Abstract. Effective anomaly detection can reduce the electricity consumption and carbon emissions in aluminium extrusion processes.
Transfer learning for aluminium extrusion electricity consumption ...
www.tandfonline.com › doi › abs
Abstract. Effective anomaly detection can reduce the electricity consumption and carbon emissions in aluminium extrusion processes.
The experimental results show that the proposed approach can identify electricity anomaly events in real time and it is shown that transferring learning ...
Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Int. J. Comput. Integr. Manuf.,. 31(4-5):396 ...
Apr 25, 2024 · Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Int. J. Comput. Integr. Manuf ...
Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Peng Liang, Haidong Yang, Wen-Si Chen, Si-Yuan ...
Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Int. J. Comput. Integr. Manuf. 31(4-5): 396 ...
Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Authors. Peng Liang · ORCID ID · Hai-Dong Yang ...
Within this paper, transfer learning is investigated for the quality prediction of aluminum gravity die casting to try to overcome these hurdles and create ...
This often leads to hardly generalizable and weak models. Within this paper, transfer learning is investigated for the quality prediction of aluminum gravity ...