Coal analysis based on visible-infrared spectroscopy and a deep neural network

Le, Ba Tuan; Xiao, Dong; Mao, Yachun; He, Dakuo

doi:10.1016/j.infrared.2018.07.013

Cited by 53 publications

(20 citation statements)

References 23 publications

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“…Before using traditional machine learning methods such as BP neural network and support vector machine (SVM), it is necessary to extract features in time or frequency domain manually. However, the scene is different in deep learning methods, which automatically extract the abstract features through a series of convolutional kernels . The whole feature extraction process is very similar to the process of observing an object from micro to macro through a microscope.…”

Section: Introductionmentioning

confidence: 99%

End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

Chen

Wang

2019

Journal of Chemometrics

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During the near‐infrared spectroscopy analysis process, modeling the quantitative relationship between the collected spectral information and target components is an important procedure. Before using the traditional modeling methods, it is often necessary to select the most featured wavelengths and eliminate those uninformative wavelengths. However, the wavelength selection algorithms can not only increase the model complexity but also may contain some adjustable parameters, which need the users to have more expertise knowledge and experiences. To solve this problem, this paper proposed a novel end‐to‐end quantitative analysis modeling method for near‐infrared spectroscopy based on convolutional neural network (CNN), which directly takes the whole range of collected raw spectral information as input without wavelength selection. The public corn NIR dataset was taken as example to validate the efficiency of proposed method. The experimental results showed that, firstly, if all the whole range of raw spectral information was taken as the input of modeling, the generalized performance of CNN outperforms the traditional methods, and the difference is statistically significant; secondly, if the traditional methods were combined with wavelength selection algorithms, their generalized performances were similar to CNN model; there is no statistical difference. The results indicated that applying the deep learning methods (take CNN as representative) to establish the quantitative analysis model of near‐infrared spectroscopy is easy to use and has more potential popularize values.

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Section: Introductionmentioning

confidence: 99%

End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

Chen

Wang

2019

Journal of Chemometrics

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“…It is thus advantageous to apply deep neural networks for the analysis of vibrational spectra, which are a complex superposition of all vibrational information within the sample. Applications of deep learning were reported for both infrared and Raman spectroscopy in order to achieve tasks like brain function investigations , biological diagnostics , cytopathology , microbial identification , pathogenic bacteria identification , food science investigations , tobacco leaves characterization and mineral analysis . Furthermore, it was reported in references that deep learning can perform better than classical machine learning methods .…”

Section: Deep Learning For Vibrational Spectroscopymentioning

confidence: 99%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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“…is equivalent to solving equation (29). Because ( ) ( ) = ( ) −1 ( ) ( ) is equivalent to ( ) ( ) = ( ) ( ) , the calculation formula for the elements (V ) ( ) of ( ) ( ) can be obtained by comparing the elements on both sides of the equation…”

Section: Process Of Matrices Decomposition For Fc-imrelmmentioning

confidence: 99%

“…. For the regression model, the rootmean-square error (RMSE) and the coefficient of determination (R2) [29] are used as the model performance evaluation indexes in this study to verify the effectiveness of the proposed FC-IMRELM algorithm. R2 and RMSE are expressed as follows:…”

Section: Regression Problemsmentioning

confidence: 99%

Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

Liu

2019

Mathematical Problems in Engineering

Self Cite

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The theory and implementation of extreme learning machine (ELM) prove that it is a simple, efficient, and accurate machine learning method. Compared with other single hidden layer feedforward neural network algorithms, ELM is characterized by simpler parameter selection rules, faster convergence speed, and less human intervention. The multiple hidden layer regularized extreme learning machine (MRELM) inherits these advantages of ELM and has higher prediction accuracy. In the MRELM model, the number of hidden layers is randomly initiated and fixed, and there is no iterative tuning process. However, the optimal number of hidden layers is the key factor to determine the generalization ability of MRELM. Given this situation, it is obviously unreasonable to determine this number by trial and random initialization. In this paper, an incremental MRELM training algorithm (FC-IMRELM) based on forced positive-definite Cholesky factorization is put forward to solve the network structure design problem of MRELM. First, an MRELM-based prediction model with one hidden layer is constructed, and then a new hidden layer is added to the prediction model in each training step until the generalization performance of the prediction model reaches its peak value. Thus, the optimal network structure of the prediction model is determined. In the training procedure, forced positive-definite Cholesky factorization is used to calculate the output weights of MRELM, which avoids the calculation of the inverse matrix and Moore-Penrose generalized inverse of matrix involved in the training process of hidden layer parameters. Therefore, FC-IMRELM prediction model can effectively reduce the computational cost brought by the process of increasing the number of hidden layers. Experiments on classification and regression problems indicate that the algorithm can be effectively used to determine the optimal network structure of MRELM, and the prediction model training by the algorithm has excellent performance in prediction accuracy and computational cost.

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Coal analysis based on visible-infrared spectroscopy and a deep neural network

Cited by 53 publications

References 23 publications

End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

Deep learning a boon for biophotonics?

Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

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