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ORCIDDirk Söffker
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- affiliation: University of Duisburg-Essen, Germany
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2020 – today
- 2024
[j29]Edwin Kipchirchir
, M. Hung Do, Jackson G. Njiri, Dirk Söffker:
Adaptive Robust Observer-Based Control for Structural Load Mitigation and Speed Regulation in Commercial Wind Turbines. IEEE Access 12: 38335-38350 (2024)- [c83]Olena Shyshova, Pooja Gadhavi, Matthias Tenzer, Dirk Söffker:
Preparation Times: An Experimental-Based Discussion About Limits for Takeover in Highly Automated Systems. CogSIMA 2024: 71-78 - [c82]Soheil Salighe, Dirk Söffker:
On the PID-structured model-free adaptive control: a comparison of different approaches. ECC 2024: 1309-1314 - [c81]Kathrin Donandt, Dirk Söffker:
Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach. FUSION 2024: 1-8 - [c80]Olena Shyshova, Pooja Gadhavi, Matthias Tenzer, Foghor Tanshi, Dirk Söffker:
Takeover time: Requirements for highly automated inland vessels - First experimental-based results. ICHMS 2024: 1-7 - [c79]Alvin Immanuel Surjana, Elmar Ahle, Dirk Söffker:
Application of Event-based Cloud NMPC with Time Delay Compensation. ICPS 2024: 1-6 - [c78]Imene Benrabia, Dirk Söffker:
Energy management of residential buildings based on model predictive control across varied prediction horizons, price models, and storage configurations. SmartGridComm 2024: 123-128 - [i9]Edwin Kipchirchir, Jonathan Liebeton, Dirk Söffker:
A hybrid prognosis approach for robust lifetime control of commercial wind turbines. CoRR abs/2404.18593 (2024) - [i8]Dominik Polke, Tim Kösters, Elmar Ahle, Dirk Söffker:
Polynomial Chaos Expanded Gaussian Process. CoRR abs/2405.01052 (2024) - [i7]Kathrin Donandt, Dirk Söffker:
Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach. CoRR abs/2406.02344 (2024) - [i6]Kathrin Donandt, Dirk Söffker:
Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems. CoRR abs/2406.02767 (2024) - [i5]Kathrin Donandt, Karim Böttger, Dirk Söffker:
Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models. CoRR abs/2406.02770 (2024) - [i4]Kathrin Donandt, Karim Böttger, Dirk Söffker:
Improved context-sensitive transformer model for inland vessel trajectory prediction. CoRR abs/2406.02771 (2024) - [i3]Foghor Tanshi, Dirk Söffker:
A quantitative model of takeover request time budget for conditionally automated driving. CoRR abs/2408.15682 (2024) - [i2]Zahra Rastin, Dirk Söffker:
Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction. CoRR abs/2408.15722 (2024) - [i1]Foghor Tanshi, Dirk Söffker:
A Lane Change Assistance System Based on Prediction of Driver Intention. CoRR abs/2409.10551 (2024) - 2023
- [j28]Ahmed M. Ali, Bedatri Moulik, Dirk Söffker:
Intelligent Real-Time Power Management of Multi-Source HEVs Based on Driving State Recognition and Offline Optimization. IEEE Trans. Intell. Transp. Syst. 24(1): 247-257 (2023) - [c77]Dominik Polke, Alvin Surjana, Florian Diepers, Elmar Ahle, Dirk Söffker:
Development of a Modular Automation Framework for Data-Driven Modeling and Optimization of Coating Formulations. ETFA 2023: 1-8 - [c76]Kathrin Donandt, Karim Böttger, Dirk Söffker:
Improved Context-Sensitive Transformer Model for Inland Vessel Trajectory Prediction. ITSC 2023: 5903-5908 - [c75]Waldemar Boschmann, Dirk Söffker:
Quantification of Reliable Detection Range Using Lidar-Based Object Detection Approaches and Varying Process Parameters. SMC 2023: 2838-2843 - [c74]Kathrin Donandt, Dirk Söffker:
Spatial and Social Situation-Aware Transformer-Based Trajectory Prediction of Autonomous Systems. SMC 2023: 4488-4493 - 2022
- [j27]Daniel Adofo Ameyaw, Qi Deng, Dirk Söffker:
Evaluating Machine Learning-Based Classification Approaches: A New Method for Comparing Classifiers Applied to Human Driver Prediction Intentions. IEEE Access 10: 62429-62439 (2022) - [j26]Ruth David, Dirk Söffker:
A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition. IEEE Access 10: 122954-122964 (2022) - [j25]Qi Deng, Dirk Söffker:
A Review of HMM-Based Approaches of Driving Behaviors Recognition and Prediction. IEEE Trans. Intell. Veh. 7(1): 21-31 (2022) - [c73]Ruth David, Dirk Söffker:
Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition. CogSIMA 2022: 16-22 - [c72]Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, Dirk Söffker:
Mixed-Sensitivity Robust Disturbance Accommodating Control for Load Mitigation and Speed Regulation of Wind Turbines. ECC 2022: 1012-1017 - [c71]Florian Diepers, Dominik Polke, Elmar Ahle, Dirk Söffker:
Data-Driven Force Control of an Automated Scratch Test. ICCMA 2022: 94-99 - [c70]Abderahman Bejaoui, Dirk Söffker:
Supervision concept for situated human driving applied to inland shipping. ITSC 2022: 244-249 - [c69]Navreet S. Thind, Dirk Söffker:
Probabilistic ship behavior prediction using generic models. ITSC 2022: 250-255 - [c68]Waldemar Boschmann, Dirk Söffker:
Complementary and situation sensitive object detection, review performance, and performance dependencies of common approaches. ITSC 2022: 888-892 - [c67]Kathrin Donandt, Karim Böttger, Dirk Söffker:
Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models. ITSC 2022: 974-979 - 2021
- [c66]Mark Spiller, Dirk Söffker:
Robust Control of Relative Degree Two Systems Subject to Output Constraints with Time-Varying Bounds. CDC 2021: 5402-5409 - [c65]Chao He, Yuan Yao Lum, Kar Yen Lee, Dirk Söffker:
Human reliability estimation based on fuzzy logic-modified CREAM approach. CogSIMA 2021: 45-50 - [c64]Chao He, Dirk Söffker:
Human reliability analysis in situated driving context considering human experience using a fuzzy-based clustering approach. ICHMS 2021: 1-6 - [c63]Ruth David, Sandra Rothe, Dirk Söffker:
Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach. ITSC 2021: 3444-3449 - 2020
- [j24]Arezoo Sarkheyli-Hägele, Dirk Söffker:
Integration of case-based reasoning and fuzzy approaches for real-time applications in dynamic environments: current status and future directions. Artif. Intell. Rev. 53(3): 1943-1974 (2020) - [j23]Mark Spiller, Dirk Söffker:
On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter. Frontiers Appl. Math. Stat. 6: 585439 (2020) - [j22]Georg Hägele, Dirk Söffker:
Risk Areas Determination for Autonomous- and Semi-autonomous Aerial Systems Considering Run-Time Technical Reliability Assessment. J. Intell. Robotic Syst. 97(3): 511-529 (2020) - [j21]Qi Deng, Jiao Wang, Kevin Hillebrand, Christoper Ragenold Benjamin, Dirk Söffker:
Prediction Performance of Lane Changing Behaviors: A Study of Combining Environmental and Eye-Tracking Data in a Driving Simulator. IEEE Trans. Intell. Transp. Syst. 21(8): 3561-3570 (2020) - [c62]Qi Deng, Maryam Saleh, Foghor Tanshi, Dirk Söffker:
Online intention recognition applied to real simulated driving maneuvers. CogSIMA 2020: 1-6 - [c61]Chao He, Foghor Tanshi, Dirk Söffker:
Human online reliability estimation applied to real driving maneuvers. CogSIMA 2020: 149-154 - [c60]Hoang Anh Pham, Dirk Söffker:
Improved model-free adaptive control method using recursive least-squares estimation algorithm. ECC 2020: 47-52 - [c59]Mark Spiller, Dirk Söffker:
Automated Interceptor Guidance Based on Multiple Object Tracking. ECC 2020: 1763-1768 - [c58]M. Hung Do, Dirk Söffker:
Robust Structural Load Mitigation Control for Wind Turbines in Low Wind Speed Region. ECC 2020: 2074-2079 - [c57]Chao He, Dirk Söffker:
Establishing a modified CREAM approach for reliability evaluation. ICHMS 2020: 1-6
2010 – 2019
- 2019
- [j20]Sandra Rothe, Bastian Kudszus, Dirk Söffker:
Does Classifier Fusion Improve the Overall Performance? Numerical Analysis of Data and Fusion Method Characteristics Influencing Classifier Fusion Performance. Entropy 21(9): 866 (2019) - [j19]Gaowei Xu, Min Liu, Zhuofu Jiang, Dirk Söffker, Weiming Shen:
Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning. Sensors 19(5): 1088 (2019) - [j18]Lina Owino, Marvin Hilkens, Friederike Kögler, Dirk Söffker:
Automated Measurement and Control of Germination Paper Water Content. Sensors 19(10): 2232 (2019) - [j17]Jiao Wang, Dirk Söffker:
Bridging Gaps Among Human, Assisted, and Automated Driving With DVIs: A Conceptional Experimental Study. IEEE Trans. Intell. Transp. Syst. 20(6): 2096-2108 (2019) - [j16]Ahmed M. Ali, Ahmad Ghanbar, Dirk Söffker:
Optimal Control of Multi-Source Electric Vehicles in Real Time Using Advisory Dynamic Programming. IEEE Trans. Veh. Technol. 68(11): 10394-10405 (2019) - [j15]Ahmed M. Ali, Rushikesh Shivapurkar, Dirk Söffker:
Optimal Situation-Based Power Management and Application to State Predictive Models for Multi-Source Electric Vehicles. IEEE Trans. Veh. Technol. 68(12): 11473-11482 (2019) - [c56]Foghor Tanshi, Dirk Söffker:
Modeling Drivers' Takeover Behavior Depending on the Criticality of Driving Situations and the Complexity of Secondary Tasks. CogSIMA 2019: 67-73 - [c55]Rosmawati Jihin, Friederike Kögler, Dirk Söffker:
Data Driven State Machine Model for Industry 4.0 Lifetime Modeling and Identification of Irrigation Control Parameters. GIoTS 2019: 1-6 - [c54]Qi Deng, Dirk Söffker:
Classifying Human Behaviors: Improving Training of Conventional Algorithms. ITSC 2019: 1060-1065 - [c53]Qi Deng, Dirk Söffker:
Modeling and Prediction of Human Behaviors based on Driving Data using Multi-Layer HMMs. ITSC 2019: 2014-2019 - [c52]Foghor Tanshi, Dirk Söffker:
Modeling of takeover variables with respect to driver situation awareness and workload for intelligent driver assistance. IV 2019: 1667-1672 - 2018
- [j14]Arezoo Sarkheyli, Dirk Söffker:
Learning and representation of event-discrete situations for individualized situation recognition using fuzzy Situation-Operator Modeling. Eng. Appl. Artif. Intell. 72: 357-367 (2018) - [j13]Sebastian F. Wirtz, Adauto P. A. Cunha, Marc Labusch, Galina Marzun, Stephan Barcikowski, Dirk Söffker:
Development of A Low-Cost FPGA-Based Measurement System for Real-Time Processing of Acoustic Emission Data: Proof of Concept Using Control of Pulsed Laser Ablation in Liquids. Sensors 18(6): 1775 (2018) - [j12]Mark Spiller, Fateme Bakhshande, Dirk Söffker:
The uncertainty learning filter: A revised smooth variable structure filter. Signal Process. 152: 217-226 (2018) - [c51]M. Hung Do, Jackson G. Njiri, Dirk Söffker:
Structural load mitigation control for nonlinear wind turbines with unmodeled dynamics. ACC 2018: 3466-3471 - [c50]Qi Deng, Dirk Söffker:
Improved Driving Behaviors Prediction Based on Fuzzy Logic-Hidden Markov Model (FL-HMM). Intelligent Vehicles Symposium 2018: 2003-2008 - [c49]Qi Deng, Jiao Wang, Dirk Söffker:
Prediction of human driver behaviors based on an improved HMM approach. Intelligent Vehicles Symposium 2018: 2066-2071 - 2017
- [j11]Arezoo Sarkheyli, Dirk Söffker:
Fuzzy SOM-based Case-Based Reasoning for individualized situation recognition applied to supervision of human operators. Knowl. Based Syst. 137: 42-53 (2017) - [c48]Fateme Bakhshande, Dirk Söffker:
Robust control approach for a hydraulic differential cylinder system using a Proportional-Integral-Observer-based backstepping control. ACC 2017: 3102-3107 - [c47]Elmira Madadi, Dirk Söffker:
Comparison of different model-free iterative learning control methods concerning a nonlinear mechanical structure. ASCC 2017: 1560-1565 - [c46]Andreas Bathelt, Dirk Söffker, Mohieddine Jelali:
An approach to recursive subspace identification. CDC 2017: 4638-4643 - [c45]Arezoo Sarkheyli-Hägele, Dirk Söffker:
Online learning for an individualized lane-change situation recognition system applied to driving assistance. CogSIMA 2017: 1-6 - [c44]Min Zhao, David Käthner, Meike Jipp, Dirk Söffker, Karsten Lemmer:
Modeling driver behavior at roundabouts: Results from a field study. Intelligent Vehicles Symposium 2017: 908-913 - [c43]Georg Hägele, Dirk Söffker:
Safety unit-based safe behavior assurance for autonomous and semi-autonomous aerial systems: Requirements, concept, and simulation results. Intelligent Vehicles Symposium 2017: 1546-1551 - 2016
- [j10]Chunsheng Wei, Dirk Söffker:
Optimization Strategy for PID-Controller Design of AMB Rotor Systems. IEEE Trans. Control. Syst. Technol. 24(3): 788-803 (2016) - [c42]Arezoo Sarkheyli, Dirk Söffker:
Feature selection for situation recognition in Fuzzy SOM-based Case-based Reasoning. CogSIMA 2016: 145-151 - [c41]Sandra Rothe, Dirk Söffker:
Comparison of different information fusion methods using ensemble selection considering Benchmark data. FUSION 2016: 73-78 - [c40]Arezoo Sarkheyli, Dirk Söffker:
Automatic selection of relevant features using Rough Set Theory for real-time situation recognition based on fuzzy SOM-based CBR. SMC 2016: 832-837 - [c39]Georg Hägele, Dirk Söffker:
Strictly Formalized Situation-Operator-Modeling technique for fall-back layer modeling for autonomous or semi-autonomous systems requiring software-based fail-safe behavior. SMC 2016: 886-891 - [c38]Georg Hägele, Dirk Söffker:
Fall-back layer concept for autonomous or semi-autonomous systems and processes: Requirements, concepts, and first tests. SMC 2016: 916-921 - [c37]Jiao Wang, Dirk Söffker:
Improving driving efficiency for hybrid electric vehicle with suitable interface. SMC 2016: 928-933 - 2015
- [j9]Andreas Hasselberg, Dirk Söffker:
Petri-Net-Based Modeling of Human Operator's Planning for the Evaluation of Task Performance Using the Example of Air Traffic Control. IEEE Trans. Hum. Mach. Syst. 45(6): 676-685 (2015) - [c36]Andreas Bathelt, Dirk Söffker, Mohieddine Jelali:
An algorithm combining the subspace identification methods ORT and CCA. CDC 2015: 3361-3366 - [c35]Nejra Beganovic, Jackson G. Njiri, Sandra Rothe, Dirk Söffker:
Application of diagnosis and prognosis to wind turbine system based on fatigue load. ICPHM 2015: 1-6 - [c34]Nejra Beganovic, Dirk Söffker:
Wear process lifetime prediction based on parametric model applied to experimental data. ICPHM 2015: 1-6 - 2014
- [j8]Lou'i Al-Shrouf, Mahmud-Sami Saadawia, Dirk Söffker:
Improved process monitoring and supervision based on a reliable multi-stage feature-based pattern recognition technique. Inf. Sci. 259: 282-294 (2014) - [c33]Dirk Söffker, Olga Muthig, Xingguang Fu:
Modeling for cooperation and coordination within structured and complex situations like unknown emergency situations. CSCWD 2014: 146-152 - [c32]Jiao Wang, Dirk Söffker:
Towards a driver supervision and assistance system: intention detecting and option providing. SMC 2014: 3966-3971 - 2013
- [j7]Xiaojun Wang, Zhiping Qiu, Dirk Söffker:
Uncertainty-Based Design Optimization in Engineering: Model, Algorithm, and Application. J. Appl. Math. 2013: 595719:1-595719:2 (2013) - [c31]Marcel Langer, Dirk Söffker:
User assistance by situational, task- and model-based process information filtering in interactive, semi-automated human-automation systems. CogSIMA 2013: 106-113 - [c30]Xingguang Fu, Marcel Langer, Dirk Söffker:
Toward a modeling of human-centered, rule-based cooperative teamwork. CSCWD 2013: 471-476 - [c29]Dirk Söffker, Jiao Wang, Sandra Schiffer, Matthias Marx, Xingguang Fu:
Know Your Options - Interfacing Consequences and Forecasted Performance Analysis: A Concept for the Novel Type of Information System KYO-ICPA. IFAC HMS 2013: 218-225 - [c28]Andreas Hasselberg, Dirk Söffker:
A Human Cognitive Performance Measure Based on Available Options for Adaptive Aiding. IFAC HMS 2013: 442-449 - 2012
- [j6]Dirk Söffker, Xingguang Fu, Andreas Hasselberg, Marcel Langer:
Modeling of Complex Human-Process Interaction as Framework for Assistance and Supervisory Control of Technical Processes. Int. J. Inf. Technol. Web Eng. 7(1): 46-66 (2012) - [j5]Yan Liu, Dirk Söffker:
Variable high-gain disturbance observer design with online adaption of observer gains embedded in numerical integration. Math. Comput. Simul. 82(5): 847-857 (2012) - [c27]Dirk Söffker, Marcel Langer, Andreas Hasselberg, Gregor Flesch:
Modeling of cooperative Human-Machine-Human Systems based on Game Theory. CSCWD 2012: 274-281 - [c26]Mustafa Turki Hussein, Dirk Söffker:
Variable gain control of elastic crane using vision sensor data. ICARCV 2012: 1783-1788 - [c25]Philipp Ertle, Michel Tokic, Richard Cubek, Holger Voos, Dirk Söffker:
Towards learning of safety knowledge from human demonstrations. IROS 2012: 5394-5399 - [c24]Philipp Ertle, Michel Tokic, Tobias Bystricky, Marius Ebel, Holger Voos, Dirk Söffker:
Conceptual Design of a Dynamic Risk-Assessment Server for Autonomous Robots. ROBOTIK 2012 - [c23]Philipp Ertle, Holger Voos, Dirk Söffker:
Utilizing dynamic hazard knowledge for risk sensitive action planning of autonomous robots. ROSE 2012: 162-167 - [c22]Marcel Langer, Dirk Söffker:
Situation-based process guiding and supervision assistance system for semi-automated manufacturing processes. SMC 2012: 820-825 - [c21]Xingguang Fu, Dirk Söffker:
Toward a human-centered intelligent supervision and assistance for complex technical systems. SMC 2012: 1536-1541 - 2011
- [c20]Fan Zhang, Dirk Söffker:
A data-driven online stability monitoring method for unknown discrete-time nonlinear systems. CDC/ECC 2011: 7356-7361 - [c19]Xingguang Fu, Dirk Söffker:
Concept for SOM-based computer supported cooperative work. CSCWD 2011: 261-267 - 2010
- [j4]Dirk Söffker, Markus Özbek, Matthias Marx:
Powermanagement eines hybridelektrischen Antriebs - Auslegung, Optimierung und Leistungsregelung (Powermanagement of a Hybrid Electric Powertrain - Design Aspects, Optimization Targets and Power Flow Control). Autom. 58(10): 580-589 (2010) - [c18]Xingguang Fu, Dirk Söffker:
System theoretic modeling of human interaction with respect to rule-based driving interactions. IFAC HMS 2010: 99-104 - [c17]Philipp Ertle, Holger Voos, Dirk Söffker:
Development of Safe Autonomous Mobile Service Robots using an Active Integrated Approach. ISR/ROBOTIK 2010: 1-8 - [c16]Dennis Gamrad, Dirk Söffker:
Learning from conflicts in real world environments for the realization of Cognitive Technical Systems. SMC 2010: 1995-2002 - [c15]Philipp Ertle, Dennis Gamrad, Holger Voos, Dirk Söffker:
Action planning for autonomous systems with respect to safety aspects. SMC 2010: 2465-2472
2000 – 2009
- 2009
- [c14]Yan Liu, Dirk Söffker:
Improvement of optimal high-gain PI-Observer design. ECC 2009: 4564-4569 - [c13]Marc ter Beek, Dirk Söffker:
Observer-based identification of nonlinear clutch friction under operating conditions. ECC 2009: 5051-5056 - [c12]Dennis Gamrad, Dirk Söffker:
Reduction of Complexity for the Analysis of Human-Machine-Interaction. SMC 2009: 1263-1268 - [c11]Dennis Gamrad, Dirk Söffker:
Simulation of Learning and Planning by a Novel Architecture for Cognitive Technical Systems. SMC 2009: 2233-2238 - 2008
- [c10]Hendrik Oberheid, Dennis Gamrad, Dirk Söffker:
Closed loop state space analysis and simulation for Cognitive Systems. ACSD 2008: 39-44 - [c9]Hendrik Oberheid, Dirk Söffker:
Cooperative Arrival Management in Air Traffic Control - A Coloured Petri Net Model of Sequence Planning. Petri Nets 2008: 348-367 - 2007
- [j3]Dirk Söffker, Elmar Ahle:
Idea, conception and realisation of learning abilities for robot control using a Situation-Operator-Model. Int. J. Intell. Syst. Technol. Appl. 2(2/3): 271-283 (2007) - [c8]Hendrik Oberheid, Dirk Söffker:
Designing for cooperation - mechanisms and procedures for air-ground integrated arrival management. SMC 2007: 253-259 - [c7]Dennis Gamrad, Hendrik Oberheid, Dirk Söffker:
Supervision of open systems using a situation-operator-modeling approach and higher petri net formalisms. SMC 2007: 925-930 - 2006
- [c6]Elmar Ahle, Dirk Söffker:
A Cognitive-Oriented Architecture to Realize Autonomous Behavior - Part I: Theoretical Background. SMC 2006: 2215-2220 - [c5]Elmar Ahle, Dirk Söffker:
A Cognitive-Oriented Architecture to Realize Autonomous Behavior - Part II: Application to Mobile Robotics. SMC 2006: 2221-2227 - 2005
- [j2]Dirk Söffker:
Dirk Söffker, Professor in Duisburg. Autom. 53(6): 286 (2005) - [c4]Dirk Söffker, Elmar Ahle:
Idea, Conception, and Realization of Learning Abilities for Robot Control Using a Situation-Operator- Model. ALaRT 2005: 131-141 - [c3]Elmar Ahle, Dirk Söffker:
A concept for a cognitive-oriented approach to build autonomous systems. SMC 2005: 2929-2935 - 2004
- [c2]Elmar Ahle, Dirk Söffker:
Learning from interaction with the environment using a situation-operator calculus with application to mobile robots. SMC (4) 2004: 3839-3844 - 2001
- [j1]Dirk Söffker:
From Human-Machine-Interaction Modeling to New Concepts Constructing Autonomous Systems: A Phenomenological Engineering-Oriented Approach. J. Intell. Robotic Syst. 32(2): 191-205 (2001) - [c1]Dirk Söffker:
Modeling of the knowledge-based 'intelligent system'-environment interaction: description application to human-machine interaction and system-theoretic aspects leading to a new type of autonomous systems. SMC 2001: 213-220
Coauthor Index
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