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To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

This paper investigates a novel anti-disturbance speed tracking control problem for permanent magnet synchronous motor (PMSM) systems with unknown mismatched disturbances. In order to realise the rejection and compensation for load torque, a cascaded PMSM system is constructed by using a coordinate transformation such that the load disturbances become matched with respect to the virtual control input. By combining disturbance observer with proportional-integral feedback control structure, a composite speed controller is proposed on this basis to ensure the PMSM system stability, and convergence of the tracking error of angular velocity to zero. The favourable observation performance for the load torque and its derivative can also be achieved simultaneously. Meanwhile, the [Formula: see text] performance index is used to further optimise the robustness of the PMSM system. Finally, the effectiveness of the proposed control scheme is verified by simulations for the PMSM system with monotonous disturbances and harmonic disturbances respectively.

In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the method is used to improve the playing power of the computer game of a special kind of chess. JIU chess, also called Tibetan Go chess, is mainly played in places where Tibetan tribes gather. Its play process is divided two sequential stages: preparation and battle. The layout at preparation is vital for the successive battle, even for the final winning. Studies on Tibetan JIU chess have focused on Bayesian network based pattern extraction and chess shape based strategy, which do not perform well. To address the low chess power of JIU chess from the view of artificial intelligence, we developed a reinforcement learning model based on temporal difference (TD) algorithm for the preparation stage of JIU. First, the search range was limited within a 6 × 6 area at the center of the chessboard, and the TD learning architecture was combined with chess shapes to construct an intelligent environmental feedback system. Second, optimal state transition strategies were obtained by self-play. In addition, the results of the reinforcement learning model were output as SGF files, which act as a pattern library for the battle stage. The experimental results demonstrate that this reinforcement learning model can effectively improve the playing strength of JIU program and outperform the other methods.INDEX TERMS Artificial intelligence, reinforcement learning, temporal difference algorithm, JIU chess.

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