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A New Approach for Supervised Power Disaggregation by Using..:
, In:
2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
,
Wang, T. S.
;
Ji, T. Y.
;
Li, M. S.
- p. 507-512 , 2019
Link:
https://doi.org/10.1109/DEMPED.2019.8864870
RT T1 <cTitle>2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)</cTitle>: T1 <Title>A New Approach for Supervised Power Disaggregation by Using a Denoising Autoencoder and Recurrent LSTM Network</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-8864870&Exemplar=1&LAN=DE A1 Wang, T. S. A1 Ji, T. Y. A1 Li, M. S. YR 2019 K1 Noise reduction K1 Hidden Markov models K1 Neural networks K1 Training data K1 Feature extraction K1 Logic gates K1 Washing machines K1 Non-intrusive load monitoring (NILM) K1 load disaggregation K1 deep neural network (DNN) K1 long short-term memory network (LSTM) K1 denoising autoencoder (dAE) SP 507 OP 512 LK http://dx.doi.org/https://doi.org/10.1109/DEMPED.2019.8864870 DO https://doi.org/10.1109/DEMPED.2019.8864870 SF ELIB - SuUB Bremen
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