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Using unsupervised learning for feature detection in a coal mine roof.

Authors
King-RL; Hicks-MA; Signer-SP
Source
Eng Appl Artif Intell 1993 Dec; 6(6):565-573
NIOSHTIC No.
10012303
Abstract
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.
Keywords
Mining-industry; Underground-mining; Coal-mining; Author Keywords: Clusters; feature detection; artificial neural networks; underground mining; unsupervised learning
CODEN
EAAIE6
Publication Date
19931201
Document Type
Journal Article
Fiscal Year
1994
NTIS Accession No.
NTIS Price
Identifying No.
OP 30-94
Issue of Publication
6
ISSN
0952-1976
NIOSH Division
SRC
Source Name
Engineering Applications of Artificial Intelligence
State
MS; WA
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