RETRACTED ARTICLE: A robust vector field correction method via a mixture statistical model of PIV signal
RETRACTED ARTICLE: A robust vector field correction method via a mixture statistical model of PIV signal
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DOI：
10.1007/s00348-016-2115-y
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发表时间：
2016-02
影响因子：
2.4
通讯作者：
Yong Lee;Hua Yang;Zhouping Yin
中科院分区：
文献类型：
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作者：
Yong Lee;Hua Yang;Zhouping Yin
在粒子图像测速技术（PIV）的实际速度场测量中，异常值（伪矢量）是一个常见的问题，需要对其进行验证并替换为可靠的值。在存在测量噪声或湍流的情况下，如何正确标记异常值是最具挑战性的问题之一。此外，异常值的聚类出现使得很难挑出所有的异常值。目前的方法大多是利用局部统计模型一次验证和校正离群值。本文提出了一种基于PIV信号混合统计模型的矢量场校正方法。实际上，这个问题被表述为一个带有隐藏/潜在变量的贝叶斯模型的最大后验（MAP）估计，标记原始领域中的异常值。使用期望最大化算法迭代优化MAP估计的解，即离群值集和恢复的流场。我们在两种合成速度场和两种实验数据上说明了这种VFC方法，并证明了它对大量的异常值（甚至高达60%）具有鲁棒性。此外，与现有方法相比，所提出的VFC方法具有较高的准确率和对聚类异常点的良好兼容性。我们的VFC算法计算效率高，并提供了相应的Matlab代码供其他人使用。此外，我们的方法是通用的，可以无缝扩展到三维三组件（3D3C） PIV数据。
Outlier (spurious vector) is a common problem in practical velocity field measurement using particle image velocimetry technology (PIV), and it should be validated and replaced by a reliable value. One of the most challenging problems is to correctly label the outliers under the circumstance that measurement noise exists or the flow becomes turbulent. Moreover, the outlier’s cluster occurrence makes it difficult to pick out all the outliers. Most of current methods validate and correct the outliers using local statistical models in a single pass. In this work, a vector field correction (VFC) method is proposed directly from a mixture statistical model of PIV signal. Actually, this problem is formulated as a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables, labeling the outliers in the original field. The solution of this MAP estimation, i.e., the outlier set and the restored flow field, is optimized iteratively using an expectation–maximization algorithm. We illustrated this VFC method on two kinds of synthetic velocity fields and two kinds of experimental data and demonstrated that it is robust to a very large number of outliers (even up to 60 %). Besides, the proposed VFC method has high accuracy and excellent compatibility for clustered outliers, compared with the state-of-the-art methods. Our VFC algorithm is computationally efficient, and corresponding Matlab code is provided for others to use it. In addition, our approach is general and can be seamlessly extended to three-dimensional-three-component (3D3C) PIV data.