This image shows Andrey Morozov

Andrey Morozov

Jun.-Prof. Dr.-Ing.

Junior professorship
Institute of Industrial Automation and Software Engineering

Contact

+49 711 685 67312
+49 711 685 67302

Pfaffenwaldring 47
70550 Stuttgart
Germany
Room: 2.139

Journals and Conferences :
  1. 2024

    1. V. Naik, T. Fabarisov, and A. Morozov, “Machine Learning Based Search for Access Points in Anomaly Detection Model,” 2024.
  2. 2023

    1. J. Grimstad and A. Morozov, “Adversarial Multi-Agent Reinforcement Learning for Fault-Tolerant Design of Complex Systems,” 2023, pp. 3399–3405.
    2. P. Grimmeisen, R. Golwalkar, Y. Ma, and A. Morozov, “Automated and Continuous Risk Assessment for ROS-Based Software-Defined Robotic Systems,” 2023, pp. 1–7.
    3. S. Ding, A. Wolf, and A. Morozov, “Automated and Self-Adapting Approach to AI-based Anomaly Detection,” 2023, pp. 3056–3063.
    4. Y. Ma, P. Grimmeisen, and A. Morozov, “Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023, pp. 1--6.
    5. S. Bickelhaupt, M. Hahn, N. Nuding, A. Morozov, and M. Weyrich, “Challenges and Opportunities of Future Vehicle Diagnostics in Software-Defined Vehicles,” 2023.
    6. S. Bickelhaupt, M. Hahn, N. Nuding, A. Morozov, and M. Weyrich, “Comprehensive Evaluation of Logging Frameworks for Future Vehicle Diagnostics,” 2023.
    7. B. Schürrle, P. Grimmeisen, J. Pfeiffer, T. Zimmermann, A. Morozov, and A. Wortmann, “Educating Future Software Engineers for Industrial Robotics,” 2023.
    8. A. Aghaei Attar, T. Fabarisov, A. Morozov, M. Artelt, and I. Mamaev, “Hybrid Lightweight Deep Learning-Based Error Detection Model on Edge Computing Devices,” 2023, pp. 1–4.
    9. S. Ding, A. Wolf, and A. Morozov, “Interpretation of Influential Factors for AI-Based Anomaly Detection,” 2023, pp. 1762–1769.
    10. J. Grimstad, T. Ruppert, J. Abonyi, and A. Morozov, “Preventive Risk-based Maintenance Scheduling using Discrete-Time Markov Chain Models,” 2023, pp. 1135–1142.
    11. T. Fabarisov, V. Naik, A. Aghaei Attar, and A. Morozov, “Remedy: Automated Design and Deployment of Hybrid Deep Learning-based Error Detectors,” 2023, pp. 1–8.
    12. B. Schürrle, V. Sankarappan, and A. Morozov, “SynthiCAD: Generation of Industrial Image Data Sets for Resilience Evaluation of Safety-Critical Classifiers,” 2023, pp. 2199–2206.
  3. 2022

    1. P. Grimmeisen, M. Diaconeasa, Y. Ma, and A. Morozov, “Automated Generation of Hybrid Probabilistic Risk Models from SysML v2 Models of Software-Defined Manufacturing Systems,” in Morozov ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
    2. P. Grimmeisen, A. Morozov, T. Fabarisov, A. Wortmann, and C. H. Koo, “Automated Model-Based Reliability Assessment of Software-Defined Manufacturing,” in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
    3. P. Grimmeisen, A. Wortmann, and A. Morozov, “Case study on automated and continuous reliability assessment of software-defined manufacturing based on digital twins,” MODELS ’22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, 2022.
    4. Y. Ma, P. Grimmeisen, and A. Morozov, “Detection and classification of robotic manipulator anomalies using MLSTM-FCN models,” ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
    5. T. Fabarisov, A. Morozov, I. Mamaev, and P. Grimmeisen, “FIDGET: Deep Learning-Based Fault Injection Framework for Safety Analysis and Intelligent Generation of Labeled Training Data,” 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September 2022, 2022.
    6. S. Ding, N. Chakraborty, and A. Morozov, “IMU Sensor Faults Detection for UAV using Machine Learning,” in European Conference on Safety and Reliability 2022, 28 Aug - 3 Sept. 2022, Dublin, Ireland, 2022, 2022.
    7. S. Ding, S. Ayoub, and A. Morozov, “Tool Paper: Time Series Anomaly Detection Platform for MATLAB Simulink,” In: 8th International Symposium on Model-Based Safety Assessment, 5-7 Sept. 2022, Munich, Germany, 2022, 2022.
    8. G. Siedel, S. Vock, A. Morozov, and S. Voß, “Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers,” in The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety (AISafety), 2022, 2022.
  4. 2021

    1. Y. Ma, A. Morozov, and S. Ding, “Anomaly Detection for Cyber Physical Systems using Transformers,” The International Mechanical Engineering Congress and Exposition, IMECE, 2021.
    2. T. Fabarisov, I. Mamaev, A. Morozov, and K. Janschek, “Deep Learning-based Error Mitigation for Assistive Exoskeleton with Computational-Resource-Limited Platform and Edge Tensor Processing Unit,” in The International Mechanical Engineering Congress and Exposition, IMECE, 2021.
    3. S. Ding, A. Morozov, T. Fabarisov, and S. Vock, “KrakenBox: Deep Learning based Error Detector for Industrial Cyber-Physical Systems,” in The International Mechanical Engineering Congress and Exposition, IMECE, 2021.
  5. 2020

    1. A. Morozov, M. A. Diaconeasa, and M. Steurer, “A Hybrid Methodology For Model-based Probabilistic Resilience, Evaluation Of Dynamic Systems,” in International Mechanical Engineering Congress and Exposition (IMECE 2020 - Virtual Conference), 15-19 Nov. 2020, Portland, OR, USA, 2020.
    2. A. Morozov, M. B. Emil Valiev, L. G. Kai Ding, and C. Schorn, “Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers,” in Proceedings of the Workshop on Artificial Intelligence Safety 2020 co-located with the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), 2020.
    3. E. Valiev, N. Yusupova, A. Morozov, K. Janschek, and M. Beyer, “Evaluation of the Impact of Random Computing Hardware Faults on the Performance of Convolutional Neural Networks,” in In: Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020), Atlantis Press, pp. 307-312, 2020.
    4. M. Moradi, B. J. Oakes, M. Saraoglu, A. Morozov, K. Janschek, and J. Denil, “Exploring Fault Parameter Space Using Reinforcement Learning-based Fault Injection,” in 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W - Virtual Conference), 29 June - 2 July 2020, Valencia, Spain, pp. 102-109, 2020.
    5. M. Beyer, A. Morozov, E. Valiev, C. Schorn, L. Gauerhof, K. Ding, and K. Janschek, “Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, 2020.
    6. C. Dubslaff, A. Morozov, C. Baier, and K. Janschek, “Iterative Variable Reordering: Taming Huge System Families,” Proceedings of the 4th Workshop on Models for Formal Analysis of Real Systems (MARS 2020), pp. 121-133, 2020.
    7. T. Mutzke, M. Steurer, A. Morozov, and K. Janschek, “Model-based Analysis of Timing Errors for Reliable UAV Design,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, pp. 2057-2064, 2020.
    8. M. Steurer, A. Morozov, K. Janschek, and K.-P. Neitzke, “Model-Based Dependability Assessment of Phased-Mission Unmanned Aerial Vehicles,” in 21st IFAC World Congress (Virtual Conference), 11-17 July 2020, Berlin, 2020.
    9. S. Ding, A. Morozov, S. Vock, M. Weyrich, and K. Janschek, “Model-Based Error Detection for Industrial Automation Systems Using LSTM Networks,” In: International Symposium on Model-Based Safety and Assessment, pp. 212-226. Springer, Cham, 2020.
    10. A. M. T. Fabarisov, I. Mamaev and K. Janschek, “Model-based Fault Injection Experiments for the Safety Analysis of Exoskeleton System,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, 2020.
    11. T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “Model-based Stochastic Error Propagation Analysis for Cyber-Physical Systems,” Acta Polytechnica Hungarica, 17(8), pp.15-28, 2020.
    12. C. Dubslaff, A. Morozov, C. Baier, and K. Janschek, “Reduction Methods on Probabilistic Control-flow Programs for Reliability Analysis,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, pp. 4843-4850, 2020.
    13. M. Steurer, T. Mutzke, A. Morozov, K. Janschek, and K.-P. Neitzke, “Utilizing Model-based Timing Analysis for Holistic Dependability Assessment of Unmanned Aerial Vehicle,” in 30th European Safety and Reliability Conference (ESREL - Virtual Conference), 1-5 Nov. 2020, Venice, Italy, pp. 2065-2072, 2020.
    14. M. Saraoğlu, Q. Shi, A. Morozov, and K. Janschek, “Virtual validation of autonomous vehicle safety through simulation-based testing,” In 20. In: Internationales Stuttgarter Symposium, pp. 419-434, Springer Vieweg, Wiesbaden, 2020.
  6. 2019

    1. T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “Analytical toolset for model-based stochastic error propagation analysis: extension and optimization towards industrial requirements,” Системная инженерия и информационные технологии, 1(1), pp.41-46, 2019.
    2. K. Ding, S. Ding, A. Morozov, T. Fabarisov, and K. Janschek, “On-line error detection and mitigation for time-series data of cyber-physical systems using deep learning based methods,” in 2019 15th European Dependable Computing Conference (EDCC) (pp. 7-14). IEEE, 2019.
  7. 2018

    1. T. Fabarisov, N. Yusupova, K. Ding, A. Morozov, and K. Janschek, “The efficiency comparison of the prism and storm probabilistic model checkers for error propagation analysis tasks,” Industry 4.0, 3(5), pp.229-231., 2018.

The research interest of Jun.-Prof. Morozov lies at the intersection of three domains, namely, (i) Networked Automation Systems (NAS), (ii) Dependability, and (iii) Artificial Intelligence (AI). Modern NAS is a particular case of Cyber-Physical Systems (CPS) with the focus on the cooperation of heterogeneous industrial robotic systems. Accurate assessment of reliability, safety, and resilience is essential for NAS because of the high cost of downtime and strict safety requirements. However, the analytical capabilities of dependability evaluation methods, which are currently applied in the industry, are far behind the technical level of the systems in question. These methods cannot adequately describe sophisticated failure scenarios of highly dynamic and intelligent NAS. Besides that, future NAS will include more and more AI components. However, the reliability and safety analysis of AI is an entirely open question at the moment. An inevitable revolution in the dependability methods is expected in the next years. So, the main goal is to build a strong research team capable of taking a leading role in the development of the next generation of dependability analysis methods for modern and future NAS.

Jun.-Prof. Morozov takes over the "Technologien und Methoden der Softwaresysteme I" course for bachelor students from the winter term 2020-2021. Besides that, he is creating a new 4SWS course "Modeling and Analysis of Automation Systems (MAAS)" for master students of the study programs “Autonome Systeme”, “Elektrotechnik und Informationstechnologie”, and “Electrical Engineering”.

Jun.-Prof. Andrey Morozov has started at IAS in April 2020. He holds a six-years tenure-track position. During this time, he has to establish a new professorship for "Networked Automation Systems" and achieve the research and teaching goals defined by the rectorate.

Jun.-Prof. Morozov received his diploma in Computer Science and Mathematics from Ufa State Aviation Technical University in 2007 in Ufa, Russia. In 2009 he moved to Germany and, in 2012, got a doctoral degree (Dr.-Ing.) in the Institute of Automation (IfA), Faculty of Electrical and Computer Engineering, Technische Universität Dresden. After that, being a postdoc researcher, he worked on several R&D projects funded by DLR, ESA, NASA, and DFG. In 2014, Jun.-Prof. Morozov built a new research group at IfA with the main focus on the model-based analysis of safety-critical mechatronic systems. Jun.-Prof. Morozov has published 40 research papers and made more than 20 scientific talks, including the presentations in research centers of ESA, DLR, Bosch, MathWorks, Sandford Research Institute, and the University of California Los Angeles.

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