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Human activity recognition is widely used in many fields, such as the monitoring of smart homes, fire detecting and rescuing, hospital patient management, etc. Acoustic waves are an effective method for human activity recognition. In traditional ways, one or a few ultrasonic sensors are used to receive signals, which require many feature quantities of extraction from the received data to improve recognition accuracy. In this study, we propose an approach for human activity recognition based on a two-dimensional acoustic array and convolutional neural networks. A single feature quantity is utilized to characterize the sound of human activities and identify those activities. The results show that the total accuracy of the activities is 97.5% for time-domain data and 100% for frequency-domain data. The influence of the array size on recognition accuracy is discussed, and the accuracy of the proposed approach is compared with traditional recognition approaches such as k-nearest neighbor and support vector machines where it outperformed them.

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In some high-precision control applications, such as defense industry and computerized numerical control machine tools, fast and stable electromagnetic torque response is required to ensure the high dynamic performance of the system, while traditional PI control often cannot meet its requirements. For this purpose, a predictive control algorithm based on the deadbeat control algorithm is proposed in order to improve the performance of the motor current loop. In order to solve the problem that the conventional deadbeat control algorithm has a large dependence on system parameters and low robustness, this paper proposes an improved deadbeat control scheme for the permanent magnet synchronous motor based on the three-level inverters. The scheme is based on the second theorem of Lyapunov stability. The improved deadbeat control algorithm can achieve a good output waveform when the switching frequency is not high and the response speed is fast. The robustness of the system is improved, and there are good characteristics in reduced torque ripple. Compared to the traditional PI regulators, the improved deadbeat control can quickly track the current commands without overshoot and oscillation and suppress torque ripple. The simulation and experimental results show that the improved deadbeat control proposed in this paper has good dynamic and static performance.

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