Sajad Shahsavari
Doctoral Researcher
sajad.s.shahsavari@utu.fi Joukahaisenkatu 3-5 Turku Työhuone: B6018 ORCID-tunniste: https://orcid.org/0000-0003-3754-1743 |
Asiantuntijuusalueet
Data processing; Statistical machine learning; Deep learning; Reinforcement learning; Software engineering
Data processing; Statistical machine learning; Deep learning; Reinforcement learning; Software engineering
Tutkimusyhteisö tai tutkimusaihe
Machine-learning based digital twin for autonomous systems
Machine-learning based digital twin for autonomous systems
Biografia
Sajad Shahsavari is a Ph.D. student at University of Turku, Finland, and works as a researcher at Computational Engineering and Analysis (COMEA) research group in Turku University of Applied Sciences. His research interests include deep neural networks, time-series prediction, reinforcement learning and data analysis. He received his B.Sc. degree in Computer Engineering from Amirkabir University of Technology, Tehran, Iran in 2014 and his M.Sc. degree in Artificial Intelligence from Sharif University of Technology, Tehran, Iran in 2017.
Tutkimus
Julkaisut
- A Coordinated Approach to Control Mechanical and Computing Resources in Mobile Robots (2025)
- IEEE Transactions on Robotics
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - A Novel Approach for Battery State-of-Health Estimation Using Convolutional Auto-Encoders (2025)
- European Control Conference
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Co-Management of Computational and Mechanical Energy in Mobile Robots Using Reinforcement Learning (2025)
- European Control Conference
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - A Coupled Battery State-of-Charge and Voltage Model for Optimal Control Applications (2023)
- Proceedings : Design, Automation, and Test in Europe Conference and Exhibition
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - An Extension of the Kinetic Battery Model for Optimal Control Applications (2023)
- Proceedings of the IEEE International Symposium on Industrial Electronics
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Capacity loss estimation for li-ion batteries based on a semi-empirical model (2021)
- Proceedings: European Conference for Modelling and Simulation
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - MCX - An open-source framework for digital twins (2021)
- Proceedings: European Conference for Modelling and Simulation
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Remote Run-Time Failure Detection and Recovery Control For Quadcopters (2021)
- Journal of integrated design and process science
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )
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