---
_id: '52816'
abstract:
- lang: eng
text: "Manufacturing companies face the challenge of reaching required quality standards.
Using\r\noptical sensors and deep learning might help. However, training deep
learning algorithms\r\nrequire large amounts of visual training data. Using domain
randomization to generate synthetic\r\nimage data can alleviate this bottleneck.
This paper presents the application of synthetic\r\nimage training data for optical
quality inspections using visual sensor technology. The results\r\nshow synthetically
generated training data are appropriate for visual quality inspections."
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Hieb, Michael
id: '72252'
last_name: Hieb
citation:
ama: 'Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in
Machine Vision Quality Gates in Manufacturing. In: Lectures. AMA Service
GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:10.5162/smsi2023/d7.4'
apa: Gräßler, I., & Hieb, M. (2023). Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing. Lectures,
253–524. https://doi.org/10.5162/smsi2023/d7.4
bibtex: '@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={10.5162/smsi2023/d7.4},
booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515
Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524}
}'
chicago: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing.” In Lectures,
253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023.
https://doi.org/10.5162/smsi2023/d7.4.
ieee: 'I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection
in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg,
2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.'
mla: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing.” Lectures,
AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp.
253–524, doi:10.5162/smsi2023/d7.4.
short: 'I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str.
49, 31515 Wunstorf, Germany, 2023, pp. 253–524.'
conference:
end_date: 2023-05-11
location: Nuremberg
name: SMSI 2023. Sensor and Measurement Science International
start_date: 2023-05-08
date_created: 2024-03-25T10:16:24Z
date_updated: 2024-03-25T11:05:53Z
department:
- _id: '152'
doi: 10.5162/smsi2023/d7.4
keyword:
- synthetic training data
- machine vision quality gates
- deep learning
- automated inspection and quality control
- production control
language:
- iso: eng
page: 253-524
publication: Lectures
publication_status: published
publisher: AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany
quality_controlled: '1'
status: public
title: Creating Synthetic Training Datasets for Inspection in Machine Vision Quality
Gates in Manufacturing
type: conference
user_id: '5905'
year: '2023'
...
---
_id: '55850'
abstract:
- lang: eng
text: This release covers the state of this prototype app at the end of the funding
phase for the Paderborn University part of the Beethoven in the House project.
It uses https://api.domestic-beethoven.eu/ld/BithCollection.jsonld as starting
point for traversing the LOD graph, and reads data from the project pod available
from https://bith.solidcommunity.net/public/bith.ttl (which has no content at
the time of the release).
author:
- first_name: Johannes
full_name: Kepper, Johannes
last_name: Kepper
citation:
ama: 'Kepper J. DomesticBeethoven/Bith-Annotator: Release 2023-04. Zenodo;
2023. doi:10.5281/ZENODO.7877741'
apa: 'Kepper, J. (2023). DomesticBeethoven/bith-annotator: Release 2023-04.
Zenodo. https://doi.org/10.5281/ZENODO.7877741'
bibtex: '@book{Kepper_2023, title={DomesticBeethoven/bith-annotator: Release 2023-04},
DOI={10.5281/ZENODO.7877741},
publisher={Zenodo}, author={Kepper, Johannes}, year={2023} }'
chicago: 'Kepper, Johannes. DomesticBeethoven/Bith-Annotator: Release 2023-04.
Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877741.'
ieee: 'J. Kepper, DomesticBeethoven/bith-annotator: Release 2023-04. Zenodo,
2023.'
mla: 'Kepper, Johannes. DomesticBeethoven/Bith-Annotator: Release 2023-04.
Zenodo, 2023, doi:10.5281/ZENODO.7877741.'
short: 'J. Kepper, DomesticBeethoven/Bith-Annotator: Release 2023-04, Zenodo, 2023.'
date_created: 2024-08-28T11:48:31Z
date_updated: 2024-08-28T13:59:52Z
department:
- _id: '874'
doi: 10.5281/ZENODO.7877741
keyword:
- MEI
- Edirom
- Music Encoding Initiative
- Linked Open Data
- MELD
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://zenodo.org/record/7877741
oa: '1'
publication_status: published
publisher: Zenodo
status: public
title: 'DomesticBeethoven/bith-annotator: Release 2023-04'
type: misc
user_id: '1684'
year: '2023'
...
---
_id: '55833'
abstract:
- lang: eng
text: We present a new multi-layered, conceptual model for associating musical source
materials to musicological arguments. We describe our proposal for operationalizing
these concepts through a framework for musical annotation which we have implemented
using RDF. Briefly stated, this model shows how portions of digitized data in
various files and formats can be identified, selected, labelled, and compared.
author:
- first_name: Elisabete
full_name: Shibata, Elisabete
last_name: Shibata
- first_name: David
full_name: Lewis, David
last_name: Lewis
- first_name: Mark
full_name: Saccomano, Mark
last_name: Saccomano
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
- first_name: Kevin
full_name: Page, Kevin
last_name: Page
citation:
ama: 'Shibata E, Lewis D, Saccomano M, Kepper J, Page K. A New Conceptual Model
for Musical Sources and Musicological Studies. In: Weigl D, Bain J, Ang A, eds.
Proceedings of the Music Encoding Conference 2022. ; 2023:145–150. doi:https://doi.org/10.17613/8p2c-1q77'
apa: Shibata, E., Lewis, D., Saccomano, M., Kepper, J., & Page, K. (2023). A
New Conceptual Model for Musical Sources and Musicological Studies. In D. Weigl,
J. Bain, & A. Ang (Eds.), Proceedings of the Music Encoding Conference
2022 (pp. 145–150). https://doi.org/10.17613/8p2c-1q77
bibtex: '@inproceedings{Shibata_Lewis_Saccomano_Kepper_Page_2023, place={Halifax,
Nova Scotia, Canada}, title={A New Conceptual Model for Musical Sources and Musicological
Studies}, DOI={https://doi.org/10.17613/8p2c-1q77},
booktitle={Proceedings of the Music Encoding Conference 2022}, author={Shibata,
Elisabete and Lewis, David and Saccomano, Mark and Kepper, Johannes and Page,
Kevin}, editor={Weigl, David and Bain, Jennifer and Ang, Ailynn}, year={2023},
pages={145–150} }'
chicago: Shibata, Elisabete, David Lewis, Mark Saccomano, Johannes Kepper, and Kevin
Page. “A New Conceptual Model for Musical Sources and Musicological Studies.”
In Proceedings of the Music Encoding Conference 2022, edited by David Weigl,
Jennifer Bain, and Ailynn Ang, 145–150. Halifax, Nova Scotia, Canada, 2023. https://doi.org/10.17613/8p2c-1q77.
ieee: 'E. Shibata, D. Lewis, M. Saccomano, J. Kepper, and K. Page, “A New Conceptual
Model for Musical Sources and Musicological Studies,” in Proceedings of the
Music Encoding Conference 2022, 2023, pp. 145–150, doi: https://doi.org/10.17613/8p2c-1q77.'
mla: Shibata, Elisabete, et al. “A New Conceptual Model for Musical Sources and
Musicological Studies.” Proceedings of the Music Encoding Conference 2022,
edited by David Weigl et al., 2023, pp. 145–150, doi:https://doi.org/10.17613/8p2c-1q77.
short: 'E. Shibata, D. Lewis, M. Saccomano, J. Kepper, K. Page, in: D. Weigl, J.
Bain, A. Ang (Eds.), Proceedings of the Music Encoding Conference 2022, Halifax,
Nova Scotia, Canada, 2023, pp. 145–150.'
date_created: 2024-08-28T11:41:59Z
date_updated: 2024-08-28T14:12:33Z
department:
- _id: '874'
doi: https://doi.org/10.17613/8p2c-1q77
editor:
- first_name: David
full_name: Weigl, David
last_name: Weigl
- first_name: Jennifer
full_name: Bain, Jennifer
last_name: Bain
- first_name: Ailynn
full_name: Ang, Ailynn
last_name: Ang
keyword:
- BitH
- Linked Data
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hcommons.org/deposits/item/hc:59295/
oa: '1'
page: 145–150
place: Halifax, Nova Scotia, Canada
publication: Proceedings of the Music Encoding Conference 2022
publication_status: published
status: public
title: A New Conceptual Model for Musical Sources and Musicological Studies
type: conference
user_id: '1684'
year: '2023'
...
---
_id: '55832'
abstract:
- lang: eng
text: Digital musicology research often proceeds by extending and enriching its
evidence base as it progresses, rather than starting with a complete corpus of
data and metadata, as a consequence of an emergent research need. In this paper,
we consider a research workflow which assumes an incremental approach to data
gathering and annotation. We describe tooling which implements parts of this workflow,
developed to support the study of nineteenth-century music arrangements, and evaluate
the applicability of our approach through interviews with musicologists and music
editors who have used the tools. We conclude by considering extensions of this
approach and the wider implications for digital musicology and music information
retrieval.
author:
- first_name: David
full_name: Lewis, David
last_name: Lewis
- first_name: Elisabete
full_name: Shibata, Elisabete
last_name: Shibata
- first_name: Andrew
full_name: Hankinson, Andrew
last_name: Hankinson
- first_name: Johannes
full_name: Kepper, Johannes
id: '1684'
last_name: Kepper
orcid: 0000-0003-4891-260X
- first_name: Kevin R.
full_name: Page, Kevin R.
last_name: Page
- first_name: Lisa
full_name: Rosendahl, Lisa
last_name: Rosendahl
- first_name: Mark
full_name: Saccomano, Mark
last_name: Saccomano
- first_name: Christine
full_name: Siegert, Christine
last_name: Siegert
citation:
ama: 'Lewis D, Shibata E, Hankinson A, et al. Supporting Musicological Investigations
With Information Retrieval Tools: An Iterative Approach to Data Collection. In:
; 2023. doi:10.5281/ZENODO.10265407'
apa: 'Lewis, D., Shibata, E., Hankinson, A., Kepper, J., Page, K. R., Rosendahl,
L., Saccomano, M., & Siegert, C. (2023). Supporting Musicological Investigations
With Information Retrieval Tools: An Iterative Approach to Data Collection.
https://doi.org/10.5281/ZENODO.10265407'
bibtex: '@inproceedings{Lewis_Shibata_Hankinson_Kepper_Page_Rosendahl_Saccomano_Siegert_2023,
title={Supporting Musicological Investigations With Information Retrieval Tools:
An Iterative Approach to Data Collection}, DOI={10.5281/ZENODO.10265407},
author={Lewis, David and Shibata, Elisabete and Hankinson, Andrew and Kepper,
Johannes and Page, Kevin R. and Rosendahl, Lisa and Saccomano, Mark and Siegert,
Christine}, year={2023} }'
chicago: 'Lewis, David, Elisabete Shibata, Andrew Hankinson, Johannes Kepper, Kevin
R. Page, Lisa Rosendahl, Mark Saccomano, and Christine Siegert. “Supporting Musicological
Investigations With Information Retrieval Tools: An Iterative Approach to Data
Collection,” 2023. https://doi.org/10.5281/ZENODO.10265407.'
ieee: 'D. Lewis et al., “Supporting Musicological Investigations With Information
Retrieval Tools: An Iterative Approach to Data Collection,” 2023, doi: 10.5281/ZENODO.10265407.'
mla: 'Lewis, David, et al. Supporting Musicological Investigations With Information
Retrieval Tools: An Iterative Approach to Data Collection. 2023, doi:10.5281/ZENODO.10265407.'
short: 'D. Lewis, E. Shibata, A. Hankinson, J. Kepper, K.R. Page, L. Rosendahl,
M. Saccomano, C. Siegert, in: 2023.'
date_created: 2024-08-28T11:41:41Z
date_updated: 2024-08-28T14:14:55Z
department:
- _id: '874'
doi: 10.5281/ZENODO.10265407
keyword:
- BitH
- Linked Data
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://zenodo.org/records/10265407/files/000094.pdf?download=1
oa: '1'
publication_status: published
status: public
title: 'Supporting Musicological Investigations With Information Retrieval Tools:
An Iterative Approach to Data Collection'
type: conference
user_id: '1684'
year: '2023'
...
---
_id: '37497'
abstract:
- lang: eng
text: Since historical times, cartographic maps have revealed spatial relations
and enabled decisions and processes. Geographic Information Systems (GIS) allow
for acquisition, management, analysis, and presentation of geospatial objects.
With free geospatial data becoming available through open data policies and an
increasing amount of digitally connected objects in the Internet of Things (IoT),
GIS are becoming indispensable to Information Systems (IS) research. However,
the consideration and relevance of GIS has only been investigated rarely. We examine,
how and in which fields of application GIS have been studied in the IS literature
and elicit the importance of GIS regarding their design and usage. A systematic
literature review leads us to develop four research propositions. Our results
indicate that GIS are still an undeservedly underrepresented discipline in IS
and should be more theorized, put center-stage in design-oriented research, and
considered for creating superior value co-creation in service systems.
author:
- first_name: Jennifer
full_name: Priefer, Jennifer
id: '82872'
last_name: Priefer
citation:
ama: 'Priefer J. Geographic Information Systems in Information Systems Research
- Review and Research Prospects. In: Bui TX, Sprague RH, eds. Proceedings of
the 56th Hawaii International Conference on System Sciences. ; 2023.'
apa: Priefer, J. (2023). Geographic Information Systems in Information Systems Research
- Review and Research Prospects. In T. X. Bui & R. H. Sprague (Eds.), Proceedings
of the 56th Hawaii International Conference on System Sciences.
bibtex: '@inproceedings{Priefer_2023, title={Geographic Information Systems in Information
Systems Research - Review and Research Prospects}, booktitle={Proceedings of the
56th Hawaii International Conference on System Sciences}, author={Priefer, Jennifer},
editor={Bui, T.X. and Sprague, R.H.}, year={2023} }'
chicago: Priefer, Jennifer. “Geographic Information Systems in Information Systems
Research - Review and Research Prospects.” In Proceedings of the 56th Hawaii
International Conference on System Sciences, edited by T.X. Bui and R.H. Sprague,
2023.
ieee: J. Priefer, “Geographic Information Systems in Information Systems Research
- Review and Research Prospects,” in Proceedings of the 56th Hawaii International
Conference on System Sciences, 2023.
mla: Priefer, Jennifer. “Geographic Information Systems in Information Systems Research
- Review and Research Prospects.” Proceedings of the 56th Hawaii International
Conference on System Sciences, edited by T.X. Bui and R.H. Sprague, 2023.
short: 'J. Priefer, in: T.X. Bui, R.H. Sprague (Eds.), Proceedings of the 56th Hawaii
International Conference on System Sciences, 2023.'
conference:
name: 56th Hawaii International Conference on System Sciences
date_created: 2023-01-19T06:32:14Z
date_updated: 2023-01-19T06:32:30Z
editor:
- first_name: T.X.
full_name: Bui, T.X.
last_name: Bui
- first_name: R.H.
full_name: Sprague, R.H.
last_name: Sprague
keyword:
- GIS
- Industry 4.0
- and Sustainability
- geographic information systems
- geospatial data
- gis
- information systems research
- literature review
language:
- iso: eng
main_file_link:
- url: https://hdl.handle.net/10125/103252
publication: Proceedings of the 56th Hawaii International Conference on System Sciences
status: public
title: Geographic Information Systems in Information Systems Research - Review and
Research Prospects
type: conference
user_id: '82872'
year: '2023'
...
---
_id: '45793'
abstract:
- lang: eng
text: The global megatrends of digitization and sustainability lead to new challenges
for the design and management of technical products in industrial companies. Product
management - as the bridge between market and company - has the task to absorb
and combine the manifold requirements and make the right product-related decisions.
In the process, product management is confronted with heterogeneous information,
rapidly changing portfolio components, as well as increasing product, and organizational
complexity. Combining and utilizing data from different sources, e.g., product
usage data and social media data leads to promising potentials to improve the
quality of product-related decisions. In this paper, we reinforce the need for
data-driven product management as an interdisciplinary field of action. The state
of data-driven product management in practice was analyzed by conducting workshops
with six manufacturing companies and hosting a focus group meeting with experts
from different industries. We investigate the expectations and derive requirements
leading us to open research questions, a vision for data-driven product management,
and a research agenda to shape future research efforts.
author:
- first_name: Khoren
full_name: Grigoryan, Khoren
last_name: Grigoryan
- first_name: Timm
full_name: Fichtler, Timm
id: '66731'
last_name: Fichtler
orcid: https://orcid.org/0000-0001-6034-4399
- first_name: Nick
full_name: Schreiner, Nick
last_name: Schreiner
- first_name: Martin
full_name: Rabe, Martin
last_name: Rabe
- first_name: Melina
full_name: Panzner, Melina
id: '72658'
last_name: Panzner
- first_name: Arno
full_name: Kühn, Arno
last_name: Kühn
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
- first_name: Christian
full_name: Koldewey, Christian
id: '43136'
last_name: Koldewey
orcid: https://orcid.org/0000-0001-7992-6399
citation:
ama: 'Grigoryan K, Fichtler T, Schreiner N, et al. Data-Driven Product Management:
A Practitioner-Driven Research Agenda. In: Procedia CIRP 33. ; 2023.'
apa: 'Grigoryan, K., Fichtler, T., Schreiner, N., Rabe, M., Panzner, M., Kühn, A.,
Dumitrescu, R., & Koldewey, C. (2023). Data-Driven Product Management: A Practitioner-Driven
Research Agenda. Procedia CIRP 33. 33rd CIRP Design Conference, Sydney.'
bibtex: '@inproceedings{Grigoryan_Fichtler_Schreiner_Rabe_Panzner_Kühn_Dumitrescu_Koldewey_2023,
title={Data-Driven Product Management: A Practitioner-Driven Research Agenda},
booktitle={Procedia CIRP 33}, author={Grigoryan, Khoren and Fichtler, Timm and
Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu,
Roman and Koldewey, Christian}, year={2023} }'
chicago: 'Grigoryan, Khoren, Timm Fichtler, Nick Schreiner, Martin Rabe, Melina
Panzner, Arno Kühn, Roman Dumitrescu, and Christian Koldewey. “Data-Driven Product
Management: A Practitioner-Driven Research Agenda.” In Procedia CIRP 33,
2023.'
ieee: 'K. Grigoryan et al., “Data-Driven Product Management: A Practitioner-Driven
Research Agenda,” presented at the 33rd CIRP Design Conference, Sydney, 2023.'
mla: 'Grigoryan, Khoren, et al. “Data-Driven Product Management: A Practitioner-Driven
Research Agenda.” Procedia CIRP 33, 2023.'
short: 'K. Grigoryan, T. Fichtler, N. Schreiner, M. Rabe, M. Panzner, A. Kühn, R.
Dumitrescu, C. Koldewey, in: Procedia CIRP 33, 2023.'
conference:
location: Sydney
name: 33rd CIRP Design Conference
date_created: 2023-06-27T13:46:45Z
date_updated: 2023-06-27T13:57:42Z
department:
- _id: '563'
- _id: '241'
keyword:
- Product Management
- Data Analytics
- Data-Driven Design
- Product-related data
- Lifecycle Data
- Tool-support
language:
- iso: eng
publication: Procedia CIRP 33
status: public
title: 'Data-Driven Product Management: A Practitioner-Driven Research Agenda'
type: conference
user_id: '66731'
year: '2023'
...
---
_id: '44146'
abstract:
- lang: eng
text: "Many Android applications collect data from users. When they do, they must\r\nprotect
this collected data according to the current legal frameworks. Such\r\ndata protection
has become even more important since the European Union rolled\r\nout the General
Data Protection Regulation (GDPR). App developers have limited\r\ntool support
to reason about data protection throughout their app development\r\nprocess. Although
many Android applications state a privacy policy, privacy\r\npolicy compliance
checks are currently manual, expensive, and prone to error.\r\nOne of the major
challenges in privacy audits is the significant gap between\r\nlegal privacy statements
(in English text) and technical measures that Android\r\napps use to protect their
user's privacy. In this thesis, we will explore to\r\nwhat extent we can use static
analysis to answer important questions regarding\r\ndata protection. Our main
goal is to design a tool based approach that aids app\r\ndevelopers and auditors
in ensuring data protection in Android applications,\r\nbased on automated static
program analysis."
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
citation:
ama: 'Khedkar M. Static Analysis for Android GDPR Compliance Assurance. In: 2023
IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings
(ICSE-Companion), Melbourne, Australia, 2023, Pp. 197-199. doi:10.1109/ICSE-Companion58688.2023.00054'
apa: 'Khedkar, M. (n.d.). Static Analysis for Android GDPR Compliance Assurance.
2023 IEEE/ACM 45th International Conference on Software Engineering: Companion
Proceedings (ICSE-Companion), Melbourne, Australia, 2023, Pp. 197-199. https://doi.org/10.1109/ICSE-Companion58688.2023.00054'
bibtex: '@inproceedings{Khedkar, title={Static Analysis for Android GDPR Compliance
Assurance}, DOI={10.1109/ICSE-Companion58688.2023.00054},
booktitle={2023 IEEE/ACM 45th International Conference on Software Engineering:
Companion Proceedings (ICSE-Companion), Melbourne, Australia, 2023, pp. 197-199},
author={Khedkar, Mugdha} }'
chicago: 'Khedkar, Mugdha. “Static Analysis for Android GDPR Compliance Assurance.”
In 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion
Proceedings (ICSE-Companion), Melbourne, Australia, 2023, Pp. 197-199, n.d.
https://doi.org/10.1109/ICSE-Companion58688.2023.00054.'
ieee: 'M. Khedkar, “Static Analysis for Android GDPR Compliance Assurance,” doi:
10.1109/ICSE-Companion58688.2023.00054.'
mla: 'Khedkar, Mugdha. “Static Analysis for Android GDPR Compliance Assurance.”
2023 IEEE/ACM 45th International Conference on Software Engineering: Companion
Proceedings (ICSE-Companion), Melbourne, Australia, 2023, Pp. 197-199, doi:10.1109/ICSE-Companion58688.2023.00054.'
short: 'M. Khedkar, in: 2023 IEEE/ACM 45th International Conference on Software
Engineering: Companion Proceedings (ICSE-Companion), Melbourne, Australia, 2023,
Pp. 197-199, n.d.'
date_created: 2023-04-24T12:14:17Z
date_updated: 2024-09-16T08:46:25Z
ddc:
- '004'
department:
- _id: '76'
doi: 10.1109/ICSE-Companion58688.2023.00054
external_id:
arxiv:
- '2303.09606'
file:
- access_level: closed
content_type: application/pdf
creator: khedkarm
date_created: 2023-04-24T12:15:27Z
date_updated: 2023-04-24T12:15:27Z
file_id: '44147'
file_name: 2023047614.pdf
file_size: 85313
relation: main_file
success: 1
file_date_updated: 2023-04-24T12:15:27Z
has_accepted_license: '1'
keyword:
- static analysis
- data protection and privacy
- GDPR compliance
language:
- iso: eng
publication: '2023 IEEE/ACM 45th International Conference on Software Engineering:
Companion Proceedings (ICSE-Companion), Melbourne, Australia, 2023, pp. 197-199'
publication_status: accepted
status: public
title: Static Analysis for Android GDPR Compliance Assurance
type: conference
user_id: '88024'
year: '2023'
...
---
_id: '34171'
abstract:
- lang: eng
text: State estimation when only a partial model of a considered system is available
remains a major challenge in many engineering fields. This work proposes a joint,
square-root unscented Kalman filter to estimate states and model uncertainties
simultaneously by linear combinations of physics-motivated library functions.
Using a sparsity promoting approach, a selection of those linear combinations
is chosen and thus an interpretable model can be extracted. Results indicate a
small estimation error compared to a traditional square-root unscented Kalman
filter and exhibit the enhancement of physically meaningful models.
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly
by a Sparsity Promoting UKF. In: 12th IFAC Symposium on Nonlinear Control Systems
(NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015'
apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015},
number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS
2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90}
}'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” In 12th IFAC Symposium
on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015.
ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90,
doi: https://doi.org/10.1016/j.ifacol.2023.02.015.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on
Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90,
doi:https://doi.org/10.1016/j.ifacol.2023.02.015.
short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 2023, pp. 85–90.'
conference:
end_date: 2023-01-06
location: Canberra, Australien
name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022
start_date: 2023-01-04
date_created: 2022-12-01T07:17:00Z
date_updated: 2024-11-13T08:43:05Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2023.02.015
intvolume: ' 56'
issue: '1'
keyword:
- joint estimation
- unscented transform
- Kalman filter
- sparsity
- data-driven
- compressed sensing
language:
- iso: eng
page: 85-90
publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)
quality_controlled: '1'
status: public
title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '48012'
abstract:
- lang: eng
text: '3D printing is a well-established technology with rapidly increasing usage
scenarios both in the industry and consumer context. The growing popularity of
3D printing has also attracted security researchers, who have analyzed possibilities
for weakening 3D models or stealing intellectual property from 3D models. We extend
these important aspects and provide the first comprehensive security analysis
of 3D printing data formats. We performed our systematic study on the example
of the 3D Manufacturing Format (3MF), which offers a large variety of features
that could lead to critical attacks. Based on 3MF’s features, we systematized
three attack goals: Data Exfiltration (dex), Denial of Service, and UI Spoofing
(uis). We achieve these goals by exploiting the complexity of 3MF, which is based
on the Open Packaging Conventions (OPC) format and uses XML to define 3D models.
In total, our analysis led to 352 tests. To create and run these tests automatically,
we implemented an open-source tool named 3MF Analyzer (tool), which helped us
evaluate 20 applications.'
author:
- first_name: Jost
full_name: Rossel, Jost
id: '58331'
last_name: Rossel
orcid: 0000-0002-3182-4059
- first_name: Vladislav
full_name: Mladenov, Vladislav
last_name: Mladenov
- first_name: Juraj
full_name: Somorovsky, Juraj
id: '83504'
last_name: Somorovsky
orcid: 0000-0002-3593-7720
citation:
ama: 'Rossel J, Mladenov V, Somorovsky J. Security Analysis of the 3MF Data Format.
In: Proceedings of the 26th International Symposium on Research in Attacks,
Intrusions and Defenses. ACM; 2023. doi:10.1145/3607199.3607216'
apa: Rossel, J., Mladenov, V., & Somorovsky, J. (2023). Security Analysis of
the 3MF Data Format. Proceedings of the 26th International Symposium on Research
in Attacks, Intrusions and Defenses. 26th International Symposium on Research
in Attacks, Intrusions and Defenses, Hongkong. https://doi.org/10.1145/3607199.3607216
bibtex: '@inproceedings{Rossel_Mladenov_Somorovsky_2023, title={Security Analysis
of the 3MF Data Format}, DOI={10.1145/3607199.3607216},
booktitle={Proceedings of the 26th International Symposium on Research in Attacks,
Intrusions and Defenses}, publisher={ACM}, author={Rossel, Jost and Mladenov,
Vladislav and Somorovsky, Juraj}, year={2023} }'
chicago: Rossel, Jost, Vladislav Mladenov, and Juraj Somorovsky. “Security Analysis
of the 3MF Data Format.” In Proceedings of the 26th International Symposium
on Research in Attacks, Intrusions and Defenses. ACM, 2023. https://doi.org/10.1145/3607199.3607216.
ieee: 'J. Rossel, V. Mladenov, and J. Somorovsky, “Security Analysis of the 3MF
Data Format,” presented at the 26th International Symposium on Research in Attacks,
Intrusions and Defenses, Hongkong, 2023, doi: 10.1145/3607199.3607216.'
mla: Rossel, Jost, et al. “Security Analysis of the 3MF Data Format.” Proceedings
of the 26th International Symposium on Research in Attacks, Intrusions and Defenses,
ACM, 2023, doi:10.1145/3607199.3607216.
short: 'J. Rossel, V. Mladenov, J. Somorovsky, in: Proceedings of the 26th International
Symposium on Research in Attacks, Intrusions and Defenses, ACM, 2023.'
conference:
end_date: 2023-10-18
location: Hongkong
name: 26th International Symposium on Research in Attacks, Intrusions and Defenses
start_date: 2023-10-16
date_created: 2023-10-11T13:42:09Z
date_updated: 2025-07-16T11:06:49Z
ddc:
- '000'
department:
- _id: '632'
doi: 10.1145/3607199.3607216
file:
- access_level: open_access
content_type: application/pdf
creator: jrossel
date_created: 2023-10-16T03:48:08Z
date_updated: 2024-09-05T11:14:40Z
file_id: '48065'
file_name: Security_Analysis_of_the_3mf_Data_Format.pdf
file_size: 1054999
relation: main_file
file_date_updated: 2024-09-05T11:14:40Z
has_accepted_license: '1'
keyword:
- Data Format Security
- 3D Manufacturing Format
- 3D Printing
- Additive Manufacturing
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/abs/10.1145/3607199.3607216
oa: '1'
publication: Proceedings of the 26th International Symposium on Research in Attacks,
Intrusions and Defenses
publication_status: published
publisher: ACM
quality_controlled: '1'
status: public
title: Security Analysis of the 3MF Data Format
type: conference
user_id: '58331'
year: '2023'
...
---
_id: '48878'
abstract:
- lang: eng
text: Due to the rise of continuous data-generating applications, analyzing data
streams has gained increasing attention over the past decades. A core research
area in stream data is stream classification, which categorizes or detects data
points within an evolving stream of observations. Areas of stream classification
are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing
a wide range of (social) media applications. Research in stream classification
is related to developing methods that adapt to the changing and potentially volatile
data stream. It focuses on individual aspects of the stream classification pipeline,
e.g., designing suitable algorithm architectures, an efficient train and test
procedure, or detecting so-called concept drifts. As a result of the many different
research questions and strands, the field is challenging to grasp, especially
for beginners. This survey explores, summarizes, and categorizes work within the
domain of stream classification and identifies core research threads over the
past few years. It is structured based on the stream classification process to
facilitate coordination within this complex topic, including common application
scenarios and benchmarking data sets. Thus, both newcomers to the field and experts
who want to widen their scope can gain (additional) insight into this research
area and find starting points and pointers to more in-depth literature on specific
issues and research directions in the field.
author:
- first_name: Lena
full_name: Clever, Lena
last_name: Clever
- first_name: Janina Susanne
full_name: Pohl, Janina Susanne
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream
Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(18):9094.
doi:10.3390/app12189094'
apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022).
Process-Oriented Stream Classification Pipeline: A Literature Review. Applied
Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094'
bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented
Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094},
number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital
Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek,
Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }'
chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and
Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.'
ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented
Stream Classification Pipeline: A Literature Review,” Applied Sciences,
vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.'
mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature
Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital
Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.'
short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences
12 (2022) 9094.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:50:56Z
department:
- _id: '819'
doi: 10.3390/app12189094
intvolume: ' 12'
issue: '18'
keyword:
- big data
- data mining
- data stream analysis
- machine learning
- stream classification
- supervised learning
language:
- iso: eng
page: '9094'
publication: Applied Sciences
publication_identifier:
issn:
- 2076-3417
publisher: '{Multidisciplinary Digital Publishing Institute}'
status: public
title: 'Process-Oriented Stream Classification Pipeline: A Literature Review'
type: journal_article
user_id: '102979'
volume: 12
year: '2022'
...
---
_id: '35728'
abstract:
- lang: eng
text: Technological developments such as Cloud Computing, the Internet of Things,
Big Data and Artificial Intelligence continue to drive the digital transformation
of business and society. With the advent of platform-based ecosystems and their
potential to address complex challenges, there is a trend towards greater interconnectedness
between different stakeholders to co-create services based on the provision and
use of data. While previous research on digital transformation mainly focused
on digital transformation within organizations, it is of growing importance to
understand the implications for digital transformation on different layers (e.g.,
interorganizational cooperation and platform ecosystems). In particular, the conceptualization
and implications of public data spaces and related ecosystems provide promising
research opportunities. This special issue contains five papers on the topic of
digital transformation and, with the editorial, further contributes by providing
an initial conceptualization of public data spaces' potential to foster innovative
progress and digital transformation from a management perspective.
article_type: letter_note
author:
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Thomas
full_name: Hess, Thomas
last_name: Hess
- first_name: Antonia
full_name: Köster, Antonia
last_name: Köster
- first_name: Christiane
full_name: Lehrer, Christiane
last_name: Lehrer
citation:
ama: 'Beverungen D, Hess T, Köster A, Lehrer C. From private digital platforms to
public data spaces: implications for the digital transformation. Electronic
Markets. 2022;32:493-501. doi:10.1007/s12525-022-00553-z'
apa: 'Beverungen, D., Hess, T., Köster, A., & Lehrer, C. (2022). From private
digital platforms to public data spaces: implications for the digital transformation.
Electronic Markets, 32, 493–501. https://doi.org/10.1007/s12525-022-00553-z'
bibtex: '@article{Beverungen_Hess_Köster_Lehrer_2022, title={From private digital
platforms to public data spaces: implications for the digital transformation},
volume={32}, DOI={10.1007/s12525-022-00553-z},
journal={Electronic Markets}, publisher={Springer Science and Business Media LLC},
author={Beverungen, Daniel and Hess, Thomas and Köster, Antonia and Lehrer, Christiane},
year={2022}, pages={493–501} }'
chicago: 'Beverungen, Daniel, Thomas Hess, Antonia Köster, and Christiane Lehrer.
“From Private Digital Platforms to Public Data Spaces: Implications for the Digital
Transformation.” Electronic Markets 32 (2022): 493–501. https://doi.org/10.1007/s12525-022-00553-z.'
ieee: 'D. Beverungen, T. Hess, A. Köster, and C. Lehrer, “From private digital platforms
to public data spaces: implications for the digital transformation,” Electronic
Markets, vol. 32, pp. 493–501, 2022, doi: 10.1007/s12525-022-00553-z.'
mla: 'Beverungen, Daniel, et al. “From Private Digital Platforms to Public Data
Spaces: Implications for the Digital Transformation.” Electronic Markets,
vol. 32, Springer Science and Business Media LLC, 2022, pp. 493–501, doi:10.1007/s12525-022-00553-z.'
short: D. Beverungen, T. Hess, A. Köster, C. Lehrer, Electronic Markets 32 (2022)
493–501.
date_created: 2023-01-10T09:52:01Z
date_updated: 2024-04-18T12:37:41Z
ddc:
- '380'
department:
- _id: '526'
doi: 10.1007/s12525-022-00553-z
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2024-04-18T12:35:42Z
date_updated: 2024-04-18T12:35:42Z
file_id: '53572'
file_name: EM - From Private Platforms o Pubic Data Spaces.pdf
file_size: 861383
relation: main_file
success: 1
file_date_updated: 2024-04-18T12:35:42Z
has_accepted_license: '1'
intvolume: ' 32'
keyword:
- Digital transformation
- Public data spaces
- Digital platforms
- GAIA-X
language:
- iso: eng
page: 493-501
publication: Electronic Markets
publication_identifier:
issn:
- 1019-6781
- 1422-8890
publication_status: published
publisher: Springer Science and Business Media LLC
quality_controlled: '1'
status: public
title: 'From private digital platforms to public data spaces: implications for the
digital transformation'
type: journal_article
user_id: '59677'
volume: 32
year: '2022'
...
---
_id: '35732'
abstract:
- lang: eng
text: While the Information Systems (IS) discipline has researched digital platforms
extensively, the body of knowledge appertaining to platforms still appears fragmented
and lacking conceptual consistency. Based on automated text mining and unsupervised
machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive
research on platforms—comprising 11,049 papers spanning 44 years of research activity.
From a cluster analysis concerning platform concepts’ semantically most similar
words, we identify six research streams on platforms, each with their own platform
terms. Based on interpreting the identified concepts vis-à-vis the extant research
and considering a temporal perspective on the concepts’ application, we present
a lexicon of platform concepts, to guide further research on platforms in the
IS discipline. Researchers and managers can build on our results to position their
work appropriately, applying a specific theoretical perspective on platforms in
isolation or combining multiple perspectives to study platform phenomena at a
more abstract level.
article_type: original
author:
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
- first_name: Hedda
full_name: Lüttenberg, Hedda
last_name: Lüttenberg
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
citation:
ama: 'Bartelheimer C, zur Heiden P, Lüttenberg H, Beverungen D. Systematizing the
lexicon of platforms in information systems: a data-driven study. Electronic
Markets. 2022;32:375-396. doi:10.1007/s12525-022-00530-6'
apa: 'Bartelheimer, C., zur Heiden, P., Lüttenberg, H., & Beverungen, D. (2022).
Systematizing the lexicon of platforms in information systems: a data-driven study.
Electronic Markets, 32, 375–396. https://doi.org/10.1007/s12525-022-00530-6'
bibtex: '@article{Bartelheimer_zur Heiden_Lüttenberg_Beverungen_2022, title={Systematizing
the lexicon of platforms in information systems: a data-driven study}, volume={32},
DOI={10.1007/s12525-022-00530-6},
journal={Electronic Markets}, publisher={Springer Science and Business Media LLC},
author={Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda
and Beverungen, Daniel}, year={2022}, pages={375–396} }'
chicago: 'Bartelheimer, Christian, Philipp zur Heiden, Hedda Lüttenberg, and Daniel
Beverungen. “Systematizing the Lexicon of Platforms in Information Systems: A
Data-Driven Study.” Electronic Markets 32 (2022): 375–96. https://doi.org/10.1007/s12525-022-00530-6.'
ieee: 'C. Bartelheimer, P. zur Heiden, H. Lüttenberg, and D. Beverungen, “Systematizing
the lexicon of platforms in information systems: a data-driven study,” Electronic
Markets, vol. 32, pp. 375–396, 2022, doi: 10.1007/s12525-022-00530-6.'
mla: 'Bartelheimer, Christian, et al. “Systematizing the Lexicon of Platforms in
Information Systems: A Data-Driven Study.” Electronic Markets, vol. 32,
Springer Science and Business Media LLC, 2022, pp. 375–96, doi:10.1007/s12525-022-00530-6.'
short: C. Bartelheimer, P. zur Heiden, H. Lüttenberg, D. Beverungen, Electronic
Markets 32 (2022) 375–396.
date_created: 2023-01-10T10:00:55Z
date_updated: 2024-04-18T12:40:34Z
ddc:
- '380'
department:
- _id: '526'
doi: 10.1007/s12525-022-00530-6
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2024-04-18T12:39:00Z
date_updated: 2024-04-18T12:39:00Z
file_id: '53573'
file_name: EM - Lexicon of Platform Terms.pdf
file_size: 1262427
relation: main_file
success: 1
file_date_updated: 2024-04-18T12:39:00Z
has_accepted_license: '1'
intvolume: ' 32'
jel:
- L86
keyword:
- Platform
- Text mining
- Machine learning
- Data communications
- Interpretive research
- Systems design and implementation
language:
- iso: eng
page: 375-396
publication: Electronic Markets
publication_identifier:
issn:
- 1019-6781
- 1422-8890
publication_status: published
publisher: Springer Science and Business Media LLC
quality_controlled: '1'
status: public
title: 'Systematizing the lexicon of platforms in information systems: a data-driven
study'
type: journal_article
user_id: '59677'
volume: 32
year: '2022'
...
---
_id: '26539'
abstract:
- lang: eng
text: In control design most control strategies are model-based and require accurate
models to be applied successfully. Due to simplifications and the model-reality-gap
physics-derived models frequently exhibit deviations from real-world-systems.
Likewise, purely data-driven methods often do not generalise well enough and may
violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired
loss functions separately have shown promising results to conquer these drawbacks.
In this contribution we extend existing methods towards the identification of
non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN
and a physics-inspired loss term (-L) to successfully identify the system's dynamics,
while maintaining the consistency with physical laws. The proposed method is demonstrated
on two real-world nonlinear systems and outperforms existing techniques regarding
complexity and reliability.
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Composed Physics- and Data-driven System Identification
for Non-autonomous Systems in Control Engineering. In: 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC). ; 2022:67-76.
doi:10.1109/AIRC56195.2022.9836982'
apa: Götte, R.-S., & Timmermann, J. (2022). Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering. 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76. https://doi.org/10.1109/AIRC56195.2022.9836982
bibtex: '@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering}, DOI={10.1109/AIRC56195.2022.9836982},
booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022},
pages={67–76} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” In 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76, 2022. https://doi.org/10.1109/AIRC56195.2022.9836982.
ieee: 'R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System
Identification for Non-autonomous Systems in Control Engineering,” in 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), Cairo, Egypt, 2022, pp. 67–76, doi: 10.1109/AIRC56195.2022.9836982.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 2022, pp. 67–76, doi:10.1109/AIRC56195.2022.9836982.
short: 'R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.'
conference:
end_date: 2021-12-10
location: Cairo, Egypt
name: 3rd International Conference on Artificial Intelligence, Robotics and Control
start_date: 2021-12-08
date_created: 2021-10-19T14:47:17Z
date_updated: 2024-11-13T08:43:28Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836982
keyword:
- data-driven
- physics-based
- physics-informed
- neural networks
- system identification
- hybrid modelling
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.08148
oa: '1'
page: 67-76
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)
quality_controlled: '1'
status: public
title: Composed Physics- and Data-driven System Identification for Non-autonomous
Systems in Control Engineering
type: conference
user_id: '43992'
year: '2022'
...
---
_id: '31066'
abstract:
- lang: eng
text: 'While trade-offs between modeling effort and model accuracy remain a major
concern with system identification, resorting to data-driven methods often leads
to a complete disregard for physical plausibility. To address this issue, we propose
a physics-guided hybrid approach for modeling non-autonomous systems under control.
Starting from a traditional physics-based model, this is extended by a recurrent
neural network and trained using a sophisticated multi-objective strategy yielding
physically plausible models. While purely data-driven methods fail to produce
satisfying results, experiments conducted on real data reveal substantial accuracy
improvements by our approach compared to a physics-based model. '
author:
- first_name: Oliver
full_name: Schön, Oliver
last_name: Schön
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent
Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55.
; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282'
apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12),
19–24. https://doi.org/10.1016/j.ifacol.2022.07.282
bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55},
DOI={https://doi.org/10.1016/j.ifacol.2022.07.282},
number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems
(ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2022}, pages={19–24} }'
chicago: Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective
Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical
Systems.” In 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS
2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282.
ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in
14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022),
Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.'
mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks
for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24,
doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.'
conference:
end_date: 2022-07-01
location: Casablanca, Morocco
name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
start_date: 2022-06-29
date_created: 2022-05-05T06:22:55Z
date_updated: 2024-11-13T08:43:16Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2022.07.282
intvolume: ' 55'
issue: '12'
keyword:
- neural networks
- physics-guided
- data-driven
- multi-objective optimization
- system identification
- machine learning
- dynamical systems
language:
- iso: eng
page: 19-24
publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
quality_controlled: '1'
status: public
title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous
Dynamical Systems
type: conference
user_id: '43992'
volume: 55
year: '2022'
...
---
_id: '22480'
abstract:
- lang: eng
text: In this publication important aspects for the implementation of inductive
locating are explained. The miniaturized sensor platform called Sens-o-Spheres
is used as an application of this locating method. The sensor platform is applied
in bioreactors in order to obtain the environmental parameters, which makes a
localization by magnetic fields necessary. Since the properties of magnetic fields
in the localization area are very different from the wave characteristics, the
principle of inductive localization is investigated in this publication and explained
by using electrical equivalent circuit diagrams. Thereby, inductive localization
uses the coupling or the mutual inductivities between coils, which is noticeable
by an induced voltage. Therefore some properties and procedures are explained
to extract the location of Sens-o-Spheres or other industrial sensor platforms
from the couplings of the coils. One method calculates the location from an adapted
ratio calculation and the other method uses neural networks and stochastic filters
to obtain the results. In the end, these results are evaluated and compared.
author:
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Dominik
full_name: Schröder, Dominik
last_name: Schröder
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
citation:
ama: 'Lange S, Schröder D, Hedayat C, Kuhn H, Hilleringmann U. Development of Methods
for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms
in Bioprocesses. In: 22nd IEEE International Conference on Industrial Technology
(ICIT). Valencia, Spain : IEEE; 2021. doi:10.1109/icit46573.2021.9453609'
apa: 'Lange, S., Schröder, D., Hedayat, C., Kuhn, H., & Hilleringmann, U. (2021).
Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized
Sensor Platforms in Bioprocesses. In 22nd IEEE International Conference on
Industrial Technology (ICIT). Valencia, Spain : IEEE. https://doi.org/10.1109/icit46573.2021.9453609'
bibtex: '@inproceedings{Lange_Schröder_Hedayat_Kuhn_Hilleringmann_2021, place={
Valencia, Spain }, title={Development of Methods for Coil-Based Localization by
Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}, DOI={10.1109/icit46573.2021.9453609},
booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)},
publisher={IEEE}, author={Lange, Sven and Schröder, Dominik and Hedayat, Christian
and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021} }'
chicago: 'Lange, Sven, Dominik Schröder, Christian Hedayat, Harald Kuhn, and Ulrich
Hilleringmann. “Development of Methods for Coil-Based Localization by Magnetic
Fields of Miniaturized Sensor Platforms in Bioprocesses.” In 22nd IEEE International
Conference on Industrial Technology (ICIT). Valencia, Spain : IEEE, 2021.
https://doi.org/10.1109/icit46573.2021.9453609.'
ieee: S. Lange, D. Schröder, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Development
of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor
Platforms in Bioprocesses,” in 22nd IEEE International Conference on Industrial
Technology (ICIT), Valencia, Spain , 2021.
mla: Lange, Sven, et al. “Development of Methods for Coil-Based Localization by
Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” 22nd IEEE
International Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453609.
short: 'S. Lange, D. Schröder, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 22nd IEEE
International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain
, 2021.'
conference:
end_date: 2021-03-12
location: 'Valencia, Spain '
name: 22nd IEEE International Conference on Industrial Technology (ICIT)
start_date: 2021-03-10
date_created: 2021-06-20T23:25:54Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/icit46573.2021.9453609
keyword:
- Location awareness
- Coils
- Couplings
- Nonuniform electric fields
- Magnetic separation
- Neural networks
- Training data
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9453609
place: ' Valencia, Spain '
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: 22nd IEEE International Conference on Industrial Technology (ICIT)
publication_identifier:
isbn:
- '9781728157306'
publication_status: published
publisher: IEEE
status: public
title: Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized
Sensor Platforms in Bioprocesses
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '27381'
abstract:
- lang: eng
text: Graph neural networks (GNNs) have been successfully applied in many structured
data domains, with applications ranging from molecular property prediction to
the analysis of social networks. Motivated by the broad applicability of GNNs,
we propose the family of so-called RankGNNs, a combination of neural Learning
to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
between graphs, suggesting that one of them is preferred over the other. One practical
application of this problem is drug screening, where an expert wants to find the
most promising molecules in a large collection of drug candidates. We empirically
demonstrate that our proposed pair-wise RankGNN approach either significantly
outperforms or at least matches the ranking performance of the naive point-wise
baseline approach, in which the LtR problem is solved via GNN-based graph regression.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference
on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science.
Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5'
apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph
Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th
International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180).
Springer. https://doi.org/10.1007/978-3-030-88942-5
bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
DOI={10.1007/978-3-030-88942-5},
booktitle={Proceedings of The 24th International Conference on Discovery Science
(DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
Notes in Computer Science} }'
chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
Graph Neural Networks.” In Proceedings of The 24th International Conference
on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80.
Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.
ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
Networks,” in Proceedings of The 24th International Conference on Discovery
Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.'
mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
Neural Networks.” Proceedings of The 24th International Conference on Discovery
Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.
short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
pp. 166–180.'
conference:
end_date: 2021-10-13
location: Halifax, Canada
name: 24th International Conference on Discovery Science
start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
full_name: Soares, Carlos
last_name: Soares
- first_name: Luis
full_name: Torgo, Luis
last_name: Torgo
external_id:
arxiv:
- '2104.08869'
intvolume: ' 12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
(DS 2021)
publication_identifier:
isbn:
- '9783030889418'
- '9783030889425'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '24551'
abstract:
- lang: eng
text: "Access to precise meteorological data is crucial to be able to plan and install
renewable energy systems \r\nsuch as solar power plants and wind farms. In case
of solar energy, knowledge of local irradiance and air temperature \r\nvalues
is very important. For this, various methods can be used such as installing local
weather stations or using \r\nmeteorological data from different organizations
such as Meteonorm or official Deutscher Wetterdienst (DWD). An \r\nalternative
is to use satellite reanalysis datasets provided by organizations like the National
Aeronautics and Space \r\nAdministration (NASA) and European Centre for Medium-Range
Weather Forecasts (ECMWF). In this paper the \r\n“Modern-Era Retrospective analysis
for Research and Applications” dataset version 2 (MERRA-2) will be presented,
\r\nand its performance will be evaluated by comparing it to locally measured
datasets provided by Meteonorm and DWD. \r\nThe analysis shows very high correlation
between MERRA-2 and local measurements (correlation coefficients of 0.99) \r\nfor
monthly global irradiance and air temperature values. The results prove the suitability
of MERRA-2 data for \r\napplications requiring long historical data. Moreover,
availability of MERRA-2 for the whole world with an acceptable \r\nresolution
makes it a very valuable dataset."
author:
- first_name: Arash
full_name: Khatibi, Arash
id: '43538'
last_name: Khatibi
- first_name: Stefan
full_name: Krauter, Stefan
id: '28836'
last_name: Krauter
orcid: 0000-0002-3594-260X
citation:
ama: 'Khatibi A, Krauter S. Comparison and Validation of Irradiance Data: Satellite
Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD).
In: Proceedings of the 38th European Photovoltaic Solar Energy Conference and
Exhibition (EUPVSEC 2021). ; 2021:1141-1147. doi:10.4229/EUPVSEC20212021-5BV.4.11'
apa: 'Khatibi, A., & Krauter, S. (2021). Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD). Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021), 1141–1147. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11'
bibtex: '@inproceedings{Khatibi_Krauter_2021, title={Comparison and Validation of
Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German
Weather Service (DWD)}, DOI={10.4229/EUPVSEC20212021-5BV.4.11},
booktitle={Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)}, author={Khatibi, Arash and Krauter, Stefan}, year={2021},
pages={1141–1147} }'
chicago: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD).” In Proceedings of the 38th European Photovoltaic Solar Energy
Conference and Exhibition (EUPVSEC 2021), 1141–47, 2021. https://doi.org/10.4229/EUPVSEC20212021-5BV.4.11.'
ieee: 'A. Khatibi and S. Krauter, “Comparison and Validation of Irradiance Data:
Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service
(DWD),” in Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147, doi: 10.4229/EUPVSEC20212021-5BV.4.11.'
mla: 'Khatibi, Arash, and Stefan Krauter. “Comparison and Validation of Irradiance
Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather
Service (DWD).” Proceedings of the 38th European Photovoltaic Solar Energy
Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–47, doi:10.4229/EUPVSEC20212021-5BV.4.11.'
short: 'A. Khatibi, S. Krauter, in: Proceedings of the 38th European Photovoltaic
Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp. 1141–1147.'
conference:
end_date: 2021-09-10
name: 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC
2021)
start_date: 2021-09-06
date_created: 2021-09-16T10:20:41Z
date_updated: 2022-01-06T13:29:51Z
ddc:
- '550'
department:
- _id: '53'
doi: 10.4229/EUPVSEC20212021-5BV.4.11
file:
- access_level: closed
content_type: application/pdf
creator: krauter
date_created: 2022-01-06T13:26:47Z
date_updated: 2022-01-06T13:26:47Z
file_id: '29176'
file_name: Khatibi Krauter - MERRA 2 vs Meteonorm - EUPVSEC 2021.pdf
file_size: 2475972
relation: main_file
success: 1
file_date_updated: 2022-01-06T13:26:47Z
has_accepted_license: '1'
keyword:
- Energy potential estimation
- Photovoltaic
- Solar radiation
- Temperature measurement
- Satellite data
- Meteonorm
- MERRA-2
- DWD
language:
- iso: eng
page: 1141 - 1147
publication: Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)
publication_identifier:
isbn:
- 3-936338-78-7
publication_status: published
quality_controlled: '1'
status: public
title: 'Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset
MERRA-2 vs. Meteonorm and German Weather Service (DWD)'
type: conference
user_id: '28836'
year: '2021'
...
---
_id: '24547'
abstract:
- lang: eng
text: 'Over the last years, several approaches for the data-driven estimation of
expected possession value (EPV) in basketball and association football (soccer)
have been proposed. In this paper, we develop and evaluate PIVOT: the first such
framework for team handball. Accounting for the fast-paced, dynamic nature and
relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
learning architecture that relies solely on tracking data. This efficient approach
is capable of predicting the probability that a team will score within the near
future given the fine-grained spatio-temporal distribution of all players and
the ball over the last seconds of the game. Our experiments indicate that PIVOT
is able to produce accurate and calibrated probability estimates, even when trained
on a relatively small dataset. We also showcase two interactive applications of
PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Michael
full_name: Döring, Michael
last_name: Döring
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jochen
full_name: Baumeister, Jochen
id: '46'
last_name: Baumeister
orcid: 0000-0003-2683-5826
citation:
ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021).'
apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.).
PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data. 8th Workshop on Machine Learning and Data
Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
Online.'
bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning
and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data,” presented at the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on
Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).'
short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
end_date: 2021-09-17
location: Online
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery (ECML PKDD 2021)
start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '27491'
abstract:
- lang: eng
text: ' Students often have a lack of understanding and awareness of where, how,
and why personal data about them is collected and processed. Especially, when
interacting with data-driven digital artifacts, an appropriate perception of the
data collection and processing is necessary for self-determination. This dissertation
deals with the development and evaluation of a concept called data awareness which
aims to foster students’ self-determination interacting with data-driven digital
artifacts.'
author:
- first_name: Lukas
full_name: Höper, Lukas
id: '58041'
last_name: Höper
citation:
ama: 'Höper L. Developing and Evaluating the Concept Data Awareness for K12 Computing
Education. In: 21st Koli Calling International Conference on Computing Education
Research. Koli Calling ’21. Association for Computing Machinery; 2021. doi:10.1145/3488042.3490509'
apa: Höper, L. (2021). Developing and Evaluating the Concept Data Awareness for
K12 Computing Education. 21st Koli Calling International Conference on Computing
Education Research. https://doi.org/10.1145/3488042.3490509
bibtex: '@inproceedings{Höper_2021, place={New York, NY, USA}, series={Koli Calling
’21}, title={Developing and Evaluating the Concept Data Awareness for K12 Computing
Education}, DOI={10.1145/3488042.3490509},
booktitle={21st Koli Calling International Conference on Computing Education Research},
publisher={Association for Computing Machinery}, author={Höper, Lukas}, year={2021},
collection={Koli Calling ’21} }'
chicago: 'Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for
K12 Computing Education.” In 21st Koli Calling International Conference on
Computing Education Research. Koli Calling ’21. New York, NY, USA: Association
for Computing Machinery, 2021. https://doi.org/10.1145/3488042.3490509.'
ieee: 'L. Höper, “Developing and Evaluating the Concept Data Awareness for K12 Computing
Education,” 2021, doi: 10.1145/3488042.3490509.'
mla: Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for K12
Computing Education.” 21st Koli Calling International Conference on Computing
Education Research, Association for Computing Machinery, 2021, doi:10.1145/3488042.3490509.
short: 'L. Höper, in: 21st Koli Calling International Conference on Computing Education
Research, Association for Computing Machinery, New York, NY, USA, 2021.'
date_created: 2021-11-16T07:59:49Z
date_updated: 2024-09-16T08:32:39Z
department:
- _id: '67'
doi: 10.1145/3488042.3490509
keyword:
- data awareness
- machine learning
- data science education
- data-driven digital artifacts
- artificial intelligence
language:
- iso: eng
place: New York, NY, USA
publication: 21st Koli Calling International Conference on Computing Education Research
publication_identifier:
isbn:
- '9781450384889'
publisher: Association for Computing Machinery
quality_controlled: '1'
series_title: Koli Calling '21
status: public
title: Developing and Evaluating the Concept Data Awareness for K12 Computing Education
type: conference
user_id: '58041'
year: '2021'
...
---
_id: '24540'
abstract:
- lang: eng
text: "With its growing population and industrialization, DREs, and solar technologies
in particular, provide a \r\nsustainable means of bridging the current energy
deficit in Africa, increasing supply reliability and meeting future \r\ndemand.
Data acquisition and data management systems allow real time monitoring and control
of energy systems as \r\nwell as performance analysis. However commercial data
acquisition systems often have cost implications that are \r\nprohibitive for
small PV systems and installations in developing countries.\r\nIn this paper,
a multi-user, multi-purpose microgrid database system is designed and implemented.
MAVOWATT \r\n270 power quality analyzers by GOSSEN METRAWATT, raspberry pi modules
and sensors are used for measuring, \r\nrecording and storing electrical and meteorological
data in East Africa. Socio-economic data is also stored in the\r\ndatabase. The
designed system employs open source software and hardware solutions which are
best suited to \r\ndeveloping regions like East Africa due to the lower cost implications.\r\nThe
expected results promise a comprehensive database covering different electro-technical
and socio-economic \r\nparameters useful for optimal design of microgrid systems."
author:
- first_name: Josephine Nakato
full_name: Kakande, Josephine Nakato
id: '88649'
last_name: Kakande
- first_name: Godiana Hagile
full_name: Philipo, Godiana Hagile
id: '88505'
last_name: Philipo
- first_name: Stefan
full_name: Krauter, Stefan
id: '28836'
last_name: Krauter
orcid: 0000-0002-3594-260X
citation:
ama: 'Kakande JN, Philipo GH, Krauter S. Load Data Acquisition in Rural East Africa
for the Layout of Microgrids and Demand–Side–Management Measures. In: Proceedings
of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC
2021). ; 2021:1505-1510. doi:10.4229/EUPVSEC20212021-6BV.5.38'
apa: Kakande, J. N., Philipo, G. H., & Krauter, S. (2021). Load Data Acquisition
in Rural East Africa for the Layout of Microgrids and Demand–Side–Management Measures.
Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition
(EUPVSEC 2021), 1505–1510. https://doi.org/10.4229/EUPVSEC20212021-6BV.5.38
bibtex: '@inproceedings{Kakande_Philipo_Krauter_2021, title={Load Data Acquisition
in Rural East Africa for the Layout of Microgrids and Demand–Side–Management Measures},
DOI={10.4229/EUPVSEC20212021-6BV.5.38},
booktitle={Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)}, author={Kakande, Josephine Nakato and Philipo,
Godiana Hagile and Krauter, Stefan}, year={2021}, pages={1505–1510} }'
chicago: Kakande, Josephine Nakato, Godiana Hagile Philipo, and Stefan Krauter.
“Load Data Acquisition in Rural East Africa for the Layout of Microgrids and Demand–Side–Management
Measures.” In Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021), 1505–10, 2021. https://doi.org/10.4229/EUPVSEC20212021-6BV.5.38.
ieee: 'J. N. Kakande, G. H. Philipo, and S. Krauter, “Load Data Acquisition in Rural
East Africa for the Layout of Microgrids and Demand–Side–Management Measures,”
in Proceedings of the 38th European Photovoltaic Solar Energy Conference and
Exhibition (EUPVSEC 2021), 2021, pp. 1505–1510, doi: 10.4229/EUPVSEC20212021-6BV.5.38.'
mla: Kakande, Josephine Nakato, et al. “Load Data Acquisition in Rural East Africa
for the Layout of Microgrids and Demand–Side–Management Measures.” Proceedings
of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC
2021), 2021, pp. 1505–10, doi:10.4229/EUPVSEC20212021-6BV.5.38.
short: 'J.N. Kakande, G.H. Philipo, S. Krauter, in: Proceedings of the 38th European
Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021), 2021, pp.
1505–1510.'
conference:
end_date: 2021-09-10
name: 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC
2021)
start_date: 2021-09-06
date_created: 2021-09-16T05:52:50Z
date_updated: 2024-10-17T08:45:50Z
ddc:
- '620'
department:
- _id: '53'
doi: 10.4229/EUPVSEC20212021-6BV.5.38
file:
- access_level: closed
content_type: application/pdf
creator: krauter
date_created: 2022-01-06T13:11:59Z
date_updated: 2022-01-06T13:11:59Z
file_id: '29174'
file_name: Kakande Philipo Krauter - Load Data Aquisition ART-D - EUPVSEC 2021.pdf
file_size: 1343406
relation: main_file
success: 1
file_date_updated: 2022-01-06T13:11:59Z
has_accepted_license: '1'
keyword:
- Art-D
- Afrika
- Demand side management
- MySQL
- Raspberry pi
- Data acquisition
language:
- iso: eng
page: 1505-1510
publication: Proceedings of the 38th European Photovoltaic Solar Energy Conference
and Exhibition (EUPVSEC 2021)
publication_identifier:
isbn:
- 3-936338-78-7
publication_status: published
quality_controlled: '1'
status: public
title: Load Data Acquisition in Rural East Africa for the Layout of Microgrids and
Demand–Side–Management Measures
type: conference
user_id: '16148'
year: '2021'
...