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In this paper, frequency is regarded as a new characteristic parameter to analyze vibration features. Firstly, the principle of vibration signal feature extraction was proposed, and the characteristic frequency function was obtained through the vibration feature extraction test of quick-point grinding. Then, the influence of different levels of technological parameters on grinding vibration under the machining or idle condition was analyzed. And the variation law of characteristic frequency was analyzed by comparing the contact and energy transfer relationship between the grinding wheel and workpiece. The results showed that the machining characteristic frequency is always smaller than the idle and the relative deviation of characteristic frequency decreases with the increase of the grinding wheel ' s linear velocity, and increases with the increase of grinding depth and feed velocity. At the same time, the influence weight of each technological parameter on the characteristic frequency was compared, and the idea of improving the machining efficiency was put forward. [ABSTRACT FROM AUTHOR]

Aiming at the problem of inaccurate locating of traditional iris locating algorithms, an improved YOLOV3 iris locating model is proposed to improve the accuracy of iris locating and make it better applied to production practice. Using the Densenet-121 model as the feature extraction module, and on the basis of it, the auxiliary network is obtained by copying the backbone network to make it more conducive to the detection of small targets, and the non-local attention mechanism is used to enhance the semantic information of the features obtained by the image. The YOLOV3 model, Daugman model and Wilde model based on DarkNet-53 are used for comparative experiments. The experimental results show that the accuracy of the experimental model in this paper is as high as 97. 1 % in iris locating, which has obvious advantages compared with other iris locating models. [ABSTRACT FROM AUTHOR]

The information of step line is of great importance to open-pit mining. The existing method of obtaining step line has large workload， low efficiency and poor accuracy， which reduces the production efficiency and acceptance accuracy of the mine.Thus， a method of automatically extracting open-pit mine step lines from the dense point cloud data of open-pit mines generated by sequence UAV images is proposed in this paper. This method uses progressive morphological filtering algorithm to preprocess the point cloud， and a three-dimensional edge detection and curvature index weighting method that takes into account the geometric properties of the neighborhood is proposed to extract the feature points of the step line， then， it uses the moving least squares method to accurately fit the step line. The experimental results show that the algorithm can automatically， efficiently and accurately extract the step line of the open-pit mine， and generate open-pit mining status map. It has important application value for open-pit mine production and safety. [ABSTRACT FROM AUTHOR]