论文标题
通过投影多元奇异频谱分析对压敏油漆数据的时间序列图像降级
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
论文作者
论文摘要
时间序列数据，例如不稳定的压力敏感涂料（PSP）测量数据，可能包含大量随机噪声。因此，在这项研究中，我们研究了一种将多元奇异频谱分析（MSSA）与低维数据表示结合的降噪方法。 MSSA是一种使用时间延迟嵌入的状态空间重建技术，并且通过将数据投影到奇异值分解（SVD）基础上来实现低维表示。将提出的不稳定PSP数据（即预计的MSSA）的降噪性能与截短的SVD方法的降噪性能，即使用的MSSA，这是使用最多的降噪方法之一。结果表明，与截短的SVD方法相比，预测的MSSA在减少随机噪声方面表现出更好的性能。此外，与截短的SVD方法相比，投影的MSSA的性能对截断等级不太敏感。此外，预测的MSSA通过从嘈杂的输入数据中提取状态空间中的平滑轨迹来有效地实现了脱氧。预计，预计的MSSA将有效地减少不仅PSP测量数据中的随机噪声，还可以有效地降低各种高维时序列数据。
Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement data, may contain a significant amount of random noise. Thus, in this study, we investigated a noise-reduction method that combines multivariate singular spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a state-space reconstruction technique that utilizes time-delay embedding, and the low-dimensional representation is achieved by projecting data onto the singular value decomposition (SVD) basis. The noise-reduction performance of the proposed method for unsteady PSP data, i.e., the projected MSSA, is compared with that of the truncated SVD method, one of the most employed noise-reduction methods. The result shows that the projected MSSA exhibits better performance in reducing random noise than the truncated SVD method. Additionally, in contrast to that of the truncated SVD method, the performance of the projected MSSA is less sensitive to the truncation rank. Furthermore, the projected MSSA achieves denoising effectively by extracting smooth trajectories in a state space from noisy input data. Expectedly, the projected MSSA will be effective for reducing random noise in not only PSP measurement data, but also various high-dimensional time-series data.
