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 illumination changes, target deformation and background clutter in the target tracking field, a visual tracking algorithm based on peak sidelobe ratio is proposed. The object-interference model is used to represent the target appearance model, and context information is added to the relevant filtering framework. Training the filter internally to enhance the ability of filter discrimination. In the model update process, it is easier to introduce samples that cannot characterize the target, and use peak sidelobe comparison to update the tracking parameters, which can enhance the generalization ability of the model. Tested with some classic and recently algorithms in the OTB50, OTB100, UAV123, TC128 experimental video data set, the experiment’ results show that the visual tracking algorithm that is proposed in the article can track the target more accurately. It has important research on the development of intelligent video surveillance value.

According to the motion balance for a two-wheeled robot control problems, we put forward a bionic learning algorithm based on growing cell structure (GCS) network and Q-learning. GCS network has in addition to the competitive mechanism of SOM network, and it can also carry out self-organizationally evolution through the continuous growth of new neurons. Q-learning algorithm is a model free reinforcement learning algorithm, and it can improve the learning ability of the control system, but it is only suitable for the discrete state. We made the growth characteristics of GCS network apply to the Q-learning algorithm, and optimized the Q value through the information of the winning neuron which comes from the network. Ultimately, we achieved the model free control of a continuous state system, and made simulation experiments on two-wheeled robot. The results showed that the robot learned to effectively control the movement balance through continuous growth and improvement of neurons, and verified that it was effective and feasible of the bionic learning algorithm based on the growth network for the robot's motion balance control.

Siamese network based trackers describe object tracking as a similarity matching problem and these trackers achieve state-of-the-art performance on multiple benchmarks. However, due to the non-update of the appearance template and the change of the object appearance, the tracking drift problem often occurs, especially in the background clutter scene. Effective appearance template update methods can improve tracker performance, but most trackers use simple linear interpolation to update the template or do not update the initial template at all. Through experiments, we find that the channel response of the search region with the adjacent frame appearance template is often better than that with the initial frame appearance template. So we add the results of the previous frame prediction as a new template branch to the Siamese network to form a Triplet network. We applied the Triplet network to the SiamFC and SiamCAR, called TripFC and TripCAR. We tested on four challenging benchmarks (GOT-10K, OTB2013, OTB2015, UAV123). The experiments show that our method is powerful and effective, it can be easily embedded into the Siamese trackers. TripFC has a good effect on solving the problem of tracking drift. If necessary we can publish the code to facilitate research in this area.

Electromagnetic separators are widely used in new energy battery purification, resource recycling, and mineral processing. However, coil heating can cause a decline in separation performance and damage to coil insulation. To ensure the stable operation of electromagnetic separators, cooling plates are employed to effectively mitigate temperature rise. To explore a high-performance and economical cooling method, this paper employs CFD finite element analysis for the structural optimization of cooling plates. First, the paper investigates the flow heat characteristics of S-shaped cooling plates. Numerical simulations are performed to analyze the variation of fluid characteristics with different numbers of water channels. Regression equations linking structural parameters to performance indicators are derived, and the optimal channel number and hydraulic diameter are determined. Furthermore, to enhance heat transfer efficiency, an innovative semicircular groove structure is introduced on the cooling plate walls. An optimization strategy based on a genetic algorithm is developed to determine the optimal groove parameters. A simulation shows that the optimized cooling plate reduces coil temperature by 12.63 °C with a decrease of 15.31% compared with the original design. Finally, a prototype with optimized parameters is manufactured after the experimental results of the two test points and the simulation results reveal errors of 0.26% and 0.96%, respectively. The experimental results align well with the simulations, confirming the reliability of the experimental results and the feasibility of the optimization strategy, and providing a reference for future cooling plate designs.

Aiming at the problems of target occlusion, illumination change, and fast motion in target tracking, an anomaly suppression correlation filtering algorithm is proposed. The histogram of oriented gradient (HOG) feature and color name (CN) feature are extracted to construct the target appearance model, and an abnormal suppression correlation filter is constructed to suppress sudden changes in the filter response graph. The alternating direction method of multipliers (ADMM) method is used to speed up the calculation of the filter. A scale filter is added in the article to solve the problem of inaccurate tracking caused by a single scale. In the model update process, the three-layer rotating circle memory model is used to update, which improves the tracking ability of the algorithm. It is better to update the target model in the scene where the target is occluded. The proposed algorithm and BACF, KCC, KCF, MKCFup, ARCF are tested in the OTB50, OTB100, UAV123, TC128 experimental data sets. The results show that the visual tracking algorithm proposed in the article has a high success rate and accuracy, and has certain research value.