Ali, A.M.; Ghanbar, A.; Söffker, D.: Optimal control of multi-source electric vehicles in real-time using Advisory Dynamic Programming. IEEE, Transactions on Vehicular Technology, Vol. 68, No. 11, 2019, pp. 10394-10405.[LINK]

Ali, A.M.; Shivapurkar, R.; Söffker, D.: Optimal situation-based power management and application to state predictive models for multi-source electric vehicles. IEEE Transactions on Vehicular Technology, Vol. 68, No. 12, 2019, pp. 11473-11482.[LINK]

Deng, Q.; Wang, J.; Hillebrand, K.; Benjamin, C.R.; Söffker, D.: Prediction performance of lane changing behaviors: a study of combining environmental and eye-tracking data in a driving simulator. IEEE Transactions on Intelligent Transportation Systems (ITS), Vol. 21, No.8, 2019, pp. 3561-3570.[LINK]

Hägele, G.; Söffker, D.: Risk Areas Determination for Autonomous- and Semi-autonomous Aerial Systems Considering Run-time Technical Reliability Assessment. Journal of Intelligent & Robotic Systems, 2019.[LINK]

Kögler, F.; Söffker, D.: Explorative Frequency Analysis of Leaf Temperature Behavior of Corn (zea mays) at Water Deficit. Plants, MDPI, Vol. 8, No. 4, 2019, pp. 105.[LINK]

Labsuch, M.; Cunha, A.P.A.; Wirtz, S.F.; Reichenberger, S.; Cleve, E.; Söffker, D.; Barcikowski, S.: Acoustic emission control avoids fluence shifts caused by target runaway during laser synthesis of colloids. Applied Surface Science, 2019, pp. 887-895.

Owino, L.; Hilkens, M.; Kögler, F.; Söffker, D.: Automated Measurement and Control of Germination Paper Water Content. Sensors, MDPI, Basel, Vol. 19, No 10. Special Issue: Smart Sensing Technologies for Agriculture, 2019, pp. 2232.[PDF][LINK]

Rothe, S.; Kudszus, B.; Söffker, D.: Does classifier fusion improve the overall performance? Numerical analysis of data and fusion method characteristics influencing classifier fusion performance. Entropy, Vol. 21, No. 9, 2019, pp. 866.[LINK]

Sarkheyli-Hägele, A.; Söffker, D.: Integration of Case-Based Reasoning and Fuzzy Approaches for Real-time Applications in Dynamic Environments: Current Status and Future Directions. Artificial Intelligence Review, Springer Netherlands, 2019, pp. 1-32.[PDF][LINK]

Wang, J.; Söffker, D.: Bridging gaps among human, assisted, and automated driving with DVIs: a conceptional experimental study. IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 6, 2019, pp. 2096-2108.[LINK]

Xu, G.; Liu, M.; Jiang, Z.; Söffker, D.; Shen, W.: Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning. Sensors, Vol. 19, No. 5, 2019, pp. 1088.[PDF][LINK]

Ali, M.A.; Moulik, B.; Beganovic, N.; Söffker, D.: A state-of-health-oriented power management strategy for multi-source electric vehicles considering situation-based optimized solutions in real-time. Annual Conference of the Prognostics and Health Management (PHM) Society, Scottsdale, Arizona, USA, 2019.[LINK]

Ameyaw, D. A.; Deng, Q.; Söffker, D.: Probability of Detection (POD)-based metric for evaluation of Classifiers used in Driving Behavior Prediction. Proceedings of the Annual Conference of the PHM Society, 11(1) , Scottsdale, Arizona, USA, 2019.[LINK]

Beganovic, N.; Moulik, B.; Ali, A.M.; Söffker, D.: Lifetime model developement for integration in power management of HEVs by terms of minimizing fuel consumption and battery degradation. Annual Conference of the Prognostics and Health Management (PHM) Society, Scottsdale, Arizona, USA, 2019.[LINK]

Deng, Q.; Söffker, D.: Modeling and Prediction of Human Behaviors based on Driving Data using Multi-Layer HMMs. IEEE Transactions on Intelligent Transportation Systems Conference (ITSC 2019), Auckland, New Zealand, 2019, pp. 2014-2019.

Deng, Q.; Söffker, D.: Classifying Human Behaviors: Improving Training of Conventional Algorithms. IEEE Transactions on Intelligent Transportation Systems Conference (ITSC 2019), Auckland, New Zealand, 2019, pp. 1060-1065.

Do, M. H.; Söffker, D.: Robust observer-based load extenuation control for wind turbines. ASME 15th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC), Anaheim, CA, 2019.

Jihin, R.; Kögler, F.; Söffker, D. : Data Driven State Machine Model for Industry 4.0 Lifetime Modeling and Identification of Irrigation Control Parameters. GIoTS'19 : 3rd Global IoT Summit (2019), Aarhus, Denmark, 2019.[LINK]

Jihin, R.; Söffker, D.: Lifetime Prediction Considering Nonlinearity in Degradation Progression. ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE), Anaheim, CA, USA, 2019.[LINK]

Jihin, R.; Söffker, D.: Health State Assessment and Lifetime Prediction Based On Unsupervised State Estimation. ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE), Anaheim, CA, USA, 2019.[LINK]

Kahar, H.; Söffker, D.: On nonlinear dynamics and control of an inverted flexible pendulum system with chaos. ASME 2019 International Design Engineering Technical Conferences & Conference on Mechanical Vibration and Noise, California, USA, 2019, Vol. 8, pp. V008T10A034.[LINK]

Kahar, H.; Söffker, D.: Time-frequency energy analysis of chaotic mechanical system affected by additive impulses. Vibroengineering PROCEDIA for 38th International Vibroengineering (JVE) Conference, Rome, Italy, Vol.24, 2019, pp. 68-73.

Owino, L.; Söffker, D.: Development and test of predictive model for maize leaf appearance during vegetative phase. IFAC Agricontrol conference, Sydney, 2019, pp. 126-131.[LINK]

Pham, H. A.; Söffker, D.: Modified model-free adaptive control method applied to vibration control of an elastic crane. ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Anaheim, CA, USA, 2019, DETC2019-97654, V006T09A042.[LINK]

Söffker, D.; Kögler, F.; Owino, L.: Crop growth modeling- a new data driven approach. IFAC Agricontrol conference, Sydney, 2019, pp. 132-136.

Tanshi, F.; Dargahi Nobari, K.; Wang, J.; Söffker, D.: Design of Conditional Driving Automation Variables to Improve Takeover Performance. 10th IFAC Symposium on Intelligent Autonomous Vehicles, Gdansk, Poland, 2019, pp. 170-175.

Tanshi, F.; Söffker, D.: Modeling drivers takeover behavior depending on the criticality of driving situations and the complexity of secondary tasks. IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) 2019, Las Vegas, USA, 2019.[LINK]

Tanshi, F.; Söffker, D.: Modeling of takeover variables with respect to driver situation awareness and workload for intelligent driver assistance.  2019 IEEE Intelligent Vehicles Symposium (IV'19), Paris, France, 2019.[LINK]

Wirtz, S.F.; Bach, S.; Söffker, D.: Experimental results of acoustic emission attenuation due to wave propagation in composites. Annual Conference of the Prognostics and Health Management Society, Scottsdale, AZ, USA, 2019.[LINK]

Kahar, H.; Söffker, D.: Experimental study on an elastic mechanical system showing chaotic dynamical behavior. ECCOMAS Multibody Dynamics Conference 2019, Duisburg, Germany, 2019.

Owino, L.; Söffker, D.: Deficit irrigation-based control of leaf appearance in early vegetative stages of maize growth. Gesellschaft für Pflanzenbauwissenschaften e.V, Berlin, 62. Jahrestagung, 2019, pp. 63-64.[LINK]

Pham, H. A.; Söffker, D.: Model-Free Adaptive Control Method Applied to Vibration Reduction of a Flexible Crane as MIMO System. Proceedings in Applied Mathematics and Mechanics (PAMM) - Wiley Online, 2019.[PDF][LINK]

Deng, Q.; Söffker, D.: Multi-Level HMMs-based Cognitive modeling for Human Driving Intentions Recognition. 2019 KS Workshop, Duisburg, Germany, März 26-28, 2019.

He, C.; Söffker, D.: A modified CREAM approach to situated human driving context. 8. Interdisziplinärer Workshop Kognitive Systeme: Mensch, Teams, Systeme und Automaten, Duisburg, März 26-28, 2019.

Söffker, D.: From data-driven NDT of systemsto BIG DATA-based modeling. Graduate Seminar, Dept. Mechanical Engineering (Prof. G. Mauer), University of Nevada, Las Vegas, Apr 03, 2019.

Tanshi, F.; Söffker, D.: Understanding the Dependencies Of Human Takeover Behavior from Various Dimensions of Criticality. 8. Interdisziplinärer Workshop Kognitive Systeme, Duisburg, Germany, March 26-28, 2019.