Výsledky vyhledávání - "Klaeger, Tilman"

Conference

Selbstlernende Bedienerassistenzsysteme in Verarbeitungsmaschinen

Autor: Schult, Andre, Klaeger, Tilman, Carsch, Sebastian, Oehm, Lukas

Publikováno v: VVD 2018 Verarbeitungsmaschinen und Verpackungstechnik - Verarbeitung & Verpackung 4.0 : 9. wissenschaftliche Fachtagung am 15./16. März in Dresden/Radebeul

Die Effizienz von Verarbeitungs- und Verpackungsmaschinen hängt ganz wesentlich vom Erfahrungs- und Prozesswissen der Bediener ab. Unerfahrenen Bedienern ist nur selten möglich, Störungen im Prozess mit einer fundierten Ursachenanalyse nachhaltig

Externí odkaz: https://tud.qucosa.de/id/qucosa%3A31014
https://tud.qucosa.de/api/qucosa%3A31014/attachment/ATT-0/

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Conference

Grundlagen des maschinellen Lernens: Was wird gemacht? Wie wird es gemacht? Ist das wirklich Lernen?

Autor: Klaeger, Tilman

Externí odkaz: https://slub.qucosa.de/id/qucosa%3A16500
https://slub.qucosa.de/api/qucosa%3A16500/attachment/ATT-0/

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Report

Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept

Autor: Schroth, Moritz, Hake, Felix, Merker, Konstantin, Becher, Alexander, Klaeger, Tilman, Huesmann, Robin, Eichhorn, Detlef, Oehm, Lukas

Publikováno v: EnviroInfo 2022, Gesellschaft f\"ur Informatik e.V

Nowadays cross-industry ranging challenges include the reduction of greenhouse gas emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy co

Externí odkaz: http://arxiv.org/abs/2206.11581

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Report

FastIoT -- A framework and holistic approach for rapid development of IIoT systems

Autor: Klaeger, Tilman, Merker, Konstantin

While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapi

Externí odkaz: http://arxiv.org/abs/2201.13243

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Report

Data science on industrial data -- Today's challenges in brown field applications

Autor: Klaeger, Tilman, Gottschall, Sebastian, Oehm, Lukas

Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the applicat

Externí odkaz: http://arxiv.org/abs/2006.05757

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Report

Using anomaly detection to support classification of fast running (packaging) processes

Autor: Klaeger, Tilman, Schult, Andre, Oehm, Lukas

In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumv

Externí odkaz: http://arxiv.org/abs/1906.02473

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