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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Analyzing point cloud of coal mining process in much dust environment based on dynamic graph convolution neural

Zhizhong Xing1, Shuanfeng Zhao2, Wei Guo1

  • 1College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Environmental Science and Pollution Research International
|August 13, 2022
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Summary

This study introduces a new method to identify marker balls in coal mine point clouds, improving coordinate conversion for sustainable mining. The approach enhances geometric feature recognition for better safety and intelligent operations.

Keywords:
Coal energyDeep learningDust explosionEnvironmental perceptionFully mechanized mining faceGeologic modelGraph neural networkPoint cloud

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Area of Science:

  • Geosciences
  • Computer Science
  • Mining Engineering

Background:

  • Underground coal mines generate significant dust, impacting environmental perception and worker safety.
  • Accurate point cloud data is crucial for converting mining coordinates to geodetic coordinates, essential for large-scale operations.

Purpose of the Study:

  • To develop a robust method for identifying marker balls within coal mine point clouds.
  • To enable accurate coordinate conversion for large-scale, fully mechanized mining faces.
  • To enhance the intelligence and safety of underground mining operations.

Main Methods:

  • Generated multi-density geometry point clouds from complete and incomplete data, addressing uneven distribution.
  • Increased the weight of point cloud normal vectors in network training to enhance sensitivity to geometric features.
  • Utilized advanced deep neural networks, including dynamic graph convolution neural network (DGCNN), for direct point cloud analysis.

Main Results:

  • The proposed method, combined with DGCNN, accurately identifies marker balls in massive coal mining point clouds.
  • The approach effectively handles uneven point cloud distribution and enhances geometric feature extraction.
  • Demonstrated improved sensitivity of the network model to subtle geometric features.

Conclusions:

  • The developed method significantly improves the accuracy of marker ball identification in coal mine point clouds.
  • This research enhances production efficiency and safety in fully mechanized mining.
  • It lays the groundwork for intelligent mining and mitigation of hazards like dust explosions.