Ore segmentation and proportion detection (PD) in conveyor belt images (CBI) are significant for the safe operation of ore engineering. However, the actual accuracy of segmentation and detection is low due to the influence of ore flow shape, irregular distribution, and dust interference. This paper proposed RSFA-Net, a high-performance and lightweight network for segmentation and PD in ore conveyor belts (OCB). Firstly, a new double-layer residual depth-separable (DRDS) algorithm is proposed for the first time and applied to encoder-decoder networks. The module improves the ability of the proposed network to identify ores in images and represent complex features, while greatly reducing the parameters and computational burden required to segment the network. Secondly, we also propose a new channel spatial fusion attention (NCSFA) to fully tap the potential of the proposed network for ore feature extraction, which can capture complex and variable ore features and extract more critical information in the context of the image. Finally, we used high-performance image sensors to build a large, high-quality OCB dataset, solving the problem of the unavailability of public datasets. We proposed and adopted the maximum relative error (MRE) as the precision index of the ore proportion detection task for the first time. RSFA-Net was compared to nine of the most representative ore segmentation networks. RSFA-Net outperforms all methods in terms of segmentation performance and lightweight. The segmentation accuracy of RSFA-Net is 97%, while the model size is only 1.06MB, and the accuracy of PD is as high as 98%.

This paper presents an improved Fast-Segmentation Convolutional Neural Network (Fast-SCNN) and U-Net networks based on the channel attention mechanism. While ensuring the speed of network detection, the accuracy of image segmentation is also considered. The experimental results show that the accuracy rate of improved Fast-SCNN based on the channel attention mechanism is greatly improved compared with the original Fast-SCNN, reaching 88.056%, and the mean intersection over union is also improved to a certain extent, reaching 81.087%, and the detection speed is better than the original Fast-SCNN network. The accuracy of improved U-Net network based on the channel attention mechanism is 0.91805, which is better than the original U-Net network. In terms of detection speed, the improved U-Net network based on channel attention mechanism has greatly improved compared with the original U-Net network, reaching 24.02 frames per second. In addition, a method of preventing clogging of ore conveyor belts based on static image detection is proposed in this paper. By judging and predicting the blockage of the ore conveyor belt. When the conveyor belt is about to be blocked or has been blocked, the fuzzy algorithm is used to control the ore conveyor belt to slow down and stop, to improve the safety and efficiency of the conveyor belt.

Autonomous aerial refueling technology can significantly extend the operational endurance of unmanned aerial vehicles (UAVs), enhancing their ability to perform long-duration missions efficiently. In this paper, we address the identification of refueling drogues in the close docking phase of autonomous aerial refueling. We propose a high-precision real-time drogue recognition network called DREP-Net. The backbone of this network employs the DGST module for efficient feature extraction and improved representation of multi-scale information. For occlusion and complex background problems, we designed the RGConv module, which combines the re-parameterization module with the GhostNet idea to improve the detection of an occluded drogue. Meanwhile, we introduced the efficient local attention mechanism into the neck network to enhance the overall attention to the target region. Then, we designed Phead, a lightweight detection head that combines the advantages of decoupling and coupling heads to improve the detection speed. Finally, we compared our network with mainstream algorithms on a real drogue dataset, and the results show that DREP-Net has 2.7% higher mean average precision (mAP) compared to the YOLOv8n model, and the detection speed is improved by 31.4 frames per second.

Conventional control strategy for permanent magnet synchronous machine (PMSM) has the defect of low steady-state efficiency as a result of ignoring iron loss resistance and unreasonable current distribution. In this brief, a steady-state efficiency improvement method considering the current distribution coefficient and iron loss resistance is proposed. Firstly, iron loss resistance is incorporated into equivalent circuit and mathematical model of the PMSM in d-q rotating coordinate frame. Secondly, steady-state reference current components of the d-axis and q-axis are analytically calculated by current distribution coefficients, two computational models that can accurately predict iron loss resistance under the condition of known and unknown hysteresis and eddy current loss parameters are derived and proposed for the first time. Finally, experimental comparison with conventional methods such as id = 0 and MTPA proves validity and superiority for our proposed method.

An enhanced algorithm, YOLOv5s-FCW, is put forward in this study to tackle the problems that exist in the current helmet detection (HD) methods. These issues include having too many parameters, a complex network, and large computation requirements, making it unsuitable for deployment on embedded and other devices. Additionally, existing algorithms struggle with detecting small targets and do not achieve high enough recognition accuracy. Firstly, the YOLOv5s backbone network is replaced by FasterNet for feature extraction (FE), which reduces the number of parameters and computational effort in the network. Secondly, a convolutional block attention module (CBAM) is added to the YOLOv5 model to improve the detection model’s ability to detect small objects such as helmets by increasing its attention to them. Finally, to enhance model convergence, the WIoU_Loss loss function is adopted instead of the GIoU_Loss loss function. As reported by the experimental results, the YOLOv5s-FCW algorithm proposed in this study has improved accuracy by 4.6% compared to the baseline algorithm. The proposed approach not only enhances detection concerning small and obscured targets but also reduces computation for the YOLOv5s model by 20%, thereby decreasing the hardware cost while maintaining a higher average accuracy regarding detection.