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Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo

机译：基于极端学习机整合蒙特卡罗的非织造面料渗流时间软传感器模型

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摘要

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.
机译：非织造纤维材料是具有多功能用途的材料，并且被广泛地用于制造掩模，用于防止由于复杂性和其结构的特殊性的新冠状病毒病2019，则难以渗透建模和流动在非织造纤维材料的液体特征。在本文中，非织造织物的新的渗流时间软传感器模型，基于蒙特卡罗（MC），结合极端学习机（ELM）（MCELM）提出。蒙特卡罗方法用于扩展数据样本。然后，ELM方法被用于建立与兽皮的不同量的多孔介质，以及所述插入程度和高度的非织造纤维材料的重叠的染色时间的预测模型。与反向传播（BP）神经网络和径向基函数（RBF）神经网络比较，结果表明，基于该MCELM方法预测模型具有显著功率在精度和预测速度，这有利于精确和方面快速制造的非织造纤维材料在液体渗流特性和多孔介质的结构特征之间的实际应用的。此外，所提出的模型之间的关系具有用于预测患有不同的结构特征和相关的研究过程中的非织造纤维材料的特性和使用某些价值。

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