The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

In this study, the wear characteristics of the die were tested and analyzed through compaction tests, and the distribution of wear depth along the direction toward the extrusion outlet was obtained. A discrete element method (DEM) model of the die’s wear process was established. The results show that the severe wear area is located near the stop position of the compression rod, forming a plow-shaped wear area along the extrusion direction, accompanied by fatigue peeling. The wear depth gradually decreases towards the extrusion outlet. The DEM model partially reveals the occurrence of the wear phenomenon, but the particle motion speed deviates from the actual situation. The maximum compression force value range during the DEM compression stage is within the actual maximum compression force value range, and the relative error range of the average maximum compression force is less than 2%. By verifying the formula to calibrate the model, the calibrated model is compared with the actual mold wear, and the predicted value is close to the actual test result. The DEM can be used to explore the wear mechanism and predict the die’s wear failure process, laying the foundation for optimizing die wear resistance design.

The split-stream rushing exhaust muffler is a design that improves acoustic attenuation performance and reduces exhaust resistance by lowering the internal airflow velocity. In the past, researchers often applied a rigid treatment to the muffler wall when studying this new muffler and neglected the acoustic–structure coupling effect. As a result, differences existed between the calculated results and practical situations. This research employed COMSOL 6.2 software to perform finite element calculations on the acoustic performance of this muffler under acoustic–structural coupling. It analyzed the causes of transmission loss discrepancies with and without the acoustic–structural coupling effect, and the findings were validated experimentally. Building upon this foundation, further optimization and refinement of the muffler’s structural parameters were conducted. The results demonstrated that the transmission loss curve under the acoustic–structure coupling effect followed a similar trend to that observed without the acoustic–structure coupling effect. However, the transmission loss curve changed owing to the influence of the acoustic–structure coupling effect on sound pressure, which resulted in a 6.24% decrease in the average transmission loss. The transmission loss curve accounting for the acoustic–structure coupling effect aligned more closely with the test results than the curve that did not account for the coupling effect. Furthermore, this study delved into the influences of the wall thickness, inner tube diameter, and inner tube length on the muffler’s acoustic performance under acoustic–structural coupling. Subsequently, the muffler was optimized based on the findings.

In order to reduce the exhaust noise, which is the main source of gasoline engine noise, this paper uses on orthogonal test to optimize the parameters of the main structure of split-stream rushing muffler (the perforation rate of the perforated pipe, perforation aperture of the perforated pipe, perforation rate of the perforated plate). It also comprehensively evaluates its acoustic and aerodynamic performances to determine the optimal structural model. The acoustic performance and aerodynamic performance of the split-stream rushing muffler and the original muffler are compared and analyzed in the simulation test. The results show that the average Transmission Loss of the split-stream rushing muffler is greater than that of the original muffler, and its Pressure Loss is also lower than that of the original muffler. Through the insertion loss experiment, it was found that the average insertion loss of the main muffling frequency band is 19.14 dB, which is higher than that of the original muffler, and the peak frequency of the insertion loss of the split-stream rushing muffler corresponds well with the peak frequency of the engine exhaust noise. The Pressure Loss of the split-stream rushing muffler was tested on the experiment bench, and the relative error between the experiment data and the simulation data is within 8%. In summary, the split-stream rushing muffler can effectively reduce the exhaust noise problem of gasoline engines.