DocumentCode
526405
Title
Notice of Retraction
Coal rock interface recognition based on independent component analysis and BP neural network
Author
Wei Liu
Author_Institution
Sch. of Mech. Electron. & Inf. Eng., China Univ. of Min. & Technol., Beijing, China
Volume
5
fYear
2010
fDate
9-11 July 2010
Firstpage
556
Lastpage
558
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, a new method of coal rock interface recognition(CRIR) based on independent component analysis and BP neural network is presented. ICA algorithm was used to separate the preprocessed acoustic signal and extract independent components of coal and rock. By analyzing power spectrum of the first two independent components, we found the difference of coal and rock in specific frequency interval. Then the feature vectors created from energy function were served as the input patterns of BP neural network. Experimental result shows that the proposed approach can efficiently extract important features and has proven to be reliable in CRIR.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, a new method of coal rock interface recognition(CRIR) based on independent component analysis and BP neural network is presented. ICA algorithm was used to separate the preprocessed acoustic signal and extract independent components of coal and rock. By analyzing power spectrum of the first two independent components, we found the difference of coal and rock in specific frequency interval. Then the feature vectors created from energy function were served as the input patterns of BP neural network. Experimental result shows that the proposed approach can efficiently extract important features and has proven to be reliable in CRIR.
Keywords
backpropagation; coal; independent component analysis; mining; neural nets; rocks; BP neural network; ICA algorithm; acoustic signal; coal rock interface recognition; energy function; feature vectors; frequency interval; independent component analysis; power spectrum; Silicon; BP neural network; acoustic signal; coal rock interface recognition; fully mechanized mining face; independent component analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563951
Filename
5563951
Link To Document