| Technical evaluation of the flexibility of water electrolysis systems to increase energy flexibility: A review H Lange, A Klose, W Lippmann, L Urbas International Journal of Hydrogen Energy 48 (42), 15771-15783, 2023 | 201 | 2023 |
| A secure hybrid dynamic-state estimation approach for power systems under false data injection attacks Z Kazemi, AA Safavi, F Naseri, L Urbas, P Setoodeh IEEE Transactions on Industrial Informatics 16 (12), 7275-7286, 2020 | 106 | 2020 |
| Linked data as integrating technology for industrial data M Graube, J Pfeffer, J Ziegler, L Urbas 2011 14th International Conference on Network-Based Information Systems, 162-167, 2011 | 98 | 2011 |
| Open source as enabler for OPC UA in industrial automation F Palm, S Grüner, J Pfrommer, M Graube, L Urbas 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation …, 2015 | 97 | 2015 |
| Integrated virtual commissioning an essential activity in the automation engineering process: From virtual commissioning to simulation supported engineering M Oppelt, L Urbas IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society …, 2014 | 91 | 2014 |
| Integration of modular process units into process control systems J Ladiges, A Fay, T Holm, U Hempen, L Urbas, M Obst, T Albers IEEE Transactions on Industry Applications 54 (2), 1870-1880, 2017 | 84 | 2017 |
| Individual differences in navigation between sharable content objects—an evaluation study of a learning module prototype B Gauss, L Urbas British Journal of Educational Technology 34 (4), 499-509, 2003 | 74 | 2003 |
| R43ples: Revisions for triples M Graube, S Hensel, L Urbas Proc. of LDQ, 2014 | 72* | 2014 |
| Automatic model generation for virtual commissioning based on plant engineering data O Mathias, W Gerrit, D Oliver, L Benjamin, S Markus, U Leon IFAC Proceedings Volumes 47 (3), 11635-11640, 2014 | 70 | 2014 |
| Information models in OPC UA and their advantages and disadvantages M Graube, S Hensel, C Iatrou, L Urbas 2017 22nd IEEE International Conference on Emerging Technologies and Factory …, 2017 | 63 | 2017 |
| Big bang-big crunch learning method for fuzzy cognitive maps E Yesil, L Urbas World Acad. Sci. Eng. Technol 71, 815-8124, 2010 | 63 | 2010 |
| Orchestration requirements for modular process plants in chemical and pharmaceutical industries A Klose, S Merkelbach, A Menschner, S Hensel, S Heinze, L Bittorf, ... Chemical Engineering & Technology 42 (11), 2282-2291, 2019 | 60 | 2019 |
| Two-stage learning based fuzzy cognitive maps reduction approach MF Hatwágner, E Yesil, MF Dodurka, E Papageorgiou, L Urbas, LT Kóczy IEEE Transactions on Fuzzy Systems 26 (5), 2938-2952, 2018 | 54 | 2018 |
| High-level behavior representation languages revisited FE Ritter, SR Haynes, M Cohen, A Howes, B John, B Best, C Lebiere, ... | 54 | 2006 |
| A review on machine learning approaches for microalgae cultivation systems T Syed, F Krujatz, Y Ihadjadene, G Mühlstädt, H Hamedi, J Mädler, ... Computers in biology and medicine 172, 108248, 2024 | 51 | 2024 |
| The digital twin–your ingenious companion for process engineering and smart production A Bamberg, L Urbas, S Bröcker, M Bortz, N Kockmann Chemical Engineering & Technology 44 (6), 954-961, 2021 | 48 | 2021 |
| Process Control Systems Engineering L Urbas Oldenbourg Industrieverlag, 2012 | 46 | 2012 |
| Beyond app-chaining: Mobile app orchestration for efficient model driven software generation J Ziegler, M Graube, J Pfeffer, L Urbas Proceedings of 2012 IEEE 17th International Conference on Emerging …, 2012 | 40 | 2012 |
| The machine learning life cycle in chemical operations–status and open challenges M Gaertler, V Khaydarov, B Klöpper, L Urbas Chemie Ingenieur Technik 93 (12), 2063-2080, 2021 | 37 | 2021 |
| Information modeling for middleware in automation W Mahnke, A Gössling, M Graube, L Urbas ETFA2011, 1-7, 2011 | 37 | 2011 |
Markus GraubeTechnische Universität Dresden, Professur für ProzessleittechnikVerified email at tu-dresden.de
