Linked data as integrating technology for industrial data M Graube, J Pfeffer, J Ziegler, L Urbas International Journal of Distributed Systems and Technologies (IJDST) 3 (3 …, 2012 | 93 | 2012 |
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 | 92 | 2015 |
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 | 77 | 2020 |
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 | 76 | 2014 |
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 | 68 | 2003 |
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 | 67 | 2017 |
R43ples: Revisions for triples M Graube, S Hensel, L Urbas Proc. of LDQ, 2014 | 61* | 2014 |
Big bang–big crunch learning method for fuzzy cognitive maps E Yesil, L Urbas International Journal of Computer and Information Engineering 4 (11), 1756-1765, 2010 | 61 | 2010 |
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 | 54 | 2014 |
High-level behavior representation languages revisited FE Ritter, SR Haynes, M Cohen, A Howes, B John, B Best, C Lebiere, ... Proceedings of ICCM-2006-Seventh International Conference on Cognitive …, 2006 | 54 | 2006 |
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, 2023 | 52 | 2023 |
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 | 52 | 2017 |
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 | 50 | 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 | 45 | 2018 |
Process Control Systems Engineering L Urbas Oldenbourg Industrieverlag, 2012 | 43 | 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 | 38 | 2012 |
Information modeling for middleware in automation W Mahnke, A Gössling, M Graube, L Urbas ETFA2011, 1-7, 2011 | 38 | 2011 |
The digital twin–your ingenious companion for process engineering and smart production A Bamberg, L Urbas, S Broecker, M Bortz, N Kockmann Chemical Engineering & Technology 44 (6), 954-961, 2021 | 34 | 2021 |
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 | 31 | 2021 |
Co-simulation with OPC UA S Hensel, M Graube, L Urbas, T Heinzerling, M Oppelt 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), 20-25, 2016 | 31 | 2016 |