---
_id: '12806'
abstract:
- lang: eng
text: Cyber-Physical Systems (CPS) play an essential role in today’s production
processes, leveraging Artificial Intelligence (AI) to enhance operations such
as optimization, anomaly detection, and predictive maintenance. This article reviews
a cognitive architecture for Artificial Intelligence, which has been developed
to establish a standard framework for integrating AI solutions into existing production
processes. Given that machines in these processes continuously generate large
streams of data, Online Machine Learning (OML) is identified as a crucial extension
to the existing architecture. To substantiate this claim, real-world experiments
using a slitting machine are conducted, to compare the performance of OML to traditional
Batch Machine Learning. The assessment of contemporary OML algorithms using a
real production system is a fundamental innovation in this research. The evaluations
clearly indicate that OML adds significant value to CPS, and it is strongly recommended
as an extension of related architectures, such as the cognitive architecture for
AI discussed in this article. Additionally, surrogate-model-based optimization
is employed, to determine the optimal hyperparameter settings for the corresponding
OML algorithms, aiming to achieve peak performance in their respective tasks.
article_number: '11506'
author:
- first_name: Alexander
full_name: Hinterleitner, Alexander
last_name: Hinterleitner
- first_name: Richard
full_name: Schulz, Richard
last_name: Schulz
- first_name: Lukas
full_name: Hans, Lukas
last_name: Hans
- first_name: Aleksandr
full_name: Subbotin, Aleksandr
last_name: Subbotin
- first_name: Nils
full_name: Barthel, Nils
last_name: Barthel
- first_name: Noah
full_name: Pütz, Noah
last_name: Pütz
- first_name: Martin
full_name: Rosellen, Martin
last_name: Rosellen
- first_name: Thomas
full_name: Bartz-Beielstein, Thomas
last_name: Bartz-Beielstein
- first_name: Christoph
full_name: Geng, Christoph
id: '61408'
last_name: Geng
- first_name: Phillip
full_name: Priss, Phillip
last_name: Priss
citation:
ama: 'Hinterleitner A, Schulz R, Hans L, et al. Online Machine Learning and Surrogate-Model-Based
Optimization for Improved Production Processes Using a Cognitive Architecture.
Applied Sciences : open access journal. 2023;13(20). doi:10.3390/app132011506'
apa: 'Hinterleitner, A., Schulz, R., Hans, L., Subbotin, A., Barthel, N., Pütz,
N., Rosellen, M., Bartz-Beielstein, T., Geng, C., & Priss, P. (2023). Online
Machine Learning and Surrogate-Model-Based Optimization for Improved Production
Processes Using a Cognitive Architecture. Applied Sciences : Open Access
Journal, 13(20), Article 11506. https://doi.org/10.3390/app132011506'
bjps: 'Hinterleitner A et al. (2023) Online Machine Learning and Surrogate-Model-Based
Optimization for Improved Production Processes Using a Cognitive Architecture.
Applied Sciences : open access journal 13.'
chicago: 'Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin,
Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng,
and Phillip Priss. “Online Machine Learning and Surrogate-Model-Based Optimization
for Improved Production Processes Using a Cognitive Architecture.” Applied
Sciences : Open Access Journal 13, no. 20 (2023). https://doi.org/10.3390/app132011506.'
chicago-de: 'Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin,
Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng
und Phillip Priss. 2023. Online Machine Learning and Surrogate-Model-Based Optimization
for Improved Production Processes Using a Cognitive Architecture. Applied
Sciences : open access journal 13, Nr. 20. doi:10.3390/app132011506,
.'
din1505-2-1: 'Hinterleitner,
Alexander ; Schulz, Richard
; Hans, Lukas ; Subbotin,
Aleksandr ; Barthel, Nils
; Pütz, Noah ; Rosellen,
Martin ; Bartz-Beielstein, Thomas
; u. a.: Online Machine Learning and Surrogate-Model-Based Optimization
for Improved Production Processes Using a Cognitive Architecture. In: Applied
Sciences : open access journal Bd. 13. Basel, MDPI AG (2023), Nr. 20'
havard: 'A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz,
M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Online Machine Learning and
Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive
Architecture, Applied Sciences : Open Access Journal. 13 (2023).'
ieee: 'A. Hinterleitner et al., “Online Machine Learning and Surrogate-Model-Based
Optimization for Improved Production Processes Using a Cognitive Architecture,”
Applied Sciences : open access journal, vol. 13, no. 20, Art. no. 11506,
2023, doi: 10.3390/app132011506.'
mla: 'Hinterleitner, Alexander, et al. “Online Machine Learning and Surrogate-Model-Based
Optimization for Improved Production Processes Using a Cognitive Architecture.”
Applied Sciences : Open Access Journal, vol. 13, no. 20, 11506, 2023,
https://doi.org/10.3390/app132011506.'
short: 'A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz,
M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Applied Sciences : Open
Access Journal 13 (2023).'
ufg: 'Hinterleitner, Alexander u. a.: Online Machine Learning and Surrogate-Model-Based
Optimization for Improved Production Processes Using a Cognitive Architecture,
in: Applied Sciences : open access journal 13 (2023), H. 20.'
van: 'Hinterleitner A, Schulz R, Hans L, Subbotin A, Barthel N, Pütz N, et al. Online
Machine Learning and Surrogate-Model-Based Optimization for Improved Production
Processes Using a Cognitive Architecture. Applied Sciences : open access journal.
2023;13(20).'
date_created: 2025-04-16T07:27:52Z
date_updated: 2025-06-26T07:50:56Z
department:
- _id: DEP5023
doi: 10.3390/app132011506
external_id:
isi:
- '001096019200001'
intvolume: ' 13'
isi: '1'
issue: '20'
keyword:
- machine learning
- online algorithms
- cyber-physical production systems
- surrogate-based optimization
language:
- iso: eng
place: Basel
publication: ' Applied Sciences : open access journal'
publication_identifier:
issn:
- 2076-3417
publication_status: published
publisher: MDPI AG
status: public
title: Online Machine Learning and Surrogate-Model-Based Optimization for Improved
Production Processes Using a Cognitive Architecture
type: scientific_journal_article
user_id: '83781'
volume: 13
year: '2023'
...
---
_id: '12804'
abstract:
- lang: eng
text: 'Data in many applications follows systems of Ordinary Differential Equations
(ODEs). This paper presents a novel algorithmic and symbolic construction for
covariance functions of Gaussian Processes (GPs) with realizations strictly following
a system of linear homogeneous ODEs with constant coefficients, which we call
LODE-GPs. Introducing this strong inductive bias into a GP improves modelling
of such data. Using smith normal form algorithms, a symbolic technique, we overcome
two current restrictions in the state of the art: (1) the need for certain uniqueness
conditions in the set of solutions, typically assumed in classical ODE solvers
and their probabilistic counterparts, and (2) the restriction to controllable
systems, typically assumed when encoding differential equations in covariance
functions. We show the effectiveness of LODE-GPs in a number of experiments, for
example learning physically interpretable parameters by maximizing the likelihood.'
author:
- first_name: Andreas
full_name: Besginow, Andreas
id: '61743'
last_name: Besginow
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Besginow A, Lange-Hegermann M. Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations. Vol 35. (Koyejo S, Mohamed S, Agarwal
A, et al., eds.). Curran Associates, Inc.; 2022:29386-29399.
apa: Besginow, A., & Lange-Hegermann, M. (2022). Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations. In S. Koyejo, S. Mohamed,
A. Agarwal, D. Belgrave, K. Cho, A. Oh, & Neural Information Processing Systems
Foundation (Eds.), 36th Conference on Neural Information Processing Systems
(NeurIPS 2022) (Vol. 35, pp. 29386–29399). Curran Associates, Inc.
bjps: 'Besginow A and Lange-Hegermann M (2022) Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations, Koyejo S et al. (eds).
Red Hook, NY : Curran Associates, Inc.'
chicago: 'Besginow, Andreas, and Markus Lange-Hegermann. Constraining Gaussian
Processes to Systems of Linear Ordinary Differential Equations. Edited by
S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, and Neural Information
Processing Systems Foundation . 36th Conference on Neural Information Processing
Systems (NeurIPS 2022) . Vol. 35. Advances in Neural Information Processing
Systems. Red Hook, NY : Curran Associates, Inc., 2022.'
chicago-de: 'Besginow, Andreas und Markus Lange-Hegermann. 2022. Constraining
Gaussian Processes to Systems of Linear Ordinary Differential Equations. Hg.
von S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, und Neural
Information Processing Systems Foundation . 36th Conference on Neural Information
Processing Systems (NeurIPS 2022) . Bd. 35. Advances in Neural Information
Processing Systems. Red Hook, NY : Curran Associates, Inc.'
din1505-2-1: 'Besginow, Andreas ;
Lange-Hegermann, Markus ; Koyejo, S. ; Mohamed,
S. ; Agarwal, A. ; Belgrave, D. ; Cho,
K. ; Oh, A. ; Neural
Information Processing Systems Foundation (Hrsg.): Constraining Gaussian
Processes to Systems of Linear Ordinary Differential Equations, Advances
in Neural Information Processing Systems. Bd. 35. Red Hook, NY : Curran Associates,
Inc., 2022'
havard: A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook,
NY , 2022.
ieee: 'A. Besginow and M. Lange-Hegermann, Constraining Gaussian Processes to
Systems of Linear Ordinary Differential Equations, vol. 35. Red Hook, NY :
Curran Associates, Inc., 2022, pp. 29386–29399.'
mla: Besginow, Andreas, and Markus Lange-Hegermann. “Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations.” 36th Conference on Neural
Information Processing Systems (NeurIPS 2022) , edited by S. Koyejo et al.,
vol. 35, Curran Associates, Inc., 2022, pp. 29386–99.
short: A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook,
NY , 2022.
ufg: 'Besginow, Andreas/Lange-Hegermann, Markus: Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations, Bd. 35, hg. von Koyejo,
S. u. a., Red Hook, NY 2022 (Advances in Neural Information Processing Systems).'
van: 'Besginow A, Lange-Hegermann M. Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations. Koyejo S, Mohamed S, Agarwal A, Belgrave
D, Cho K, Oh A, et al., editors. 36th Conference on Neural Information Processing
Systems (NeurIPS 2022) . Red Hook, NY : Curran Associates, Inc.; 2022. (Advances
in Neural Information Processing Systems; vol. 35).'
conference:
end_date: 2022-12-09
location: New Orleans, La.; Online
name: 36th Conference on Neural Information Processing Systems (NeurIPS)
start_date: 2022-11-28
corporate_editor:
- 'Neural Information Processing Systems Foundation '
date_created: 2025-04-16T06:58:04Z
date_updated: 2025-06-26T13:37:53Z
department:
- _id: DEP5000
editor:
- first_name: S.
full_name: Koyejo, S.
last_name: Koyejo
- first_name: S.
full_name: Mohamed, S.
last_name: Mohamed
- first_name: A.
full_name: Agarwal, A.
last_name: Agarwal
- first_name: D.
full_name: Belgrave, D.
last_name: Belgrave
- first_name: K.
full_name: Cho, K.
last_name: Cho
- first_name: A.
full_name: Oh, A.
last_name: Oh
intvolume: ' 35'
keyword:
- SMITH NORMAL-FORM
- ALGORITHMS
- REDUCTION
language:
- iso: eng
page: 29386 - 29399
place: 'Red Hook, NY '
publication: '36th Conference on Neural Information Processing Systems (NeurIPS 2022) '
publication_identifier:
eisbn:
- 978-1-7138-7312-9
isbn:
- '978-1-7138-7108-8 '
issn:
- 1049-5258
publication_status: published
publisher: Curran Associates, Inc.
series_title: Advances in Neural Information Processing Systems
status: public
title: Constraining Gaussian Processes to Systems of Linear Ordinary Differential
Equations
type: conference_editor_article
user_id: '83781'
volume: 35
year: '2022'
...
---
_id: '12786'
abstract:
- lang: eng
text: 'One goal in Bayesian machine learning is to encode prior knowledge into prior
distributions, to model data efficiently. We consider prior knowledge from systems
of linear partial differential equations together with their boundary conditions.
We construct multi-output Gaussian process priors with realizations in the solution
set of such systems, in particular only such solutions can be represented by Gaussian
process regression. The construction is fully algorithmic via Grobner bases and
it does not employ any approximation. It builds these priors combining two parametrizations
via a pullback: the first parametrizes the solutions for the system of differential
equations and the second parametrizes all functions adhering to the boundary conditions.'
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Lange-Hegermann M. Linearly Constrained Gaussian Processes with Boundary
Conditions. Vol 130. (Banerjee A, Fukumizu K, eds.). MLResearchPress ; 2021.
apa: Lange-Hegermann, M. (2021). Linearly Constrained Gaussian Processes with Boundary
Conditions. In A. Banerjee & K. Fukumizu (Eds.), 24th International Conference
on Artificial Intelligence and Statistics (AISTATS) (Vol. 130). MLResearchPress
.
bjps: Lange-Hegermann M (2021) Linearly Constrained Gaussian Processes
with Boundary Conditions, Banerjee A and Fukumizu K (eds). MLResearchPress
.
chicago: 'Lange-Hegermann, Markus. Linearly Constrained Gaussian Processes with
Boundary Conditions. Edited by A. Banerjee and K. Fukumizu. 24th International
Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 130.
Proceedings of Machine Learning Research : PMLR . MLResearchPress , 2021.'
chicago-de: 'Lange-Hegermann, Markus. 2021. Linearly Constrained Gaussian Processes
with Boundary Conditions. Hg. von A. Banerjee und K. Fukumizu. 24th International
Conference on Artificial Intelligence and Statistics (AISTATS). Bd. 130. Proceedings
of machine learning research : PMLR . MLResearchPress .'
din1505-2-1: 'Lange-Hegermann, Markus
; Banerjee, A. ; Fukumizu,
K. (Hrsg.): Linearly Constrained Gaussian Processes with Boundary Conditions,
Proceedings of machine learning research : PMLR . Bd. 130 : MLResearchPress
, 2021'
havard: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, MLResearchPress , 2021.
ieee: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, vol. 130. MLResearchPress , 2021.
mla: Lange-Hegermann, Markus. “Linearly Constrained Gaussian Processes with Boundary
Conditions.” 24th International Conference on Artificial Intelligence and Statistics
(AISTATS), edited by A. Banerjee and K. Fukumizu, vol. 130, MLResearchPress
, 2021.
short: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, MLResearchPress , 2021.
ufg: 'Lange-Hegermann, Markus: Linearly Constrained Gaussian Processes with
Boundary Conditions, Bd. 130, hg. von Banerjee, A./Fukumizu, K., o. O. 2021 (Proceedings
of machine learning research : PMLR ).'
van: 'Lange-Hegermann M. Linearly Constrained Gaussian Processes with Boundary Conditions.
Banerjee A, Fukumizu K, editors. 24th International Conference on Artificial Intelligence
and Statistics (AISTATS). MLResearchPress ; 2021. (Proceedings of machine learning
research : PMLR ; vol. 130).'
conference:
end_date: 2021-04-15
location: Virtual
name: 24th International Conference on Artificial Intelligence and Statistics (AISTATS)
start_date: 2021-04-13
date_created: 2025-04-14T13:58:16Z
date_updated: 2025-06-26T13:42:36Z
department:
- _id: DEP5000
- _id: DEP5023
editor:
- first_name: A.
full_name: Banerjee, A.
last_name: Banerjee
- first_name: K.
full_name: Fukumizu, K.
last_name: Fukumizu
intvolume: ' 130'
keyword:
- FUNCTIONAL REGRESSION
- PREDICTION
- ALGORITHMS
- COMPLEXITY
- MODELS
language:
- iso: eng
publication: 24th International Conference on Artificial Intelligence and Statistics
(AISTATS)
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: 'MLResearchPress '
quality_controlled: '1'
series_title: 'Proceedings of machine learning research : PMLR '
status: public
title: Linearly Constrained Gaussian Processes with Boundary Conditions
type: conference_editor_article
user_id: '83781'
volume: 130
year: '2021'
...
---
_id: '2007'
abstract:
- lang: eng
text: Multisensor systems are susceptible to sensor ageing effects as well as to
environmental changes. Due to these effects, the distribution of sensor measurements
may change over time, which is referred to as sensor drift. A multisensor system
which adapts to drift by self-monitoring is more durable, requires less manual
maintenance, and provides information of higher quality. This contribution proposes
an approach for detecting and adapting to sensor drift. The proposed detection
algorithm determines the reliability of a sensor based on fuzzy pattern classifiers
and a consistency measure. By this means, the inherent redundancy in multisensor
systems is exploited to detect drift. Detected drift leads then to a retraining
of the classifier on batched data guided by information fusion. The retraining
incorporates the estimated magnitude of the drift. The proposed algorithms are
evaluated in comparison with state-of-the-art methods in the scope of a publicly
available dataset. It is shown that the drift detection algorithm yields results
similar to the benchmark algorithm but is less computationally complex. Relearning
with the drift-adapted approach results in more robust classifiers with regard
to potential future drift.
author:
- first_name: Christoph-Alexander
full_name: Holst, Christoph-Alexander
id: '64782'
last_name: Holst
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Holst C-A, Lohweg V. A Conflict-Based Drift Detection And Adaptation Approach
for Multisensor Information Fusion. In: 23rd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA). Torino, Italy; 2018.
doi:10.1109/ETFA.2018.8502571'
apa: Holst, C.-A., & Lohweg, V. (2018). A Conflict-Based Drift Detection And
Adaptation Approach for Multisensor Information Fusion. In 23rd IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA). Torino,
Italy. https://doi.org/10.1109/ETFA.2018.8502571
bjps: Holst C-A and Lohweg V (2018) A Conflict-Based Drift Detection And
Adaptation Approach for Multisensor Information Fusion. 23rd IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA). Torino,
Italy.
chicago: Holst, Christoph-Alexander, and Volker Lohweg. “A Conflict-Based Drift
Detection And Adaptation Approach for Multisensor Information Fusion.” In 23rd
IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). Torino, Italy, 2018. https://doi.org/10.1109/ETFA.2018.8502571.
chicago-de: 'Holst, Christoph-Alexander und Volker Lohweg. 2018. A Conflict-Based
Drift Detection And Adaptation Approach for Multisensor Information Fusion. In:
23rd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). Torino, Italy. doi:10.1109/ETFA.2018.8502571,
.'
din1505-2-1: 'Holst, Christoph-Alexander
; Lohweg, Volker: A Conflict-Based
Drift Detection And Adaptation Approach for Multisensor Information Fusion. In:
23rd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). Torino, Italy, 2018'
havard: 'C.-A. Holst, V. Lohweg, A Conflict-Based Drift Detection And Adaptation
Approach for Multisensor Information Fusion, in: 23rd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.'
ieee: C.-A. Holst and V. Lohweg, “A Conflict-Based Drift Detection And Adaptation
Approach for Multisensor Information Fusion,” in 23rd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.
mla: Holst, Christoph-Alexander, and Volker Lohweg. “A Conflict-Based Drift Detection
And Adaptation Approach for Multisensor Information Fusion.” 23rd IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA), 2018, doi:10.1109/ETFA.2018.8502571.
short: 'C.-A. Holst, V. Lohweg, in: 23rd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA), Torino, Italy, 2018.'
ufg: 'Holst, Christoph-Alexander/Lohweg, Volker (2018): A Conflict-Based
Drift Detection And Adaptation Approach for Multisensor Information Fusion, in:
23rd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA), Torino, Italy.'
van: 'Holst C-A, Lohweg V. A Conflict-Based Drift Detection And Adaptation Approach
for Multisensor Information Fusion. In: 23rd IEEE International Conference on
Emerging Technologies and Factory Automation (ETFA). Torino, Italy; 2018.'
conference:
end_date: 2018-09-07
location: Torino, Italy
name: IEEE 23rd International Conference on Emerging Technologies and Factory Automation
(ETFA) 2018
start_date: 2018-09-04
date_created: 2019-11-25T08:35:47Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: 10.1109/ETFA.2018.8502571
keyword:
- Multisensor systems
- Temperature measurement
- Current measurement
- Redundancy
- Pollution measurement
- Detection algorithms
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/abstract/document/8502571
place: Torino, Italy
publication: 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA)
publication_status: published
status: public
title: A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information
Fusion
type: conference
user_id: '15514'
year: 2018
...