Testing of Fiber Contents in Mixture Textiles by NIR Combined with BP Neural Network

2014 ◽  
Vol 651-653 ◽  
pp. 301-304
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yao Cheng Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Human Life ◽  
Back Propagation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Least Square ◽  
Neural Network Models ◽  
Mixture Samples

Textiles are necessaries of human life. The fiber content is index of textile quality and how to measure it has important meaning. A method for testing fiber contents in mixture textiles by near infrared spectroscopy (NIR) was researched. The near infrared Spectra of samples in the range of 4000 cm-1 - 10000 cm-1 were obtained. Noise reduction and compression of spectra data was done by wavelet transform (WT). The reconstructed spectral signals were established based on WT and the correction models based on back propagation (BP) neural network were built. Comparisons between the BP neural network models at different analysis scale and the model of partial least square method (PLS) were given. When the structure of neural network is 11-9-2 for cotton/ terylene mixture samples and 21-13-2 for cotton/wool mixture samples, the best accuracy and fastest convergence speed is achieved. Experimental results have shown that this approach by Fourier transform NIR based on the BP neural network to predict the fiber content of textile mixture can satisfy the requirement of quantitative analysis and is also suitable for other fiber contents measurement of mixture textiles.

scholarly journals Determination of Fiber Contents in Blended Textiles by NIR Combined with BP Neural Network

ISRN Textiles ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yaocheng Xie ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Fiber Content ◽  
Cotton Wool ◽  
Near Infrared Spectra ◽  
Hidden Neurons

Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.

Variable selection in classification of environmental soil samples for partial least square and neural network models

2001 ◽  
Vol 446 (1-2) ◽  
pp. 231-242 ◽  
Author(s):  
Ziad Ramadan ◽  
Xin-Hua Song ◽  
Philip K Hopke ◽  
Mara J Johnson ◽  
Kate M Scow
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Models ◽  
Soil Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Neural Network Models

scholarly journals A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2808
Author(s):  
Li Li ◽  
Jiahui Yu ◽  
Hang Cheng ◽  
Miaojuan Peng
Keyword(s):  
Neural Network ◽  
Body Temperature ◽  
Temperature Measurement ◽  
Error Compensation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Least Square Regression ◽  
Bpnn Model

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

scholarly journals Study on Nanosecond Laser Ablation of 40Cr13 Die Steel Based on ANOVA and BP Neural Network

2021 ◽  
Vol 11 (21) ◽  
pp. 10331
Author(s):  
Zhenshuo Yin ◽  
Qiang Liu ◽  
Pengpeng Sun ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Removal Rate ◽  
Back Propagation ◽  
Pulse Frequency ◽  
Ablation Depth ◽  
Nanosecond Laser ◽  
Neural Network Models

Microstructured steel 40Cr13, which is considered a hard-to-machine steel due to its high mechanical strength and hardness, has wide applications in the dies industry. This study investigates the influence of three process parameters of a 355 nm nanosecond pulse laser on the ablation results of 40Cr13, based on analysis of variance (ANOVA) and back propagation (BP) neural network. The ANOVA results show that laser power has the greatest influence on the ablation depth, width, and material removal rate (MRR), with influence levels of 52.5%, 60.9%, and 70.4%, respectively. The scan speed affects the ablation depth and width to a certain extent, and the influence of the pulse frequency on the ablation depth and MRR is non-negligible. BP neural network models with 3-8-3, 3-10-3, and 3-12-3 structures were applied to predict the ablation results. The results show that the prediction accuracy is relatively high for the ablation width and MRR, with average prediction accuracies of 96.0% and 93.5%. The 3-8-3 network model has the highest prediction accuracy for the ablation width, and the 3-10-3 network model has the highest prediction accuracy for the ablation depth and MRR.

scholarly journals Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum

2019 ◽  
Vol 16 (2) ◽  
pp. 701
Author(s):  
Mohd Nazrul Effendy Mohd Idrus ◽  
Kim Seng Chia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Latent Variables ◽  
Near Infrared ◽  
Spectroscopic Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and  of 0.9787, and PLS with RMSEP of 0.4669 gd/L and  of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.</p>

scholarly journals ON-LINE DETECTION OF CERASUS HUMILIS FRUIT BASED ON VIS/NIR SPECTROSCOPY COMBINED WITH VARIABLE SELECTION METHODS AND GA-BP MODEL

2021 ◽  
pp. 199-210
Author(s):  
Bin Wang ◽  
Junlin He ◽  
Shujuan Zhang ◽  
Lili Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Prediction Models ◽  
Recognition Rate ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Full Spectrum ◽  
Cerasus Humilis ◽  
Bp Model

In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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