用类朊病毒机制提升光纤斑点图温度预测精度
Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams
- 借鉴朊病毒记忆机制，设计持续记忆状态捕捉长期依赖
- 将平均绝对误差降至0.71℃，优于传统CNN与ViT模型
- 通过注意力与显著性图实现可解释性分析，适合传感器研发者
光纤斑点图传感器（FSS）因高温度敏感性在环境监测中至关重要，但其复杂数据给预测模型带来挑战。本文提出受朊病毒启发的视觉变压器模型Prion-ViT，模拟生物朊病毒的记忆机制，增强对长程依赖的建模能力，从而提升温度预测精度。该模型通过持久记忆状态在各层间传递关键特征，使平均绝对误差（MAE）降低至0.71℃，显著优于ResNet、Inception Net V2和标准视觉变压器。论文还结合可解释人工智能（XAI）技术，利用注意力图与显著性图揭示预测关键区域，为斑点图分析提供直观视角。
原文摘要 · Abstract (English)
Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature prediction accuracy using FSS data. Prion-ViT leverages a persistent memory state to retain and propagate key features across layers, reducing mean absolute error (MAE) to 0.71$^\circ$C and outperforming models like ResNet, Inception Net V2, and Standard Vision Transformers. This paper also discusses Explainable AI (XAI) techniques, providing a perspective on specklegrams through attention and saliency maps, which highlight key regions contributing to predictions
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