The correct functioning of photovoltaic (PV) cells is critical to ensuring the optimal performance of a solar plant. Anomaly detection techniques for PV cells can result in significant cost savings in operation and maintenance (O&M). Recent research has focused on deep learning techniques for automatically detecting anomalies in Electroluminescence (EL) images. Automated anomaly annotations can improve current O&M methodologies and help develop decision-making systems to extend the life-cycle of the PV cells and predict failures. This paper addresses the lack of anomaly segmentation annotations in the literature by proposing a combination of state-of-the-art data-driven techniques to create a Golden Standard benchmark. The proposed method stands out for (1) its adaptability to new PV cell types, (2) cost-efficient fine-tuning, and (3) leverage public datasets to generate advanced annotations. The methodology has been validated in the annotation of a widely used dataset, obtaining a reduction of the annotation cost by 60%.

精确的温度测量对于适当的监测和控制工业炉是必不可少的。然而，测量不确定性是这种关键参数的风险。当使用谱带辐射热度技术时，必须考虑某些乐器和环境误差，例如目标表面发射率的不确定性，反射周围物体的辐射或大气吸收和发射，以命名几个。可以使用测量模型来分离测量辐射的不期望的贡献，也称为纠错模型。本文介绍了石油化学炉场景中的温度测量期间预算重要误差和不确定性的方法。还通过基于深度学习的测量校正模型来介绍连续监控系统，以允许域专家实时分析炉的操作。为了验证所提出的系统的功能，提出了一种在石化工厂中的真实应用案例。所提出的解决方案展示了精确的工业炉监测的可行性，从而增加了运行安全性并提高了这种能量密集型系统的效率。