Lukas Furtner
Research Assistant
NameMr M.Sc. Lukas Furtner
Hybrid Modeling, Simulation & Opimization Group
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Process Systems Engineering Group
Process Systems Engineering Group
Visiting address:
Merkelbau Helmholtzstraße 14
01069 Dresden
Visiting address:
Barkhausen-Bau, BAR/E30 Helmholtzstr. 18
01069 Dresden
Research interests
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Hybrid Modelling
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Soft Sensoring
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Sensor Placement
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Fouling
Short biography
Period | Activity |
Seit 2022 |
Doctoral Researcher, TU Dresden - Chair of Process Control Systems & Process Systems Engineering Group |
2021-2022 |
Master Thesis, R&D at MAN Truck & Bus SE |
2019-2022 | Master's course Chemical Engineering, TU Munich |
2018-2019 |
Industrial Intern, Automation department ZETA GmbH |
2015-2019 |
Bachelor's course Chemical Engineering, TU Munich |
Publications
2024
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Self-Evaluation of Trajectory Predictors for Autonomous Driving , 29 Feb 2024, In: Electronics. 13, 5, p. 1-16, 16 p., 946Electronic (full-text) versionResearch output: Contribution to journal > Research article
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An Uncertainty Analysis Based Approach to Sensor Selection in Chemical Processes , 2024, 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation, ETFA 2024. Facchinetti, T., Cenedese, A., Bello, L. L., Vitturi, S., Sauter, T. & Tramarin, F. (eds.). Institute of Electrical and Electronics Engineers Inc., 4 p.Electronic (full-text) versionResearch output: Contribution to book/conference proceedings/anthology/report > Conference contribution
2023
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Über die Abhängigkeit der Güte hybrider semi-parametrischer Softsensoren von der Verteilung wissensbasierter und datengetriebener Modellanteile , 20 Nov 2023Research output: Contribution to conferences > Poster
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Standards for Information Models Considering Knowledge Distribution in Modular Plants , 2023, 2023 IEEE 21st International Conference on Industrial Informatics, INDIN 2023. Dorksen, H., Scanzio, S., Jasperneite, J., Wisniewski, L., Man, K. F., Sauter, T., Seno, L., Trsek, H. & Vyatkin, V. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 1-7Electronic (full-text) versionResearch output: Contribution to book/conference proceedings/anthology/report > Conference contribution
2020
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Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo , 7 Dec 2020, In: Journal of manufacturing systems : an official journal of the Society of Manufacturing Engineers (SME). 61, p. 799 - 807Electronic (full-text) versionResearch output: Contribution to journal > Research article