用视觉变压器+物理模型，让低成本氧传感器在长周期海藻污染中仍精准测氧。
Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring
- 结合视觉变压器与物理方程构建新神经网络，抑制信号漂移。
- 在藻类污染环境下误差降至约2微摩尔/升，比传统方法降92%以上。
- 可自诊断误差，适合长期海洋监测与低预算设备部署。
气候危机与生态系统退化亟需智能、低成本的传感器，在真实环境中实现稳定、长期监测。溶解氧（DO）浓度是预测气候临界点的关键参数。基于微结构聚合物薄膜掺杂磷光染料的低成本光电传感器易于部署，但信号漂移和海洋生物污损仍是主要挑战。本文提出一种新型传感范式：将基于相机的DO传感器与基于视觉变换器（ViT）的物理信息神经网络（PINN）结合，在生物污损条件下实现高保真传感。训练与测试数据来自藻类富集水箱持续14天的实验，以模拟加速生物污损。所提ViT-PINN将斯特恩-沃尔默（SV）方程嵌入损失函数，相比经典统计与机器学习方法，平均绝对误差（MAE）分别降低92%和89%，达到约2 μmol/L的绝对误差。深度集成模型进一步量化预测不确定性，实现自诊断传感能力。
原文摘要 · Abstract (English)
The escalating climate crisis and ecosystem degradation demand intelligent, low-cost sensors capable of robust, long-term monitoring in real-world environments. Absolute dissolved oxygen (DO) concentration is a key parameter for predicting climate tipping points. Inexpensive optoelectronic sensors based on microstructured polymer films doped with phosphorescent dyes could be readily deployable; however, signal drift and marine biofouling remain major challenges. Here, we introduce a sensing paradigm that combines camera-based DO sensors with a visual transformer (ViT)-based physics-informed neural network (PINN) for high-fidelity sensing under biofouling conditions. Training and testing data were obtained from an algae-laden water tank over 14 days to capture accelerated biofouling. The ViT-PINN, which embeds the Stern-Volmer (SV) equation into the loss function, reduces mean average error (MAE) by 92% and 89% compared to classical statistical and ML approaches, achieving ~2 umol/L absolute error. A deep ensemble further quantifies predictive uncertainty, enabling self-diagnostic sensing.
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