---
_id: '11808'
abstract:
- lang: eng
text: The application of hydrogen for energy storage and as a vehicle fuel necessitates
efficient and effective storage technologies. In addition to traditional cryogenic
and high-pressure tanks, an alternative approach involves utilizing porous materials
such as activated carbons within the storage tank. The adsorption behaviour of
hydrogen in porous structures is described using the Dubinin-Astakhov isotherm.
To model the flow of hydrogen within the tank, we rely on the equations of mass
conservation, the Navier-Stokes equations, and the equation of energy conservation,
which are implemented in a computational fluid dynamics code and additional terms
account for the amount of hydrogen involved in sorption and the corresponding
heat release. While physical models are valuable, data-driven models often offer
computational advantages. Based on the data from the physical adsorption model,
a data-driven model is derived using various machine learning techniques. This
model is then incorporated as source terms in the governing conservation equations,
resulting in a novel hybrid formulation which is computationally more efficient.
Consequently, a new method is presented to compute the temperature and concentration
distribution during the charging and discharging of hydrogen tanks and identifying
any limiting phenomena more easily.
article_number: '132318'
author:
- first_name: Georg Heinrich
full_name: Klepp, Georg Heinrich
id: '49011'
last_name: Klepp
citation:
ama: 'Klepp GH. Modelling activated carbon hydrogen storage tanks using machine
learning models. Energy : the international journal ; technologies, resources,
reserves, demands, impact, conservation, management, policy. 2024;306. doi:10.1016/j.energy.2024.132318'
apa: 'Klepp, G. H. (2024). Modelling activated carbon hydrogen storage tanks using
machine learning models. Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy, 306,
Article 132318. https://doi.org/10.1016/j.energy.2024.132318'
bjps: 'Klepp GH (2024) Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models. Energy : the international journal ; technologies,
resources, reserves, demands, impact, conservation, management, policy 306.'
chicago: 'Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models.” Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306
(2024). https://doi.org/10.1016/j.energy.2024.132318.'
chicago-de: 'Klepp, Georg Heinrich. 2024. Modelling activated carbon hydrogen storage
tanks using machine learning models. Energy : the international journal ; technologies,
resources, reserves, demands, impact, conservation, management, policy 306.
doi:10.1016/j.energy.2024.132318,
.'
din1505-2-1: 'Klepp, Georg Heinrich:
Modelling activated carbon hydrogen storage tanks using machine learning models.
In: Energy : the international journal ; technologies, resources, reserves,
demands, impact, conservation, management, policy Bd. 306. Amsterdam, Elsevier
BV (2024)'
havard: 'G.H. Klepp, Modelling activated carbon hydrogen storage tanks using machine
learning models, Energy : The International Journal ; Technologies, Resources,
Reserves, Demands, Impact, Conservation, Management, Policy. 306 (2024).'
ieee: 'G. H. Klepp, “Modelling activated carbon hydrogen storage tanks using machine
learning models,” Energy : the international journal ; technologies, resources,
reserves, demands, impact, conservation, management, policy, vol. 306, Art.
no. 132318, 2024, doi: 10.1016/j.energy.2024.132318.'
mla: 'Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models.” Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy, vol.
306, 132318, 2024, https://doi.org/10.1016/j.energy.2024.132318.'
short: 'G.H. Klepp, Energy : The International Journal ; Technologies, Resources,
Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).'
ufg: 'Klepp, Georg Heinrich: Modelling activated carbon hydrogen storage
tanks using machine learning models, in: Energy : the international journal ;
technologies, resources, reserves, demands, impact, conservation, management,
policy 306 (2024).'
van: 'Klepp GH. Modelling activated carbon hydrogen storage tanks using machine
learning models. Energy : the international journal ; technologies, resources,
reserves, demands, impact, conservation, management, policy. 2024;306.'
date_created: 2024-07-31T14:23:52Z
date_updated: 2024-08-01T08:16:04Z
department:
- _id: DEP6017
doi: 10.1016/j.energy.2024.132318
intvolume: ' 306'
keyword:
- Hydrogen storage
- Adsorption
- Activated carbon
- Machine learning
- Simulation
- Computational fluid dynamics
language:
- iso: eng
place: Amsterdam
publication: 'Energy : the international journal ; technologies, resources, reserves,
demands, impact, conservation, management, policy'
publication_identifier:
eissn:
- 1873-6785
issn:
- 0360-5442
publication_status: published
publisher: Elsevier BV
status: public
title: Modelling activated carbon hydrogen storage tanks using machine learning models
type: scientific_journal_article
user_id: '83781'
volume: 306
year: '2024'
...
---
_id: '12167'
abstract:
- lang: eng
text: 'Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments
is significantly constrained by adverse weather conditions, limiting their operation
to clear weather due to safety concerns. Ensuring that AVs remain within their
designated Operational Design Domain (ODD) is a formidable challenge, making boundary
monitoring strategies essential for safe navigation. This study explores the critical
role of an ODD monitoring system (OMS) in addressing these challenges. It reviews
various methodologies for designing an OMS and presents a comprehensive visualization
framework incorporating trigger points for ODD exits. These trigger points serve
as essential references for effective OMS design. The study also delves into a
specific use case concerning ODD exits: the reduction in road friction due to
adverse weather conditions. It emphasizes the importance of contactless computer
vision-based methods for road condition estimation (RCE), particularly using vision
sensors such as cameras. The study details a timeline of methods involving classical
machine learning and deep learning feature extraction techniques, identifying
contemporary challenges such as class imbalance, lack of comprehensive datasets,
annotation methods, and the scarcity of generalization techniques. Furthermore,
it provides a factual comparison of two state-of-the-art RCE datasets. In essence,
the study aims to address and explore ODD exits due to weather-induced road conditions,
decoding the practical solutions and directions for future research in the realm
of AVs.'
article_type: original
author:
- first_name: Ramakrishnan
full_name: Subramanian, Ramakrishnan
id: '85499'
last_name: Subramanian
- first_name: Ulrich
full_name: Büker, Ulrich
id: '81453'
last_name: Büker
orcid: 0000-0002-4403-3889
citation:
ama: 'Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition
Estimation Methods Within the Framework of an Operational Design Domain Monitoring
System. Eng : advances in engineering. 2024;5(4):2778-2804. doi:10.3390/eng5040145'
apa: 'Subramanian, R., & Büker, U. (2024). Study of Contactless Computer Vision-Based
Road Condition Estimation Methods Within the Framework of an Operational Design
Domain Monitoring System. Eng : Advances in Engineering, 5(4), 2778–2804.
https://doi.org/10.3390/eng5040145'
bjps: 'Subramanian R and Büker U (2024) Study of Contactless Computer Vision-Based
Road Condition Estimation Methods Within the Framework of an Operational Design
Domain Monitoring System. Eng : advances in engineering 5, 2778–2804.'
chicago: 'Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer
Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
Design Domain Monitoring System.” Eng : Advances in Engineering 5, no.
4 (2024): 2778–2804. https://doi.org/10.3390/eng5040145.'
chicago-de: 'Subramanian, Ramakrishnan und Ulrich Büker. 2024. Study of Contactless
Computer Vision-Based Road Condition Estimation Methods Within the Framework of
an Operational Design Domain Monitoring System. Eng : advances in engineering
5, Nr. 4: 2778–2804. doi:10.3390/eng5040145,
.'
din1505-2-1: 'Subramanian, Ramakrishnan
; Büker, Ulrich: Study of Contactless
Computer Vision-Based Road Condition Estimation Methods Within the Framework of
an Operational Design Domain Monitoring System. In: Eng : advances in engineering
Bd. 5. Basel, MDPI AG (2024), Nr. 4, S. 2778–2804'
havard: 'R. Subramanian, U. Büker, Study of Contactless Computer Vision-Based Road
Condition Estimation Methods Within the Framework of an Operational Design Domain
Monitoring System, Eng : Advances in Engineering. 5 (2024) 2778–2804.'
ieee: 'R. Subramanian and U. Büker, “Study of Contactless Computer Vision-Based
Road Condition Estimation Methods Within the Framework of an Operational Design
Domain Monitoring System,” Eng : advances in engineering, vol. 5, no. 4,
pp. 2778–2804, 2024, doi: 10.3390/eng5040145.'
mla: 'Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer
Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
Design Domain Monitoring System.” Eng : Advances in Engineering, vol. 5,
no. 4, 2024, pp. 2778–804, https://doi.org/10.3390/eng5040145.'
short: 'R. Subramanian, U. Büker, Eng : Advances in Engineering 5 (2024) 2778–2804.'
ufg: 'Subramanian, Ramakrishnan/Büker, Ulrich: Study of Contactless Computer
Vision-Based Road Condition Estimation Methods Within the Framework of an Operational
Design Domain Monitoring System, in: Eng : advances in engineering 5 (2024),
H. 4, S. 2778–2804.'
van: 'Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition
Estimation Methods Within the Framework of an Operational Design Domain Monitoring
System. Eng : advances in engineering. 2024;5(4):2778–804.'
date_created: 2024-12-04T16:46:30Z
date_updated: 2024-12-05T13:19:17Z
department:
- _id: DEP5023
- _id: DEP5000
doi: 10.3390/eng5040145
intvolume: ' 5'
issue: '4'
keyword:
- autonomous vehicles
- operational design domain
- computer vision
- machine learning
- road surface detection
language:
- iso: eng
page: 2778-2804
place: Basel
publication: 'Eng : advances in engineering'
publication_identifier:
eissn:
- 2673-4117
publication_status: published
publisher: MDPI AG
quality_controlled: '1'
status: public
title: Study of Contactless Computer Vision-Based Road Condition Estimation Methods
Within the Framework of an Operational Design Domain Monitoring System
type: scientific_journal_article
user_id: '83781'
volume: 5
year: '2024'
...
---
_id: '10216'
abstract:
- lang: eng
text: Wet granulation is a frequent process in the pharmaceutical industry. As a
starting point for numerous dosage forms, the quality of the granulation not only
affects subsequent production steps but also impacts the quality of the final
product. It is thus crucial and economical to monitor this operation thoroughly.
Here, we report on identifying different phases of a granulation process using
a machine learning approach. The phases reflect the water content which, in turn,
influences the processability and quality of the granule mass. We used two kinds
of microphones and an acceleration sensor to capture acoustic emissions and vibrations.
We trained convolutional neural networks (CNNs) to classify the different phases
using transformed sound recordings as the input. We achieved a classification
accuracy of up to 90% using vibrational data and an accuracy of up to 97% using
the audible microphone data. Our results indicate the suitability of using audible
sound and machine learning to monitor pharmaceutical processes. Moreover, since
recording acoustic emissions is contactless, it readily complies with legal regulations
and presents Good Manufacturing Practices.
article_number: '2153'
author:
- first_name: Ruwen
full_name: Fulek, Ruwen
id: '79527'
last_name: Fulek
- first_name: Selina
full_name: Ramm, Selina
id: '68713'
last_name: Ramm
orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Christian
full_name: Kiera, Christian
last_name: Kiera
- first_name: Miriam
full_name: Pein-Hackelbusch, Miriam
id: '64952'
last_name: Pein-Hackelbusch
orcid: 0000-0002-7920-0595
- first_name: Ulrich
full_name: Odefey, Ulrich
id: '74218'
last_name: Odefey
citation:
ama: Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning
approach to qualitatively evaluate different granulation phases by acoustic emissions.
Pharmaceutics. 2023;15(8). doi:https://doi.org/10.3390/pharmaceutics15082153
apa: Fulek, R., Ramm, S., Kiera, C., Pein-Hackelbusch, M., & Odefey, U. (2023).
A machine learning approach to qualitatively evaluate different granulation phases
by acoustic emissions. Pharmaceutics, 15(8), Article 2153. https://doi.org/10.3390/pharmaceutics15082153
bjps: Fulek R et al. (2023) A Machine Learning Approach to Qualitatively
Evaluate Different Granulation Phases by Acoustic Emissions. Pharmaceutics
15.
chicago: Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch, and
Ulrich Odefey. “A Machine Learning Approach to Qualitatively Evaluate Different
Granulation Phases by Acoustic Emissions.” Pharmaceutics 15, no. 8 (2023).
https://doi.org/10.3390/pharmaceutics15082153.
chicago-de: Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch
und Ulrich Odefey. 2023. A machine learning approach to qualitatively evaluate
different granulation phases by acoustic emissions. Pharmaceutics 15, Nr.
8. doi:https://doi.org/10.3390/pharmaceutics15082153,
.
din1505-2-1: 'Fulek, Ruwen ; Ramm, Selina ; Kiera,
Christian ; Pein-Hackelbusch, Miriam
; Odefey, Ulrich: A machine learning
approach to qualitatively evaluate different granulation phases by acoustic emissions.
In: Pharmaceutics Bd. 15. Basel, MDPI (2023), Nr. 8'
havard: R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, A machine learning
approach to qualitatively evaluate different granulation phases by acoustic emissions,
Pharmaceutics. 15 (2023).
ieee: 'R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, and U. Odefey, “A machine
learning approach to qualitatively evaluate different granulation phases by acoustic
emissions,” Pharmaceutics, vol. 15, no. 8, Art. no. 2153, 2023, doi: https://doi.org/10.3390/pharmaceutics15082153.'
mla: Fulek, Ruwen, et al. “A Machine Learning Approach to Qualitatively Evaluate
Different Granulation Phases by Acoustic Emissions.” Pharmaceutics, vol.
15, no. 8, 2153, 2023, https://doi.org/10.3390/pharmaceutics15082153.
short: R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, Pharmaceutics
15 (2023).
ufg: 'Fulek, Ruwen u. a.: A machine learning approach to qualitatively evaluate
different granulation phases by acoustic emissions, in: Pharmaceutics 15
(2023), H. 8.'
van: Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning
approach to qualitatively evaluate different granulation phases by acoustic emissions.
Pharmaceutics. 2023;15(8).
date_created: 2023-08-15T10:48:15Z
date_updated: 2025-07-29T13:21:40Z
department:
- _id: DEP4022
- _id: DEP4028
- _id: DEP4014
doi: https://doi.org/10.3390/pharmaceutics15082153
external_id:
isi:
- '001119084200001'
pmid:
- '37631367'
intvolume: ' 15'
isi: '1'
issue: '8'
keyword:
- wet granulation
- acoustic classification
- machine learning
- convolutional neural networks
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/1999-4923/15/8/2153
oa: '1'
place: Basel
pmid: '1'
publication: Pharmaceutics
publication_identifier:
eissn:
- '1999-4923 '
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: A machine learning approach to qualitatively evaluate different granulation
phases by acoustic emissions
type: scientific_journal_article
user_id: '83781'
volume: 15
year: '2023'
...
---
_id: '12785'
abstract:
- lang: eng
text: Due to the demographic aging of society, the demand for skilled caregiving
is increasing. However, the already existing shortage of professional caregivers
will exacerbate in the future. As a result, family caregivers must shoulder a
heavier share of the care burden. To ease the burden and promote a better work-life
balance, we developed the Digital Case Manager. This tool uses machine learning
algorithms to learn the relationship between a care situation and the next care
steps and helps family caregivers balance their professional and private lives
so that they are able to continue caring for their family members without sacrificing
their own jobs and personal ambitions. The data for the machine learning model
are generated by means of a questionnaire based on professional assessment instruments.
We implemented a proof-of-concept of the Digital Case Manager and initial tests
show promising results. It offers a quick and easy-to-use tool for family caregivers
in the early stages of a care situation.
article_number: '1215'
author:
- first_name: Paul
full_name: Wunderlich, Paul
id: '52317'
last_name: Wunderlich
- first_name: Frauke
full_name: Wiegräbe, Frauke
id: '76510'
last_name: Wiegräbe
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
citation:
ama: Wunderlich P, Wiegräbe F, Dörksen H. Digital Case Manager-A Data-Driven Tool
to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF
ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2023;20(2). doi:10.3390/ijerph20021215
apa: Wunderlich, P., Wiegräbe, F., & Dörksen, H. (2023). Digital Case Manager-A
Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL
JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 20(2), Article
1215. https://doi.org/10.3390/ijerph20021215
bjps: Wunderlich P, Wiegräbe F and Dörksen H (2023) Digital Case Manager-A
Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL
JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20.
chicago: Wunderlich, Paul, Frauke Wiegräbe, and Helene Dörksen. “Digital Case Manager-A
Data-Driven Tool to Support Family Caregivers with Initial Guidance.” INTERNATIONAL
JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20, no. 2 (2023). https://doi.org/10.3390/ijerph20021215.
chicago-de: Wunderlich, Paul, Frauke Wiegräbe und Helene Dörksen. 2023. Digital
Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20, Nr.
2. doi:10.3390/ijerph20021215,
.
din1505-2-1: 'Wunderlich, Paul ; Wiegräbe, Frauke ; Dörksen,
Helene: Digital Case Manager-A Data-Driven Tool to Support Family Caregivers
with Initial Guidance. In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH
AND PUBLIC HEALTH Bd. 20. Basel, MDPI (2023), Nr. 2'
havard: P. Wunderlich, F. Wiegräbe, H. Dörksen, Digital Case Manager-A Data-Driven
Tool to Support Family Caregivers with Initial Guidance, INTERNATIONAL JOURNAL
OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 20 (2023).
ieee: 'P. Wunderlich, F. Wiegräbe, and H. Dörksen, “Digital Case Manager-A Data-Driven
Tool to Support Family Caregivers with Initial Guidance,” INTERNATIONAL JOURNAL
OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 20, no. 2, Art. no. 1215,
2023, doi: 10.3390/ijerph20021215.'
mla: Wunderlich, Paul, et al. “Digital Case Manager-A Data-Driven Tool to Support
Family Caregivers with Initial Guidance.” INTERNATIONAL JOURNAL OF ENVIRONMENTAL
RESEARCH AND PUBLIC HEALTH, vol. 20, no. 2, 1215, 2023, https://doi.org/10.3390/ijerph20021215.
short: P. Wunderlich, F. Wiegräbe, H. Dörksen, INTERNATIONAL JOURNAL OF ENVIRONMENTAL
RESEARCH AND PUBLIC HEALTH 20 (2023).
ufg: 'Wunderlich, Paul/Wiegräbe, Frauke/Dörksen, Helene: Digital Case Manager-A
Data-Driven Tool to Support Family Caregivers with Initial Guidance, in: INTERNATIONAL
JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20 (2023), H. 2.'
van: Wunderlich P, Wiegräbe F, Dörksen H. Digital Case Manager-A Data-Driven Tool
to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL
RESEARCH AND PUBLIC HEALTH. 2023;20(2).
date_created: 2025-04-14T13:39:52Z
date_updated: 2025-06-25T13:11:41Z
department:
- _id: DEP5023
- _id: DEP5000
doi: 10.3390/ijerph20021215
external_id:
isi:
- '000918039900001'
pmid:
- '36673969'
intvolume: ' 20'
isi: '1'
issue: '2'
keyword:
- machine learning
- healthcare
- case management
- caring
- multi-label classification
language:
- iso: eng
place: Basel
pmid: '1'
publication: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
publication_identifier:
eissn:
- 1660-4601
issn:
- '1661-7827 '
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial
Guidance
type: scientific_journal_article
user_id: '83781'
volume: 20
year: '2023'
...
---
_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: '6689'
abstract:
- lang: eng
text: "Free amino nitrogen (FAN) concentrations in beer mash can be determined with
machine learning algorithms\r\nfrom near-infrared (NIR) spectra. NIR spectroscopy
is an alternative to a classical chemical analysis and\r\nallows for the application
of inline process quality control. This study investigates the capabilities of\r\ndifferent
machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision
Tree\r\nRegressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR),
K-nearest neighbours (KNN)\r\nregression as well as Support Vector Regression
(SVR) to predict the FAN content in beer mash from NIR\r\nspectra. Various pre-processing
strategies such as principal component analysis (PCA) and data\r\nstandardization
were used to process NIR data that were used to train the machine learning algorithms.\r\nAlgorithm
training was conducted with NIR data obtained from 16 beer mashes with varying
FAN\r\nconcentrations. The trained models were then validated with 4 beer mashes
that were not used for model\r\ntraining. Machine learning algorithms based on
linear regression showed the highest prediction accuracy on\r\nunpre-processed
data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L
(R2 = 0.96)\r\nand a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The
FAN concentration range of the investigated\r\nsamples was between approx. 180
and 220 mg/L. Machine learning based NIR spectra analysis is an alternative\r\nto
classical chemical FAN level determination methods and can also be used as inline
sensor system."
article_type: original
author:
- first_name: Patrick
full_name: Wefing, Patrick
id: '68976'
last_name: Wefing
- first_name: Florian
full_name: Conradi, Florian
id: '68967'
last_name: Conradi
- first_name: Johannes
full_name: Rämisch, Johannes
last_name: Rämisch
- first_name: Peter
full_name: Neubauer, Peter
last_name: Neubauer
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free
amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation
by machine learning algorithms. Brewing science . 2021;74(9/10):107-121.
doi:https://doi.org/10.23763/BrSc21-10wefing
apa: Wefing, P., Conradi, F., Rämisch, J., Neubauer, P., & Schneider, J. (2021).
Determination of free amino nitrogen in beer mash with an inline NIR transflectance
probe and data evaluation by machine learning algorithms. Brewing Science ,
74(9/10), 107–121. https://doi.org/10.23763/BrSc21-10wefing
bjps: Wefing P et al. (2021) Determination of Free Amino Nitrogen
in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine
Learning Algorithms. Brewing science 74, 107–121.
chicago: 'Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer, and
Jan Schneider. “Determination of Free Amino Nitrogen in Beer Mash with an Inline
NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.”
Brewing Science 74, no. 9/10 (2021): 107–21. https://doi.org/10.23763/BrSc21-10wefing.'
chicago-de: 'Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer
und Jan Schneider. 2021. Determination of free amino nitrogen in beer mash with
an inline NIR transflectance probe and data evaluation by machine learning algorithms.
Brewing science 74, Nr. 9/10: 107–121. doi:https://doi.org/10.23763/BrSc21-10wefing,
.'
din1505-2-1: 'Wefing, Patrick ; Conradi, Florian ; Rämisch,
Johannes ; Neubauer, Peter
; Schneider, Jan: Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms. In: Brewing science Bd.
74, Carl (2021), Nr. 9/10, S. 107–121'
havard: P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms, Brewing Science . 74 (2021) 107–121.
ieee: 'P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, and J. Schneider, “Determination
of free amino nitrogen in beer mash with an inline NIR transflectance probe and
data evaluation by machine learning algorithms,” Brewing science , vol.
74, no. 9/10, pp. 107–121, 2021, doi: https://doi.org/10.23763/BrSc21-10wefing.'
mla: Wefing, Patrick, et al. “Determination of Free Amino Nitrogen in Beer Mash
with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning
Algorithms.” Brewing Science , vol. 74, no. 9/10, 2021, pp. 107–21, https://doi.org/10.23763/BrSc21-10wefing.
short: P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Brewing Science 74
(2021) 107–121.
ufg: 'Wefing, Patrick u. a.: Determination of free amino nitrogen in beer
mash with an inline NIR transflectance probe and data evaluation by machine learning
algorithms, in: Brewing science 74 (2021), H. 9/10, S. 107–121.'
van: Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free
amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation
by machine learning algorithms. Brewing science . 2021;74(9/10):107–21.
date_created: 2021-11-02T10:06:04Z
date_updated: 2025-01-30T15:43:53Z
department:
- _id: DEP1308
- _id: DEP4028
doi: https://doi.org/10.23763/BrSc21-10wefing
intvolume: ' 74'
issue: 9/10
keyword:
- mashing
- NIR
- machine learning
- FAN
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.researchgate.net/publication/355735532_Determination_of_free_amino_nitrogen_in_beer_mash_with_an_inline_NIR_transflectance_probe_and_data_evaluation_by_machine_learning_algorithms
oa: '1'
page: 107 - 121
publication: 'Brewing science '
publication_identifier:
eissn:
- 0723-1520
issn:
- 1866-5195
publication_status: published
publisher: Carl
quality_controlled: '1'
status: public
title: Determination of free amino nitrogen in beer mash with an inline NIR transflectance
probe and data evaluation by machine learning algorithms
type: journal_article
user_id: '83781'
volume: 74
year: '2021'
...
---
_id: '12800'
abstract:
- lang: eng
text: his paper presents the cognitive module of the Cognitive Architecture for
Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The
goal of this architecture is to reduce the implementation effort of artificial
intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
algorithm-knowledge base allow the dynamic pipeline orchestration and configuration.
A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance
for further evaluation through the cognitive module. Thus, the cognitive module
is able to select feasible and robust configurations for process pipelines in
varying use cases. Furthermore, it automatically adapts the models and algorithms
based on model quality and resource consumption. The cognitive module also instantiates
additional pipelines to evaluate algorithms from different classes on test functions.
CAAI relies on well-defined interfaces to enable the integration of additional
modules and reduce implementation effort. Finally, an implementation based on
Docker, Kubernetes, and Kafka for the virtualization and orchestration of the
individual modules and as messaging technology for module communication is used
to evaluate a real-world use case.
author:
- first_name: Jan
full_name: Strohschein, Jan
last_name: Strohschein
- first_name: Andreas
full_name: Fischbach, Andreas
last_name: Fischbach
- first_name: Andreas
full_name: Bunte, Andreas
id: '58885'
last_name: Bunte
- first_name: Heide
full_name: Faeskorn-Woyke, Heide
last_name: Faeskorn-Woyke
- first_name: Natalia
full_name: Moriz, Natalia
id: '44238'
last_name: Moriz
- first_name: Thomas
full_name: Bartz-Beielstein, Thomas
last_name: Bartz-Beielstein
citation:
ama: Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein
T. Cognitive capabilities for the CAAI in cyber-physical production systems. The
International Journal of Advanced Manufacturing Technology. 2021;115(11-12):3513-3532.
doi:10.1007/s00170-021-07248-3
apa: Strohschein, J., Fischbach, A., Bunte, A., Faeskorn-Woyke, H., Moriz, N., &
Bartz-Beielstein, T. (2021). Cognitive capabilities for the CAAI in cyber-physical
production systems. The International Journal of Advanced Manufacturing Technology,
115(11–12), 3513–3532. https://doi.org/10.1007/s00170-021-07248-3
bjps: Strohschein J et al. (2021) Cognitive Capabilities for the CAAI
in Cyber-Physical Production Systems. The International Journal of Advanced
Manufacturing Technology 115, 3513–3532.
chicago: 'Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke,
Natalia Moriz, and Thomas Bartz-Beielstein. “Cognitive Capabilities for the CAAI
in Cyber-Physical Production Systems.” The International Journal of Advanced
Manufacturing Technology 115, no. 11–12 (2021): 3513–32. https://doi.org/10.1007/s00170-021-07248-3.'
chicago-de: 'Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke,
Natalia Moriz und Thomas Bartz-Beielstein. 2021. Cognitive capabilities for the
CAAI in cyber-physical production systems. The International Journal of Advanced
Manufacturing Technology 115, Nr. 11–12: 3513–3532. doi:10.1007/s00170-021-07248-3,
.'
din1505-2-1: 'Strohschein, Jan ; Fischbach, Andreas ; Bunte,
Andreas ; Faeskorn-Woyke, Heide
; Moriz, Natalia ; Bartz-Beielstein,
Thomas: Cognitive capabilities for the CAAI in cyber-physical production
systems. In: The International Journal of Advanced Manufacturing Technology
Bd. 115. London [u.a.], Springer (2021), Nr. 11–12, S. 3513–3532'
havard: J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T.
Bartz-Beielstein, Cognitive capabilities for the CAAI in cyber-physical production
systems, The International Journal of Advanced Manufacturing Technology. 115 (2021)
3513–3532.
ieee: 'J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, and
T. Bartz-Beielstein, “Cognitive capabilities for the CAAI in cyber-physical production
systems,” The International Journal of Advanced Manufacturing Technology,
vol. 115, no. 11–12, pp. 3513–3532, 2021, doi: 10.1007/s00170-021-07248-3.'
mla: Strohschein, Jan, et al. “Cognitive Capabilities for the CAAI in Cyber-Physical
Production Systems.” The International Journal of Advanced Manufacturing Technology,
vol. 115, no. 11–12, 2021, pp. 3513–32, https://doi.org/10.1007/s00170-021-07248-3.
short: J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T. Bartz-Beielstein,
The International Journal of Advanced Manufacturing Technology 115 (2021) 3513–3532.
ufg: 'Strohschein, Jan u. a.: Cognitive capabilities for the CAAI in cyber-physical
production systems, in: The International Journal of Advanced Manufacturing
Technology 115 (2021), H. 11–12, S. 3513–3532.'
van: Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein
T. Cognitive capabilities for the CAAI in cyber-physical production systems. The
International Journal of Advanced Manufacturing Technology. 2021;115(11–12):3513–32.
date_created: 2025-04-15T13:05:17Z
date_updated: 2025-06-26T13:39:22Z
department:
- _id: DEP5023
doi: 10.1007/s00170-021-07248-3
external_id:
isi:
- '000659025000010'
intvolume: ' 115'
isi: '1'
issue: 11-12
keyword:
- Cognition
- Industry 40
- Big data platform
- Machine learning
- CPPS
- Optimization
- Algorithm selection
- Simulation
language:
- iso: eng
page: 3513-3532
place: London [u.a.]
publication: The International Journal of Advanced Manufacturing Technology
publication_identifier:
eissn:
- 1433-3015
issn:
- 0268-3768
publication_status: published
publisher: 'Springer '
status: public
title: Cognitive capabilities for the CAAI in cyber-physical production systems
type: scientific_journal_article
user_id: '83781'
volume: 115
year: '2021'
...
---
_id: '4097'
abstract:
- lang: eng
text: The capabilities of object detection are well known, but many projects don’t
use them, despite potential benefit. Even though the use of object detection algorithms
is facilitated through frameworks and publications, a big issue is the creation
of the necessary training data. To tackle this issue, this work shows the design
and evaluation of a prototype, which allows users to create synthetic datasets
for object detection in images. The prototype is evaluated using YOLOv3 as the
underlying detector and shows that the generated datasets are equally good in
quality as manually created data. This encourages a wide adoption of object detection
algorithms in different areas, since image creation and labeling is often the
most time consuming step.
author:
- first_name: Andreas
full_name: Besginow, Andreas
id: '61743'
last_name: Besginow
- first_name: Sebastian
full_name: Büttner, Sebastian
id: '61868'
last_name: Büttner
- first_name: Carsten
full_name: Röcker, Carsten
id: '61525'
last_name: Röcker
citation:
ama: 'Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone
- A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd
International Conference on Human-Computer Interaction. Vol 12203. Lecture
Notes in Computer Science . Springer; 2020:178-192. doi:https://doi.org/10.1007/978-3-030-50344-4_14'
apa: Besginow, A., Büttner, S., & Röcker, C. (2020). Making Object Detection
Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data
Generation. 22nd International Conference on Human-Computer Interaction,
12203, 178–192. https://doi.org/10.1007/978-3-030-50344-4_14
bjps: 'Besginow A, Büttner S and Röcker C (2020) Making Object Detection
Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data
Generation. 22nd International Conference on Human-Computer Interaction,
vol. 12203. Berlin: Springer, pp. 178–192.'
chicago: 'Besginow, Andreas, Sebastian Büttner, and Carsten Röcker. “Making Object
Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic
Data Generation.” In 22nd International Conference on Human-Computer Interaction,
12203:178–92. Lecture Notes in Computer Science . Berlin: Springer, 2020. https://doi.org/10.1007/978-3-030-50344-4_14.'
chicago-de: 'Besginow, Andreas, Sebastian Büttner und Carsten Röcker. 2020. Making
Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic
Synthetic Data Generation. In: 22nd International Conference on Human-Computer
Interaction, 12203:178–192. Lecture Notes in Computer Science . Berlin: Springer.
doi:https://doi.org/10.1007/978-3-030-50344-4_14,
.'
din1505-2-1: 'Besginow, Andreas ;
Büttner, Sebastian ; Röcker,
Carsten: Making Object Detection Available to Everyone - A Hardware Prototype
for Semi-automatic Synthetic Data Generation. In: 22nd International Conference
on Human-Computer Interaction, Lecture Notes in Computer Science .
Bd. 12203. Berlin : Springer, 2020, S. 178–192'
havard: 'A. Besginow, S. Büttner, C. Röcker, Making Object Detection Available to
Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation,
in: 22nd International Conference on Human-Computer Interaction, Springer, Berlin,
2020: pp. 178–192.'
ieee: 'A. Besginow, S. Büttner, and C. Röcker, “Making Object Detection Available
to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation,”
in 22nd International Conference on Human-Computer Interaction, Copenhagen,
Denmark, 2020, vol. 12203, pp. 178–192. doi: https://doi.org/10.1007/978-3-030-50344-4_14.'
mla: Besginow, Andreas, et al. “Making Object Detection Available to Everyone -
A Hardware Prototype for Semi-Automatic Synthetic Data Generation.” 22nd International
Conference on Human-Computer Interaction, vol. 12203, Springer, 2020, pp.
178–92, https://doi.org/10.1007/978-3-030-50344-4_14.
short: 'A. Besginow, S. Büttner, C. Röcker, in: 22nd International Conference on
Human-Computer Interaction, Springer, Berlin, 2020, pp. 178–192.'
ufg: 'Besginow, Andreas/Büttner, Sebastian/Röcker, Carsten: Making Object
Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic
Data Generation, in: o. Hg.: 22nd International Conference on Human-Computer Interaction,
Bd. 12203, Berlin 2020 (Lecture Notes in Computer Science ), S. 178–192.'
van: 'Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone
- A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd
International Conference on Human-Computer Interaction. Berlin: Springer; 2020.
p. 178–92. (Lecture Notes in Computer Science ; vol. 12203).'
conference:
end_date: 2020-07-24
location: Copenhagen, Denmark
name: 22nd International Conference on Human-Computer Interaction
start_date: 2020-07-19
date_created: 2020-11-26T14:10:04Z
date_updated: 2025-06-26T13:28:35Z
department:
- _id: DEP5023
doi: https://doi.org/10.1007/978-3-030-50344-4_14
intvolume: ' 12203'
keyword:
- Object detection
- Synthetic datasets
- Machine learning
- Deep learning
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/chapter/10.1007/978-3-030-50344-4_14
oa: '1'
page: 178-192
place: Berlin
publication: 22nd International Conference on Human-Computer Interaction
publication_identifier:
eisbn:
- 978-3-030-50344-4
isbn:
- 978-3-030-50343-7
publication_status: published
publisher: Springer
series_title: 'Lecture Notes in Computer Science '
status: public
title: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic
Synthetic Data Generation
type: conference
user_id: '83781'
volume: 12203
year: '2020'
...
---
_id: '12812'
abstract:
- lang: eng
text: Discerning unexpected from expected data patterns is the key challenge of
anomaly detection. Although a multitude of solutions has been applied to this
modern Industry 4.0 problem, it remains an open research issue to identify the
key characteristics subjacent to an anomaly, sc. generate hypothesis as to why
they appear. In recent years, machine learning models have been regarded as universal
solution for a wide range of problems. While most of them suffer from non-self-explanatory
representations, Gaussian Processes (GPs) deliver interpretable and robust statistical
data models, which are able to cope with unreliable, noisy, or partially missing
data. Thus, we regard them as a suitable solution for detecting and appropriately
representing anomalies and their respective characteristics. In this position
paper, we discuss the problem of automatic and interpretable anomaly detection
by means of GPs. That is, we elaborate on why GPs are well suited for anomaly
detection and what the current challenges are when applying these probabilistic
models to large-scale production data.
author:
- first_name: Fabian
full_name: Berns, Fabian
last_name: Berns
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Christian
full_name: Beecks, Christian
last_name: Beecks
citation:
ama: Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0. (Panetto H, Madani K,
Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92.
doi:10.5220/0010130300870092
apa: Berns, F., Lange-Hegermann, M., & Beecks, C. (2020). Towards Gaussian Processes
for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto,
K. Madani, & A. Smirnov (Eds.), Proceedings of the International Conference
on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1
(pp. 87–92). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010130300870092
bjps: Berns F, Lange-Hegermann M and Beecks C (2020) Towards Gaussian
Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0,
Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.
chicago: Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. Towards
Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
4.0. Edited by H. Panetto, K. Madani, and A. Smirnov. Proceedings of the
International Conference on Innovative Intelligent Industrial Production and Logistics
IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications, 2020.
https://doi.org/10.5220/0010130300870092.
chicago-de: Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. Towards
Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
4.0. Hg. von H. Panetto, K. Madani, und A. Smirnov. Proceedings of the
International Conference on Innovative Intelligent Industrial Production and Logistics
IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications. doi:10.5220/0010130300870092,
.
din1505-2-1: 'Berns, Fabian ; Lange-Hegermann, Markus ; Beecks,
Christian ; Panetto, H. ;
Madani, K. ; Smirnov,
A. (Hrsg.): Towards Gaussian Processes for Automatic and Interpretable
Anomaly Detection in Industry 4.0 : SCITEPRESS - Science and Technology Publications,
2020'
havard: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for
Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science
and Technology Publications, 2020.
ieee: 'F. Berns, M. Lange-Hegermann, and C. Beecks, Towards Gaussian Processes
for Automatic and Interpretable Anomaly Detection in Industry 4.0. SCITEPRESS
- Science and Technology Publications, 2020, pp. 87–92. doi: 10.5220/0010130300870092.'
mla: Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable
Anomaly Detection in Industry 4.0.” Proceedings of the International Conference
on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1,
edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications,
2020, pp. 87–92, https://doi.org/10.5220/0010130300870092.
short: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and
Technology Publications, 2020.
ufg: 'Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian: Towards Gaussian
Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg.
von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.'
van: Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov
A, editors. Proceedings of the International Conference on Innovative Intelligent
Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and
Technology Publications; 2020.
conference:
end_date: 2020-11-04
location: Budapest, HUNGARY
name: International Conference on Innovative Intelligent Industrial Production and
Logistics (IN4PL)
start_date: 2020-11-02
date_created: 2025-04-17T06:20:07Z
date_updated: 2025-06-26T13:31:38Z
department:
- _id: DEP5000
doi: 10.5220/0010130300870092
editor:
- first_name: H.
full_name: Panetto, H.
last_name: Panetto
- first_name: K.
full_name: Madani, K.
last_name: Madani
- first_name: A.
full_name: Smirnov, A.
last_name: Smirnov
keyword:
- Anomaly Detection
- Gaussian Processes
- Explainable Machine Learning
- Industry 4.0
language:
- iso: eng
page: 87-92
publication: ' Proceedings of the International Conference on Innovative Intelligent
Industrial Production and Logistics IN4PL - Volume 1'
publication_identifier:
isbn:
- 978-989-758-476-3
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
status: public
title: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection
in Industry 4.0
type: conference_editor_article
user_id: '83781'
year: '2020'
...
---
_id: '4327'
abstract:
- lang: eng
text: In ever changing world, the industrial systems become more and more complex.
Machine feedback in the form of alarms and notifications, due to its growing volume,
becomes overwhelming for the operator. In addition, expectations in relation to
system availability are growing as well. Therefore, there exists strong need for
new solutions guaranteeing fast troubleshooting of problems that arise during
system operation. The approach proposed in this study uses advantages of the Asset
Administration Shell, machine learning, and human-machine interaction in order
to create the assistance system which holistically addresses the issue of troubleshooting
complex industrial systems.
author:
- first_name: Dorota
full_name: Lang, Dorota
id: '68941'
last_name: Lang
- first_name: Paul
full_name: Wunderlich, Paul
id: '52317'
last_name: Wunderlich
- first_name: Mario
full_name: Heinz, Mario
id: '68913'
last_name: Heinz
- first_name: Lukasz
full_name: Wisniewski, Lukasz
id: '1710'
last_name: Wisniewski
- first_name: Jürgen
full_name: Jasperneite, Jürgen
id: '1899'
last_name: Jasperneite
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Carsten
full_name: Röcker, Carsten
id: '61525'
last_name: Röcker
citation:
ama: 'Lang D, Wunderlich P, Heinz M, et al. Assistance System to Support Troubleshooting
of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory
Communication Systems (WFCS). Piscataway, NJ: IEEE; 2018. doi:10.1109/WFCS.2018.8402380'
apa: 'Lang, D., Wunderlich, P., Heinz, M., Wisniewski, L., Jasperneite, J., Niggemann,
O., & Röcker, C. (2018). Assistance System to Support Troubleshooting of Complex
Industrial Systems. In 14th IEEE International Workshop on Factory Communication
Systems (WFCS). Piscataway, NJ: IEEE. https://doi.org/10.1109/WFCS.2018.8402380'
bjps: 'Lang D et al. (2018) Assistance System to Support Troubleshooting
of Complex Industrial Systems. 14th IEEE International Workshop on Factory
Communication Systems (WFCS). Piscataway, NJ: IEEE.'
chicago: 'Lang, Dorota, Paul Wunderlich, Mario Heinz, Lukasz Wisniewski, Jürgen
Jasperneite, Oliver Niggemann, and Carsten Röcker. “Assistance System to Support
Troubleshooting of Complex Industrial Systems.” In 14th IEEE International
Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE, 2018.
https://doi.org/10.1109/WFCS.2018.8402380.'
chicago-de: 'Lang, Dorota, Paul Wunderlich, Mario Heinz, Lukasz Wisniewski, Jürgen
Jasperneite, Oliver Niggemann und Carsten Röcker. 2018. Assistance System to Support
Troubleshooting of Complex Industrial Systems. In: 14th IEEE International
Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE. doi:10.1109/WFCS.2018.8402380,
.'
din1505-2-1: 'Lang, Dorota ; Wunderlich, Paul ; Heinz,
Mario ; Wisniewski, Lukasz
; Jasperneite, Jürgen ; Niggemann,
Oliver ; Röcker, Carsten:
Assistance System to Support Troubleshooting of Complex Industrial Systems. In:
14th IEEE International Workshop on Factory Communication Systems (WFCS).
Piscataway, NJ : IEEE, 2018'
havard: 'D. Lang, P. Wunderlich, M. Heinz, L. Wisniewski, J. Jasperneite, O. Niggemann,
C. Röcker, Assistance System to Support Troubleshooting of Complex Industrial
Systems, in: 14th IEEE International Workshop on Factory Communication Systems
(WFCS), IEEE, Piscataway, NJ, 2018.'
ieee: D. Lang et al., “Assistance System to Support Troubleshooting of Complex
Industrial Systems,” in 14th IEEE International Workshop on Factory Communication
Systems (WFCS), Imperia, Italy , 2018.
mla: Lang, Dorota, et al. “Assistance System to Support Troubleshooting of Complex
Industrial Systems.” 14th IEEE International Workshop on Factory Communication
Systems (WFCS), IEEE, 2018, doi:10.1109/WFCS.2018.8402380.
short: 'D. Lang, P. Wunderlich, M. Heinz, L. Wisniewski, J. Jasperneite, O. Niggemann,
C. Röcker, in: 14th IEEE International Workshop on Factory Communication Systems
(WFCS), IEEE, Piscataway, NJ, 2018.'
ufg: 'Lang, Dorota et. al. (2018): Assistance System to Support Troubleshooting
of Complex Industrial Systems, in: 14th IEEE International Workshop on Factory
Communication Systems (WFCS), Piscataway, NJ.'
van: 'Lang D, Wunderlich P, Heinz M, Wisniewski L, Jasperneite J, Niggemann O, et
al. Assistance System to Support Troubleshooting of Complex Industrial Systems.
In: 14th IEEE International Workshop on Factory Communication Systems (WFCS).
Piscataway, NJ: IEEE; 2018.'
conference:
end_date: 2018-06-15
location: 'Imperia, Italy '
name: 14th IEEE International Workshop on Factory Communication Systems (WFCS)
start_date: 2018-06-13
date_created: 2021-01-08T08:26:30Z
date_updated: 2023-03-15T13:49:52Z
department:
- _id: DEP5023
- _id: DEP5019
doi: 10.1109/WFCS.2018.8402380
keyword:
- Maintenance engineering
- Adaptation models
- Machine learning
- Data models
- Standards
- Software
- Bayes methods
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
place: Piscataway, NJ
publication: 14th IEEE International Workshop on Factory Communication Systems (WFCS)
publication_identifier:
eisbn:
- 978-1-5386-1066-4
publication_status: published
publisher: IEEE
status: public
title: Assistance System to Support Troubleshooting of Complex Industrial Systems
type: conference
user_id: '45673'
year: 2018
...
---
_id: '4254'
abstract:
- lang: eng
text: The current trend of integrating machines and factories into cyber-physical
systems (CPS) creates an enormous complexity for operators of such systems. Especially
the search for the root cause of cascading failures becomes highly time-consuming.
Within this paper, we address the question on how to help human users to better
and faster understand root causes of such situations. We propose a concept of
interactive alarm flood reduction and present the implementation of a first vertical
prototype for such a system. We consider this prototype as a first artifact to
be discussed by the research community and aim towards an incremental further
development of the system in order to support humans in complex error situations.
author:
- first_name: Sebastian
full_name: Büttner, Sebastian
id: '61868'
last_name: Büttner
- first_name: Paul
full_name: Wunderlich, Paul
id: '52317'
last_name: Wunderlich
- first_name: Mario
full_name: Heinz, Mario
id: '68913'
last_name: Heinz
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Carsten
full_name: Röcker, Carsten
id: '61525'
last_name: Röcker
citation:
ama: 'Büttner S, Wunderlich P, Heinz M, Niggemann O, Röcker C. Managing Complexity:
Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction.
In: Holzinger A, ed. Machine Learning and Knowledge Extraction : First IFIP
TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio,
Italy, August 29 – September 1, 2017, Proceedings. Vol 10410. Lecture Notes
in Computer Science . Cham: Springer; 2017:69-82.'
apa: 'Büttner, S., Wunderlich, P., Heinz, M., Niggemann, O., & Röcker, C. (2017).
Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive
Alarm Flood Reduction. In A. Holzinger (Ed.), Machine Learning and Knowledge
Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference,
CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings (Vol.
10410, pp. 69–82). Cham: Springer.'
bjps: 'Büttner S et al. (2017) Managing Complexity: Towards Intelligent
Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In Holzinger
A (ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4,
8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy,
August 29 – September 1, 2017, Proceedings, vol. 10410. Cham: Springer, pp.
69–82.'
chicago: 'Büttner, Sebastian, Paul Wunderlich, Mario Heinz, Oliver Niggemann, and
Carsten Röcker. “Managing Complexity: Towards Intelligent Error-Handling Assistance
Trough Interactive Alarm Flood Reduction.” In Machine Learning and Knowledge
Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference,
CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, edited
by Andreas Holzinger, 10410:69–82. Lecture Notes in Computer Science . Cham: Springer,
2017.'
chicago-de: 'Büttner, Sebastian, Paul Wunderlich, Mario Heinz, Oliver Niggemann
und Carsten Röcker. 2017. Managing Complexity: Towards Intelligent Error-Handling
Assistance Trough Interactive Alarm Flood Reduction. In: Machine Learning
and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain
Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings,
hg. von Andreas Holzinger, 10410:69–82. Lecture Notes in Computer Science . Cham:
Springer.'
din1505-2-1: 'Büttner, Sebastian ;
Wunderlich, Paul ; Heinz,
Mario ; Niggemann, Oliver
; Röcker, Carsten: Managing Complexity:
Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction.
In: Holzinger, A. (Hrsg.): Machine
Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International
Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1,
2017, Proceedings, Lecture Notes in Computer Science . Bd. 10410. Cham :
Springer, 2017, S. 69–82'
havard: 'S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, C. Röcker, Managing
Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm
Flood Reduction, in: A. Holzinger (Ed.), Machine Learning and Knowledge Extraction :
First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE
2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, Springer, Cham,
2017: pp. 69–82.'
ieee: 'S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, and C. Röcker, “Managing
Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm
Flood Reduction,” in Machine Learning and Knowledge Extraction : First IFIP
TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio,
Italy, August 29 – September 1, 2017, Proceedings, Reggio, Italy, 2017, vol.
10410, pp. 69–82.'
mla: 'Büttner, Sebastian, et al. “Managing Complexity: Towards Intelligent Error-Handling
Assistance Trough Interactive Alarm Flood Reduction.” Machine Learning and
Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain
Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings,
edited by Andreas Holzinger, vol. 10410, Springer, 2017, pp. 69–82.'
short: 'S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, C. Röcker, in: A. Holzinger
(Ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9,
12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August
29 – September 1, 2017, Proceedings, Springer, Cham, 2017, pp. 69–82.'
ufg: 'Büttner, Sebastian et. al. (2017): Managing Complexity: Towards Intelligent
Error-Handling Assistance Trough Interactive Alarm Flood Reduction, in: Andreas
Holzinger (Hg.): Machine Learning and Knowledge Extraction : First IFIP TC
5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio,
Italy, August 29 – September 1, 2017, Proceedings (=Lecture Notes in Computer
Science 10410), Cham, S. 69–82.'
van: 'Büttner S, Wunderlich P, Heinz M, Niggemann O, Röcker C. Managing Complexity:
Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction.
In: Holzinger A, editor. Machine Learning and Knowledge Extraction : First IFIP
TC 5, WG 84, 89, 129 International Cross-Domain Conference, CD-MAKE 2017, Reggio,
Italy, August 29 – September 1, 2017, Proceedings. Cham: Springer; 2017. p. 69–82.
(Lecture Notes in Computer Science ; vol. 10410).'
conference:
end_date: 2017-09-01
location: Reggio, Italy
name: International Cross-Domain Conference, CD-MAKE 2017
start_date: 2017-08-29
date_created: 2020-12-10T13:40:04Z
date_updated: 2023-03-15T13:49:51Z
department:
- _id: DEP5023
editor:
- first_name: Andreas
full_name: Holzinger, Andreas
last_name: Holzinger
intvolume: ' 10410'
keyword:
- Alarm flood reduction
- Machine learning
- Assistive system
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
page: 69-82
place: Cham
publication: ' Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4,
8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August
29 – September 1, 2017, Proceedings'
publication_identifier:
eisbn:
- '9783319668086 '
isbn:
- 978-3-319-66807-9
publication_status: published
publisher: Springer
series_title: 'Lecture Notes in Computer Science '
status: public
title: 'Managing Complexity: Towards Intelligent Error-Handling Assistance Trough
Interactive Alarm Flood Reduction'
type: conference
user_id: '15514'
volume: 10410
year: 2017
...
---
_id: '4298'
abstract:
- lang: eng
text: In this paper, we present the current state-of-the-art of decision making
(DM) and machine learning (ML) and bridge the two research domains to create an
integrated approach of complex problem solving based on human and computational
agents. We present a novel classification of ML, emphasizing the human-in-the-loop
in interactive ML (iML) and more specific on collaborative interactive ML (ciML),
which we understand as a deep integrated version of iML, where humans and algorithms
work hand in hand to solve complex problems. Both humans and computers have specific
strengths and weaknesses and integrating humans into machine learning processes
might be a very efficient way for tackling problems. This approach bears immense
research potential for various domains, e.g., in health informatics or in industrial
applications. We outline open questions and name future challenges that have to
be addressed by the research community to enable the use of collaborative interactive
machine learning for problem solving in a large scale.
author:
- first_name: Sebastian
full_name: Robert, Sebastian
last_name: Robert
- first_name: Sebastian
full_name: Büttner, Sebastian
id: '61868'
last_name: Büttner
- first_name: Carsten
full_name: Röcker, Carsten
id: '61525'
last_name: Röcker
- first_name: Andreas
full_name: Holzinger, Andreas
last_name: Holzinger
citation:
ama: 'Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards
Collaborative Interactive Machine Learning. In: Holzinger A, ed. Machine Learning
for Health Informatics : State-of-the-Art and Future Challenges . Vol 9605.
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence
. Cham, CH: Springer; 2016:357-376. doi:10.1007/978-3-319-50478-0_18'
apa: 'Robert, S., Büttner, S., Röcker, C., & Holzinger, A. (2016). Reasoning
Under Uncertainty: Towards Collaborative Interactive Machine Learning. In A. Holzinger
(Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future
Challenges (Vol. 9605, pp. 357–376). Cham, CH: Springer. https://doi.org/10.1007/978-3-319-50478-0_18'
bjps: 'Robert S et al. (2016) Reasoning Under Uncertainty: Towards
Collaborative Interactive Machine Learning. In Holzinger A (ed.), Machine Learning
for Health Informatics : State-of-the-Art and Future Challenges , vol. 9605.
Cham, CH: Springer, pp. 357–376.'
chicago: 'Robert, Sebastian, Sebastian Büttner, Carsten Röcker, and Andreas Holzinger.
“Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.”
In Machine Learning for Health Informatics : State-of-the-Art and Future Challenges
, edited by Andreas Holzinger, 9605:357–76. Lecture Notes in Computer Science
/ Lecture Notes in Artificial Intelligence . Cham, CH: Springer, 2016. https://doi.org/10.1007/978-3-319-50478-0_18.'
chicago-de: 'Robert, Sebastian, Sebastian Büttner, Carsten Röcker und Andreas Holzinger.
2016. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.
In: Machine Learning for Health Informatics : State-of-the-Art and Future Challenges
, hg. von Andreas Holzinger, 9605:357–376. Lecture Notes in Computer Science
/ Lecture Notes in Artificial Intelligence . Cham, CH: Springer. doi:10.1007/978-3-319-50478-0_18,
.'
din1505-2-1: 'Robert, Sebastian ;
Büttner, Sebastian ; Röcker,
Carsten ; Holzinger, Andreas:
Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.
In: Holzinger, A. (Hrsg.): Machine
Learning for Health Informatics : State-of-the-Art and Future Challenges ,
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence
. Bd. 9605. Cham, CH : Springer, 2016, S. 357–376'
havard: 'S. Robert, S. Büttner, C. Röcker, A. Holzinger, Reasoning Under Uncertainty:
Towards Collaborative Interactive Machine Learning, in: A. Holzinger (Ed.), Machine
Learning for Health Informatics : State-of-the-Art and Future Challenges , Springer,
Cham, CH, 2016: pp. 357–376.'
ieee: 'S. Robert, S. Büttner, C. Röcker, and A. Holzinger, “Reasoning Under Uncertainty:
Towards Collaborative Interactive Machine Learning,” in Machine Learning for
Health Informatics : State-of-the-Art and Future Challenges , vol. 9605, A.
Holzinger, Ed. Cham, CH: Springer, 2016, pp. 357–376.'
mla: 'Robert, Sebastian, et al. “Reasoning Under Uncertainty: Towards Collaborative
Interactive Machine Learning.” Machine Learning for Health Informatics : State-of-the-Art
and Future Challenges , edited by Andreas Holzinger, vol. 9605, Springer,
2016, pp. 357–76, doi:10.1007/978-3-319-50478-0_18.'
short: 'S. Robert, S. Büttner, C. Röcker, A. Holzinger, in: A. Holzinger (Ed.),
Machine Learning for Health Informatics : State-of-the-Art and Future Challenges
, Springer, Cham, CH, 2016, pp. 357–376.'
ufg: 'Robert, Sebastian et. al. (2016): Reasoning Under Uncertainty: Towards
Collaborative Interactive Machine Learning, in: Andreas Holzinger (Hg.): Machine
Learning for Health Informatics : State-of-the-Art and Future Challenges
(=Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence 9605),
Cham, CH, S. 357–376.'
van: 'Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards
Collaborative Interactive Machine Learning. In: Holzinger A, editor. Machine Learning
for Health Informatics : State-of-the-Art and Future Challenges . Cham, CH: Springer;
2016. p. 357–76. (Lecture Notes in Computer Science / Lecture Notes in Artificial
Intelligence ; vol. 9605).'
date_created: 2020-12-22T14:11:00Z
date_updated: 2023-03-15T13:49:51Z
department:
- _id: DEP5023
doi: 10.1007/978-3-319-50478-0_18
editor:
- first_name: Andreas
full_name: Holzinger, Andreas
last_name: Holzinger
intvolume: ' 9605'
keyword:
- Decision making
- Reasoning
- Interactive machine learning
- Collaborative interactive machine learning
language:
- iso: eng
page: 357-376
place: Cham, CH
publication: 'Machine Learning for Health Informatics : State-of-the-Art and Future
Challenges '
publication_identifier:
eisbn:
- '978-3-319-50478-0 '
isbn:
- '978-3-319-50477-3 '
publication_status: published
publisher: Springer
series_title: 'Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence '
status: public
title: 'Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning'
type: book_chapter
user_id: '15514'
volume: 9605
year: 2016
...
---
_id: '4336'
abstract:
- lang: eng
text: "Prolonged life expectancy along with the increasing complexity of medicine
and health services raises health costs worldwide dramatically. Whilst the smart
health concept has much potential to support the concept of the emerging P4-medicine
(preventive, participatory, predictive, and personalized), such high-tech medicine
produces large amounts of high-dimensional, weakly-structured data sets and massive
amounts of unstructured information. All these technological approaches along
with “big data” are turning the medical sciences into a data-intensive science.
To keep pace with the growing amounts of complex data, smart hospital approaches
are a commandment of the future, necessitating context aware computing along with
advanced interaction paradigms in new physical-digital ecosystems.\r\n\r\nThe
very successful synergistic combination of methodologies and approaches from Human-Computer
Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions
for the vision to support human intelligence with machine learning.\r\n\r\nThe
papers selected for this volume focus on hot topics in smart health; they discuss
open problems and future challenges in order to provide a research agenda to stimulate
further research and progress."
citation:
ama: 'Holzinger A, Röcker C, Ziefle M, eds. Smart Health: Open Problems and Future
Challenges. Vol 8700. Heidelberg: Springer; 2015. doi:10.1007/978-3-319-16226-3'
apa: 'Holzinger, A., Röcker, C., & Ziefle, M. (Eds.). (2015). Smart Health:
Open Problems and Future Challenges (Vol. 8700). Heidelberg: Springer. https://doi.org/10.1007/978-3-319-16226-3'
bjps: 'Holzinger A, Röcker C and Ziefle M (eds) (2015) Smart Health: Open
Problems and Future Challenges. Heidelberg: Springer.'
chicago: 'Holzinger, Andreas, Carsten Röcker, and Martina Ziefle, eds. Smart
Health: Open Problems and Future Challenges. Vol. 8700. Lecture Notes in Computer
Science / Information Systems and Applications, Incl. Internet/Web, and HCI.
Heidelberg: Springer, 2015. https://doi.org/10.1007/978-3-319-16226-3.'
chicago-de: 'Holzinger, Andreas, Carsten Röcker und Martina Ziefle, Hrsg. 2015.
Smart Health: Open Problems and Future Challenges. Bd. 8700. Lecture Notes
in Computer Science / Information Systems and Applications, incl. Internet/Web,
and HCI. Heidelberg: Springer. doi:10.1007/978-3-319-16226-3,
.'
din1505-2-1: 'Holzinger, A. ; Röcker, C. ; Ziefle,
M. (Hrsg.): Smart Health: Open Problems and Future Challenges, Lecture
Notes in Computer Science / Information Systems and Applications, incl. Internet/Web,
and HCI. Bd. 8700. Heidelberg : Springer, 2015'
havard: 'A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and
Future Challenges, Springer, Heidelberg, 2015.'
ieee: 'A. Holzinger, C. Röcker, and M. Ziefle, Eds., Smart Health: Open Problems
and Future Challenges, vol. 8700. Heidelberg: Springer, 2015.'
mla: 'Holzinger, Andreas, et al., editors. Smart Health: Open Problems and Future
Challenges. Vol. 8700, Springer, 2015, doi:10.1007/978-3-319-16226-3.'
short: 'A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and
Future Challenges, Springer, Heidelberg, 2015.'
ufg: 'Holzinger, Andreas et. al. (Hgg.) (2015): Smart Health: Open Problems
and Future Challenges (=Lecture Notes in Computer Science / Information Systems
and Applications, incl. Internet/Web, and HCI 8700), Heidelberg.'
van: 'Holzinger A, Röcker C, Ziefle M, editors. Smart Health: Open Problems and
Future Challenges. Heidelberg: Springer; 2015. 275 p. (Lecture Notes in Computer
Science / Information Systems and Applications, incl. Internet/Web, and HCI;
vol. 8700).'
date_created: 2021-01-08T12:03:52Z
date_updated: 2023-03-15T13:49:52Z
department:
- _id: DEP5023
doi: 10.1007/978-3-319-16226-3
editor:
- first_name: Andreas
full_name: Holzinger, Andreas
last_name: Holzinger
- first_name: Carsten
full_name: Röcker, Carsten
id: '61525'
last_name: Röcker
- first_name: Martina
full_name: Ziefle, Martina
last_name: Ziefle
intvolume: ' 8700'
keyword:
- HCI
- ambient assisted living
- big data
- computational intelligence
- context awareness
- data centric medicine
- decision support
- interactive data mining
- keyword detection
- knoweldge bases
- knoweldge discovery
- machine learning
- medical decision support
- medical informatics
- natural language processing
- pervasive health
- smart home
- ubiquitous computing
- visualization
- wearable sensors
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'http://www.springerlink.com/content/978-3-319-16226-3 '
oa: '1'
page: '275'
place: Heidelberg
publication_identifier:
eisbn:
- 978-3-319-16226-3
eissn:
- 1611-3349
isbn:
- 978-3-319-16225-6
issn:
- 0302-9743
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science / Information Systems and Applications,
incl. Internet/Web, and HCI
status: public
title: 'Smart Health: Open Problems and Future Challenges'
type: book_editor
user_id: '15514'
volume: 8700
year: 2015
...
---
_id: '2167'
abstract:
- lang: eng
text: "Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry
and politics: By applying new IT and new computer science solutions, production
systems will become more adaptable, more resource ef- ficient and more user friendly.
The analysis and diagnosis of such systems is a major part of this trend: Plants
should detect automatically wear, faults and suboptimal configurations. This paper
reflects the current state-of- the-art in diagnosis against the requirements of
CPPSs, identifies three main gaps and gives application scenarios to outline first
ideas for potential solutions to close these gaps.\r\n"
author:
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems
- State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial
Intelligence (AAAI-15). Austin, Texas, USA; 2015.'
apa: Niggemann, O., & Lohweg, V. (2015). On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. In Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.
bjps: Niggemann O and Lohweg V (2015) On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. Twenty-Ninth Conference
on Artificial Intelligence (AAAI-15). Austin, Texas, USA.
chicago: Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda.” In Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA, 2015.
chicago-de: 'Niggemann, Oliver und Volker Lohweg. 2015. On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.'
din1505-2-1: 'Niggemann, Oliver ;
Lohweg, Volker: On the Diagnosis
of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In:
Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas,
USA, 2015'
havard: 'O. Niggemann, V. Lohweg, On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth Conference on
Artificial Intelligence (AAAI-15), Austin, Texas, USA, 2015.'
ieee: O. Niggemann and V. Lohweg, “On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda,” in Twenty-Ninth Conference
on Artificial Intelligence (AAAI-15), 2015.
mla: Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda.” Twenty-Ninth Conference on
Artificial Intelligence (AAAI-15), 2015.
short: 'O. Niggemann, V. Lohweg, in: Twenty-Ninth Conference on Artificial Intelligence
(AAAI-15), Austin, Texas, USA, 2015.'
ufg: 'Niggemann, Oliver/Lohweg, Volker (2015): On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA.'
van: 'Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems
- State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial
Intelligence (AAAI-15). Austin, Texas, USA; 2015.'
date_created: 2019-12-04T12:43:12Z
date_updated: 2023-03-15T13:49:39Z
department:
- _id: DEP5023
keyword:
- Cyber-Physical Systems
- Machine Learning
- Diagnosis
- Anomaly Detection
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9530/9691
oa: '1'
place: Austin, Texas, USA
publication: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)
status: public
title: On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and
Research Agenda
type: conference
user_id: '68554'
year: 2015
...
---
_id: '2087'
abstract:
- lang: eng
text: It is likely in real-world applications that only little data isavailable
for training a knowledge-based system. We present a method forautomatically training
the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification
system that works also when only littledata is available and the universal set
is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s
member-ship functions are trained using probability distribution functions.
author:
- first_name: Uwe
full_name: Mönks, Uwe
id: '1825'
last_name: Mönks
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
- first_name: Denis
full_name: Petker, Denis
last_name: Petker
citation:
ama: 'Mönks U, Lohweg V, Petker D. Fuzzy-Pattern-Classifier Training with Small
Data Sets. In: IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02
July 2010, Dortmund, Germany; 2010.'
apa: Mönks, U., Lohweg, V., & Petker, D. (2010). Fuzzy-Pattern-Classifier Training
with Small Data Sets. In IPMU 2010 - International Conference on Information
Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun
2010 - 02 July 2010, Dortmund, Germany.
bjps: Mönks U, Lohweg V and Petker D (2010) Fuzzy-Pattern-Classifier Training
with Small Data Sets. IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02
July 2010, Dortmund, Germany.
chicago: Mönks, Uwe, Volker Lohweg, and Denis Petker. “Fuzzy-Pattern-Classifier
Training with Small Data Sets.” In IPMU 2010 - International Conference on
Information Processing and Management of Uncertainty in Knowledge Based Systems.
28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.
chicago-de: 'Mönks, Uwe, Volker Lohweg und Denis Petker. 2010. Fuzzy-Pattern-Classifier
Training with Small Data Sets. In: IPMU 2010 - International Conference on
Information Processing and Management of Uncertainty in Knowledge Based Systems.
28 Jun 2010 - 02 July 2010, Dortmund, Germany.'
din1505-2-1: 'Mönks, Uwe ; Lohweg,
Volker ; Petker, Denis: Fuzzy-Pattern-Classifier
Training with Small Data Sets. In: IPMU 2010 - International Conference on
Information Processing and Management of Uncertainty in Knowledge Based Systems :
28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010'
havard: 'U. Mönks, V. Lohweg, D. Petker, Fuzzy-Pattern-Classifier Training with
Small Data Sets, in: IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems, 28 Jun 2010 - 02 July
2010, Dortmund, Germany, 2010.'
ieee: U. Mönks, V. Lohweg, and D. Petker, “Fuzzy-Pattern-Classifier Training with
Small Data Sets,” in IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems, 2010.
mla: Mönks, Uwe, et al. “Fuzzy-Pattern-Classifier Training with Small Data Sets.”
IPMU 2010 - International Conference on Information Processing and Management
of Uncertainty in Knowledge Based Systems, 28 Jun 2010 - 02 July 2010, Dortmund,
Germany, 2010.
short: 'U. Mönks, V. Lohweg, D. Petker, in: IPMU 2010 - International Conference
on Information Processing and Management of Uncertainty in Knowledge Based Systems,
28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.'
ufg: 'Mönks, Uwe et. al. (2010): Fuzzy-Pattern-Classifier Training with Small
Data Sets, in: IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems.'
van: 'Mönks U, Lohweg V, Petker D. Fuzzy-Pattern-Classifier Training with Small
Data Sets. In: IPMU 2010 - International Conference on Information Processing
and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July
2010, Dortmund, Germany; 2010.'
date_created: 2019-12-02T08:15:18Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
keyword:
- Fuzzy Logic
- Probability Theory
- Fuzzy-Pattern-Classification
- Machine Learning
- Artificial Intelligence
- Pattern Recognition
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.th-owl.de/init/uploads/tx_initdb/00800426_01.pdf
oa: '1'
publication: IPMU 2010 - International Conference on Information Processing and Management
of Uncertainty in Knowledge Based Systems
publication_status: published
publisher: 28 Jun 2010 - 02 July 2010, Dortmund, Germany
status: public
title: Fuzzy-Pattern-Classifier Training with Small Data Sets
type: conference
user_id: '45673'
year: 2010
...