Brushless direct current (BLDC) permanent magnet (PM) synchronous motors are in high demand for ventilator applications owing to their high speed, high efficiency, and other significant features. However, it has become an important problem in eddy current loss calculations with high-speed motors, which leads to low motor (ventilator) life and PM demagnetization. This paper focuses on the eddy current loss calculation and the structure improvement design for the two-pole 90 W, 47,000 r/min toothless BLDC motor. First, the influencing factors of eddy current loss are comprehensively investigated, and a multiparameter improvement methodology is proposed accordingly. Second, by finite element analysis (FEA), the effective winding length ratio and the number of parallel wires were mainly researched for the winding, and the influence on the eddy current loss and the efficiency was determined, providing a reference for BLDC high-speed motors. This study has resulted in a 34.75% reduction in the winding losses, and a 4.6% increase in the efficiency of the improved model compared with the original design. Third, the new rotor structure is proposed, saving PM volume 15% more than original. THD of gap flux density is decreased 20.97%; the eddy current loss in the new rotor is decreased 22% more than original. Furthermore, by coupling simulation of the magnetic–thermal field, the maximum temperature of winding of the improved model is 13.4% lower than that of the original model at the thermal steady state. Finally, the electromagnetic and thermal properties simulation results were verified by testing the prototype. It is of great significance to the structure design and efficiency improvement of the BLDC high-speed motor.

Aiming at the problems of occlusion, drift, and background change in target tracking, a background learning correlation filtering algorithm based on multi-feature fusion is proposed. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion. The fast directional gradient histogram (FHOG), color feature (CN), and texture feature (ULBP) were extracted, and the feature channels were connected in series. Then the depth features of Conv4-4 and Conv5-4 layers were extracted through the VGG-19 network, and the appearance model of the target was constructed. To reduce the sensitivity of the filter to the sudden change of background, a background learning filter is constructed, and the alternate direction multiplier method (ADMM) is used to speed up the calculation of the filter. In the model update stage, aiming at the problem of pollution of the original template caused by target occlusion, a high-confidence multi-template fusion update strategy is proposed by fusing the template with the highest confidence in the current frame, the previous frame, and the history frame. Finally, the proposed algorithm is tested on OTB50, OTB100, UAV123, and TC128 experimental data sets, and some classical and latest algorithms. The experimental results show that the tracking accuracy and robustness of the correlation filtering algorithm are improved.INDEX TERMS Target tracking, Machine vision, Correlation filtering, Background learning, More templates.

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