论文标题
两相流动表征的噪声多元多尺度置换熵
A noise-robust Multivariate Multiscale Permutation Entropy for two-phase flow characterisation
论文作者
论文摘要
使用基于图的方法，我们提出了一个多尺度置换熵，以在多个时间尺度上探索多元时间序列的复杂性。该多元多尺度置换熵（MPEG）通过为每个粗粒度时间序列构造一个基础图，然后应用图形信号的最新置换熵，从而结合了通道之间的相互作用。鉴于在现实世界数据分析中噪声构成的挑战，我们使用合成时间序列研究了MPEG噪声的鲁棒性，并且比类似的多元熵指标表现出更好的性能。 两相流数据是一个重要的工业过程，其特征是复杂的动态行为。 MPEG通过合并来自不同尺度的信息来表征两相流的流动行为转变。实验结果表明，MPEG对流动模式的动态敏感，从而使我们能够区分不同的流动模式。
Using a graph-based approach, we propose a multiscale permutation entropy to explore the complexity of multivariate time series over multiple time scales. This multivariate multiscale permutation entropy (MPEG) incorporates the interaction between channels by constructing an underlying graph for each coarse-grained time series and then applying the recent permutation entropy for graph signals. Given the challenge posed by noise in real-world data analysis, we investigate the robustness to noise of MPEG using synthetic time series and demonstrating better performance than similar multivariate entropy metrics. Two-phase flow data is an important industrial process characterised by complex, dynamic behaviour. MPEG characterises the flow behaviour transition of two-phase flow by incorporating information from different scales. The experimental results show that MPEG is sensitive to the dynamic of flow patterns, allowing us to distinguish between different flow patterns.
