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Modified Landweber algorithm for robust particle sizing by using Fraunhofer diffraction

机译:改进的Landweber算法,通过Fraunhofer衍射实现鲁棒的颗粒尺寸

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摘要

In this paper, a robust modified Landweber algorithm was proposed to retrieve the particle size distributions from Fraunhofer diffraction. Three typical particle size distributions, i.e., Rosin-Rammler, lognormal, and bimodal normal distributions for particles ranging from 4.8 to 96 μm, were employed to verify the performance of the algorithm. To show its merits, the proposed algorithm was compared with the Tikhonov regularization algorithm and the l_1-norm-based algorithm. Simulation results showed that, for noise-free data, both the modified Landweber algorithm and the l_1-norm-based algorithm were better than the Tikhonov regularization algorithm in terms of accuracy. When the data was noise-contaminated, the modified Landweber algorithm was superior to the other two algorithms in both accuracy and speed. An experimental setup was also established and the results validated the feasibility and effectiveness of the proposed method.
机译:本文提出了一种鲁棒的改进的Landweber算法,以从Fraunhofer衍射中检索粒径分布。三种典型的粒度分布(即Rosin-Rammler分布,对数正态分布和双峰正态分布)的范围为4.8至96μm,用于验证算法的性能。为了显示其优点,将该算法与Tikhonov正则化算法和基于l_1-norm的算法进行了比较。仿真结果表明,对于无噪声数据,改进的Landweber算法和基于l_1-norm的算法在准确性方面均优于Tikhonov正则化算法。当数据受到噪声污染时,改进的Landweber算法在准确性和速度上均优于其他两种算法。建立了实验装置,结果验证了该方法的可行性和有效性。

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