Name
Abstract | ||
|---|---|---|
Coal mine disaster has a serious threat to production and safety, mine safety prediction is an extremely challenging problem from many perspectives. This paper describes a generic fusion model for coal mine safety combining information from several physically different sensors aiming to the detection, monitoring and crisis management of such natural hazards. A conduct model base on least squares support vector machine (LSSVM) is proposed. Experimental results from the coal mine sensors are presented |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MVHI.2010.71 | MVHI |
Keywords | Field | DocType |
different sensor,crisis management,coal mine safety,safety prediction,serious threat,support vector machine,challenging problem,natural hazard,conduct model base,generic fusion model,multisensor fusiion,squares support vector machine,safety level,remote monitoring,security,sensor fusion,occupational safety,least squares support vector machine,support vector machines,coal,coal mining,mining industry | Least squares support vector machine,Computer science,Coal mining,Support vector machine,Sensor fusion,Crisis management,Coal,Artificial intelligence,Condition monitoring,Natural hazard,Machine learning,Mining engineering | Conference |
ISBN | Citations | PageRank |
978-1-4244-6596-5 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Desheng Liu | 1 | 0 | 1.01 |
| Zhiru Xu | 2 | 0 | 1.01 |
| Wei Wang | 3 | 1679 | 168.84 |
| Lei Wang | 4 | 401 | 111.60 |