Active Hard Sample Learning for Violation Action Recognition in Power Grid Operation
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, re...
Saved in:
Main Authors: | Lingwen Meng, Di He, Guobang Ban, Guanghui Xi, Anjun Li, Xinshan Zhu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2078-2489/16/1/67 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Supervised and Self-Supervised Learning for Assembly Line Action Recognition
by: Christopher Indris, et al.
Published: (2025-01-01) -
A Named Entity Recognition Model for Chinese Electricity Violation Descriptions Based on Word-Character Fusion and Multi-Head Attention Mechanisms
by: Lingwen Meng, et al.
Published: (2025-01-01) -
Building Arabic Speech Recognition System Using HuBERT Model and Studying the Sources of Errors [Arabic]
by: Rima Sbih, et al.
Published: (2025-01-01) -
Safeguards-related event detection in surveillance video using semi-supervised learning approach
by: Se-Hwan Park, et al.
Published: (2025-02-01) -
Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers
by: Manuel Milling, et al.
Published: (2025-01-01)