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Updated: Sep 18, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

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MetaIndux-TS:用于工业时间序列的频率感知AIGC基础模型.

Haiteng Wang, Lei Ren, Yikang Li

    IEEE transactions on neural networks and learning systems
    |June 23, 2025
    PubMed
    概括

    MetaIndux-TS使用人工智能生成的内容 (AIGC) 来生成工业时间序列数据,以克服收集挑战. 这种基于频率的扩散模型实现了卓越的真实性和预测得分,使人工智能能够在制造业中发挥作用.

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