Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant...
Saved in:
| Main Authors: | Mainak Mallick, Young-Dae Shim, Hong-In Won, Seung-Kyum Choi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1745 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Irregular Openings Identification at Construction Sites Based on Few-Shot Learning
by: Minjo Seo, et al.
Published: (2025-05-01) -
Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning
by: Junlong Li, et al.
Published: (2025-08-01) -
Prediction method of gas content in deep coal seams based on logging parameters: A case study of the Baijiahai region in the Junggar Basin
by: Yijie Wen, et al.
Published: (2025-08-01) -
Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning
by: Pablo Corona-Fraga, et al.
Published: (2025-01-01) -
Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning
by: Yulong Ji, et al.
Published: (2024-11-01)