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Identifying the temporal distribution structure in multivariate data for time-series segmentation based on two-sample test
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DOI：10.1016/j.inffus.2025.103445.png)
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摘要
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• 通过识别状态变化，设计了复杂系统的多传感器监测方法。
• 提出了MIDAST方法学，用于基于融合的多变量数据分割。
• 在计算机模拟过程中评估了两种不同的多变量数据模型。
• MIDAST优于两种基线方法，即e-Divisive和KCPA。
• MIDAST能够实现颅内高压事件的非侵入式重建。
Keyword:
Multivariate data segmentation
Two-sample test
Non-Gaussian distributions
Gaussian distribution
Multisensor fusion
Intracranial hypertension detection
Non-invasive measurement
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
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