Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Milot Gashi"'
Publikováno v:
Applied Sciences, Vol 13, Iss 5, p 3088 (2023)
Taking the multi-component perspective in Predictive Maintenance (PdM) is one promising approach to improve prediction quality. Therefore, detection and modeling of interdependencies within systems are important, especially as systems become more com
Externí odkaz:
https://doaj.org/article/c34fdb64e344457ba4a4a475e66b6a4e
Autor:
Muaaz Abdul Hadi, Daniel Kraus, Amer Kajmakovic, Josef Suschnigg, Ouijdane Guiza, Milot Gashi, Georgios Sopidis, Matej Vukovic, Katarina Milenkovic, Michael Haslgruebler, Markus Brillinger, Konrad Diwold
Publikováno v:
Applied Sciences, Vol 12, Iss 17, p 8696 (2022)
Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be
Externí odkaz:
https://doaj.org/article/1ef71244732a4413ac0f8b4d297d1441
Autor:
Milot Gashi, Heimo Gursch, Hannes Hinterbichler, Stefan Pichler, Stefanie Lindstaedt, Stefan Thalmann
Publikováno v:
Sensors, Vol 22, Iss 8, p 2837 (2022)
Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of
Externí odkaz:
https://doaj.org/article/3fac0c24ebe141b896df7d7f383ca560
Publikováno v:
2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f1f8bfe516b13b1cb8df1a97662f59a
https://doi.org/10.1109/ipsn54338.2022.00059
https://doi.org/10.1109/ipsn54338.2022.00059
Publikováno v:
2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbe043e385164d1fb8c97ace2b86acca
https://doi.org/10.1109/ipsn54338.2022.00062
https://doi.org/10.1109/ipsn54338.2022.00062
Publikováno v:
Proceedings of the Annual Hawaii International Conference on System Sciences.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de8bd8c6c9749fe9bb512d54cff44607
https://doi.org/10.24251/hicss.2022.211
https://doi.org/10.24251/hicss.2022.211
Autor:
Milot Gashi, Matej Vuković, Nikolina Jekic, Stefan Thalmann, Andreas Holzinger, Claire Jean-Quartier, Fleur Jeanquartier
Publikováno v:
BioMedInformatics; Volume 2; Issue 1; Pages: 139-158
This study aims to reflect on a list of libraries providing decision support to AI models. The goal is to assist in finding suitable libraries that support visual explainability and interpretability of the output of their AI model. Especially in sens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::088be2bcde27ceec4571af55ef317c8e
http://openlib.tugraz.at/61fa37513214a
http://openlib.tugraz.at/61fa37513214a
Publikováno v:
HICSS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ccffbb679b2b48342d772e1a3f97f96
https://doi.org/10.24251/hicss.2021.177
https://doi.org/10.24251/hicss.2021.177
Autor:
Stefan Thalmann, Milot Gashi
Publikováno v:
Information Systems ISBN: 9783030443214
EMCIS
EMCIS
One of the most prominent use cases of a digitized industry is predictive maintenance. Advances in sensor and data technology enable continuous condition monitoring, thus, extending the opportunities for predictive maintenance. However, so far, most
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aab53cab78b8877ba97a6d6b59d5b5bf
https://doi.org/10.1007/978-3-030-44322-1_3
https://doi.org/10.1007/978-3-030-44322-1_3
Publikováno v:
Computers in Industry. 132:103505
End-of-line (EoL) testing is performed to determine product quality by ensuring reliable performance. Even though low-quality products may pass EoL testing, they have a high probability of failure over time. Analyzing product usage data can help to i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3693d47ef2af8737e94a1edfb091eb04
https://doi.org/10.1016/j.compind.2021.103505
https://doi.org/10.1016/j.compind.2021.103505