Resilience of smart integrated energy systems Babazadeh, Davood; Teimourzadeh Baboli, Payam; Mayer, Christoph; Brand, Michael; Becker, Christian; Lehnhoff, Sebastian In: Fathi, M., Zio, E., Pardalos, P.M. (eds): Handbook of Smart Energy Systems. Springer, Cham, 1887-1913 (2023)
Experiences with System-Level Validation Approach Baboli, Payam Teimourzadeh; Babazadeh, Davood; Siagkas, D.; Manikas, S.; Anastasakis, K.; Merino, Julia In: Strasser T., de Jong E., Sosnina M. (eds) European Guide to Power System Testing. Springer, Cham. (2020) Verlags DOI
Test Procedure and Description for System Testing Heussen, Kai; Babazadeh, Davood; Degefa, Merkebu Z.; Taxt, H.; Merino, Julia; Nguyen, V. H.; Baboli, Payam Teimourzadeh; Moghim Khavari, A.; Rikos, E.; Pellegrino, L.; Tran, Q. T.; Jensen, Tue V.; Kotsampopoulos, P.; Strasser, Thomas I. In: Strasser T., de Jong E., Sosnina M. (eds) European Guide to Power System Testing. Springer, Cham. (2020) Verlags DOI
Modern high-frequency systems benefit massively from machine learning methods. In applications where rule-based algorithms reach their limits, these data-driven approaches enable a significant increase in resolution and accuracy. This is exemplified by current research challenges, namely for the classification of targets in autonomous driving radar systems, radar-based gesture recognition for smart home applications and device control as well as in the field of medical technology for the contactless monitoring of human vital signs.
Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>