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Jianjian Yang
China University of Mining and Technology - Beijing Campus
Corresponding Author
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Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing links, this article proposes a CNN coal and rock identification method based on hyperspectral data. First, collect coal and rock spectrum data by a near-infrared spectrometer, and then use four methods such as first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing to filter the 120 sets of collected data. The coal and rock reflectance spectrum data is preprocessed to enhance the intensity of spectral reflectance and absorption characteristics, and effectively remove the spectral curve noise generated by instrument performance and environmental factors.Construct a CNN model, judge the pros and cons of the model by comparing the accuracy of the three parameter combinations, select the most appropriate learning rate, the number of feature extraction layers, and the dropout rate, and generate the best CNN classifier for hyperspectral data. Rock recognition. Experiments show that the recognition accuracy of the one-dimensional convolutional neural network model proposed in this paper reaches 94.6%, which is higher than BP (57%), SVM (72%) and DBN (86%). Verify the advantages and effectiveness of the method proposed in this article.
Keywords
Hyperspectral data, Data preprocessing, 1D-CNN, Coal Gangue Identification
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CURRENT STATUS: Under Revision
Version 1
Posted 11 May, 2021
No community comments so far
Editorial decision: Major Revision
On 18 Jul, 2021
Reviewer #1 agreed
On 25 Jun, 2021
Review #1 received
Received 25 Jun, 2021
Reviewers invited
Invitations sent on 20 Jun, 2021
Editor assigned
On 02 May, 2021
Submission checks complete
On 02 May, 2021
Editor invited
On 02 May, 2021
First submitted
On 01 May, 2021
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This preprint is under consideration at International Journal of Coal Science & Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Jianjian Yang
China University of Mining and Technology - Beijing Campus
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 11 May, 2021
No community comments so far
Editorial decision: Major Revision
On 18 Jul, 2021
Reviewer #1 agreed
On 25 Jun, 2021
Review #1 received
Received 25 Jun, 2021
Reviewers invited
Invitations sent on 20 Jun, 2021
Editor assigned
On 02 May, 2021
Submission checks complete
On 02 May, 2021
Editor invited
On 02 May, 2021
First submitted
On 01 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing links, this article proposes a CNN coal and rock identification method based on hyperspectral data. First, collect coal and rock spectrum data by a near-infrared spectrometer, and then use four methods such as first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing to filter the 120 sets of collected data. The coal and rock reflectance spectrum data is preprocessed to enhance the intensity of spectral reflectance and absorption characteristics, and effectively remove the spectral curve noise generated by instrument performance and environmental factors.Construct a CNN model, judge the pros and cons of the model by comparing the accuracy of the three parameter combinations, select the most appropriate learning rate, the number of feature extraction layers, and the dropout rate, and generate the best CNN classifier for hyperspectral data. Rock recognition. Experiments show that the recognition accuracy of the one-dimensional convolutional neural network model proposed in this paper reaches 94.6%, which is higher than BP (57%), SVM (72%) and DBN (86%). Verify the advantages and effectiveness of the method proposed in this article.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The full text of this article is available to read as a PDF.
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