A fast artificial neural network approach for dynamic light scattering time series processing
A fast artificial neural network approach for dynamic light scattering time series processing
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DOI：
10.1088/1361-6501/aad937
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发表时间：
2018-10-01
影响因子：
2.4
通讯作者：
Rei, Silviu Mihai
中科院分区：
文献类型：
--
作者：
Chicea, Dan;Rei, Silviu Mihai
本文提出了一种简单的替代动态光散射（DLS）的时间序列处理，通过使用人工神经网络。给出了一个记录DLS时间序列的简单实验。DLS时间序列处理的参考方法包括将洛伦兹线的分析形式拟合到记录的散射光强度的频谱。设计并训练了一个单隐层人工神经网络。训练数据包括一个大的自相关的模拟时间序列的单分散球形颗粒的直径范围在10-1200 μ m。神经网络的输出精度进行了测试，在模拟和实验的时间序列记录在含有纳米粒子和微粒的流体。人工神经网络输出相对于参考直径的误差足够小，并且数据处理过程快三个数量级，证明了人工神经网络方法尽管简单，但可以更快地替代DLS时间序列处理。
This paper presents a simple alternative to dynamic light scattering (DLS) time series processing by using an artificial neural network. A simple experiment for recording a DLS time series is presented. The reference method for DLS time series processing consisted of fitting the analytical form of the Lorentzian line to the frequency spectrum of the recorded scattered light intensity. An artificial neural network with one hidden layer was designed and trained. The training data consisted of a big set of autocorrelations of simulated time series for monodispersed spherical particles with diameters in the range 10-1200 mu m. The neural network output precision was tested both on simulated and on experimental time series recorded on fluids containing nanoparticles and microparticles. The errors of the artificial neural network output relative to the reference diameters were small enough and the data processing procedure was three orders of magnitude faster, proving that, in spite of the simplicity, the artificial neural networks approach can be a faster alternative for DLS time series processing.