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Yong Zhang
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Research on Rock Burst Monitoring and Early Warning Technology Based on RBF Neural Network Yong Zhang; Hui Cai; Yunfu Cheng
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 10: October 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i10.pp7478-7485

Abstract

China is one of the most serious coal mine accidents inthe countries of the world. All of the accidents, rock burst is one of them. Therock burst in coal and rock mass, refers to the sudden power failure, release alarge number of catastrophic dynamic phenomena of energy. It can be destroy theroadway roof, cause other mine disasters, casualties and so on. In China, themine number with rock burst dangerous accounted for more than 20% of the total,Shandong QufuXing cun coal mine among them. In order to prevent to the happen of accident,the coal mine enterprise had been install all kinds of monitoring system, suchas SOS micro seismic system , Fully mechanized working face resistance ofsupport system and so on. Using sensors measuring and computer technology, thedata had been getting from the underground 1000 meters. According to the internal link ofpressure behavior between the basic regularity and variable, RBF neural networkhad been set up. From the model, it can forecast the risk index of rock burst,reveal the superincumbent stratum roof movement; master the process of stateand changes in the laws of underground pressure. It is important significanceto guide safe production of coal mine enterprises.
Computation of the Normalized Prediction Error of the Electroencephalogram Signal Chaofeng Cai; Yong Zhang; Jingying Ren; Liying Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 5: September 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, the normalized prediction error of the electroencephalogram (EEG) signal recorded at five different mental tasks was computed. The results indicate that there exists predictability in the EEG signal beyond the baseline prediction of the mean and the one-step-ahead normalized prediction error of EEG signal vary greatly when different mental tasks are implemented, which implies that the one-step-ahead normalized prediction error can be employed as a feature of EEG signal to distinct different mental tasks. For different subjects, the one-step-ahead normalized prediction error vary greatly even the EEG signal are recorded from the same electrode under the same mental task, which implies that the subjects’ individual differences should be considered adequately when the one-step-ahead normalized prediction error is employed to distinct different mental tasks. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1268 
Co-Authors Chaofeng Cai Hui Cai Jingying Ren Liying Jiang Yunfu Cheng