A New Safety Evaluation Model of Coal Mine Roof based on Multi-sensor Fusion in case of Information Confliction

被引:5
作者
Jia, Rui-sheng [1 ,2 ]
Sun, Hong-mei [1 ]
Zhang, Chong-qing [1 ]
Lv, Xue-ting [1 ]
机构
[1] Shandong Univ Sci Tech, Coll Info Sci Eng, Qingdao, Peoples R China
[2] Shandong Univ Sci Tech, Key Lab Mine Disaster Prevent & Control, Qingdao, Peoples R China
关键词
roof safety evaluation model; high conflict evidence; D-S evidence theory; multi-sensor data fusion;
D O I
10.4304/jcp.7.2.499-506
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Factors that affect the safety of coal mine roof is a multi-faceted, information fusion technology can take full advantage of multi-source information complementary and comprehensive, and improving information quality and credibility of coal mine roof safety. In analyzing the current monitoring means, a coal mine roof safety evaluation model is presented based on Dempster-Shafer evidence fusion theory, After normalizing the various sensor data, the model can acquire the basic probability assignment of the system judging the security situation of the roof, and then using the Dempster-Shafe synthesis rule to synthesize the multi-evidence, and acquiring the whole judgment of the security situation of the roof; as to the problem of the failure of Dempster evidence synthesis rule under the high conflict, establish similarity matrix through the evidence distance, determine the weight coefficient of the evidence, and use the Dempster rule to combine after the pretreatment of the evidence. Through the simulation and compared with other improved methods, the model is proved to decrease the influences that the conflict makes to the combination result, and at the same time improve the convergence rate of evidence combination and reduce the risk of decision-making under the high conflict evidence.
引用
收藏
页码:499 / 506
页数:8
相关论文
共 24 条
[1]   UPPER AND LOWER PROBABILITIES INDUCED BY A MULTIVALUED MAPPING [J].
DEMPSTER, AP .
ANNALS OF MATHEMATICAL STATISTICS, 1967, 38 (02) :325-&
[2]   EVCLUS: Evidential clustering of proximity data [J].
Denoeux, T ;
Masson, MH .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (01) :95-109
[3]  
DUBOIS H, 1998, COMPUTATIONAL INTELL, P88
[4]  
Guo Hua-wei, 2006, Journal of Shanghai Jiaotong University, V40, P1895
[5]  
Hu Chang-hua, 2009, Acta Electronica Sinica, V37, P1578
[6]  
[贾瑞生 Jia Ruisheng], 2010, [煤炭学报, Journal of China Coal Society], V35, P1496
[7]  
JIA Yong-hong, 2005, MULTIPLE REMOTE SENS
[8]  
Joussleme A.-L., 2001, Information Fusion, V2, P91, DOI 10.1016/S1566-2535(01)00026-4
[9]  
Klein Lawrence A., 2004, THEORY APPL MULTISEN
[10]  
Lan Jinhui, 2001, Journal of Tsinghua University (Science and Technology), V41, P53