
of Structures Exhibiting Nonlinear Peak Bending.” In Instrumentation and Measurement
Technology Conference (I2MTC) Proceedings, 2014 IEEE International, 320–24,
2014. https://doi.org/10.1109/I2MTC.2014.6860760.
ieee: C. Sprock and W. Sextro, “Time-efficient dynamic analysis of structures exhibiting
nonlinear peak bending,” in Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–324.
mla: Sprock, Christian, and Walter Sextro. “Time-Efficient Dynamic Analysis of Structures
Exhibiting Nonlinear Peak Bending.” Instrumentation and Measurement Technology
Conference (I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–24,
doi:10.1109/I2MTC.2014.6860760.
short: 'C. Sprock, W. Sextro, in: Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–324.'
date_created: 2019-05-20T13:25:22Z
date_updated: 2019-05-20T13:25:53Z
department:
- _id: '151'
doi: 10.1109/I2MTC.2014.6860760
keyword:
- bending
- dynamic testing
- measurement
- structural engineering
- vibrations
- measurement durations
- mechanical structures
- multisine measurement technique
- nonlinear peak bending behavior
- prediction method
- time-efficient dynamic analysis
- Heuristic algorithms
- Nonlinear systems
- Oscillators
- Time measurement
- Time-frequency analysis
- Vibrations
language:
- iso: eng
page: 320-324
publication: Instrumentation and Measurement Technology Conference (I2MTC) Proceedings,
2014 IEEE International
status: public
title: Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending
type: conference
user_id: '55222'
year: '2014'
...
---
_id: '46388'
abstract:
- lang: eng
text: Understanding the behaviour of well-known algorithms for classical NP-hard
optimisation problems is still a difficult task. With this paper, we contribute
to this research direction and carry out a feature based comparison of local search
and the well-known Christofides approximation algorithm for the Traveling Salesperson
Problem. We use an evolutionary algorithm approach to construct easy and hard
instances for the Christofides algorithm, where we measure hardness in terms of
approximation ratio. Our results point out important features and lead to hard
and easy instances for this famous algorithm. Furthermore, our cross-comparison
gives new insights on the complementary benefits of the different approaches.
author:
- first_name: Samadhi
full_name: Nallaperuma, Samadhi
last_name: Nallaperuma
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H. A Feature-Based
Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem. In: Proceedings of the Twelfth Workshop on Foundations of Genetic
Algorithms XII. FOGA XII ’13. Association for Computing Machinery; 2013:147–160.
doi:10.1145/2460239.2460253'
apa: Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., & Trautmann,
H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm
for the Travelling Salesperson Problem. Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 147–160. https://doi.org/10.1145/2460239.2460253
bibtex: '@inproceedings{Nallaperuma_Wagner_Neumann_Bischl_Mersmann_Trautmann_2013,
place={New York, NY, USA}, series={FOGA XII ’13}, title={A Feature-Based Comparison
of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem}, DOI={10.1145/2460239.2460253},
booktitle={Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII}, publisher={Association for Computing Machinery}, author={Nallaperuma, Samadhi
and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and
Trautmann, Heike}, year={2013}, pages={147–160}, collection={FOGA XII ’13} }'
chicago: 'Nallaperuma, Samadhi, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf
Mersmann, and Heike Trautmann. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” In Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, 147–160.
FOGA XII ’13. New York, NY, USA: Association for Computing Machinery, 2013. https://doi.org/10.1145/2460239.2460253.'
ieee: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann,
“A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem,” in Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 2013, pp. 147–160, doi: 10.1145/2460239.2460253.'
mla: Nallaperuma, Samadhi, et al. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, Association
for Computing Machinery, 2013, pp. 147–160, doi:10.1145/2460239.2460253.
short: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, H. Trautmann,
in: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII,
Association for Computing Machinery, New York, NY, USA, 2013, pp. 147–160.'
date_created: 2023-08-04T15:42:03Z
date_updated: 2023-10-16T13:45:53Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2460239.2460253
keyword:
- approximation algorithms
- local search
- traveling salesperson problem
- feature selection
- prediction
- classification
language:
- iso: eng
page: 147–160
place: New York, NY, USA
publication: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII
publication_identifier:
isbn:
- '9781450319904'
publisher: Association for Computing Machinery
series_title: FOGA XII ’13
status: public
title: A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '20870'
abstract:
- lang: eng
text: This study shows how venture capital investors can identify potential biases
in multi-year management forecasts before an investment decision and derive significantly
more accurate failure predictions. By advancing a cross-sectional projection method
developed by prior research and using firm-specific information in financial statements
and business plans, we derive benchmarks for management revenue forecasts. With
these benchmarks, we estimate forecast errors as an a priori measure of biased
expectations. Using this measure for our proprietary dataset on venture-backed
start-ups in Germany, we find evidence of substantial upward forecast biases.
We uncover that firms with large forecast errors fail significantly more often
than do less biased entrepreneurs in years following the investment. Overall,
our results highlight the implications of excessive optimism and overconfidence
in entrepreneurial environments and emphasize the relevance of accounting information
and business plans for venture capital investment decisions.
author:
- first_name: Sönke
full_name: Sievers, Sönke
id: '46447'
last_name: Sievers
- first_name: Christopher Frederik
full_name: Mokwa, Christopher Frederik
last_name: Mokwa
citation:
ama: Sievers S, Mokwa CF. The Relevance of Biases in Management Forecasts for
Failure Prediction in Venture Capital Investments.; 2012. doi:10.2139/ssrn.2100501
apa: Sievers, S., & Mokwa, C. F. (2012). The Relevance of Biases in Management
Forecasts for Failure Prediction in Venture Capital Investments. https://doi.org/10.2139/ssrn.2100501
bibtex: '@book{Sievers_Mokwa_2012, title={The Relevance of Biases in Management
Forecasts for Failure Prediction in Venture Capital Investments}, DOI={10.2139/ssrn.2100501},
author={Sievers, Sönke and Mokwa, Christopher Frederik}, year={2012} }'
chicago: Sievers, Sönke, and Christopher Frederik Mokwa. The Relevance of Biases
in Management Forecasts for Failure Prediction in Venture Capital Investments,
2012. https://doi.org/10.2139/ssrn.2100501.
ieee: S. Sievers and C. F. Mokwa, The Relevance of Biases in Management Forecasts
for Failure Prediction in Venture Capital Investments. 2012.
mla: Sievers, Sönke, and Christopher Frederik Mokwa. The Relevance of Biases
in Management Forecasts for Failure Prediction in Venture Capital Investments.
2012, doi:10.2139/ssrn.2100501.
short: S. Sievers, C.F. Mokwa, The Relevance of Biases in Management Forecasts for
Failure Prediction in Venture Capital Investments, 2012.
date_created: 2021-01-05T11:59:50Z
date_updated: 2022-01-06T06:54:41Z
department:
- _id: '275'
doi: 10.2139/ssrn.2100501
extern: '1'
jel:
- G24
- G32
- M13
- M41
keyword:
- Management forecast biases
- cross-sectional projection models
- venture-backed start-ups
- failure prediction
- overoptimism
- overconfidence
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2100501
page: '31'
publication_status: published
status: public
title: The Relevance of Biases in Management Forecasts for Failure Prediction in Venture
Capital Investments
type: working_paper
user_id: '46447'
year: '2012'
...
---
_id: '9784'
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. These motors can operate in
``stick-slip'' or ``slip-slip'' mode, with the fundamental frequency of the driving
signal ranging from several Hertz to more than 100 kHz. To predict the motor characteristics,
a Coulomb friction model is sufficient in many cases, but numerical simulation
requires microscopic time steps. This contribution proposes a much faster simulation
technique using one evaluation per period of the excitation signal. The proposed
technique produces results very close to those of timestep simulation for ultrasonics
inertia motors and allows direct determination of the steady-state velocity of
an inertia motor from the motion profile of the driving part. Thus it is a useful
simulation technique which can be applied in both analysis and design of inertia
motors, especially for parameter studies and optimisation.
author:
- first_name: Matthias
full_name: Hunstig, Matthias
last_name: Hunstig
- first_name: Tobias
full_name: Hemsel, Tobias
last_name: Hemsel
- first_name: Walter
full_name: Sextro, Walter
last_name: Sextro
citation:
ama: 'Hunstig M, Hemsel T, Sextro W. An efficient simulation technique for high-frequency
piezoelectric inertia motors. In: Ultrasonics Symposium (IUS), 2012 IEEE International.
; 2012:277-280. doi:10.1109/ULTSYM.2012.0068'
apa: Hunstig, M., Hemsel, T., & Sextro, W. (2012). An efficient simulation technique
for high-frequency piezoelectric inertia motors. In Ultrasonics Symposium (IUS),
2012 IEEE International (pp. 277–280). https://doi.org/10.1109/ULTSYM.2012.0068
bibtex: '@inproceedings{Hunstig_Hemsel_Sextro_2012, title={An efficient simulation
technique for high-frequency piezoelectric inertia motors}, DOI={10.1109/ULTSYM.2012.0068},
booktitle={Ultrasonics Symposium (IUS), 2012 IEEE International}, author={Hunstig,
Matthias and Hemsel, Tobias and Sextro, Walter}, year={2012}, pages={277–280}
}'
chicago: Hunstig, Matthias, Tobias Hemsel, and Walter Sextro. “An Efficient Simulation
Technique for High-Frequency Piezoelectric Inertia Motors.” In Ultrasonics
Symposium (IUS), 2012 IEEE International, 277–80, 2012. https://doi.org/10.1109/ULTSYM.2012.0068.
ieee: M. Hunstig, T. Hemsel, and W. Sextro, “An efficient simulation technique for
high-frequency piezoelectric inertia motors,” in Ultrasonics Symposium (IUS),
2012 IEEE International, 2012, pp. 277–280.
mla: Hunstig, Matthias, et al. “An Efficient Simulation Technique for High-Frequency
Piezoelectric Inertia Motors.” Ultrasonics Symposium (IUS), 2012 IEEE International,
2012, pp. 277–80, doi:10.1109/ULTSYM.2012.0068.
short: 'M. Hunstig, T. Hemsel, W. Sextro, in: Ultrasonics Symposium (IUS), 2012
IEEE International, 2012, pp. 277–280.'
date_created: 2019-05-13T13:20:17Z
date_updated: 2022-01-06T07:04:20Z
department:
- _id: '151'
doi: 10.1109/ULTSYM.2012.0068
keyword:
- friction
- ultrasonic motors
- Coulomb friction model
- efficient simulation technique
- friction contact
- high-frequency piezoelectric inertia motor
- motor characteristics prediction
- numerical simulation
- slip-slip mode
- stick-slip mode
- time-step simulation
- ultrasonic inertia motor
- Acceleration
- Acoustics
- Actuators
- Computational modeling
- Friction
- Numerical models
- Steady-state
language:
- iso: eng
page: 277-280
publication: Ultrasonics Symposium (IUS), 2012 IEEE International
publication_identifier:
issn:
- 1948-5719
quality_controlled: '1'
status: public
title: An efficient simulation technique for high-frequency piezoelectric inertia
motors
type: conference
user_id: '55222'
year: '2012'
...
---
_id: '5196'
abstract:
- lang: eng
text: 'This study shows how venture capital investors can identify potential biases
in multi-year management forecasts before an investment decision and derive significantly
more accurate failure predictions. By advancing a cross-sectional projection method
developed by prior research and using firm-specific information in financial statements
and business plans, we derive benchmarks for management revenue forecasts. With
these benchmarks, we estimate forecast errors as an a priori measure of biased
expectations. Using this measure for our proprietary dataset on venture-backed
start-ups in Germany, we find evidence of substantial upward forecast biases.
We uncover that firms with large forecast errors fail significantly more often
than do less biased entrepreneurs in years following the investment. Overall,
our results highlight the implications of excessive optimism and overconfidence
in entrepreneurial environments and emphasize the relevance of accounting information
and business plans for venture capital investment decisions. '
author:
- first_name: Christopher Frederik
full_name: Mokwa, Christopher Frederik
last_name: Mokwa
- first_name: Sönke
full_name: Sievers, Sönke
last_name: Sievers
citation:
ama: Mokwa CF, Sievers S. The Relevance of Biases in Management Forecasts for Failure
Prediction in Venture Capital Investments. SSRN Electronic Journal. 2012.
doi:10.2139/ssrn.2100501
apa: Mokwa, C. F., & Sievers, S. (2012). The Relevance of Biases in Management
Forecasts for Failure Prediction in Venture Capital Investments. SSRN Electronic
Journal. https://doi.org/10.2139/ssrn.2100501
bibtex: '@article{Mokwa_Sievers_2012, title={The Relevance of Biases in Management
Forecasts for Failure Prediction in Venture Capital Investments}, DOI={10.2139/ssrn.2100501},
journal={SSRN Electronic Journal}, author={Mokwa, Christopher Frederik and Sievers,
Sönke}, year={2012} }'
chicago: Mokwa, Christopher Frederik, and Sönke Sievers. “The Relevance of Biases
in Management Forecasts for Failure Prediction in Venture Capital Investments.”
SSRN Electronic Journal, 2012. https://doi.org/10.2139/ssrn.2100501.
ieee: C. F. Mokwa and S. Sievers, “The Relevance of Biases in Management Forecasts
for Failure Prediction in Venture Capital Investments,” SSRN Electronic Journal,
2012.
mla: Mokwa, Christopher Frederik, and Sönke Sievers. “The Relevance of Biases in
Management Forecasts for Failure Prediction in Venture Capital Investments.” SSRN
Electronic Journal, 2012, doi:10.2139/ssrn.2100501.
short: C.F. Mokwa, S. Sievers, SSRN Electronic Journal (2012).
date_created: 2018-10-31T12:12:28Z
date_updated: 2022-01-06T07:01:43Z
department:
- _id: '275'
doi: 10.2139/ssrn.2100501
jel:
- G24
- G32
- M13
- M41
keyword:
- Management forecast biases
- cross-sectional projection models
- venture-backed start-ups
- failure prediction
- overoptimism
- overconfidence
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
status: public
title: The Relevance of Biases in Management Forecasts for Failure Prediction in Venture
Capital Investments
type: journal_article
user_id: '64756'
year: '2012'
...