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Using unsupervised learning for feature detection in a coal mine roof.
King RL; Hicks MA; Signer SP
Eng Appl Artif Intell 1993 Dec; 6(6):565-573
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.
Mining-industry; Underground-mining; Coal-mining; Author Keywords: Clusters; feature detection; artificial neural networks; underground mining; unsupervised learning
Issue of Publication
Engineering Applications of Artificial Intelligence
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division