Articles you may be interested inPhotorefractive properties of Cu-doped (K 0.5 Na 0.5 ) 0.2 (Sr 0.75 Ba 0.25 ) 0.9 Nb 2 O 6 crystals with different doping levels and different dimensions Nearinfrared response of photorefractive crystals (K0.5Na0.5)0.2(Sr0.75Ba0.25)0.9Nb2O6:Cu and LiNbO3:Fe Enhancement of the response rate of internal reflection selfpumped phase conjugators with Ce and Mn doped (K0.5Na0.5)0.2(Sr0.75Ba0.25)0.9Nb2O6 crystals using intermittent light Appl. Phys. Lett. 67, 10 (1995); 10.1063/1.115499 Contrast reversal of antiparallel domains in Cudoped (Ba0.25Sr0.75)0.9(K0.5Na0.5)0.2Nb2O6 single crystal with synchrotron topography

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With the rapid development of neural networks in recent years, saliency detection based on deep learning has made great breakthroughs. Most deep saliency detection algorithms are based on convolutional neural networks, which still have great room for improvement in the edge accuracy of salient objects recognition, which may lead to fuzzy results in practical applications such as image matting. In order to improve the accuracy of detection, a saliency detection model based on semantic soft segmentation is proposed in this paper. Firstly, the semantic segmentation module combines spectral extinction and residual network model to obtain low-level color features and high-level semantic features, which can clearly segment all kinds of objects in the image. Then, the saliency detection module locates the position and contour of the main body of the object, and the edge accurate results are obtained after the processing of the two modules. Finally, compared with the other 11 algorithms on the DUTS-TEST data set, the weighted F-measure value of the proposed algorithm ranked first, which was 5.8% higher than the original saliency detection algorithm, and the accuracy was significantly improved.

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With the expansion of industrial manufacturing, a single motor is difficult to meet the needs of production, synchronous operation mode of multiple motors has been widely used in industry and production. This article takes four permanent magnet synchronous motors as the research object and adopts SVPWM control technology as the inner loop to achieve steady-state operation of every motor. The outer loop adopts conventional PI controller and sliding mode controller (SMC) to realize synchronous operation of four motors respectively. Based on the Matlab/Simulink platform the simulation model of the synchronous motor control system is designed. The deviation coupling synchronization control strategy of four motors under different speed outer loop control modes is studied. Through the load torque mutation experiments, the anti-disturbance ability and rapidity of the two controllers are compared. Simulation experiment results show that the multi-motor synchronous control system based on sliding mode controller still has good speed tracking performance under large load torque sudden changes.