Facial expression recognition(FER)is a vital application of image processing technology.In this paper,a FER model based on the residual network is proposed.The proposed model introduces the idea of the DenseNet,in whi...Facial expression recognition(FER)is a vital application of image processing technology.In this paper,a FER model based on the residual network is proposed.The proposed model introduces the idea of the DenseNet,in which the outputs of the residual blocks are not simply added but are linked to the channel dimension.In addition,transfer learning is used to reduce training costs and accelerate training speed.The accuracy and robustness of the proposed FER model were tested by K-fold cross-validation.Experimental results show that the proposed method has competitive performances on FER2013,FER plus(FERPlus),and the real-world affective faces database(RAF-DB).展开更多
Robot error compensation is a technique for enhancing the positioning accuracy of the system. This paper presented an error measuring technique for serial robots based on the multi-hole measuring method, combined with...Robot error compensation is a technique for enhancing the positioning accuracy of the system. This paper presented an error measuring technique for serial robots based on the multi-hole measuring method, combined with the intelligent particle swarm optimisation(PSO) to obtain the optimal solution of the robot’s error compensation values, thereby improving the positioning accuracy of the robot. In the experiment, the robot error was measured using self-made multi-hole measuring plates and probes, and the experimental data were combined with PSO for the error comprehensive analysis. The results showed that on this type of serial robot, the multi-hole measuring method and PSO algorithm had obvious error compensation effects, which effectively improved the positioning accuracy of the robot, with the error reduced by 35% after compensation.展开更多
基金supported by the National Natural Science Foundation of China(51105213)。
文摘Facial expression recognition(FER)is a vital application of image processing technology.In this paper,a FER model based on the residual network is proposed.The proposed model introduces the idea of the DenseNet,in which the outputs of the residual blocks are not simply added but are linked to the channel dimension.In addition,transfer learning is used to reduce training costs and accelerate training speed.The accuracy and robustness of the proposed FER model were tested by K-fold cross-validation.Experimental results show that the proposed method has competitive performances on FER2013,FER plus(FERPlus),and the real-world affective faces database(RAF-DB).
文摘Robot error compensation is a technique for enhancing the positioning accuracy of the system. This paper presented an error measuring technique for serial robots based on the multi-hole measuring method, combined with the intelligent particle swarm optimisation(PSO) to obtain the optimal solution of the robot’s error compensation values, thereby improving the positioning accuracy of the robot. In the experiment, the robot error was measured using self-made multi-hole measuring plates and probes, and the experimental data were combined with PSO for the error comprehensive analysis. The results showed that on this type of serial robot, the multi-hole measuring method and PSO algorithm had obvious error compensation effects, which effectively improved the positioning accuracy of the robot, with the error reduced by 35% after compensation.
