论文标题
耦合系统的非结构化网格方法可降低非线性降噪方法
An Unstructured Mesh Approach to Nonlinear Noise Reduction for Coupled Systems
论文作者
论文摘要
为了解决电子数据采集系统和现实世界中固有的噪声，Araki等人。 [Physica d：非线性现象，417（2021）132819]展示了一种基于网格的非线性技术，可以从混乱信号中删除噪声，从而利用同一动力学系统中的清洁高效率信号并在多维相空间中平均集合。这种方法实现了具有100％添加噪声的时间序列数据，但在低数据密度的区域中遭受了损失。为了改善这种基于网格的方法，这里使用基于三角形的非结构化网格和Voronoi图来完成相同的任务。非结构化网格更均匀地将数据样本分布在网细胞上，以提高重建信号的准确性。通过经验平衡偏差和差异误差，在选择非结构化细胞的数量作为可用样品数量的函数中，该方法可以使用已知的测试数据获得渐近统计收敛，并减少来自Hall效应推力（HETS）实验信号（HETS）的合成噪声，而不是原始网格策略更大。
To address noise inherent in electronic data acquisition systems and real world sources, Araki et al. [Physica D: Nonlinear Phenomena, 417 (2021) 132819] demonstrated a grid based nonlinear technique to remove noise from a chaotic signal, leveraging a clean high-fidelity signal from the same dynamical system and ensemble averaging in multidimensional phase space. This method achieved denoising of a time-series data with 100% added noise but suffered in regions of low data density. To improve this grid-based method, here an unstructured mesh based on triangulations and Voronoi diagrams is used to accomplish the same task. The unstructured mesh more uniformly distributes data samples over mesh cells to improve the accuracy of the reconstructed signal. By empirically balancing bias and variance errors in selecting the number of unstructured cells as a function of the number of available samples, the method achieves asymptotic statistical convergence with known test data and reduces synthetic noise on experimental signals from Hall Effect Thrusters (HETs) with greater success than the original grid-based strategy.
