Publications

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Referierte Publikationen
Konferenzbeiträge (referiert) [3]
[Ait23]	Ait Alla, A.; Kreutz, M.; Theß, M.; Lütjen, M.; Freitag, M.: Deploying a self-learning AI methodology to trigger battery-powered stock-level sensors of an eKanban system. In: Procedia CIRP 120(2023). Proc. of CIRP CMS 2023. Elsevier, Amsterdam, NL, 2023, pp. 207-212, DOI 10.1016/j.procir.2023.08.037 [ BibTeX \| DOI ]

[Kre23a]	Kreutz, M.; Ait Alla, A.; Lütjen, M.; Freitag, M.: Deep Learning based monocular fill level detection for an eKanban system. In: IFAC-PapersOnLine 56(2023)2. Proc. of IFAC World Congress 2023, Elsevier, Amsterdam, 2023, pp. 10321-10326, DOI 10.1016/j.ifacol.2023.10.990 [ BibTeX \| DOI ]

[Kre21]	Kreutz, M.; Ait Alla, A.; Lütjen, M.; Freitag, M.: Autonomous, low-cost sensor module for fill level measurement for a self-learning electronic Kanban system. In: IFAC PapersOnLine 54(2021)1. Proc. of IFAC INCOM 2021, Elsevier, Amsterdam, 2021, pp. 623-628, DOI 10.1016/j.ifacol.2021.08.173 [ BibTeX \| DOI ]


Nicht-referierte Publikationen
Konferenzbeiträge (nicht referiert) [1]
[Kre21b]	Kreutz, M.; Ait Alla, A.; Lütjen, M.; Freitag, M.: Energieeffizientes eKanban-System mit autonomen Sensormodulen zur Füllstandsmessung und Reinforcement Learning zur Messintervalanpassung. In: Logistics Journal. Tagungsband zum 17. Fallkolloquium der WGTL, WGTL, Rostock-Warnemünde, 2021, S. 106-115, DOI 10.2195/lj_Proc_kreutz_de_202112_01 [ BibTeX \| DOI ]

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