ABSTRACT
Coal and gangue (rock) identification is the essential process in a coal preparation plant. In an actual coal preparation plant, the existing classification methods have many disadvantages in safety and identification rate. We utilized the echo intensity image (EII) of lidar for coal and gangue identification for the first time, to the best of our knowledge, and achieved outstanding recognition results with a convolutional neural network. First, we acquire the information of the 3D point cloud, including the distance and the echo intensity, and decompose them into two channels. Then, we utilize the distance channel to remove the background noises and separate the object and the echo intensity channel to construct the 2D EII. Finally, we prune the dense convolutional network (DenseNet-121) to DenseNet-40 for the real-time identification and compare its F1 score with the other two traditional recognition algorithms. The experiment shows that the F1 score of the DenseNet-40 is up to 0.96, which indicates the DenseNet-40 is provably higher than other traditional algorithms in accuracy. Through trial and error, we find that the echo intensity of lidar can clearly show the texture information of coal and gangue. After combining with the DenseNet-40, it has more benefits than the existing classification methods in accuracy, efficiency, and robustness.
ABSTRACT
We demonstrate a high-power laser diode stacks double-end-pumped Nd:YVO4 1064 nm slab laser with a folded stable-unstable hybrid resonator. An output power of 220 W was obtained at the pump power of 490 W with optical conversion efficiency of 44.9%. At the output power of 202 W, the M2 factors in the unstable direction and in the stable direction were 1.7 and 2.3, respectively.
