To realize the unmanned automation of the full mechanized caving, the bottleneck problem of coal-gangue interface detection in top coal caving must be solved first. Targeting coal-gangue interface detection on fully mechanized mining face, an alternative scheme to detect coal-gangue interface based on vibration signal analysis of the tail boom support of the longwall mining machine. It is found that when coal and gangue fall, the characteristics of vibration signals generated by coal and gangue shocking the tail boom are different. First, EEMD algorithm is used to decompose the original vibration signals into intrinsic mode functions (IMFs). Each IMF represents the distribution of energy from high to low. EEMD algorithm can restrain the mode mixing phenomenon caused by empirical mode decomposition (EMD). The energy of vibration signals will change in different frequency bands when the top-coal fall down or the coal-gangue fall down. According the information theory, we define EEMD energy entropy to describe this change. Experimental results show that EEMD energy entropy of top-coal caving is always greater than that of coal-gangue caving. Thus, the Mahalanobis distance metric method based on EEMD energy entropy is proposed for coal-gangue interface detection. The results show the proposed method can be used as a robust empirical method for coal-gangue interface detection.

To obtain the quality of water more economically and flexibly, it improves the traditional node optimization layout method -dynamic proximity degree method from the perspective of unmanned ship. Based on considering the data correlation and time factors of the monitoring nodes, and considering the influence of the relative position relationship between the nodes on the length of the inspection path, a simple and efficient genetic algorithm is designed to further optimize the node selection with a faster convergence speed. The water quality monitoring experiment of lake in the university was carried out by an unmanned surface vehicle and MATLAB platform. The results show that the improved dynamic proximity algorithm retains the representativeness of the nodes to the water quality and greatly shorts the cruise path of the ship. In the optimization experiment of 14 nodes, compared with the traditional node selection method, the path length is shortened by 14.85% on average.

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