Sun Yanjing

Energy-Balanced Scheme Based Unequal Density of Backbone for Wireless Sensor Networks Under Coal Mine

In order to solve uneven data throughput phenomenon for wireless sensor networks under coal mine, which cause “hot spots” problem, energy-efficient uneven clustering scheme based on the energy and distribution density of cluster heads ( UCS-ED ) is proposed, which formed by many existing routing protocols used in wireless sensor networks. In the cluster heads election phase it put the energy and distribution density as two elements to join the election, form an uneven density of backbone around the Sink. Simulation results show that UCS-ED outperform LEACH and HEED in balancing energy consumption and prolonging the network lifetime.

Intelligent Prediction System of Coal-Gas Outburst Based on Evolutionary Neural Nets

The novel coal-gas dangerous-level prediction model established has advantages of the EA and BP neural nets, and overcomes the shortcomings of misreport and missing-report of others. This approach can accurately capture the complicated relationships among feature values of coal-gas outbursts and dangerous circumstances. We considered the characteristic of coal-gas outburst carefully, combining with the raw data of coal-gas monitor system in the Daping colliery and the 10th colliery of Pingdingshan Company as well as real-time samples of accidents, and selected pattern sets to train the proposed model and generate the corresponding rules for prediction. Results show that the ENN has better performance than the ANN or the traditional method used individually, and enhances the practical techniques for prediction of coal and gas in coal mine to guarantee safety.