IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p2064-d1074737.html
   My bibliography  Save this article

Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM -SVM

Author

Listed:
  • Murad S. Alfarzaeai

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China
    School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Eryi Hu

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China)

  • Wang Peng

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China)

  • Niu Qiang

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Maged M. A. Alkainaeai

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

Abstract

Computer-vision-based separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like density. This study achieves the classification of coal and gangue based on their mass, volume, and weight. A dataset of volume, weight and 3_side images is collected. By using 3_side images of coal gangue, the visual perception value of the volume is extracted ( E x M ) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives ( E x M ) and the weight to perform the coal gangue classification. The proposed system eliminates computer vision problems like light intensity, dust, and heterogeneous coal sources. The proposed model was tested with a collected dataset and achieved high recognition accuracy (KNN 100%, Linear SVM 100%, RBF SVM 100%, Gaussian Process 100%, Decision Tree 98%, Random Forest 100%, MLP 100%, AdaBosst 100%, Naive Bayes 98%, and QDA 99%). A cross-validation test has been done to verify the generalization ability. The results also demonstrate high classification accuracy (KNN 96%, Linear SVM 100%, RBF SVM 96%, Gaussian Process 96%, Decision Tree 99%, Random Forest 99%, MLP 100%, AdaBosst 99%, Naive Bayes 99%, and QDA 99%). The results show the high ability of the proposed technique E x M -SVM in coal gangue classification tasks.

Suggested Citation

  • Murad S. Alfarzaeai & Eryi Hu & Wang Peng & Niu Qiang & Maged M. A. Alkainaeai, 2023. "Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM -SVM," Energies, MDPI, vol. 16(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2064-:d:1074737
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/2064/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/2064/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rong Gao & Zhaoyun Sun & Wei Li & Lili Pei & Yuanjiao Hu & Liyang Xiao, 2020. "Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks," Energies, MDPI, vol. 13(4), pages 1-13, February.
    2. Yuanyuan Pu & Derek B. Apel & Alicja Szmigiel & Jie Chen, 2019. "Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning," Energies, MDPI, vol. 12(9), pages 1-11, May.
    3. Bing Wang & Chao-Qun Cui & Yi-Xin Zhao & Bo Yang & Qing-Zhou Yang, 2019. "Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1345-1364, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    2. Guoyu Wang & Jinsheng Zhou, 2022. "Multiobjective Optimization of Carbon Emission Reduction Responsibility Allocation in the Open-Pit Mine Production Process against the Background of Peak Carbon Dioxide Emissions," Sustainability, MDPI, vol. 14(15), pages 1-21, August.
    3. Jianjian Yang & Boshen Chang & Yuzeng Zhang & Yucheng Zhang & Wenjie Luo, 2022. "PCViT: A Pre-Convolutional ViT Coal Gangue Identification Method," Energies, MDPI, vol. 15(12), pages 1-16, June.
    4. Ling Du & Hasan Dinçer & İrfan Ersin & Serhat Yüksel, 2020. "IT2 Fuzzy-Based Multidimensional Evaluation of Coal Energy for Sustainable Economic Development," Energies, MDPI, vol. 13(10), pages 1-21, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2064-:d:1074737. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.