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Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets—pig faces and pig necks—which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices. [ABSTRACT FROM AUTHOR]

A trench-assisted ring few-mode erbium-doped fiber amplifier (FM-EDFA) supporting eight spatial modes is designed and proposed in this work. The gain equalization for the FM-EDFA is achieved by selecting the appropriate doping radius and concentration using a particle swarm optimization (PSO) algorithm when only the pump in the fundamental mode ( L P 01 ) is applied. When the signals in the eight spatial modes are simultaneously amplified, the average modal gain is about 20 dB, and the DMG is less than 0.3 dB for a signal at 1550 nm. Considering the gain competition of six wavelength signals, the modal gain and DMG are more than 20 and 1 dB, respectively. In addition, the tolerance analysis for manufacturing with this design is also discussed. For a fluctuation in the refractive index, the average modal gain is about 19.5 dB, and the DMG is 0.77 dB, indicating that the structure has good fabrication tolerance.