Title :
Gas Emission Rate Prediction in Fully-Mechanized Excavated Faces Based on Support Vector Machine
Author :
Wang Changlong ; Weihua, Fu
Author_Institution :
Sch. of Civil Eng., Hebei Univ. of Eng., Handan, China
Abstract :
In order to ensure safety in coal production, full assurance is given for fully-mechanized excavated faces. Based on the vector supporting machine for regression (SVR), a model is established for predicting the gas emission in fully-mechanized excavated faces. The index system is analyzed and the model parameters are chosen. Then, the sample set of gas emission in fully-mechanized coal driving workface is use to realize the model by programming based on Matlab. The predictions turn out that the precise is much larger than 90%, which satisfies the demands of design and production of coal mines and provides a reliable foundation for mine ventilation and gas management in fully-mechanized excavated faces.
Keywords :
accident prevention; coal; learning (artificial intelligence); mining industry; regression analysis; support vector machines; Matlab; coal driving workface; coal production; fully-mechanized excavated faces; gas emission rate prediction; gas management; mine ventilation; support vector machine; support vector regression; Civil engineering; Information technology; Machine intelligence; Management training; Mathematical model; Predictive models; Production; Statistical learning; Support vector machine classification; Support vector machines; SVM; Tracking; emission rate; fully-mechanized excavated faces; gas prediction;
Conference_Titel :
Intelligent Information Technology Application Workshops, 2009. IITAW '09. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-6420-3
Electronic_ISBN :
978-1-4244-6421-0
DOI :
10.1109/IITAW.2009.60
