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General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Editor-in-chief
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2017 Vol.6(2): 67-71 ISSN: 2010-3751
doi: 10.18178/ijfcc.2017.6.2.491

Study on the Prediction Method of Fusion Recognition for Characteristic Information of Coal Spontaneous Combustion

Wei-Feng Wang, Jun Deng, Yuan-Bin Hou, and Nai-Guo Wang
Abstract—To solve the technical problems of identification for the dangerous degree of coal spontaneous combustion, the coal spontaneous combustion process is divided into three stages: slow oxidation, accelerating oxidation and intense oxidation in the paper. The prediction method of fusion identification for characteristic information of coal spontaneous combustion is proposed and the sensitivity index of coal spontaneous combustion degree is determined. The quantitative relationship of the characteristic temperature of coal spontaneous combustion and the gas concentration of each index is determined by polynomial least squares fitting method according to the sample test data. The different feature states are classified by SVM and PSO-SVM algorithm. The criterion of the diagnosis and early warning of coal spontaneous combustion is given according to the results of the data level and feature level. The proposed method can effectively solve the problem of low recognition rate. The experiment shows that the prediction classification accuracy of SVM is 80%, the prediction classification accuracy of PSO - SVM is approximately 100%. The PSO-SVM algorithm can significantly improve the prediction accuracy compared with the traditional method, which provides criterion for the diagnosis and early warning of coal spontaneous combustion. The classification identification of the dangerous degree of coal spontaneous combustion is implemented. It is of great significance and practical application value for improving the level of prevention and control technology of coal spontaneous combustion early hazards.

Index Terms—Coal spontaneous combustion, characteristic information, fusion recognition, prediction method.

Wei-Feng Wang and Jun Deng are with the School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi 710054, China (corresponding author: Jun DENG; tel.: +86-29-85583749; e-mail: 251044098@qq.com, 693167478@qq.com).
Yuan-Bin Hou is with the School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi 710054, China (e-mail: 739053026 @qq.com).
Nai-Guo Wang is with the Shandong Xinjulong Energy Limited Liability Company, Heze, Shandong 274918, China (e-mail: 81864920@qq.com).

[PDF]

Cite: Wei-Feng Wang, Jun Deng, Yuan-Bin Hou, and Nai-Guo Wang, "Study on the Prediction Method of Fusion Recognition for Characteristic Information of Coal Spontaneous Combustion," International Journal of Future Computer and Communication vol. 6, no. 2, pp. 67-71, 2017.

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