Coal and Rock Classification with Rib Images and Machine Learning Techniques

Details

• Personal Author:
  Xue Y
• Description:
  This paper is aimed at helping the mining industry to better understand the challenges posed by electromagnetic interference (EMI) and to promote electromagnetic compatibility (EMC) in underground coal mines. The paper begins with a review of EMI standards in other industries, then presents some representative examples of EMI instances that have occurred in the mining industry, followed by a literature review of published EMI research in mining and, finally, a discussion on mitigation strategies and practical considerations related to EMI in mining. [Description provided by NIOSH]
• Subjects:
  Automation Mining Occupational Health Safety
• Keywords:
  Author Keywords: Rock Classification Coal Mines Computer Assisted Mining Image Processing Machine Learning Mining Industry Patch Rock Mechanics SVM Underground Mining
• Series:
  Mining Publications
• ISSN:
  0026-5187
• Document Type:
  Text
• Genre:
  Abstract or Summary
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  PMRD - Pittsburgh Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  51-53
• Volume:
  74
• Issue:
  6
• NIOSHTIC Number:
  nn:20065572
• Citation:
  Min Eng 2022 Jun; 74(6):51-53
• Email:
  qcj1@cdc.gov
• Federal Fiscal Year:
  2022
• NORA Priority Area:
  Mining
• Peer Reviewed:
  True
• Source Full Name:
  Mining Engineering
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:69363126c7bdcb81c7eb133b3f093b8683e6391b9f5a6db67349ff9671028f1d4a167f0127dd26f93752f513ead8dc5762b526a6bf7b211ec10a093c1ddd20b8
• Download URL:
  https://stacks.cdc.gov/view/cdc/215533/cdc_215533_DS1.pdf
• File Type:
  [PDF - 1.10 MB ]