Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Jing-Ke She"'
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789819934546
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a346b2500f6a454c23559ee58b9bf21e
https://doi.org/10.1007/978-981-99-3455-3_19
https://doi.org/10.1007/978-981-99-3455-3_19
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789811911804
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbd7d723fef7c2b6543ba92ce9bd7a68
https://doi.org/10.1007/978-981-19-1181-1_43
https://doi.org/10.1007/978-981-19-1181-1_43
Publikováno v:
Frontiers in Energy Research, Vol 9 (2021)
A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and post-accident prediction for Loss of Coolant Accidents (LOCAs) in Nuclear Po
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789811634550
The Long-Short Term Memory (LSTM) model is applied to the Steam Generator (SG) water level prediction in this work. The model is designed and implemented within the SIMULINK environment, where a real-time validation platform is also constructed using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::322b85a0dacfec831b461ac7e1d0cd92
https://doi.org/10.1007/978-981-16-3456-7_49
https://doi.org/10.1007/978-981-16-3456-7_49
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789811518751
The Long Short-Term Memory (LSTM) model is investigated in this work, as a proposed prediction method for the abnormal condition in Nuclear Power Plants (NPPs). Its advantage of processing long timeline data is utilized to overcome the limitation of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::669c57ee18de5cd83897eff0be0cc251
https://doi.org/10.1007/978-981-15-1876-8_46
https://doi.org/10.1007/978-981-15-1876-8_46