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1 Ergebnisse
1
Deep Learning for Predicting the Strength of 3D-Printable E..:
, In:
2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)
,
Lai, Xin
;
Gong, Chen
;
He, Enpei
... - p. 52-56 , 2024
Link:
https://doi.org/10.1109/EEBDA60612.2024.10485827
RT T1 <cTitle>2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)</cTitle>: T1 <Title>Deep Learning for Predicting the Strength of 3D-Printable Engineered Cementitious Composites</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10485827&Exemplar=1&LAN=DE A1 Lai, Xin A1 Gong, Chen A1 He, Enpei A1 Li, Yinmian A1 Zhou, Yixin A1 Zhang, Fang YR 2024 K1 Deep learning K1 Printing K1 Technological innovation K1 Three-dimensional displays K1 Recurrent neural networks K1 Costs K1 Curing K1 3DP-ECC K1 Recurrent Neural Network (RNN) K1 Long Short-Term Memory (LSTM) K1 Compressive Strength Prediction K1 Feature Engineering SP 52 OP 56 LK http://dx.doi.org/https://doi.org/10.1109/EEBDA60612.2024.10485827 DO https://doi.org/10.1109/EEBDA60612.2024.10485827 SF ELIB - SuUB Bremen
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