Using Unsupervised Learning for Feature Detection in a Coal Mine Roof

Details

• Personal Author:
  Hicks MA ; King, Roger L. ; Signer, Stephen P.
• Description:
  The use of an unsupervised learning technique for classifying geological features in the roof overlying an underground coal mine is described. The technique uses torque, thrust, drill speed, penetration rate, and drill position data from a roof bolter as inputs for the classification. Data were obtained from an underground coal mine in the western United States and initially classified using clustering. Some of the available approaches for clustering are reviewed and the rationale used in selecting the chosen approach is discussed. The cluster centers, or exemplars, obtained from this approach can be used to train two supervised neural networks involving the back-propagation of error learning algorithm. [Description provided by NIOSH]
• Subjects:
  Fires Mining Occupational Health Safety Ventilation
• Keywords:
  Artificial Neural Networks Author Keywords: Clusters Coal-mining Feature Detection Mining-industry Underground-mining Underground Mining Unsupervised Learning
• Series:
  Mining Publications
• ISSN:
  0952-1976
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Mississippi ; OSHA Region 10 ; OSHA Region 4 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  SRC - Spokane Research Center
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  6
• Issue:
  6
• NIOSHTIC Number:
  nn:10012303
• Citation:
  Eng Appl Artif Intell 1993 Dec; 6(6):565-573
• Federal Fiscal Year:
  1994
• Peer Reviewed:
  True
• Source Full Name:
  Engineering Applications of Artificial Intelligence
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d8578a68f1264d93e931525950e5848c14aa8dd1fb144f11ef7f25d49b4d66c98c35b0398cfca5b861747c3d498f9aacba4a20d968c0cede393f0cc58d8e94e3
• Download URL:
  https://stacks.cdc.gov/view/cdc/220273/cdc_220273_DS1.pdf
• File Type:
  [PDF - 694.96 KB ]