OmniEval releases a Chinese-English, audio-visual-text benchmark with fine-grained temporal grounding questions, and reports that today's omni-modal models score low and depend mainly on textual cues.
A precise detection method for transient micro short-circuit faults of lithium-ion batteries through signal processing
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abstract
A specific failure mode designated as transient micro-short circuit (TMSC) has been identified in practical battery systems, exhibiting subtle and latent characteristics with measurable voltage deviations. To further improve the safe use of lithium-ion batteries (LIBs), this letter introduces a novel method for the precise detection of this TMSC faults within LIBs. The method applies the continuous wavelet transform (CWT) to voltage and current signals, followed by the identification of micro-scale anomalies through the analysis of the coherence in the wavelet spectrum at specific frequency. Through designed fault experiments, the effec-tiveness of this method has been verified. Result demon-strates that it can effectively capture micro-faults with a voltage drop as low as 30 mV within just a few seconds. Furthermore, the proposed method is inherently highly robust and is able to effectively detect false faults and hidden faults under varying current loads, which highlights the superiority of this method.
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OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs
OmniEval releases a Chinese-English, audio-visual-text benchmark with fine-grained temporal grounding questions, and reports that today's omni-modal models score low and depend mainly on textual cues.