Salehpour, S., Eskandari, A., Nedaei, A., & Aghaei, M. [ca. 2025]. Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection [Cd]. Freiburg: Universität. https://doi.org/10.1016/j.egyai.2025.100512
ISO-690 (author-date, English)SALEHPOUR, Sherko, ESKANDARI, Aref, NEDAEI, Amir und AGHAEI, Mohammadreza, 2025. Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection. Freiburg: Universität.
Modern Language Association 9th editionSalehpour, S., A. Eskandari, A. Nedaei, und M. Aghaei. Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection. cd, Universität, 2025, https://doi.org/10.1016/j.egyai.2025.100512.
Mohr Siebeck - Recht (Deutsch - Österreich)Salehpour, Sherko/Eskandari, Aref/Nedaei, Amir/Aghaei, Mohammadreza: Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection, Freiburg 2025.
Emerald - HarvardSalehpour, S., Eskandari, A., Nedaei, A. und Aghaei, M. (2025), Two-stage deep Q-network reinforcement learning based ultra-efficient fault diagnosis and severity assessment scheme for photovoltaic protection, Bd. , Universität, Freiburg, verfügbar unter:https://doi.org/10.1016/j.egyai.2025.100512.