Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles

Rezaei, Mohammad Amin; Fathollahi, Arman; Rezaei, Sajad; Hu, Jiefeng; Gheisarnejad, Meysam; Teimouri, Ali Reza; Rituraj, Rituraj; Mosavi, Amir; Khooban, Mohammad-Hassan

Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles

Rezaei Mohammad Amin; Fathollahi Arman; Rezaei Sajad; Hu Jiefeng; Gheisarnejad Meysam; Teimouri Ali Reza; Rituraj Rituraj; Mosavi Amir; Khooban Mohammad-Hassan

DOI : 10.1109/ACCESS.2022.3228916

URI : http://hdl.handle.net/20.500.12944/19979

MTMT : 33335268

Megjelenés dátuma : 2022

Folyóirat címe : IEEE Access

Évfolyam : 10

Oldalszám : 132271-132287

Dokumentum típusa : folyóiratcikk

Kulcsszó : capacitor-bank, deep learning, power system reliability, artificial intelligence (AI), machine learning, electronics, Természettudományok, Matematika- és számítástudományok

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The aim of the paper is to present some of the general principles of data protection law that can be applied to automated decision-making applications embedded into blockchain technology in order to comply with the provision of the European

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