Andrea de Giorgio
Andrea de Giorgio
CEO of Artificial Engineering | Engineer & Researcher in Artificial Intelligence
Verified email at - Homepage
Cited by
Cited by
Human-machine Collaboration in Virtual Reality for Adaptive Production Engineering
A de Giorgio, M Romero, M Onori, L Wang
Procedia Manufacturing 11, 1279-1287, 2017
Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines
ML Ruiz Rodríguez, S Kubler, A de Giorgio, M Cordy, J Robert, ...
Robotics and Computer-Integrated Manufacturing 78, 102406, 2022
Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing
A de Giorgio, A Maffei, M Onori, L Wang
Journal of Manufacturing Systems 60, 22-34, 2021
Extended Reality in Neurosurgical Education: A Systematic Review
A Iop, VG El-Hajj, M Gharios, A de Giorgio, FM Monetti, E Edström, ...
Sensors 22 (16), 6067, 2022
Learning programs is better than learning dynamics: A programmable neural network hierarchical architecture in a multi-task scenario
F Donnarumma, R Prevete, A de Giorgio, G Montone, G Pezzulo
Adaptive Behavior 24 (1), 27-51, 2016
An experimental study of the impact of virtual reality training on manufacturing operators on industrial robotic tasks
FM Monetti, A de Giorgio, H Yu, A Maffei, M Romero
9th CIRP Conference on Assembly Technology and Systems in Procedia CIRP 106 …, 2022
Procedural knowledge and function blocks for smart process planning
A de Giorgio, M Lundgren, L Wang
Procedia Manufacturing 48, 1079-1087, 2020
Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications
A de Giorgio, L Wang
IEEE Access 8, 174833 - 174844, 2020
Measuring the effect of automatically authored video aid on assembly time for procedural knowledge transfer among operators in adaptive assembly stations
A de Giorgio, M Roci, A Maffei, M Jocevski, M Onori, L Wang
International Journal of Production Research 61 (12), 3910-3925, 2021
A study on the similarities of Deep Belief Networks and Stacked Autoencoders
A de Giorgio
KTH, Royal Institute of Technology, 2015
Assessing the influence of expert video aid on assembly learning curves
A de Giorgio, S Cacace, A Maffei, FM Monetti, M Roci, M Onori, L Wang
Journal of Manufacturing Systems 62, 263-269, 2022
Adopting extended reality? A systematic review of manufacturing training and teaching applications
A de Giorgio, FM Monetti, A Maffei, M Romero, L Wang
Journal of Manufacturing Systems 71, 645-663, 2023
Systematic review of class imbalance problems in manufacturing
A de Giorgio, G Cola, L Wang
Journal of Manufacturing Systems 71, 620-644, 2023
The Impact of Learning Factories on Teaching Lean Principles in an Assembly Environment
FM Monetti, E Boffa, A de Giorgio, A Maffei
FAIM 2022: Flexible Automation and Intelligent Manufacturing: The Human-Data …, 2023
Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review
M Ghanem, AK Ghaith, VG El-Hajj, A Bhandarkar, A de Giorgio, ...
Brain Sciences 13 (12), 1723, 2023
Industrial transformation and assembly technology: context and research trends
FM Monetti, A de Giorgio, A Maffei
Procedia CIRP 107, 1427-1432, 2022
Introducing a procedural knowledge model for enhancing industrial process adaptiveness
A de Giorgio
KTH Royal Institute of Technology, 2021
Casi d'uso di intelligenza artificiale nell'industria alimentare
A de Giorgio, 2024
Citation review of a hybridization of genetic algorithms and fuzzy logic for the single-machine scheduling with flexible maintenance problem under human resource constraints by …
A de Giorgio
Preprint, 2021
Procedural knowledge blocks for locomotive assembly video dataset
A de Giorgio, 2021
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