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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

DRL has gained wild applications in the field of urban transportation signal control. However, the existing DRL traffic signal control researches only use the traditional deep neural network, and its perception ability is limited in complex traffic scenarios. In addition, as one of the three elements of reinforcement learning, it also needs to design the traffic state carefully and manually in the existing researches. Therefore, this paper proposed a DRL traffic signal control algorithm based on attention mechanism. By introducing the attention mechanism, the neural network could automatically pay attention to the important state components to enhance the perception ability of the network, improve the signal control effect, and reduce the difficulty of state vector design. Experimental results on SUMO platform show that compared with the three classical signal control algorithms, only using a simple traffic state, the proposed algorithm has the best performance in average waiting time and travel time under the condition of low and high traffic flow at single intersections and multiple intersections. [ABSTRACT FROM AUTHOR]

Urban traffic signal control can widely use deep reinforcement learning (DRL) technique. However, in existing researches, most DRL agents only use the current traffic state to make decisions and have limited control effects when the traffic flow changes greatly. Aiming at the problem, this paper proposed a state prediction based deep reinforcement learning algorithm for traffic signal control. The algorithm used one-hot coding to design a concise and efficient traffic state, and then used a Long Short-Term Memory（LSTM） to predict the future state. The agent made optimal decisions based on the current state and the predicted state. The experimental results on the simulation platform SUMO show that compared with three typical signal control algorithms, the proposed algorithm has the best performance in terms of average waiting time, travel time, fuel consumption, CO2 emissions and cumulative reward both in a single intersection and multiple intersections under different flow conditions. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of insufficient accuracy of 3D face reconstruction and dense alignment algorithm,this paper introduced densely connected multi-scale feature fusion module and residual attention mechanism to design a powerful network. For the encoder structure, this paper introduced a densely connected multi-scale feature fusion module to obtain multi-scale fusion features, which made the encoder obtain richer information. The decoder module introduced residual attention mechanism to strengthen the network 's attention to important features while suppressing unnecessary noise. According to the experimental results, compared with other algorithms, the proposed algorithm achieves a significant improvement. Compared with the PRN et, the algorithm uses fewer parameters to achieve a performance improvement of 7. 7% to 12. 1 % in various indicators. [ABSTRACT FROM AUTHOR]