CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data...
Saved in:
Main Authors: | Evgeny Zotov, Visakan Kadirkamanathan |
---|---|
Format: | article |
Language: | EN |
Published: |
Frontiers Media S.A.
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/fe7dc4dbca884776b83c306bda6c97ef |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion
by: Zainoolabadien Karim, et al.
Published: (2021) -
Automatic analysis of cognitive presence in online discussions: An approach using deep learning and explainable artificial intelligence
by: Yuanyuan Hu, et al.
Published: (2021) -
Is One Teacher Model Enough to Transfer Knowledge to a Student Model?
by: Nicola Landro, et al.
Published: (2021) -
Proposal of a remote education model with the integration of an ICT architecture to improve learning management
by: William Villegas-Ch., et al.
Published: (2021) -
Employee Attrition Prediction Using Deep Neural Networks
by: Salah Al-Darraji, et al.
Published: (2021)