---
_id: '13543'
abstract:
- lang: ger
text: "Neue Sonderausstellung “A KInd of Art. Künstliche Intelligenz trifft (Weser-)Renaissance”
im Weserrenaissance-Museum Schloss Brake \r\n\r\n \r\n\r\n \r\n\r\nWas haben das
schillernde Zeitalter der (Weser-)Renaissance und der Bereich der künstlichen
Intelligenz bloß miteinander zu tun? Erstaunlich viel! Das innovative Weserrenaissance-Museum
Schloss Brake zeigt in seiner topaktuellen Sonderausstellung “A KInd of Art. Künstliche
Intelligenz trifft (Weser-)Renaissance” überraschende Parallelen und faszinierende
Zusammenhänge auf, die kaum jemand vermuten würde. \r\n\r\n \r\n\r\n \r\n\r\n“Zusammen
mit der TH OWL und Fraunhofer IOSB-INA wagen wir den Sprung von der Zwei- in die
Dreidimensionalität und zeigen Deutschlands erste Museumsausstellung mit KI-Skulpturen,
die aus historischen Exponaten entwickelt wurden. Diese weisen allesamt einen
unmittelbaren Bezug zur (Weser-)Renaissance auf und kombinieren die Vergangenheit
und die Zukunft aufs Vortrefflichste miteinander”, sagt Museumsleiterin Silvia
Herrmann. Die KI-Skulpturen stammen dabei allesamt aus einem Master-Kurs-Projekt
des Fachbereiches Medienproduktion unter der Leitung von Prof. Anke Stache.\r\n\r\n
\r\n\r\n \r\n\r\n \r\n\r\n“Hätten Sie beispielsweise gewusst, dass das weltberühmte
Universalgenie Leonardo da Vinci bereits vor mehr als 500 Jahren einen Automaten
entwickelt hat? Es ist uns gelungen, ein nachgebautes und bewegliches Modell seines
‘Roboter-Ritters’ als Leihgabe für die Ausstellung zu gewinnen”, sagt die Kuratorin
Dr. Susanne Hilker. Passend dazu treffen Leonardo da Vinci und der Roboter Ina
in Form eines Comics fiktiv aufeinander und unterhalten sich über die Innovationen
ihrer jeweiligen Zeit.\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nZu bestaunen sind auch
zahlreiche kunsthistorische Originale wie beispielsweise “Minerva und die Musen
auf dem Helikon” von Hans Rottenhammer oder “Die Tempelreinigung” von Hans und
Paul Vredeman de Vries. Auch hierbei gibt es spannende Verbindungen zum Gebiet
der künstlichen Intelligenz.\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nFreuen können sich
die Besucher auch auf Mitmachstationen wie eine interaktive Klanginstallation,
ein Ergometer, mit dessen Hilfe sie herausfinden können, wie viel Energie die
künstliche Intelligenz für ihre Prozesse benötigt, und auf eine Fotobox. \r\n\r\n
\r\n\r\n \r\n\r\n \r\n\r\nApropos Fotos: Im Rahmen dieser Ausstellung stellt das
Museum auch originelle Kunstdoppelgänger aus. Darüber hinaus sind im “Freiraum”
die 20 besten KI-generierten Bilder zu sehen, die im Rahmen eines Wettbewerbs
des CIIT (Centrum Industrial IT) entstanden sind. Schlussendlich wird den Besuchern
zur Abstimmung die Frage aller Fragen gestellt: Kann KI Kunst? \r\n\r\n \r\n\r\n
\r\n\r\n \r\n\r\n“Wir möchten mit dieser Sonderausstellung zeigen, wie innovativ,
interessant und relevant Museen sein können. Ganz bewusst greifen wir dieses topaktuelle
Thema auf. Wir möchten in puncto Künstliche Intelligenz zum Nachdenken und zur
Diskussion anregen. Darüber hinaus schlagen wir eine Brücke zwischen Vergangenheit,
Gegenwart und Zukunft und eröffnen neue Perspektiven”, sagt Silvia Herrmann.\r\n\r\n
\r\n\r\n \r\n\r\n \r\n\r\nPassend zur neuen Sonderausstellung bietet das Weserrenaissance-Museum
Schloss Brake kurzweilige Mitmachprogramme für Kindergärten und Schulen an. Des
Weiteren stehen zahlreiche Veranstaltungen mit Bezug zum Thema KI auf dem Programm.
Alle Infos unter www.museum-schloss-brake.de\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nUnd
wer von künstlicher Intelligenz nicht genug bekommen kann, macht einen Ausflug
zum Kooperationspartner, der “Eulenburg”. Das Universitäts- und Stadtmuseum Rinteln
zeigt ebenfalls eine Ausstellung mit KI-generierten Skulpturen.\r\n\r\n \r\n\r\n
\r\n\r\n \r\n\r\nDie Ausstellung wird gefördert vom Ministerium für Kultur und
Wissenschaft des Landes NRW und dem Regionalen Kultur Programm NRW. Die Ausstellung
findet in Kooperation mit folgenden Partnern statt: Fraunhofer IOSB-INA, Technische
Hochschule OWL, Kl Akademie OWL, inIT TH OWL, Bundesministerium für Forschung,
Technologie und Raumfahrt, KreativInstitut.OWL, Trinnovation OWL, Hochschule für
Musik Detmold, Fachbereich Ingenieurwissenschaften und Mathematik der Hochschule
Bielefeld HSBI, LWL Museum Ziegelei Lage und wird unterstützt von den „Frauen
für Lemgo”.\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nDas Weserrenaissance-Museum Schloss
Brake dankt auch seinem Träger, dem Landesverband Lippe, den Mitfinanziers, dem
LWL sowie der Alten Hansestadt Lemgo, sowie den Sponsoren, der Lippischen Landesbrandversicherung
AG und der Sparkasse Lemgo, für die Unterstützung!\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nDer
Eintritt in die neue Sonderausstellung im Weserrenaissance-Museum Schloss Brake
beträgt 7 Euro. Kinder und Jugendliche bis 18 Jahre haben freien Eintritt. Die
Ausstellung kann zwischen dem 16. September 2025 und 01. Februar 2026 dienstags
bis sonntags von 10 bis 18 Uhr besichtigt werden.\r\n\r\n (https://museum-schloss-brake.de/sonderausstellung/)"
alternative_title:
- Ko-Veranstalter und Organisator einiger Ausstellungsstücke
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Lange-Hegermann M. A KInd of Art. Landesverband Lippe; 2025.
apa: Lange-Hegermann, M. (2025). A KInd of Art. Landesverband Lippe.
bjps: 'Lange-Hegermann M (2025) A KInd of Art. Lemgo: Landesverband
Lippe.'
chicago: 'Lange-Hegermann, Markus. A KInd of Art. Lemgo: Landesverband Lippe,
2025.'
chicago-de: 'Lange-Hegermann, Markus. 2025. A KInd of Art. Lemgo: Landesverband
Lippe.'
din1505-2-1: 'Lange-Hegermann, Markus:
A KInd of Art. Lemgo : Landesverband Lippe, 2025'
havard: M. Lange-Hegermann, A KInd of Art, Landesverband Lippe, Lemgo, 2025.
ieee: 'M. Lange-Hegermann, A KInd of Art. Lemgo: Landesverband Lippe, 2025.'
mla: Lange-Hegermann, Markus. A KInd of Art. Landesverband Lippe, 2025.
short: M. Lange-Hegermann, A KInd of Art, Landesverband Lippe, Lemgo, 2025.
ufg: 'Lange-Hegermann, Markus: A KInd of Art, Lemgo 2025.'
van: 'Lange-Hegermann M. A KInd of Art. Lemgo: Landesverband Lippe; 2025.'
conference:
end_date: 2026-02-01
start_date: 2025-09-16
date_created: 2026-03-23T19:15:25Z
date_updated: 2026-03-24T07:34:51Z
department:
- _id: DEP5023
- _id: DEP5015
keyword:
- KI
- Kunst
- Weserrenaissance
place: Lemgo
publisher: Landesverband Lippe
status: public
title: A KInd of Art
type: exhibition
user_id: '83781'
year: '2025'
...
---
_id: '11605'
abstract:
- lang: eng
text: The recovery of beer from surplus yeast is to date an economical business
case only for large breweries. In this work, a here novel process with rotating
ceramic microfiltration membranes is used. This allows a very high lift force
to be achieved while still maintaining a small transmembrane pressure to reduce
the formation of a fouling layer. The results show that long running times (between
cleanings) are possible, limited only by the change in the rheological properties
of the suspension due to thickening. From a so-called "Inflexion Point" (IF),
the filtration behavior changes abruptly. The aim of the work was therefore to
use machine learning aided modeling to predict the IF from experimental data in
order to optimize the process and to achieve the most economical conditions. The
economic efficiency depends on the space-time yields. The results show that a
significant improvement in economic efficiency could be possible with the help
of modeling and this special kind of filtration technology. However, the economic
efficiency depends finally on the conditions in each individual brewery.
author:
- first_name: Marc
full_name: Trilling-Haasler, Marc
id: '81622'
last_name: Trilling-Haasler
orcid: 0000-0002-3685-6383
- first_name: Jörn
full_name: Tebbe, Jörn
id: '85958'
last_name: Tebbe
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Trilling-Haasler M, Tebbe J, Lange-Hegermann M, Schneider J. Yeast Filtration
with Rotating Membrane Filtration – a New Approach for an Economical Recovery
of Beer Form Surplus Yeast .; 2024.
apa: Trilling-Haasler, M., Tebbe, J., Lange-Hegermann, M., & Schneider, J. (2024).
Yeast filtration with rotating membrane filtration – a new approach for an
economical recovery of beer form surplus yeast . 39th EBC Congress 2024, Lille.
bjps: Trilling-Haasler M et al. (2024) Yeast Filtration with Rotating
Membrane Filtration – a New Approach for an Economical Recovery of Beer Form
Surplus Yeast . .
chicago: Trilling-Haasler, Marc, Jörn Tebbe, Markus Lange-Hegermann, and Jan Schneider.
Yeast Filtration with Rotating Membrane Filtration – a New Approach for an
Economical Recovery of Beer Form Surplus Yeast , 2024.
chicago-de: Trilling-Haasler, Marc, Jörn Tebbe, Markus Lange-Hegermann und Jan Schneider.
2024. Yeast filtration with rotating membrane filtration – a new approach
for an economical recovery of beer form surplus yeast .
din1505-2-1: 'Trilling-Haasler, Marc
; Tebbe, Jörn ; Lange-Hegermann,
Markus ; Schneider, Jan:
Yeast filtration with rotating membrane filtration – a new approach for an
economical recovery of beer form surplus yeast , 2024'
havard: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, J. Schneider, Yeast filtration
with rotating membrane filtration – a new approach for an economical recovery
of beer form surplus yeast , 2024.
ieee: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, and J. Schneider, Yeast
filtration with rotating membrane filtration – a new approach for an economical
recovery of beer form surplus yeast . 2024.
mla: Trilling-Haasler, Marc, et al. Yeast Filtration with Rotating Membrane Filtration
– a New Approach for an Economical Recovery of Beer Form Surplus Yeast .
2024.
short: M. Trilling-Haasler, J. Tebbe, M. Lange-Hegermann, J. Schneider, Yeast Filtration
with Rotating Membrane Filtration – a New Approach for an Economical Recovery
of Beer Form Surplus Yeast , 2024.
ufg: 'Trilling-Haasler, Marc u. a.: Yeast filtration with rotating membrane
filtration – a new approach for an economical recovery of beer form surplus yeast
, o. O. 2024.'
van: Trilling-Haasler M, Tebbe J, Lange-Hegermann M, Schneider J. Yeast filtration
with rotating membrane filtration – a new approach for an economical recovery
of beer form surplus yeast . 2024.
conference:
end_date: 2024-05-30
location: Lille
name: 39th EBC Congress 2024
start_date: 2024-05-26
date_created: 2024-06-28T12:57:01Z
date_updated: 2025-10-17T18:36:48Z
ddc:
- '600'
department:
- _id: DEP4028
- _id: DEP5023
- _id: DEP4018
- _id: DEP1308
has_accepted_license: '1'
keyword:
- surplus yeast
- membrane filtration
- microfiltration
language:
- iso: eng
publication_status: published
quality_controlled: '1'
status: public
title: 'Yeast filtration with rotating membrane filtration – a new approach for an
economical recovery of beer form surplus yeast '
type: conference_poster
user_id: '81304'
year: '2024'
...
---
_id: '12815'
abstract:
- lang: eng
text: Active learning of physical systems must commonly respect practical safety
constraints, which restricts the exploration of the design space. Gaussian Processes
(GPs) and their calibrated uncertainty estimations are widely used for this purpose.
In many technical applications the design space is explored via continuous trajectories,
along which the safety needs to be assessed. This is particularly challenging
for strict safety requirements in GP methods, as it employs computationally expensive
Monte-Carlo sampling of high quantiles. We address these challenges by providing
provable safety bounds based on the adaptively sampled median of the supremum
of the posterior GP. Our method significantly reduces the number of samples required
for estimating high safety probabilities, resulting in faster evaluation without
sacrificing accuracy and exploration speed. The effectiveness of our safe active
learning approach is demonstrated through extensive simulations and validated
using a real-world engine example.
author:
- first_name: Jörn
full_name: Tebbe, Jörn
id: '85958'
last_name: Tebbe
- first_name: Christoph
full_name: Zimmer, Christoph
last_name: Zimmer
- first_name: Ansgar
full_name: Steland, Ansgar
last_name: Steland
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Fabian
full_name: Mies, Fabian
last_name: Mies
citation:
ama: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. Efficiently Computable
Safety Bounds for Gaussian Processes in Active Learning. MLResearchPress ;
2024:1333-1341.
apa: Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M., & Mies, F. (2024).
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning.
In International Conference on Artificial Intelligence and Statistics (AISTATS),
Vol. 238 (pp. 1333–1341). MLResearchPress .
bjps: Tebbe J et al. (2024) Efficiently Computable Safety Bounds
for Gaussian Processes in Active Learning. MLResearchPress .
chicago: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann,
and Fabian Mies. Efficiently Computable Safety Bounds for Gaussian Processes
in Active Learning. International Conference on Artificial Intelligence
and Statistics (AISTATS), Vol. 238. Proceedings of Machine Learning Research.
MLResearchPress , 2024.
chicago-de: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann
und Fabian Mies. 2024. Efficiently Computable Safety Bounds for Gaussian Processes
in Active Learning. International Conference on Artificial Intelligence
and Statistics (AISTATS), Vol. 238. Proceedings of Machine Learning Research.
MLResearchPress .
din1505-2-1: 'Tebbe, Jörn ; Zimmer, Christoph ; Steland,
Ansgar ; Lange-Hegermann, Markus
; Mies, Fabian: Efficiently Computable
Safety Bounds for Gaussian Processes in Active Learning, Proceedings of
Machine Learning Research : MLResearchPress , 2024'
havard: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
, 2024.
ieee: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, and F. Mies, Efficiently
Computable Safety Bounds for Gaussian Processes in Active Learning. MLResearchPress
, 2024, pp. 1333–1341.
mla: Tebbe, Jörn, et al. “Efficiently Computable Safety Bounds for Gaussian Processes
in Active Learning.” International Conference on Artificial Intelligence and
Statistics (AISTATS), Vol. 238, MLResearchPress , 2024, pp. 1333–41.
short: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
, 2024.
ufg: 'Tebbe, Jörn u. a.: Efficiently Computable Safety Bounds for Gaussian
Processes in Active Learning, o. O. 2024 (Proceedings of Machine Learning Research).'
van: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. Efficiently Computable
Safety Bounds for Gaussian Processes in Active Learning. International Conference
on Artificial Intelligence and Statistics (AISTATS), Vol. 238. MLResearchPress
; 2024. (Proceedings of Machine Learning Research).
conference:
location: Valencia, SPAIN
name: 27th International Conference on Artificial Intelligence and Statistics (AISTATS)
start_date: 2024-05-02
date_created: 2025-04-17T07:58:19Z
date_updated: 2025-06-25T12:47:19Z
department:
- _id: DEP5000
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlr.press/v238/tebbe24a.html
oa: '1'
page: 1333-1341
publication: International Conference on Artificial Intelligence and Statistics (AISTATS),
Vol. 238
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: 'MLResearchPress '
series_title: Proceedings of Machine Learning Research
status: public
title: Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
type: conference_editor_article
user_id: '83781'
year: '2024'
...
---
_id: '12816'
abstract:
- lang: eng
text: Medical images need annotations with high-level semantic descriptors, so that
domain experts can search for the desired dataset among an enormous volume of
visual media within a Medical Data Integration Center. This article introduces
a processing pipeline for storing and annotating DICOM and PNG imaging data by
applying Elasticsearch, S3 and Deep Learning technologies. The proposed method
processes both DICOM and PNG images to generate annotations. These image annotations
are indexed in Elasticsearch with the corresponding raw data paths, where they
can be retrieved and analyzed.
author:
- first_name: Ka Yung
full_name: Cheng, Ka Yung
last_name: Cheng
- first_name: Santiago
full_name: Pazmino, Santiago
last_name: Pazmino
- first_name: Bjoern
full_name: Bergh, Bjoern
last_name: Bergh
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Bjorn
full_name: Schreiweis, Bjorn
last_name: Schreiweis
citation:
ama: Cheng KY, Pazmino S, Bergh B, Lange-Hegermann M, Schreiweis B. An Image
Retrieval Pipeline in a Medical Data Integration Center. Vol 310. IOS Press,
Incorporated; 2024:1388-1389. doi:10.3233/SHTI231208
apa: Cheng, K. Y., Pazmino, S., Bergh, B., Lange-Hegermann, M., & Schreiweis,
B. (2024). An Image Retrieval Pipeline in a Medical Data Integration Center. In
19th World Congress on Medical and Health Informatics (MEDINFO) (Vol. 310,
pp. 1388–1389). IOS Press, Incorporated. https://doi.org/10.3233/SHTI231208
bjps: Cheng KY et al. (2024) An Image Retrieval Pipeline in a Medical
Data Integration Center. IOS Press, Incorporated.
chicago: Cheng, Ka Yung, Santiago Pazmino, Bjoern Bergh, Markus Lange-Hegermann,
and Bjorn Schreiweis. An Image Retrieval Pipeline in a Medical Data Integration
Center. 19th World Congress on Medical and Health Informatics (MEDINFO).
Vol. 310. Studies in Health Technology and Informatics. IOS Press, Incorporated,
2024. https://doi.org/10.3233/SHTI231208.
chicago-de: Cheng, Ka Yung, Santiago Pazmino, Bjoern Bergh, Markus Lange-Hegermann
und Bjorn Schreiweis. 2024. An Image Retrieval Pipeline in a Medical Data Integration
Center. 19th World Congress on Medical and Health Informatics (MEDINFO).
Bd. 310. Studies in Health Technology and Informatics. IOS Press, Incorporated.
doi:10.3233/SHTI231208, .
din1505-2-1: 'Cheng, Ka Yung ; Pazmino, Santiago ; Bergh,
Bjoern ; Lange-Hegermann, Markus
; Schreiweis, Bjorn: An Image
Retrieval Pipeline in a Medical Data Integration Center., Studies in Health
Technology and Informatics. Bd. 310 : IOS Press, Incorporated, 2024'
havard: K.Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, B. Schreiweis, An
Image Retrieval Pipeline in a Medical Data Integration Center., IOS Press, Incorporated,
2024.
ieee: 'K. Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, and B. Schreiweis,
An Image Retrieval Pipeline in a Medical Data Integration Center., vol.
310. IOS Press, Incorporated, 2024, pp. 1388–1389. doi: 10.3233/SHTI231208.'
mla: Cheng, Ka Yung, et al. “An Image Retrieval Pipeline in a Medical Data Integration
Center.” 19th World Congress on Medical and Health Informatics (MEDINFO),
vol. 310, IOS Press, Incorporated, 2024, pp. 1388–89, https://doi.org/10.3233/SHTI231208.
short: K.Y. Cheng, S. Pazmino, B. Bergh, M. Lange-Hegermann, B. Schreiweis, An Image
Retrieval Pipeline in a Medical Data Integration Center., IOS Press, Incorporated,
2024.
ufg: 'Cheng, Ka Yung u. a.: An Image Retrieval Pipeline in a Medical Data
Integration Center., Bd. 310, o. O. 2024 (Studies in Health Technology and Informatics).'
van: Cheng KY, Pazmino S, Bergh B, Lange-Hegermann M, Schreiweis B. An Image Retrieval
Pipeline in a Medical Data Integration Center. 19th World Congress on Medical
and Health Informatics (MEDINFO). IOS Press, Incorporated; 2024. (Studies in Health
Technology and Informatics; vol. 310).
conference:
end_date: 2023-08-12
location: Sydney, AUSTRALIA
name: 19th World Congress on Medical and Health Informatics (MEDINFO)
start_date: 2023-08-08
date_created: 2025-04-17T08:25:27Z
date_updated: 2025-06-25T13:05:17Z
department:
- _id: DEP5023
doi: 10.3233/SHTI231208
external_id:
pmid:
- '38269660'
intvolume: ' 310'
keyword:
- Medical image retrieval
- data lake
- DICOM
- deep learning
- elasticsearch
language:
- iso: eng
page: 1388-1389
pmid: '1'
publication: 19th World Congress on Medical and Health Informatics (MEDINFO)
publication_identifier:
eisbn:
- 978-1-64368-457-4
eissn:
- 1879-8365
isbn:
- 978-1-64368-456-7
issn:
- 0926-9630
publication_status: published
publisher: IOS Press, Incorporated
series_title: Studies in Health Technology and Informatics
status: public
title: An Image Retrieval Pipeline in a Medical Data Integration Center.
type: conference_speech
user_id: '83781'
volume: 310
year: '2024'
...
---
_id: '12822'
abstract:
- lang: eng
text: A medical data integration center integrates a large volume of medical images
from clinical departments, including X-rays, CT scans, and MRI scans. Ideally,
all images should be indexed appropriately with standard clinical terms. However,
some images have incorrect or missing annotations, which creates challenges in
searching and integrating data centrally. To address this issue, accurate and
meaningful descriptors are needed for indexing fields, enabling users to efficiently
search for desired images and integrate them with international standards. This
paper aims to provide concise annotation for missing or incorrectly indexed fields,
incorporating essential instance -level information such as radiology modalities
(e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g.,
lateral) using a Deep Learning classification model - ResNet50. To demonstrate
the capabilities of our algorithm in generating annotations for indexing fields,
we conducted three experiments using two opensource datasets, the ROCO dataset,
and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels.
While the outcomes of these experiments are satisfactory (Precision of >75%) for
less critical tasks and serve as a valuable testing ground for image retrieval,
they also underscore the need for further exploration of potential challenges.
This essay elaborates on the identified issues and presents well-founded recommendations
for refining and advancing our proposed approach.
author:
- first_name: Ka Yung
full_name: Cheng, Ka Yung
last_name: Cheng
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Jan-Bernd
full_name: Hövener, Jan-Bernd
last_name: Hövener
- first_name: Björn
full_name: Schreiweis, Björn
last_name: Schreiweis
citation:
ama: Cheng KY, Lange-Hegermann M, Hövener JB, Schreiweis B. Instance-level medical
image classification for text-based retrieval in a medical data integration center.
Computational and Structural Biotechnology Journal. 2024;24:434-450. doi:10.1016/j.csbj.2024.06.006
apa: Cheng, K. Y., Lange-Hegermann, M., Hövener, J.-B., & Schreiweis, B. (2024).
Instance-level medical image classification for text-based retrieval in a medical
data integration center. Computational and Structural Biotechnology Journal,
24, 434–450. https://doi.org/10.1016/j.csbj.2024.06.006
bjps: Cheng KY et al. (2024) Instance-Level Medical Image Classification
for Text-Based Retrieval in a Medical Data Integration Center. Computational
and Structural Biotechnology Journal 24, 434–450.
chicago: 'Cheng, Ka Yung, Markus Lange-Hegermann, Jan-Bernd Hövener, and Björn Schreiweis.
“Instance-Level Medical Image Classification for Text-Based Retrieval in a Medical
Data Integration Center.” Computational and Structural Biotechnology Journal
24 (2024): 434–50. https://doi.org/10.1016/j.csbj.2024.06.006.'
chicago-de: 'Cheng, Ka Yung, Markus Lange-Hegermann, Jan-Bernd Hövener und Björn
Schreiweis. 2024. Instance-level medical image classification for text-based retrieval
in a medical data integration center. Computational and Structural Biotechnology
Journal 24: 434–450. doi:10.1016/j.csbj.2024.06.006,
.'
din1505-2-1: 'Cheng, Ka Yung ; Lange-Hegermann, Markus ; Hövener,
Jan-Bernd ; Schreiweis, Björn:
Instance-level medical image classification for text-based retrieval in a medical
data integration center. In: Computational and Structural Biotechnology Journal
Bd. 24. Amsterdam [u.a.], Elsevier BV (2024), S. 434–450'
havard: K.Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, B. Schreiweis, Instance-level
medical image classification for text-based retrieval in a medical data integration
center, Computational and Structural Biotechnology Journal. 24 (2024) 434–450.
ieee: 'K. Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, and B. Schreiweis, “Instance-level
medical image classification for text-based retrieval in a medical data integration
center,” Computational and Structural Biotechnology Journal, vol. 24, pp.
434–450, 2024, doi: 10.1016/j.csbj.2024.06.006.'
mla: Cheng, Ka Yung, et al. “Instance-Level Medical Image Classification for Text-Based
Retrieval in a Medical Data Integration Center.” Computational and Structural
Biotechnology Journal, vol. 24, 2024, pp. 434–50, https://doi.org/10.1016/j.csbj.2024.06.006.
short: K.Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, B. Schreiweis, Computational
and Structural Biotechnology Journal 24 (2024) 434–450.
ufg: 'Cheng, Ka Yung u. a.: Instance-level medical image classification for
text-based retrieval in a medical data integration center, in: Computational
and Structural Biotechnology Journal 24 (2024), S. 434–450.'
van: Cheng KY, Lange-Hegermann M, Hövener JB, Schreiweis B. Instance-level medical
image classification for text-based retrieval in a medical data integration center.
Computational and Structural Biotechnology Journal. 2024;24:434–50.
date_created: 2025-04-22T13:32:38Z
date_updated: 2025-06-26T08:58:59Z
department:
- _id: DEP5023
doi: 10.1016/j.csbj.2024.06.006
external_id:
isi:
- '001257361300001'
pmid:
- '38975287'
intvolume: ' 24'
isi: '1'
keyword:
- DICOM images
- Medical image captioning
- Medical image interchange
- SNOMED CT body structure
language:
- iso: eng
page: 434-450
place: Amsterdam [u.a.]
pmid: '1'
publication: Computational and Structural Biotechnology Journal
publication_identifier:
issn:
- 2001-0370
publication_status: published
publisher: Elsevier BV
status: public
title: Instance-level medical image classification for text-based retrieval in a medical
data integration center
type: scientific_journal_article
user_id: '83781'
volume: 24
year: '2024'
...
---
_id: '11381'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Selina
full_name: Ramm, Selina
id: '68713'
last_name: Ramm
orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development.
DECHEMA e.V.
apa: Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.).
A systematic, model-based workflow for risk-based decision making in upstream
development. 14th European Congress of Chemical Engineering and 7th European
Congress of Applied Biotechnology, Berlin, Germany. DECHEMA e.V.
bjps: Hernández Rodriguez T et al. (n.d.) A Systematic, Model-Based
Workflow for Risk-Based Decision Making in Upstream Development. DECHEMA e.V.
chicago: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann, and Björn
Frahm. A Systematic, Model-Based Workflow for Risk-Based Decision Making in
Upstream Development. DECHEMA e.V., n.d.
chicago-de: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann und
Björn Frahm. A systematic, model-based workflow for risk-based decision making
in upstream development. DECHEMA e.V.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Ramm, Selina ; Lange-Hegermann,
Markus ; Frahm, Björn: A
systematic, model-based workflow for risk-based decision making in upstream development :
DECHEMA e.V.'
havard: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development, DECHEMA
e.V., n.d.
ieee: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, and B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development.
DECHEMA e.V.
mla: Hernández Rodriguez, Tanja, et al. A Systematic, Model-Based Workflow for
Risk-Based Decision Making in Upstream Development. DECHEMA e.V.
short: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development, DECHEMA
e.V., n.d.
ufg: 'Hernández Rodriguez, Tanja u. a.: A systematic, model-based workflow
for risk-based decision making in upstream development, o. O. u. J. .'
van: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A systematic, model-based
workflow for risk-based decision making in upstream development. DECHEMA e.V.;
conference:
end_date: 2023-09-21
location: Berlin, Germany
name: 14th European Congress of Chemical Engineering and 7th European Congress of
Applied Biotechnology
start_date: 2023-09-17
date_created: 2024-04-26T06:18:20Z
date_updated: 2024-08-02T13:57:27Z
department:
- _id: DEP4000
language:
- iso: eng
publication_status: accepted
publisher: DECHEMA e.V.
status: public
title: A systematic, model-based workflow for risk-based decision making in upstream
development
type: conference_poster
user_id: '83781'
year: '2023'
...
---
_id: '11383'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Christoph
full_name: Posch, Christoph
last_name: Posch
- first_name: Ralf
full_name: Pörtner, Ralf
last_name: Pörtner
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Florian M.
full_name: Wurm, Florian M.
last_name: Wurm
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Posch C, Pörtner R, Lange-Hegermann M, Wurm FM, Frahm
B. Model-Assisted Design Strategies for Bioprocesses – Advanced Statistical
Methods in Industrial Upstream Cell Culture. DECHEMA e.V.; 2023.
apa: Hernández Rodriguez, T., Posch, C., Pörtner, R., Lange-Hegermann, M., Wurm,
F. M., & Frahm, B. (2023). Model-assisted design strategies for bioprocesses
– Advanced statistical methods in industrial upstream cell culture. 14th European
Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology,
Berlin, Germany. DECHEMA e.V.
bjps: Hernández Rodriguez T et al. (2023) Model-Assisted Design
Strategies for Bioprocesses – Advanced Statistical Methods in Industrial Upstream
Cell Culture. DECHEMA e.V.
chicago: Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner, Markus Lange-Hegermann,
Florian M. Wurm, and Björn Frahm. Model-Assisted Design Strategies for Bioprocesses
– Advanced Statistical Methods in Industrial Upstream Cell Culture. DECHEMA
e.V., 2023.
chicago-de: Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner, Markus Lange-Hegermann,
Florian M. Wurm und Björn Frahm. 2023. Model-assisted design strategies for
bioprocesses – Advanced statistical methods in industrial upstream cell culture.
DECHEMA e.V.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Posch, Christoph ; Pörtner,
Ralf ; Lange-Hegermann, Markus
; Wurm, Florian M. ; Frahm,
Björn: Model-assisted design strategies for bioprocesses – Advanced
statistical methods in industrial upstream cell culture : DECHEMA e.V., 2023'
havard: T. Hernández Rodriguez, C. Posch, R. Pörtner, M. Lange-Hegermann, F.M. Wurm,
B. Frahm, Model-assisted design strategies for bioprocesses – Advanced statistical
methods in industrial upstream cell culture, DECHEMA e.V., 2023.
ieee: T. Hernández Rodriguez, C. Posch, R. Pörtner, M. Lange-Hegermann, F. M. Wurm,
and B. Frahm, Model-assisted design strategies for bioprocesses – Advanced
statistical methods in industrial upstream cell culture. DECHEMA e.V., 2023.
mla: Hernández Rodriguez, Tanja, et al. Model-Assisted Design Strategies for
Bioprocesses – Advanced Statistical Methods in Industrial Upstream Cell Culture.
DECHEMA e.V., 2023.
short: T. Hernández Rodriguez, C. Posch, R. Pörtner, M. Lange-Hegermann, F.M. Wurm,
B. Frahm, Model-Assisted Design Strategies for Bioprocesses – Advanced Statistical
Methods in Industrial Upstream Cell Culture, DECHEMA e.V., 2023.
ufg: 'Hernández Rodriguez, Tanja u. a.: Model-assisted design strategies
for bioprocesses – Advanced statistical methods in industrial upstream cell culture,
o. O. 2023.'
van: Hernández Rodriguez T, Posch C, Pörtner R, Lange-Hegermann M, Wurm FM, Frahm
B. Model-assisted design strategies for bioprocesses – Advanced statistical methods
in industrial upstream cell culture. DECHEMA e.V.; 2023.
conference:
end_date: 2023-09-21
location: Berlin, Germany
name: 14th European Congress of Chemical Engineering and 7th European Congress of
Applied Biotechnology
start_date: 2023-09-17
date_created: 2024-04-26T06:40:20Z
date_updated: 2024-05-21T09:06:45Z
department:
- _id: DEP4000
language:
- iso: eng
publication_status: published
publisher: DECHEMA e.V.
status: public
title: Model-assisted design strategies for bioprocesses – Advanced statistical methods
in industrial upstream cell culture
type: conference_speech
user_id: '83781'
year: '2023'
...
---
_id: '10201'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Selina
full_name: Ramm, Selina
id: '68713'
last_name: Ramm
orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development.;
2023.
apa: Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (2023).
A systematic, model-based workflow for risk-based decision making in upstream
development. BioProcessingDays 2023, Recklinghausen, Germany.
bjps: Hernández Rodriguez T et al. (2023) A Systematic, Model-Based
Workflow for Risk-Based Decision Making in Upstream Development. .
chicago: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann, and Björn
Frahm. A Systematic, Model-Based Workflow for Risk-Based Decision Making in
Upstream Development, 2023.
chicago-de: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann und
Björn Frahm. 2023. A systematic, model-based workflow for risk-based decision
making in upstream development.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Ramm, Selina ; Lange-Hegermann,
Markus ; Frahm, Björn: A
systematic, model-based workflow for risk-based decision making in upstream development,
2023'
havard: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development, 2023.
ieee: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, and B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development.
2023.
mla: Hernández Rodriguez, Tanja, et al. A Systematic, Model-Based Workflow for
Risk-Based Decision Making in Upstream Development. 2023.
short: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development, 2023.
ufg: 'Hernández Rodriguez, Tanja u. a.: A systematic, model-based workflow
for risk-based decision making in upstream development, o. O. 2023.'
van: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A systematic, model-based
workflow for risk-based decision making in upstream development. 2023.
conference:
end_date: 2023-03-01
location: Recklinghausen, Germany
name: BioProcessingDays 2023
start_date: 2023-02-27
date_created: 2023-08-08T14:54:44Z
date_updated: 2024-08-02T09:41:01Z
department:
- _id: DEP4000
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.apz-rl.de/BioProcessingDays_2023/002_download/BPDs_2023_Tagungsbuch_Titel.pdf
oa: '1'
publication_status: published
status: public
title: A systematic, model-based workflow for risk-based decision making in upstream
development
type: conference_poster
user_id: '83781'
year: '2023'
...
---
_id: '10787'
abstract:
- lang: eng
text: Cyber-physical production systems have emerged with the rise of Industry 4.0
in different industrial fields. Especially the food sector, where inhomogeneous
input products like beer/yeast suspensions with different qualities and properties
have yet slowed down automation, has potential for this evolution. This contribution
presents optimization methods for a dynamical cross-flow filtration plant which
is driven by an advanced control concept in combination with data driven product
monitoring via inline near infrared spectroscopy (NIR) in order to improve energy
savings and filtration performance. Using a hierarchical control and optimization
structure, the non stationary batch process is steered towards a high production
rate with low energy consumption for a variety of different input products.
author:
- first_name: Jörn
full_name: Tebbe, Jörn
id: '79072'
last_name: Tebbe
- first_name: Thomas
full_name: Pawlik, Thomas
id: '58915'
last_name: Pawlik
- first_name: Marc
full_name: Trilling-Haasler, Marc
id: '81622'
last_name: Trilling-Haasler
orcid: 0000-0002-3685-6383
- first_name: Jannis
full_name: Löbner, Jannis
id: '74097'
last_name: Löbner
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Tebbe J, Pawlik T, Trilling-Haasler M, Löbner J, Lange-Hegermann M, Schneider
J. Holistic Optimization of a Dynamic Cross-Flow Filtration Process towards
a Cyber-Physical System. (Jasperneite J, Wisniewski L, Fung Man K, Institute
of Electrical and Electronics Engineers , eds.). IEEE; 2023:1-7. doi:10.1109/INDIN51400.2023.10217913
apa: Tebbe, J., Pawlik, T., Trilling-Haasler, M., Löbner, J., Lange-Hegermann, M.,
& Schneider, J. (2023). Holistic optimization of a dynamic cross-flow filtration
process towards a cyber-physical system. In J. Jasperneite, L. Wisniewski, K.
Fung Man, & Institute of Electrical and Electronics Engineers (Eds.), 2023
IEEE 21st International Conference on Industrial Informatics (INDIN) (pp.
1–7). IEEE. https://doi.org/10.1109/INDIN51400.2023.10217913
bjps: 'Tebbe J et al. (2023) Holistic Optimization of a Dynamic
Cross-Flow Filtration Process towards a Cyber-Physical System, Jasperneite
J et al. (eds). [Piscataway, NJ]: IEEE.'
chicago: 'Tebbe, Jörn, Thomas Pawlik, Marc Trilling-Haasler, Jannis Löbner, Markus
Lange-Hegermann, and Jan Schneider. Holistic Optimization of a Dynamic Cross-Flow
Filtration Process towards a Cyber-Physical System. Edited by Jürgen Jasperneite,
Lukasz Wisniewski, Kim Fung Man, and Institute of Electrical and Electronics Engineers
. 2023 IEEE 21st International Conference on Industrial Informatics (INDIN).
[Piscataway, NJ]: IEEE, 2023. https://doi.org/10.1109/INDIN51400.2023.10217913.'
chicago-de: 'Tebbe, Jörn, Thomas Pawlik, Marc Trilling-Haasler, Jannis Löbner, Markus
Lange-Hegermann und Jan Schneider. 2023. Holistic optimization of a dynamic
cross-flow filtration process towards a cyber-physical system. Hg. von Jürgen
Jasperneite, Lukasz Wisniewski, Kim Fung Man, und Institute of Electrical and
Electronics Engineers . 2023 IEEE 21st International Conference on Industrial
Informatics (INDIN). [Piscataway, NJ]: IEEE. doi:10.1109/INDIN51400.2023.10217913,
.'
din1505-2-1: 'Tebbe, Jörn ; Pawlik, Thomas ; Trilling-Haasler,
Marc ; Löbner, Jannis ; Lange-Hegermann, Markus ; Schneider,
Jan ; Jasperneite, J. ; Wisniewski, L. ; Fung
Man, K. ; Institute of Electrical
and Electronics Engineers (Hrsg.): Holistic optimization of a dynamic
cross-flow filtration process towards a cyber-physical system. [Piscataway,
NJ] : IEEE, 2023'
havard: J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
J. Schneider, Holistic optimization of a dynamic cross-flow filtration process
towards a cyber-physical system, IEEE, [Piscataway, NJ], 2023.
ieee: 'J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
and J. Schneider, Holistic optimization of a dynamic cross-flow filtration
process towards a cyber-physical system. [Piscataway, NJ]: IEEE, 2023, pp.
1–7. doi: 10.1109/INDIN51400.2023.10217913.'
mla: Tebbe, Jörn, et al. “Holistic Optimization of a Dynamic Cross-Flow Filtration
Process towards a Cyber-Physical System.” 2023 IEEE 21st International Conference
on Industrial Informatics (INDIN), edited by Jürgen Jasperneite et al., IEEE,
2023, pp. 1–7, https://doi.org/10.1109/INDIN51400.2023.10217913.
short: J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
J. Schneider, Holistic Optimization of a Dynamic Cross-Flow Filtration Process
towards a Cyber-Physical System, IEEE, [Piscataway, NJ], 2023.
ufg: 'Tebbe, Jörn u. a.: Holistic optimization of a dynamic cross-flow filtration
process towards a cyber-physical system, hg. von Jasperneite, Jürgen u. a., [Piscataway,
NJ] 2023.'
van: 'Tebbe J, Pawlik T, Trilling-Haasler M, Löbner J, Lange-Hegermann M, Schneider
J. Holistic optimization of a dynamic cross-flow filtration process towards a
cyber-physical system. Jasperneite J, Wisniewski L, Fung Man K, Institute of Electrical
and Electronics Engineers , editors. 2023 IEEE 21st International Conference on
Industrial Informatics (INDIN). [Piscataway, NJ]: IEEE; 2023.'
conference:
end_date: 2023-07-20
location: Lemgo
name: 21st International Conference on Industrial Informatics ; INDIN 2023
start_date: 2023-07-17
corporate_editor:
- 'Institute of Electrical and Electronics Engineers '
date_created: 2023-11-21T08:04:41Z
date_updated: 2025-06-26T07:48:22Z
department:
- _id: DEP4018
- _id: DEP1308
- _id: DEP4028
doi: 10.1109/INDIN51400.2023.10217913
editor:
- first_name: Jürgen
full_name: Jasperneite, Jürgen
id: '1899'
last_name: Jasperneite
- first_name: Lukasz
full_name: Wisniewski, Lukasz
id: '1710'
last_name: Wisniewski
- first_name: Kim
full_name: Fung Man, Kim
last_name: Fung Man
keyword:
- Spectroscopy
- Production systems
- Filtration
- Velocity control
- Optimization methods
- Cyber-physical systems
- Nonhomogeneous media
language:
- iso: eng
page: 1-7
place: '[Piscataway, NJ]'
publication: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
publication_identifier:
eisbn:
- 978-1-6654-9313-0
isbn:
- '978-1-6654-9314-7 '
issn:
- 1935-4576
publication_status: published
publisher: IEEE
status: public
title: Holistic optimization of a dynamic cross-flow filtration process towards a
cyber-physical system
type: conference_editor_article
user_id: '83781'
year: '2023'
...
---
_id: '12811'
abstract:
- lang: eng
text: For environmental, sustainable economic and political reasons, recycling processes
are becoming increasingly important, aiming at a much higher use of secondary
raw materials. Currently, for the copper and aluminum industries, no method for
the non-destructive online analysis of heterogeneous materials is available. The
prompt gamma neutron activation analysis (PGNAA) has the potential to overcome
this challenge. A difficulty when using PGNAA for online classification arises
from the small amount of noisy data, due to short-term measurements. In this case,
classical evaluation methods using detailed peak by peak analysis fail. Therefore,
we propose to view spectral data as probability distributions. Then, we can classify
material using maximum log-likelihood with respect to kernel density estimation
and use discrete sampling to optimize hyperparameters. For measurements of pure
aluminum alloys we achieve near-perfect classification of aluminum alloys under
0.25 s.
author:
- first_name: Helmand
full_name: Shayan, Helmand
id: '79365'
last_name: Shayan
- first_name: Kai
full_name: Krycki, Kai
last_name: Krycki
- first_name: Marco
full_name: Doemeland, Marco
last_name: Doemeland
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Shayan H, Krycki K, Doemeland M, Lange-Hegermann M. PGNAA Spectral Classification
of Metal With Density Estimations. IEEE Transactions on Nuclear Science.
2023;70(6):1171-1177. doi:10.1109/tns.2023.3242626
apa: Shayan, H., Krycki, K., Doemeland, M., & Lange-Hegermann, M. (2023). PGNAA
Spectral Classification of Metal With Density Estimations. IEEE Transactions
on Nuclear Science, 70(6), 1171–1177. https://doi.org/10.1109/tns.2023.3242626
bjps: Shayan H et al. (2023) PGNAA Spectral Classification of Metal
With Density Estimations. IEEE Transactions on Nuclear Science 70,
1171–1177.
chicago: 'Shayan, Helmand, Kai Krycki, Marco Doemeland, and Markus Lange-Hegermann.
“PGNAA Spectral Classification of Metal With Density Estimations.” IEEE Transactions
on Nuclear Science 70, no. 6 (2023): 1171–77. https://doi.org/10.1109/tns.2023.3242626.'
chicago-de: 'Shayan, Helmand, Kai Krycki, Marco Doemeland und Markus Lange-Hegermann.
2023. PGNAA Spectral Classification of Metal With Density Estimations. IEEE
Transactions on Nuclear Science 70, Nr. 6: 1171–1177. doi:10.1109/tns.2023.3242626,
.'
din1505-2-1: 'Shayan, Helmand ; Krycki, Kai ; Doemeland,
Marco ; Lange-Hegermann, Markus:
PGNAA Spectral Classification of Metal With Density Estimations. In: IEEE Transactions
on Nuclear Science Bd. 70. New York, NY, IEEE (2023), Nr. 6, S. 1171–1177'
havard: H. Shayan, K. Krycki, M. Doemeland, M. Lange-Hegermann, PGNAA Spectral Classification
of Metal With Density Estimations, IEEE Transactions on Nuclear Science. 70 (2023)
1171–1177.
ieee: 'H. Shayan, K. Krycki, M. Doemeland, and M. Lange-Hegermann, “PGNAA Spectral
Classification of Metal With Density Estimations,” IEEE Transactions on Nuclear
Science, vol. 70, no. 6, pp. 1171–1177, 2023, doi: 10.1109/tns.2023.3242626.'
mla: Shayan, Helmand, et al. “PGNAA Spectral Classification of Metal With Density
Estimations.” IEEE Transactions on Nuclear Science, vol. 70, no. 6, 2023,
pp. 1171–77, https://doi.org/10.1109/tns.2023.3242626.
short: H. Shayan, K. Krycki, M. Doemeland, M. Lange-Hegermann, IEEE Transactions
on Nuclear Science 70 (2023) 1171–1177.
ufg: 'Shayan, Helmand u. a.: PGNAA Spectral Classification of Metal With
Density Estimations, in: IEEE Transactions on Nuclear Science 70 (2023),
H. 6, S. 1171–1177.'
van: Shayan H, Krycki K, Doemeland M, Lange-Hegermann M. PGNAA Spectral Classification
of Metal With Density Estimations. IEEE Transactions on Nuclear Science. 2023;70(6):1171–7.
date_created: 2025-04-16T12:38:21Z
date_updated: 2025-06-26T07:45:59Z
department:
- _id: DEP5023
doi: 10.1109/tns.2023.3242626
external_id:
isi:
- '001012981300044'
intvolume: ' 70'
isi: '1'
issue: '6'
keyword:
- Classification of metal
- kernel density estimation
- maximum log-likelihood
- online classification
- prompt gamma neutron activation analysis (PGNAA) spectral classification
- random sampling
language:
- iso: eng
page: 1171-1177
place: New York, NY
publication: IEEE Transactions on Nuclear Science
publication_identifier:
eissn:
- 1558-1578
issn:
- 0018-9499
publication_status: published
publisher: IEEE
status: public
title: PGNAA Spectral Classification of Metal With Density Estimations
type: scientific_journal_article
user_id: '83781'
volume: 70
year: '2023'
...
---
_id: '12828'
abstract:
- lang: eng
text: Partial differential equations (PDEs) are important tools to model physical
systems and including them into machine learning models is an important way of
incorporating physical knowledge. Given any system of linear PDEs with constant
coefficients, we propose a family of Gaussian process (GP) priors, which we call
EPGP, such that all realizations are exact solutions of this system. We apply
the Ehrenpreis-Palamodov fundamental principle, which works as a non-linear Fourier
transform, to construct GP kernels mirroring standard spectral methods for GPs.
Our approach can infer probable solutions of linear PDE systems from any data
such as noisy measurements, or pointwise defined initial and boundary conditions.
Constructing EPGP-priors is algorithmic, generally applicable, and comes with
a sparse version (S-EPGP) that learns the relevant spectral frequencies and works
better for big data sets. We demonstrate our approach on three families of systems
of PDEs, the heat equation, wave equation, and Maxwell's equations, where we improve
upon the state of the art in computation time and precision, in some experiments
by several orders of magnitude.
author:
- first_name: 'Marc '
full_name: 'Härkönen, Marc '
last_name: Härkönen
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Bogdan
full_name: ' Raiţă, Bogdan'
last_name: ' Raiţă'
citation:
ama: Härkönen M, Lange-Hegermann M, Raiţă B. Gaussian Process Priors for Systems
of Linear Partial Differential Equations with Constant Coefficients. Vol 202.
MLResearchPress ; 2023.
apa: Härkönen, M., Lange-Hegermann, M., & Raiţă, B. (2023). Gaussian Process
Priors for Systems of Linear Partial Differential Equations with Constant Coefficients.
In 40th International Conference on Machine Learning (Vol. 202). MLResearchPress
.
bjps: Härkönen M, Lange-Hegermann M and Raiţă B (2023) Gaussian Process
Priors for Systems of Linear Partial Differential Equations with Constant Coefficients.
MLResearchPress .
chicago: 'Härkönen, Marc , Markus Lange-Hegermann, and Bogdan Raiţă. Gaussian
Process Priors for Systems of Linear Partial Differential Equations with Constant
Coefficients. 40th International Conference on Machine Learning. Vol.
202. Proceedings of Machine Learning Research : PMLR. MLResearchPress , 2023.'
chicago-de: 'Härkönen, Marc , Markus Lange-Hegermann und Bogdan Raiţă. 2023. Gaussian
Process Priors for Systems of Linear Partial Differential Equations with Constant
Coefficients. 40th International Conference on Machine Learning. Bd.
202. Proceedings of machine learning research : PMLR. MLResearchPress .'
din1505-2-1: 'Härkönen, Marc ; Lange-Hegermann, Markus ;
Raiţă, Bogdan: Gaussian Process Priors for Systems of Linear Partial
Differential Equations with Constant Coefficients, Proceedings of machine
learning research : PMLR. Bd. 202 : MLResearchPress , 2023'
havard: M. Härkönen, M. Lange-Hegermann, B. Raiţă, Gaussian Process Priors for
Systems of Linear Partial Differential Equations with Constant Coefficients, MLResearchPress
, 2023.
ieee: M. Härkönen, M. Lange-Hegermann, and B. Raiţă, Gaussian Process Priors
for Systems of Linear Partial Differential Equations with Constant Coefficients,
vol. 202. MLResearchPress , 2023.
mla: Härkönen, Marc, et al. “Gaussian Process Priors for Systems of Linear Partial
Differential Equations with Constant Coefficients.” 40th International Conference
on Machine Learning, vol. 202, MLResearchPress , 2023.
short: M. Härkönen, M. Lange-Hegermann, B. Raiţă, Gaussian Process Priors for Systems
of Linear Partial Differential Equations with Constant Coefficients, MLResearchPress
, 2023.
ufg: 'Härkönen, Marc/Lange-Hegermann, Markus/ Raiţă, Bogdan: Gaussian Process
Priors for Systems of Linear Partial Differential Equations with Constant Coefficients,
Bd. 202, o. O. 2023 (Proceedings of machine learning research : PMLR).'
van: 'Härkönen M, Lange-Hegermann M, Raiţă B. Gaussian Process Priors for Systems
of Linear Partial Differential Equations with Constant Coefficients. 40th International
Conference on Machine Learning. MLResearchPress ; 2023. (Proceedings of machine
learning research : PMLR; vol. 202).'
conference:
end_date: 2023-07-29
location: Honolulu, HI
name: 40th International Conference on Machine Learning
start_date: 2023-07-23
date_created: 2025-04-22T14:14:42Z
date_updated: 2025-06-26T07:56:15Z
department:
- _id: DEP5023
intvolume: ' 202'
language:
- iso: eng
publication: 40th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: 'MLResearchPress '
series_title: 'Proceedings of machine learning research : PMLR'
status: public
title: Gaussian Process Priors for Systems of Linear Partial Differential Equations
with Constant Coefficients
type: conference_editor_article
user_id: '83781'
volume: 202
year: '2023'
...
---
_id: '9930'
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Tobias
full_name: Schmohl, Tobias
id: '71782'
last_name: Schmohl
orcid: https://orcid.org/0000-0002-7043-5582
- first_name: Alice
full_name: Watanabe, Alice
id: '76856'
last_name: Watanabe
- first_name: Kathrin
full_name: Schelling, Kathrin
id: '81212'
last_name: Schelling
- first_name: Stefan
full_name: Heiss, Stefan
id: '1031'
last_name: Heiss
- first_name: Jessica
full_name: Rubart, Jessica
id: '45672'
last_name: Rubart
citation:
ama: Lange-Hegermann M, Schmohl T, Watanabe A, Schelling K, Heiss S, Rubart J. KI-basierte
Erstellung individualisierter Mathematikaufgaben für MINT-Fächer. Vol 4. (Schmohl
T, Watanabe A, Schelling K, eds.). transcript Verlag; 2023:161-172. doi:10.14361/9783839457696-009
apa: 'Lange-Hegermann, M., Schmohl, T., Watanabe, A., Schelling, K., Heiss, S.,
& Rubart, J. (2023). KI-basierte Erstellung individualisierter Mathematikaufgaben
für MINT-Fächer. In T. Schmohl, A. Watanabe, & K. Schelling (Eds.), Künstliche
Intelligenz in der Hochschulbildung: Chancen und Grenzen des KI-gestützten Lernens
und Lehrens (Vol. 4, pp. 161–172). transcript Verlag. https://doi.org/10.14361/9783839457696-009'
bjps: 'Lange-Hegermann M et al. (2023) KI-basierte Erstellung individualisierter
Mathematikaufgaben für MINT-Fächer, Schmohl T, Watanabe A and Schelling K
(eds). Bielefeld: transcript Verlag.'
chicago: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Kathrin Schelling,
Stefan Heiss, and Jessica Rubart. KI-basierte Erstellung individualisierter
Mathematikaufgaben für MINT-Fächer. Edited by Tobias Schmohl, Alice Watanabe,
and Kathrin Schelling. Künstliche Intelligenz in der Hochschulbildung: Chancen
und Grenzen des KI-gestützten Lernens und Lehrens. Vol. 4. Hochschulbildung:
Lehre und Forschung. Bielefeld: transcript Verlag, 2023. https://doi.org/10.14361/9783839457696-009.'
chicago-de: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Kathrin Schelling,
Stefan Heiss und Jessica Rubart. 2023. KI-basierte Erstellung individualisierter
Mathematikaufgaben für MINT-Fächer. Hg. von Tobias Schmohl, Alice Watanabe,
und Kathrin Schelling. Künstliche Intelligenz in der Hochschulbildung: Chancen
und Grenzen des KI-gestützten Lernens und Lehrens. Bd. 4. Hochschulbildung:
Lehre und Forschung. Bielefeld: transcript Verlag. doi:10.14361/9783839457696-009,
.'
din1505-2-1: 'Lange-Hegermann, Markus
; Schmohl, Tobias ; Watanabe,
Alice ; Schelling, Kathrin
; Heiss, Stefan ; Rubart,
Jessica ; Schmohl, T. ; Watanabe, A. ; Schelling,
K. (Hrsg.): KI-basierte Erstellung individualisierter Mathematikaufgaben
für MINT-Fächer, Hochschulbildung: Lehre und Forschung. Bd. 4. Bielefeld :
transcript Verlag, 2023'
havard: M. Lange-Hegermann, T. Schmohl, A. Watanabe, K. Schelling, S. Heiss, J.
Rubart, KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer,
transcript Verlag, Bielefeld, 2023.
ieee: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, K. Schelling, S. Heiss, and
J. Rubart, KI-basierte Erstellung individualisierter Mathematikaufgaben für
MINT-Fächer, vol. 4. Bielefeld: transcript Verlag, 2023, pp. 161–172. doi:
10.14361/9783839457696-009.'
mla: 'Lange-Hegermann, Markus, et al. “KI-basierte Erstellung individualisierter
Mathematikaufgaben für MINT-Fächer.” Künstliche Intelligenz in der Hochschulbildung:
Chancen und Grenzen des KI-gestützten Lernens und Lehrens, edited by Tobias
Schmohl et al., vol. 4, transcript Verlag, 2023, pp. 161–72, https://doi.org/10.14361/9783839457696-009.'
short: M. Lange-Hegermann, T. Schmohl, A. Watanabe, K. Schelling, S. Heiss, J. Rubart,
KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer,
transcript Verlag, Bielefeld, 2023.
ufg: 'Lange-Hegermann, Markus u. a.: KI-basierte Erstellung individualisierter
Mathematikaufgaben für MINT-Fächer, Bd. 4, hg. von Schmohl, Tobias/Watanabe, Alice/Schelling,
Kathrin, Bielefeld 2023 (Hochschulbildung: Lehre und Forschung).'
van: 'Lange-Hegermann M, Schmohl T, Watanabe A, Schelling K, Heiss S, Rubart J.
KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer.
Schmohl T, Watanabe A, Schelling K, editors. Künstliche Intelligenz in der Hochschulbildung:
Chancen und Grenzen des KI-gestützten Lernens und Lehrens. Bielefeld: transcript
Verlag; 2023. (Hochschulbildung: Lehre und Forschung; vol. 4).'
date_created: 2023-05-25T11:29:54Z
date_updated: 2023-06-19T07:39:11Z
department:
- _id: DEP5000
doi: 10.14361/9783839457696-009
editor:
- first_name: Tobias
full_name: Schmohl, Tobias
id: '71782'
last_name: Schmohl
orcid: https://orcid.org/0000-0002-7043-5582
- first_name: Alice
full_name: Watanabe, Alice
id: '76856'
last_name: Watanabe
- first_name: Kathrin
full_name: Schelling, Kathrin
id: '81212'
last_name: Schelling
intvolume: ' 4'
language:
- iso: ger
page: 161-172
place: Bielefeld
publication: 'Künstliche Intelligenz in der Hochschulbildung: Chancen und Grenzen
des KI-gestützten Lernens und Lehrens'
publication_identifier:
eisbn:
- '978-3-8394-5769-6 '
isbn:
- 978-3-8376-5769-2
publication_status: published
publisher: transcript Verlag
series_title: 'Hochschulbildung: Lehre und Forschung'
status: public
title: KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer
type: conference_editor_article
user_id: '15514'
volume: 4
year: '2023'
...
---
_id: '11377'
abstract:
- lang: eng
text: consuming and often performed rather empirically. Efficient optimization
of multiple objectives such as process time, viable cell density, number of operating
steps & cultivation scales, required medium, amount of product as well as
product quality depicts a promising approach. This contribution presents a workflow
which couples uncertainty-based upstream simulation and Bayes optimization using
Gaussian processes. Its application is demonstrated in a simulation case study
for a relevant industrial task in process development, the design of a robust
cell culture expansion process (seed train), meaning that despite uncertainties
and variabilities concerning cell growth, low variations of viable cell density
during the seed train are obtained. Compared to a non-optimized reference seed
train, the optimized process showed much lower deviation rates regarding viable
cell densities (<10% instead of 41.7%) using five or four shake flask scales
and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall,
it is shown that applying Bayes optimization allows for optimization of a multi-objective
optimization function with several optimizable input variables and under a considerable
amount of constraints with a low computational effort. This approach provides
the potential to be used in the form of a decision tool, e.g., for the choice
of an optimal and robust seed train design or for further optimization tasks within
process development.
article_number: '883'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Anton
full_name: Sekulic, Anton
last_name: Sekulic
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust
Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization
Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5).
doi:10.3390/pr10050883
apa: Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B.
(2022). Designing Robust Biotechnological Processes Regarding Variabilities Using
Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.
Processes, 10(5), Article 883. https://doi.org/10.3390/pr10050883
bjps: Hernández Rodriguez T et al. (2022) Designing Robust Biotechnological
Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
a Biopharmaceutical Seed Train Design. Processes 10.
chicago: Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and
Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities
Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.”
Processes 10, no. 5 (2022). https://doi.org/10.3390/pr10050883.
chicago-de: Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und
Björn Frahm. 2022. Designing Robust Biotechnological Processes Regarding Variabilities
Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.
Processes 10, Nr. 5. doi:10.3390/pr10050883,
.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Sekulic, Anton ; Lange-Hegermann,
Markus ; Frahm, Björn: Designing
Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
Optimization Applied to a Biopharmaceutical Seed Train Design. In: Processes
Bd. 10. Basel, MDPI AG (2022), Nr. 5'
havard: T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing
Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
Optimization Applied to a Biopharmaceutical Seed Train Design, Processes. 10 (2022).
ieee: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing
Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
Optimization Applied to a Biopharmaceutical Seed Train Design,” Processes,
vol. 10, no. 5, Art. no. 883, 2022, doi: 10.3390/pr10050883.'
mla: Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes
Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical
Seed Train Design.” Processes, vol. 10, no. 5, 883, 2022, https://doi.org/10.3390/pr10050883.
short: T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Processes
10 (2022).
ufg: 'Hernández Rodriguez, Tanja u. a.: Designing Robust Biotechnological
Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
a Biopharmaceutical Seed Train Design, in: Processes 10 (2022), H. 5.'
van: Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust
Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization
Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5).
date_created: 2024-04-25T13:35:04Z
date_updated: 2024-05-21T09:30:15Z
department:
- _id: DEP4000
doi: 10.3390/pr10050883
intvolume: ' 10'
issue: '5'
keyword:
- Gaussian processes
- Bayes optimization
- Pareto optimization
- multi-objective
- cell culture
- seed train
language:
- iso: eng
place: Basel
publication: Processes
publication_identifier:
eissn:
- 2227-9717
publication_status: published
publisher: MDPI AG
status: public
title: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
Optimization Applied to a Biopharmaceutical Seed Train Design
type: scientific_journal_article
user_id: '83781'
volume: 10
year: '2022'
...
---
_id: '7932'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Selina
full_name: Ramm, Selina
id: '68713'
last_name: Ramm
orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development.
apa: Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.).
A systematic, model-based workflow for risk-based decision making in upstream
development. 5th annual Bioprocessing Summit Europe, Barcelona, Spain.
bjps: Hernández Rodriguez T et al. (n.d.) A Systematic, Model-Based
Workflow for Risk-Based Decision Making in Upstream Development. .
chicago: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann, and Björn
Frahm. A Systematic, Model-Based Workflow for Risk-Based Decision Making in
Upstream Development, n.d.
chicago-de: Hernández Rodriguez, Tanja, Selina Ramm, Markus Lange-Hegermann und
Björn Frahm. A systematic, model-based workflow for risk-based decision making
in upstream development.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Ramm, Selina ; Lange-Hegermann,
Markus ; Frahm, Björn: A
systematic, model-based workflow for risk-based decision making in upstream development'
havard: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development, n.d.
ieee: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, and B. Frahm, A systematic,
model-based workflow for risk-based decision making in upstream development.
mla: Hernández Rodriguez, Tanja, et al. A Systematic, Model-Based Workflow for
Risk-Based Decision Making in Upstream Development.
short: T. Hernández Rodriguez, S. Ramm, M. Lange-Hegermann, B. Frahm, A Systematic,
Model-Based Workflow for Risk-Based Decision Making in Upstream Development, n.d.
ufg: 'Hernández Rodriguez, Tanja u. a.: A systematic, model-based workflow
for risk-based decision making in upstream development, o. O. u. J. .'
van: Hernández Rodriguez T, Ramm S, Lange-Hegermann M, Frahm B. A systematic, model-based
workflow for risk-based decision making in upstream development.
conference:
end_date: 2022-03-24
location: Barcelona, Spain
name: 5th annual Bioprocessing Summit Europe
start_date: 2022-03-22
date_created: 2022-05-04T19:35:20Z
date_updated: 2024-08-02T13:57:53Z
department:
- _id: DEP4021
language:
- iso: eng
publication_status: accepted
status: public
title: A systematic, model-based workflow for risk-based decision making in upstream
development
type: conference_poster
user_id: '83781'
year: '2022'
...
---
_id: '10193'
abstract:
- lang: eng
text: Development and optimization of biopharmaceutical production processes with
cell cultures is cost- and time-consuming and often performed rather empirically.
Efficient optimization of multiple objectives such as process time, viable cell
density, number of operating steps & cultivation scales, required medium, amount
of product as well as product quality depicts a promising approach. This contribution
presents a workflow which couples uncertainty-based upstream simulation and Bayes
optimization using Gaussian processes. Its application is demonstrated in a simulation
case study for a relevant industrial task in process development, the design of
a robust cell culture expansion process (seed train), meaning that despite uncertainties
and variabilities concerning cell growth, low variations of viable cell density
during the seed train are obtained. Compared to a non-optimized reference seed
train, the optimized process showed much lower deviation rates regarding viable
cell densities (<10% instead of 41.7%) using five or four shake flask scales and
seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it
is shown that applying Bayes optimization allows for optimization of a multi-objective
optimization function with several optimizable input variables and under a considerable
amount of constraints with a low computational effort. This approach provides
the potential to be used in the form of a decision tool, e.g., for the choice
of an optimal and robust seed train design or for further optimization tasks within
process development.
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Anton
full_name: Sekulic, Anton
last_name: Sekulic
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: 'Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust
biotechnological processes regarding variabilities using multi-objective optimization
applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, eds.
Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing.
Vol special issue. Processes : open access journal. MDPI; 2022:21-48. doi:https://doi.org/10.3390/pr10050883'
apa: 'Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B.
(2022). Designing robust biotechnological processes regarding variabilities using
multi-objective optimization applied to a biopharmaceutical seed train design.
In R. Pörtner & J. Möller (Eds.), Bioprocess Systems Engineering Applications
in Pharmaceutical Manufacturing: Vol. special issue (pp. 21–48). MDPI. https://doi.org/10.3390/pr10050883'
bjps: 'Hernández Rodriguez T et al. (2022) Designing Robust Biotechnological
Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
a Biopharmaceutical Seed Train Design. In Pörtner R and Möller J (eds), Bioprocess
Systems Engineering Applications in Pharmaceutical Manufacturing, vol. special
issue. Basel: MDPI, pp. 21–48.'
chicago: 'Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and
Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities
Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.”
In Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing,
edited by Ralf Pörtner and Johannes Möller, special issue:21–48. Processes : Open
Access Journal. Basel: MDPI, 2022. https://doi.org/10.3390/pr10050883.'
chicago-de: 'Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und
Björn Frahm. 2022. Designing robust biotechnological processes regarding variabilities
using multi-objective optimization applied to a biopharmaceutical seed train design.
In: Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing,
hg. von Ralf Pörtner und Johannes Möller, special issue:21–48. Processes : open
access journal. Basel: MDPI. doi:https://doi.org/10.3390/pr10050883,
.'
din1505-2-1: 'Hernández Rodriguez, Tanja
; Sekulic, Anton ; Lange-Hegermann,
Markus ; Frahm, Björn: Designing
robust biotechnological processes regarding variabilities using multi-objective
optimization applied to a biopharmaceutical seed train design. In: Pörtner,
R. ; Möller, J. (Hrsg.):
Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing,
Processes : open access journal. Bd. special issue. Basel : MDPI, 2022,
S. 21–48'
havard: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing
robust biotechnological processes regarding variabilities using multi-objective
optimization applied to a biopharmaceutical seed train design, in: R. Pörtner,
J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical
Manufacturing, MDPI, Basel, 2022: pp. 21–48.'
ieee: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing
robust biotechnological processes regarding variabilities using multi-objective
optimization applied to a biopharmaceutical seed train design,” in Bioprocess
Systems Engineering Applications in Pharmaceutical Manufacturing, vol. special
issue, R. Pörtner and J. Möller, Eds. Basel: MDPI, 2022, pp. 21–48. doi: https://doi.org/10.3390/pr10050883.'
mla: Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes
Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical
Seed Train Design.” Bioprocess Systems Engineering Applications in Pharmaceutical
Manufacturing, edited by Ralf Pörtner and Johannes Möller, vol. special issue,
MDPI, 2022, pp. 21–48, https://doi.org/10.3390/pr10050883.
short: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, in: R.
Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical
Manufacturing, MDPI, Basel, 2022, pp. 21–48.'
ufg: 'Hernández Rodriguez, Tanja u. a.: Designing robust biotechnological
processes regarding variabilities using multi-objective optimization applied to
a biopharmaceutical seed train design, in: Pörtner, Ralf/Möller, Johannes (Hgg.):
Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, Band
special issue, Basel 2022 (Processes : open access journal), S. 21–48.'
van: 'Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust
biotechnological processes regarding variabilities using multi-objective optimization
applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, editors.
Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing. Basel:
MDPI; 2022. p. 21–48. (Processes : open access journal; vol. special issue).'
date_created: 2023-08-08T12:58:36Z
date_updated: 2023-08-16T09:24:16Z
department:
- _id: DEP4000
doi: https://doi.org/10.3390/pr10050883
editor:
- first_name: Ralf
full_name: Pörtner, Ralf
last_name: Pörtner
- first_name: Johannes
full_name: Möller, Johannes
last_name: Möller
keyword:
- Gaussian processes
- Bayes optimization
- Pareto optimization
- multi-objective
- cell culture
- seed train
language:
- iso: eng
page: 21-48
place: Basel
publication: Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing
publication_identifier:
eisbn:
- 978-3-0365-5209-5
eissn:
- 2227-9717
isbn:
- 978-3-0365-5210-1
publication_status: published
publisher: MDPI
quality_controlled: '1'
series_title: 'Processes : open access journal'
status: public
title: Designing robust biotechnological processes regarding variabilities using multi-objective
optimization applied to a biopharmaceutical seed train design
type: book_chapter
user_id: '83781'
volume: special issue
year: '2022'
...
---
_id: '10198'
author:
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Ralf
full_name: Pörtner, Ralf
last_name: Pörtner
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Florian M.
full_name: Wurm, Florian M.
last_name: Wurm
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Hernández Rodriguez T, Pörtner R, Lange-Hegermann M, Wurm FM, Frahm B. A
Systematic, Model-Based Approach for Decision Making in Upstream Development –
Considerations Regarding Clone Selection and Cell Expansion.
apa: 'Hernández Rodriguez, T., Pörtner, R., Lange-Hegermann, M., Wurm, F. M., &
Frahm, B. (n.d.). A systematic, model-based approach for decision making in
upstream development – Considerations regarding clone selection and cell expansion.
27th Meeting of the European Society for Animal Cell Technology (ESACT): Advanced
Cell Technologies: Making Protein, Cell, and Gene Therapies a Reality, Lisbon,
Portugal .'
bjps: Hernández Rodriguez T et al. (n.d.) A Systematic, Model-Based
Approach for Decision Making in Upstream Development – Considerations Regarding
Clone Selection and Cell Expansion. .
chicago: Hernández Rodriguez, Tanja, Ralf Pörtner, Markus Lange-Hegermann, Florian
M. Wurm, and Björn Frahm. A Systematic, Model-Based Approach for Decision Making
in Upstream Development – Considerations Regarding Clone Selection and Cell Expansion,
n.d.
chicago-de: Hernández Rodriguez, Tanja, Ralf Pörtner, Markus Lange-Hegermann, Florian
M. Wurm und Björn Frahm. A systematic, model-based approach for decision making
in upstream development – Considerations regarding clone selection and cell expansion.
din1505-2-1: 'Hernández Rodriguez, Tanja
; Pörtner, Ralf ; Lange-Hegermann,
Markus ; Wurm, Florian M.
; Frahm, Björn: A systematic,
model-based approach for decision making in upstream development – Considerations
regarding clone selection and cell expansion'
havard: T. Hernández Rodriguez, R. Pörtner, M. Lange-Hegermann, F.M. Wurm, B. Frahm,
A systematic, model-based approach for decision making in upstream development
– Considerations regarding clone selection and cell expansion, n.d.
ieee: T. Hernández Rodriguez, R. Pörtner, M. Lange-Hegermann, F. M. Wurm, and B.
Frahm, A systematic, model-based approach for decision making in upstream development
– Considerations regarding clone selection and cell expansion.
mla: Hernández Rodriguez, Tanja, et al. A Systematic, Model-Based Approach for
Decision Making in Upstream Development – Considerations Regarding Clone Selection
and Cell Expansion.
short: T. Hernández Rodriguez, R. Pörtner, M. Lange-Hegermann, F.M. Wurm, B. Frahm,
A Systematic, Model-Based Approach for Decision Making in Upstream Development
– Considerations Regarding Clone Selection and Cell Expansion, n.d.
ufg: 'Hernández Rodriguez, Tanja u. a.: A systematic, model-based approach
for decision making in upstream development – Considerations regarding clone selection
and cell expansion, o. O. u. J. .'
van: Hernández Rodriguez T, Pörtner R, Lange-Hegermann M, Wurm FM, Frahm B. A systematic,
model-based approach for decision making in upstream development – Considerations
regarding clone selection and cell expansion.
conference:
end_date: 2022-06-29
location: 'Lisbon, Portugal '
name: '27th Meeting of the European Society for Animal Cell Technology (ESACT):
Advanced Cell Technologies: Making Protein, Cell, and Gene Therapies a Reality'
start_date: 2022-06-26
date_created: 2023-08-08T13:50:25Z
date_updated: 2024-08-02T14:14:55Z
department:
- _id: DEP4000
language:
- iso: eng
publication_status: accepted
quality_controlled: '1'
status: public
title: A systematic, model-based approach for decision making in upstream development
– Considerations regarding clone selection and cell expansion
type: conference_poster
user_id: '83781'
year: '2022'
...
---
_id: '12804'
abstract:
- lang: eng
text: 'Data in many applications follows systems of Ordinary Differential Equations
(ODEs). This paper presents a novel algorithmic and symbolic construction for
covariance functions of Gaussian Processes (GPs) with realizations strictly following
a system of linear homogeneous ODEs with constant coefficients, which we call
LODE-GPs. Introducing this strong inductive bias into a GP improves modelling
of such data. Using smith normal form algorithms, a symbolic technique, we overcome
two current restrictions in the state of the art: (1) the need for certain uniqueness
conditions in the set of solutions, typically assumed in classical ODE solvers
and their probabilistic counterparts, and (2) the restriction to controllable
systems, typically assumed when encoding differential equations in covariance
functions. We show the effectiveness of LODE-GPs in a number of experiments, for
example learning physically interpretable parameters by maximizing the likelihood.'
author:
- first_name: Andreas
full_name: Besginow, Andreas
id: '61743'
last_name: Besginow
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Besginow A, Lange-Hegermann M. Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations. Vol 35. (Koyejo S, Mohamed S, Agarwal
A, et al., eds.). Curran Associates, Inc.; 2022:29386-29399.
apa: Besginow, A., & Lange-Hegermann, M. (2022). Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations. In S. Koyejo, S. Mohamed,
A. Agarwal, D. Belgrave, K. Cho, A. Oh, & Neural Information Processing Systems
Foundation (Eds.), 36th Conference on Neural Information Processing Systems
(NeurIPS 2022) (Vol. 35, pp. 29386–29399). Curran Associates, Inc.
bjps: 'Besginow A and Lange-Hegermann M (2022) Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations, Koyejo S et al. (eds).
Red Hook, NY : Curran Associates, Inc.'
chicago: 'Besginow, Andreas, and Markus Lange-Hegermann. Constraining Gaussian
Processes to Systems of Linear Ordinary Differential Equations. Edited by
S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, and Neural Information
Processing Systems Foundation . 36th Conference on Neural Information Processing
Systems (NeurIPS 2022) . Vol. 35. Advances in Neural Information Processing
Systems. Red Hook, NY : Curran Associates, Inc., 2022.'
chicago-de: 'Besginow, Andreas und Markus Lange-Hegermann. 2022. Constraining
Gaussian Processes to Systems of Linear Ordinary Differential Equations. Hg.
von S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, und Neural
Information Processing Systems Foundation . 36th Conference on Neural Information
Processing Systems (NeurIPS 2022) . Bd. 35. Advances in Neural Information
Processing Systems. Red Hook, NY : Curran Associates, Inc.'
din1505-2-1: 'Besginow, Andreas ;
Lange-Hegermann, Markus ; Koyejo, S. ; Mohamed,
S. ; Agarwal, A. ; Belgrave, D. ; Cho,
K. ; Oh, A. ; Neural
Information Processing Systems Foundation (Hrsg.): Constraining Gaussian
Processes to Systems of Linear Ordinary Differential Equations, Advances
in Neural Information Processing Systems. Bd. 35. Red Hook, NY : Curran Associates,
Inc., 2022'
havard: A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook,
NY , 2022.
ieee: 'A. Besginow and M. Lange-Hegermann, Constraining Gaussian Processes to
Systems of Linear Ordinary Differential Equations, vol. 35. Red Hook, NY :
Curran Associates, Inc., 2022, pp. 29386–29399.'
mla: Besginow, Andreas, and Markus Lange-Hegermann. “Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations.” 36th Conference on Neural
Information Processing Systems (NeurIPS 2022) , edited by S. Koyejo et al.,
vol. 35, Curran Associates, Inc., 2022, pp. 29386–99.
short: A. Besginow, M. Lange-Hegermann, Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations, Curran Associates, Inc., Red Hook,
NY , 2022.
ufg: 'Besginow, Andreas/Lange-Hegermann, Markus: Constraining Gaussian Processes
to Systems of Linear Ordinary Differential Equations, Bd. 35, hg. von Koyejo,
S. u. a., Red Hook, NY 2022 (Advances in Neural Information Processing Systems).'
van: 'Besginow A, Lange-Hegermann M. Constraining Gaussian Processes to Systems
of Linear Ordinary Differential Equations. Koyejo S, Mohamed S, Agarwal A, Belgrave
D, Cho K, Oh A, et al., editors. 36th Conference on Neural Information Processing
Systems (NeurIPS 2022) . Red Hook, NY : Curran Associates, Inc.; 2022. (Advances
in Neural Information Processing Systems; vol. 35).'
conference:
end_date: 2022-12-09
location: New Orleans, La.; Online
name: 36th Conference on Neural Information Processing Systems (NeurIPS)
start_date: 2022-11-28
corporate_editor:
- 'Neural Information Processing Systems Foundation '
date_created: 2025-04-16T06:58:04Z
date_updated: 2025-06-26T13:37:53Z
department:
- _id: DEP5000
editor:
- first_name: S.
full_name: Koyejo, S.
last_name: Koyejo
- first_name: S.
full_name: Mohamed, S.
last_name: Mohamed
- first_name: A.
full_name: Agarwal, A.
last_name: Agarwal
- first_name: D.
full_name: Belgrave, D.
last_name: Belgrave
- first_name: K.
full_name: Cho, K.
last_name: Cho
- first_name: A.
full_name: Oh, A.
last_name: Oh
intvolume: ' 35'
keyword:
- SMITH NORMAL-FORM
- ALGORITHMS
- REDUCTION
language:
- iso: eng
page: 29386 - 29399
place: 'Red Hook, NY '
publication: '36th Conference on Neural Information Processing Systems (NeurIPS 2022) '
publication_identifier:
eisbn:
- 978-1-7138-7312-9
isbn:
- '978-1-7138-7108-8 '
issn:
- 1049-5258
publication_status: published
publisher: Curran Associates, Inc.
series_title: Advances in Neural Information Processing Systems
status: public
title: Constraining Gaussian Processes to Systems of Linear Ordinary Differential
Equations
type: conference_editor_article
user_id: '83781'
volume: 35
year: '2022'
...
---
_id: '7581'
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Tobias
full_name: Schmohl, Tobias
id: '71782'
last_name: Schmohl
orcid: https://orcid.org/0000-0002-7043-5582
- first_name: Alice
full_name: Watanabe, Alice
id: '76856'
last_name: Watanabe
- first_name: Stefan
full_name: Heiss, Stefan
id: '1031'
last_name: Heiss
- first_name: Jessica
full_name: Rubart, Jessica
id: '45672'
last_name: Rubart
citation:
ama: 'Lange-Hegermann M, Schmohl T, Watanabe A, Heiss S, Rubart J. AI-Based STEM
Education: Generating Individualized Exercises in Mathematics. Vol 10. Bologna:
Libreriauniversitaria.it; 2021:385-390.'
apa: 'Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J.
(2021). AI-based STEM education: Generating individualized exercises in mathematics.
New Perspectives in Science Education (Vol. 10, pp. 385–390). Bologna:
Libreriauniversitaria.it.'
bjps: 'Lange-Hegermann M et al. (2021) AI-Based STEM Education:
Generating Individualized Exercises in Mathematics. Bologna: Libreriauniversitaria.it.'
chicago: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Stefan Heiss,
and Jessica Rubart. AI-Based STEM Education: Generating Individualized Exercises
in Mathematics. New Perspectives in Science Education. Vol. 10. Bologna:
Libreriauniversitaria.it, 2021.'
chicago-de: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Stefan Heiss
und Jessica Rubart. 2021. AI-based STEM education: Generating individualized
exercises in mathematics. New Perspectives in Science Education. Bd.
10. Bologna: Libreriauniversitaria.it.'
din1505-2-1: 'Lange-Hegermann, Markus
; Schmohl, Tobias ; Watanabe,
Alice ; Heiss, Stefan ; Rubart, Jessica: AI-based STEM education:
Generating individualized exercises in mathematics. Bd. 10. Bologna : Libreriauniversitaria.it,
2021'
havard: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, J. Rubart, AI-based
STEM education: Generating individualized exercises in mathematics, Libreriauniversitaria.it,
Bologna, 2021.'
ieee: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, and J. Rubart, AI-based
STEM education: Generating individualized exercises in mathematics, vol. 10.
Bologna: Libreriauniversitaria.it, 2021, pp. 385–390.'
mla: 'Lange-Hegermann, Markus, et al. “AI-Based STEM Education: Generating Individualized
Exercises in Mathematics.” New Perspectives in Science Education, vol.
10, Libreriauniversitaria.it, 2021, pp. 385–90.'
short: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, J. Rubart, AI-Based
STEM Education: Generating Individualized Exercises in Mathematics, Libreriauniversitaria.it,
Bologna, 2021.'
ufg: 'Lange-Hegermann, Markus et. al. (2021): AI-based STEM education: Generating
individualized exercises in mathematics (= 10), Bologna.'
van: 'Lange-Hegermann M, Schmohl T, Watanabe A, Heiss S, Rubart J. AI-based STEM
education: Generating individualized exercises in mathematics. Vol. 10, New Perspectives
in Science Education. Bologna: Libreriauniversitaria.it; 2021.'
conference:
end_date: 2021-03-19
location: Florenz
name: New Perspectives in Science Education
start_date: 2021-03-18
date_created: 2022-04-14T10:59:50Z
date_updated: 2023-03-15T13:50:10Z
department:
- _id: DEP2000
- _id: DEP1200
intvolume: ' 10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://conference.pixel-online.net/NPSE/files/npse/ed0010/FP/6790-STEM4992-FP-NPSE10.pdf
oa: '1'
page: 385-390
place: Bologna
publication: New Perspectives in Science Education
publication_status: published
publisher: Libreriauniversitaria.it
quality_controlled: '1'
status: public
title: 'AI-based STEM education: Generating individualized exercises in mathematics'
type: conference_editor_article
user_id: '79260'
volume: 10
year: 2021
...
---
_id: '5620'
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Tobias
full_name: Schmohl, Tobias
id: '71782'
last_name: Schmohl
orcid: https://orcid.org/0000-0002-7043-5582
- first_name: Alice
full_name: Watanabe, Alice
id: '76856'
last_name: Watanabe
- first_name: Stefan
full_name: Heiss, Stefan
id: '1031'
last_name: Heiss
- first_name: Jessica
full_name: Rubart, Jessica
id: '45672'
last_name: Rubart
citation:
ama: 'Lange-Hegermann M, Schmohl T, Watanabe A, Heiss S, Rubart J. AI-Based Stem
Education: Generating Individualized Exercises in Mathematics. Bologna: Libreriauniversitaria.it;
2021:385-390. doi:10.26352/F318_2384-9509'
apa: 'Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J.
(2021). AI-Based Stem Education: Generating Individualized Exercises in Mathematics.
New Perspectives in Science Education (pp. 385–390). Bologna: Libreriauniversitaria.it.
https://doi.org/10.26352/F318_2384-9509'
bjps: 'Lange-Hegermann M et al. (2021) AI-Based Stem Education:
Generating Individualized Exercises in Mathematics. Bologna: Libreriauniversitaria.it.'
chicago: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Stefan Heiss,
and Jessica Rubart. AI-Based Stem Education: Generating Individualized Exercises
in Mathematics. New Perspectives in Science Education. Filodiritto
Editore – 10th International Conference New Perspectives in Science Education.
Bologna: Libreriauniversitaria.it, 2021. https://doi.org/10.26352/F318_2384-9509.'
chicago-de: 'Lange-Hegermann, Markus, Tobias Schmohl, Alice Watanabe, Stefan Heiss
und Jessica Rubart. 2021. AI-Based Stem Education: Generating Individualized
Exercises in Mathematics. New Perspectives in Science Education. Filodiritto
Editore – 10th International Conference New Perspectives in Science Education.
Bologna: Libreriauniversitaria.it. doi:10.26352/F318_2384-9509,
.'
din1505-2-1: 'Lange-Hegermann, Markus
; Schmohl, Tobias ; Watanabe,
Alice ; Heiss, Stefan ; Rubart, Jessica: AI-Based Stem Education:
Generating Individualized Exercises in Mathematics, Filodiritto Editore
– 10th International Conference New Perspectives in Science Education. Bologna :
Libreriauniversitaria.it, 2021'
havard: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, J. Rubart, AI-Based
Stem Education: Generating Individualized Exercises in Mathematics, Libreriauniversitaria.it,
Bologna, 2021.'
ieee: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, and J. Rubart, AI-Based
Stem Education: Generating Individualized Exercises in Mathematics. Bologna:
Libreriauniversitaria.it, 2021, pp. 385–390.'
mla: 'Lange-Hegermann, Markus, et al. “AI-Based Stem Education: Generating Individualized
Exercises in Mathematics.” New Perspectives in Science Education, Libreriauniversitaria.it,
2021, pp. 385–90, doi:10.26352/F318_2384-9509.'
short: 'M. Lange-Hegermann, T. Schmohl, A. Watanabe, S. Heiss, J. Rubart, AI-Based
Stem Education: Generating Individualized Exercises in Mathematics, Libreriauniversitaria.it,
Bologna, 2021.'
ufg: 'Lange-Hegermann, Markus et. al. (2021): AI-Based Stem Education: Generating
Individualized Exercises in Mathematics (=Filodiritto Editore – 10th International
Conference New Perspectives in Science Education), Bologna.'
van: 'Lange-Hegermann M, Schmohl T, Watanabe A, Heiss S, Rubart J. AI-Based Stem
Education: Generating Individualized Exercises in Mathematics. New Perspectives
in Science Education. Bologna: Libreriauniversitaria.it; 2021. (Filodiritto Editore
– 10th International Conference New Perspectives in Science Education).'
conference:
end_date: 2021-03-19
location: Virtual Event
name: NEW PERSPECTIVES IN SCIENCE EDUCATION 10th Edition
start_date: 2021-03-18
date_created: 2021-04-19T13:32:10Z
date_updated: 2023-03-15T13:50:00Z
department:
- _id: DEP5000
- _id: DEP5023
doi: 10.26352/F318_2384-9509
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://conference.pixel-online.net/NPSE/files/npse/ed0010/FP/6790-STEM4992-FP-NPSE10.pdf
oa: '1'
page: 385 - 390
place: Bologna
publication: New Perspectives in Science Education
publication_identifier:
isbn:
- 979-12-80225-14-6
publication_status: published
publisher: Libreriauniversitaria.it
quality_controlled: '1'
series_title: Filodiritto Editore – 10th International Conference New Perspectives
in Science Education
status: public
title: 'AI-Based Stem Education: Generating Individualized Exercises in Mathematics'
type: conference_editor_article
user_id: '79260'
year: 2021
...
---
_id: '12786'
abstract:
- lang: eng
text: 'One goal in Bayesian machine learning is to encode prior knowledge into prior
distributions, to model data efficiently. We consider prior knowledge from systems
of linear partial differential equations together with their boundary conditions.
We construct multi-output Gaussian process priors with realizations in the solution
set of such systems, in particular only such solutions can be represented by Gaussian
process regression. The construction is fully algorithmic via Grobner bases and
it does not employ any approximation. It builds these priors combining two parametrizations
via a pullback: the first parametrizes the solutions for the system of differential
equations and the second parametrizes all functions adhering to the boundary conditions.'
author:
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
citation:
ama: Lange-Hegermann M. Linearly Constrained Gaussian Processes with Boundary
Conditions. Vol 130. (Banerjee A, Fukumizu K, eds.). MLResearchPress ; 2021.
apa: Lange-Hegermann, M. (2021). Linearly Constrained Gaussian Processes with Boundary
Conditions. In A. Banerjee & K. Fukumizu (Eds.), 24th International Conference
on Artificial Intelligence and Statistics (AISTATS) (Vol. 130). MLResearchPress
.
bjps: Lange-Hegermann M (2021) Linearly Constrained Gaussian Processes
with Boundary Conditions, Banerjee A and Fukumizu K (eds). MLResearchPress
.
chicago: 'Lange-Hegermann, Markus. Linearly Constrained Gaussian Processes with
Boundary Conditions. Edited by A. Banerjee and K. Fukumizu. 24th International
Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 130.
Proceedings of Machine Learning Research : PMLR . MLResearchPress , 2021.'
chicago-de: 'Lange-Hegermann, Markus. 2021. Linearly Constrained Gaussian Processes
with Boundary Conditions. Hg. von A. Banerjee und K. Fukumizu. 24th International
Conference on Artificial Intelligence and Statistics (AISTATS). Bd. 130. Proceedings
of machine learning research : PMLR . MLResearchPress .'
din1505-2-1: 'Lange-Hegermann, Markus
; Banerjee, A. ; Fukumizu,
K. (Hrsg.): Linearly Constrained Gaussian Processes with Boundary Conditions,
Proceedings of machine learning research : PMLR . Bd. 130 : MLResearchPress
, 2021'
havard: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, MLResearchPress , 2021.
ieee: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, vol. 130. MLResearchPress , 2021.
mla: Lange-Hegermann, Markus. “Linearly Constrained Gaussian Processes with Boundary
Conditions.” 24th International Conference on Artificial Intelligence and Statistics
(AISTATS), edited by A. Banerjee and K. Fukumizu, vol. 130, MLResearchPress
, 2021.
short: M. Lange-Hegermann, Linearly Constrained Gaussian Processes with Boundary
Conditions, MLResearchPress , 2021.
ufg: 'Lange-Hegermann, Markus: Linearly Constrained Gaussian Processes with
Boundary Conditions, Bd. 130, hg. von Banerjee, A./Fukumizu, K., o. O. 2021 (Proceedings
of machine learning research : PMLR ).'
van: 'Lange-Hegermann M. Linearly Constrained Gaussian Processes with Boundary Conditions.
Banerjee A, Fukumizu K, editors. 24th International Conference on Artificial Intelligence
and Statistics (AISTATS). MLResearchPress ; 2021. (Proceedings of machine learning
research : PMLR ; vol. 130).'
conference:
end_date: 2021-04-15
location: Virtual
name: 24th International Conference on Artificial Intelligence and Statistics (AISTATS)
start_date: 2021-04-13
date_created: 2025-04-14T13:58:16Z
date_updated: 2025-06-26T13:42:36Z
department:
- _id: DEP5000
- _id: DEP5023
editor:
- first_name: A.
full_name: Banerjee, A.
last_name: Banerjee
- first_name: K.
full_name: Fukumizu, K.
last_name: Fukumizu
intvolume: ' 130'
keyword:
- FUNCTIONAL REGRESSION
- PREDICTION
- ALGORITHMS
- COMPLEXITY
- MODELS
language:
- iso: eng
publication: 24th International Conference on Artificial Intelligence and Statistics
(AISTATS)
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: 'MLResearchPress '
quality_controlled: '1'
series_title: 'Proceedings of machine learning research : PMLR '
status: public
title: Linearly Constrained Gaussian Processes with Boundary Conditions
type: conference_editor_article
user_id: '83781'
volume: 130
year: '2021'
...
---
_id: '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'
...