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This paper focused on the problem of the source localization of coal mine gas release. Due to the existence of wind leakage,it is difficult to find the location accurately by using the conventional algorithms. In this study,a multisource sensor fusion based algorithm for locating the coal mine gas release was proposed. Firstly, the gas distribution in the goaf of a fully mechanized working face was analyzed and the observation model ol mine goaf area and the diffusion model of gas release were established. Then,the gas release source parameters of goaf were estimated using the Mixed Kalman Particle Filter algorithm ( MKPF) , and the coordinate of the estimated parameters was obtained according to the iterative operation. Finally, the data of wireless sensor target source sensing nodes and cluster head nodes were fused to locate the accurate coordinate of the gas release source. The research concludes that the MKPF algorithm has obvious advantages in accuracy localization, which can effectively overcome the difficulty of gas release source localization impacted by wind leakage. It can provide support for gas warning and gas drainage in goaf. [ABSTRACT FROM AUTHOR]

The mine intelligent monitoring is an important guarantee for the realization of automatic mining and visual operation in few people or unmanned working face. To address the problems of low resolution in the existing captured videos, the image blur with noise and long processing time by using the conventional methods of Nyquist sampling and image compressing for mine monitoring images in the mine environment where the images are easy to be influenced by the noise and spray dust, on the basis of the compressed sensing and sparse reconstruction theory, a novel monitoring images improved algorithm based on sparsity adaptive matching pursuit (SAMP) is proposed. Firstly, by establishing the model of the block compressed sensing in images, the proposed method adopts sparse random measurement matrix to compress sensing block images and to obtain observations. Then, DFT as sparse basis for signal sparse representation is used. Finally, an improved adaptive matching pursuit algorithm is employed to decode the images. The results indicate that the method proposed in this paper shows the superiority in comparing with other algorithms, effectively improves the speed of image acquisition and compression, and enhances decoding quality in the reconstruction process, as well as has a strong anti-noise performance and robustness. This method is helpful to improve the image definition and real-time processing performance for mine monitoring system. [ABSTRACT FROM AUTHOR]

The existing mine networks do not have a high disaster recovery capability because most of them ignore reliability design, or critical fault caused by conventional mine communication networks. A survivability model for the mine EPON was built considering the effect of network link redundancy on mine network reliability, and the tree, bus and ring network redundant structures in the mine EPON was proposed based on the model. Thus, an algorithm to evaluate the survivability of above structures was derived. Several numerical experiments demonstrate that the survivability of the mine EPON is related to the disaster intensity, network coverage and structure, which becomes worse with the greater intensity of disaster and network coverage. On the condition of the same intensity of disaster and coverage area, the reliability of redundant structure is better than that of no-redundant structure. Also, at the same network coverage of the mine EPON, the survivability of the dual-bus structure is better than that of the dual-ring and tree-shaped redundant structure. The network survivability of dual-ring structure is better than that of the tree-shaped redundant structure. These conclusions can be used to forecast and evaluate the reliability of digital mine communication network. [ABSTRACT FROM AUTHOR]