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In the traditional belt conveyor coal flow detection device, the nuclear belt scale has certain safety and environmental protection hidden dangers, and the detection precision of electronic belt scale is easily affected by the factors such as belt tension and stiffness. Moreover, non-contact detection methods based on technologies such as ultrasound, linear laser stripes and binocular vision have problems such as poor real-time performance and large measurement errors. A coal flow detection method for conveyor belt based on time-of-flight(TOF) depth image restoration is proposed. The TOF camera is used to obtain the coal conveying image of the conveyor belt. The TOF image is equalized, and the frame difference method and the boundary following algorithm are used to remove the background noise and obtain the coal region of interest. In order to solve the problem of inaccurate edge information caused by flying pixel noise and multi-path error noise at the edge of TOF depth image, the intensity image-guided depth image restoration algorithm is proposed. The Canny edge detection algorithm is used to find similar edges between the depth image and the intensity image. Based on the effective edge information of the intensity image, the unreliable data of the edge of the depth image is corrected. Furthermore, the high-precision depth images are obtained based on Navier-Stokes equation and median filter. The coal area is divided at the pixel level, the coal volume calculation model is established to obtain coal flow of conveyor belt by combining the conveyor belt speed. The experimental results show that the detection error is less than 3.78%, the standard deviation is less than 0.491 and the average processing time is 83 ms, which meets the actual production requirements.

It is difficult to accurately measure parameters by using the traditional soft sensor algorithm when the working condition of industrial process is changed. Therefore, a transfer learning strategy is introduced based on geodesic flow kernel to solve this problem. At the same time, the method is optimized to solve the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The augmented matrix is first constructed to deal with the dynamic characteristics of the process. Independent component analysis and principal component analysis are performed on the processed data to extract the non-Gaussian and Gaussian information of the source domain and the target domain. Then, the non-Gaussian and Gaussian information of the source domain is adapted to the target domain respectively on the Grassmann manifold. Finally, the maximum mean discrepancy is used to measure the distribution between the source domain and the target domain after domain adaptation, and the calculated results are applied to construct the weight of the model based on the source domain after domain adaptation. The results show that the method not only reduces the difference of distribution between the source domain and the target domain, but also solves the problems of dynamic characteristic extraction and incomplete Gaussian distribution in industrial process. The validity and the practicability of the model are proved by experiments on Tennessee Eastman data.

Current speed regulation energy saving methods all regulate speed of belt conveyor in short time, and speed mutation is easy to cause tear and slipping of belt conveyor. In view of above problem, a speed regulation energy saving method of belt conveyor based on preview control was proposed. According to material carrying capacity of preceding belt conveyor, speed which makes succeeding belt conveyor run at rated carrying capacity is calculated, and running speed of the succeeding belt conveyor is adjusted through preview control. Aiming at negative influence of noise on speed regulation effect, Kalman filter is used to estimate state of the controlled belt conveyor in the process of preview control, which can weaken noise interference and smooth speed regulation process. The simulation results show that the method can fully utilize carrying capacity of belt conveyor, reduce power consumption of belt conveyor, and achieve purpose of speed regulation and energy saving on the basis of ensuring safe operation of belt conveyor.

Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception.