This work introduces a token-aligned annotation scheme that is efficient, scalable, and naturally integrates with modern deep learning architectures like Transformers, and shows that models trained with this scheme achieved higher localization performance than weakly supervised baselines trained on image-level labels a...
Results indicate that the proposed model provides an effective representation-learning approach for simulated transmission-line fault classification, and independent validation under a retraining protocol further supports the reproducibility of the framework on another simulated power-line fault dataset.
Yan Lu, Wenjuan Zheng, Liming Wang· Scientific Reports· 0 citations
Accurate transformer oil-temperature forecasting is important for thermal-risk assessment and operational planning. However, reported gains from complex forecasting models may be affected by future information leakage, weak seasonal baselines, inconsistent target periods, and test-based model selection. This study esta...
Yan Lu, Wen-Jing Zheng· IEEE Access· 0 citations
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