Работы автора

Приводятся результаты анализа, выполненного с целью систематизации взаимоотношений ряда базовых понятий технической диагностики: объект диагностирования ( ОД ) и его части, техническое состояние ОД , структурные диагностические модели, диагностические цепи, множество возможных дефектов, алгоритмы диагностирования и контрольные проверки. На основе энтропийного критерия Шеннона предложен ряд элементов формализации заявленного перечня в виде оценок информационной сложности ОД и информативности диагностических проверок.

In order to solve the problems of slow convergence speed and premature convergence at local minima in the traditional butterfly optimization algorithm (BOA), this paper proposes a butterfly optimization algorithm (ITBOA) based on chaos mapping improvement and adaptive distribution, to speed up the optimization process and improve global search capabilities. Chaos mapping improvement is used to generate more diverse population initial values, and adaptive T-distribution adjusts the search strategy according to the current population status. Experimental results show that ITBOA can quickly find the optimal solution under standard benchmark function tests. Compared with the original butterfly algorithm, the butterfly algorithm introducing chaotic mapping (IBOA) and the particle swarm optimization algorithm (PSO), the ITBOA algorithm has faster convergence speed and better search effect.

Solar cells are very prone to scratches, hot spots, breakage and other defects during the production process, which seriously affects their service life and photoelectric conversion efficiency. Traditional detection methods cannot meet the accuracy and real-time requirements of the actual industrial production. To address the problems of low detection accuracy, slow speed, and single type of detected defects in solar cell defect detection, this paper proposes a solar cell defect detection algorithm based on improved YOLOv8s, which is based on the original YOLOv8s network model, and introduces the GAM global attention mechanism module and the EIoU-Focal loss function. The experimental results show that compared with other algorithms, the mAP@0.5 of the improved YOLOv8s reaches 85.1%, and the algorithm has a better improvement in detection accuracy and detection effect, which can complete the task of detecting defects in solar cells more quickly and accurately.