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Notice of Retraction
Improvement Research of Evidence Theory in Mine Water Inrush Prediction

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4 Author(s)
Xiao Jianyu ; Sch. of Comput. Sci. & Technol., China Univ. of Min. & Technol., Xuzhou, China ; Tong Minming ; Feng Wei ; Guo Xijin

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

Since the highly conflicting evidence could not be combined effectively through D-S evidence theory, a novel D-S data fusion method based on evidence quality is introduced in this paper. The concrete algorithm for the reliability of observer and the measure of quality function value based on observer reliability are proposed. In the data fusion, adopting the measure of evidence quality, the collected data from multi-sensor is assigned to the different weight according to the reliability of observer, and the probability assignment value is correspondingly adjusted. The improved D-S evidence theory, along with combining the highly conflicting evidence, is applied successfully to the prediction of mine water inrush. The experimental results show that the method is effective.

Published in:

Education Technology and Computer Science (ETCS), 2010 Second International Workshop on  (Volume:1 )

Date of Conference:

6-7 March 2010