Large-scale infrastructure projects, interconnection schemes, and renewable energy developments continue to shape the ...
Abstract: Deep learning algorithms have become a trending approach for transformer fault diagnosis. However, challenges such as data imbalance leading to poor model learning performance and ...
By allowing models to actively update their weights during inference, Test-Time Training (TTT) creates a "compressed memory" that solves the latency bottleneck of long-document analysis.
Abstract: As power system capacity increases, power transformers frequently endure multiple short-circuit impulses of varying intensities. Existing evaluation methods cannot accurately predict their ...
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