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

Abstract

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.

The use of Monte Carlo simulation to evaluate the optical properties of polyester fabric treated with titanium dioxide nanopigments

“…The aim of MC simulation is to establish a random process or probability model. 23,28 In this work, the optical properties and reflectance spectra of titanium dioxide-treated polyester fabric were simulated by the MC method. The process of MC simulation is performed over several steps, namely, simulating the particle distribution, random arrangement, reflective spectrum and the occupied area of titanium dioxide nanoparticles on the fabric's surface by using partitive mixing, Mie theory and the MC method.…”

“…MC simulation is a mathematical and statistical method. The aim of MC simulation is to establish a random process or probability model 23,28 . In this work, the optical properties and reflectance spectra of titanium dioxide‐treated polyester fabric were simulated by the MC method.…”

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

“…In recent years, the extreme learning machine (ELM) [22,23] and kernel extreme learning machine (KELM) [24] are proposed and have been successfully applied to model identification, such as saliency detection [25], gesture recognition [26], image classification [27], nonlinear fault detection [28], seepage time soft sensor model of nonwoven fabric [29], and rolling bearing sub-health recognition [30]. However, different kernel functions have different characteristics, and the performances are varying in different applications.…”