Publications SRS
In accordance with the well known and accepted rule in the engineering field, the author sequence of all publications (1989 to present) is related to the FLAE system (first-last-author-emphasis system).
2021 |
Journal- and book contributions |
Do, M. H.; Söffker, D.: Wind Turbine Disturbance Accommodating Control Using H infinity Optimization. Wind Energy, June, 2021.[PDF][LINK]
Do, M. H.; Söffker, D.: State-of-the-Art in Integrated Prognostics and Health Management Control for Utility-Scale Wind Turbines. Renewable and Sustainable Energy Reviews, 2021.[PDF][LINK]
Spiller, M.; Söffker, D.: Stator-Rotor Contact Force Estimation of Rotating Machine Automation. Automation, Vol. 2, No. 3, 2021, pp. 83-97.[PDF][LINK]
Referenzierbare und mehrfach referierte Konferenzbeiträge |
Bakhshande, F.; Söffker, D.: What makes the difference? State-of-the-art and new perspectives on precision and task-oriented drones for agricultural use. AgEng2020 Conference, Portugal, 2021, accepted.
David, R.; Rothe, S.; Söffker, D.: Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach. 2021 IEEE International Conference on Intelligent Transportation ( ITSC), Indianapolis, USA, 2021.[PDF][LINK]
He, C.; Lum, Y.; Lee, K.; Söffker, D.: Human reliability estimation based on fuzzy logic-modified CREAM approach. 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Tallinn, Estonia, 2021, pp. 45-50.[PDF][LINK]
He, C.; Söffker, D.: Human reliability analysis in situated driving context considering human experience using a fuzzy-based clustering approach. 2nd IEEE International Conference on Human-Machine Systems, Magdeburg, Germany, 2021, pp. 8-10.[PDF][LINK]
Owino, L., Söffker, D.: Modeling and prediction of corn growth during vegetative phase. AgEng 2021 conference, Évora, 2021, pp. 426-432.[LINK]
Owino, L., Söffker, D.: State machine-based model for estimation and prediction of above ground biomass in corn during vegetative stage. AgEng 2021 conference, Évora, 2021, pp. 433-440.[LINK]
Owino, L.; Söffker, D.: Predictive precision irrigation-based control of maize leaf growth under laboratory conditions. Gesellschaft fur Pflanzenbauwissenschaften e.V, 63. Jahrestagung, Rostock, September 28-30, 2021, pp. 261-262.[LINK]
Spiller, M.; Söffker, D.: Robust Control of Relative Degree Two Systems Subject to Output Constraints with Time-Varying Bounds. 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, 2021, pp. 5402-5409.[PDF][LINK]
Wei, X.; Demmerling, A. L.; Söffker, D.: Metalworking Fluid Classification Based on Acoustic Emission Signals and Convolutional Neural Network. European Conference of the PHM Society, Vol. 6, No. 1, 2021, pp. 471-476.[PDF][LINK]
Weitere Konferenzbeiträge |
Vorträge/Sonstiges |
Bejaoui, A.; Bakhshande, F; Söffker, D.: Modeling and formalization of Human operator behavior for mobile systems and inland vessels using the Situation-Operator-Modeling approach. Autonomous Inland and Short Sea Shipping, Duisburg, 2021.[LINK]
Boschmann, W.; Söffker, D.: Increasing detection performance using redundant object detection approaches. Autonomous Inland and Short Sea Shipping, Duisburg, 2021.
Donandt, K.; Söffker, D.: Deep Learning-based Vessels Driving Behavior Prediction in Inland Navigation. Autonomous Inland and Short Sea Shipping Conference (AISS) 2021, Duisburg-Essen, Nov 2-3, 2021.[LINK]
Kipchirchir, E.; Do, M. H.; Njiri, J. G.; Söffker, D.: Adaptive robust observer-based structural load mitigation of wind turbines. Wind Energy Science Conference (WESC) 25-28 May 2021, Hannover, May 25-28, 2021.[LINK]
Kipchirchir, E.; Do, M. H.; Njiri, J. G.; Söffker, D.: Prognostics-based adaptive control strategy for lifetime control of wind turbines. Wind Energy Science Conference (WESC) 25-28 May 2021, Hannover, May 25-28, 2021.[PDF][LINK]
Mathew, J.; Rajput, R. R.; Söffker, D.: Optimizing hybrid powertrains using MATLAB & Simulink. MATLAB Day at University of Duisburg-Essen, Mathworks, invited talk, Apr 21, 2021.
Thind, N.S.; Spiller,M.; Söffker, D.: Data-driven prediction of inland vessel trajectories. Autonomous Inland and Short Sea Shipping Conference (AISS), Duisburg, Okt 15, 2021.