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Insulator age estimation using limited unlabeled UAV data samples
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DOI：10.1016/j.rineng.2026.111307.png)
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Abstract
En 中文
• Presents a self-supervised framework for estimating power-insulator aging. • Enhances prediction accuracy without relying on expert annotations. • Results show distinct aging levels supported by quantitative metrics and qualitative analysis.
Keywords:
Insulator age estimation
Contrastive learning
Machine learning
Deep learning and UAVs
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