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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ralston, J.C. |
| Copyright Year | 2012 |
| Description | Author affiliation: Mining Technology Research Group, CSIRO Earth Science and Resource Engineering, QCAT, 1 Technology Court, Pullenvale, Queensland 4069 Australia (Ralston, J.C.) |
| Abstract | This paper describes an industrial application of mining automation technology that uses a real-time geological sensing system to enable new machine guidance capabilities for longwall shearer operations in underground coal mining. The work is primarily motivated by the need to find ways to automatically maintain the longwall shearer's position within the coal seam so that the mining resource can be optimally extracted. This process is important for improving the safety, productivity and sustainability of longwall mining. The current approach for resource tracking relies almost exclusively on manual inspection and so has significant practical drawbacks. As a solution to this problem, this paper describes a new sensing method designed to automatically track geological coal seam features, known as marker bands, using thermal infrared imaging. The feature detection and tracking tasks are described with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a practical way to achieve automated horizon control and so help overcome a long standing issue in underground coal mining. |
| Starting Page | 20 |
| Ending Page | 25 |
| File Size | 663763 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467304290 |
| ISSN | 21618070 |
| e-ISBN | 9781467304306 |
| DOI | 10.1109/CoASE.2012.6386442 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-20 |
| Publisher Place | Korea (South) |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Coal Cameras Face Geology Sensors Feature extraction Automation |
| Content Type | Text |
| Resource Type | Article |
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