用物理引导的机器学习，从电阻瞬态推断一氧化碳浓度。
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching
- 基于物理可解释特征和频域变换，提取气体传感器周期响应。
- p型模式分类准确率达96.5%，n型模式回归误差仅1.48 ppm。
- 兼顾精度与可解释性，适合高可靠性气体检测应用。
本研究提出一种物理引导的机器学习框架，用于从具有温度依赖性p-n切换行为的混合相SnO-SnO₂材料气体传感器的实验电阻瞬态中推断一氧化碳浓度。通过物理可解释的描述符表征周期级瞬态响应，并结合快速傅里叶变换（FFT）和离散小波变换（DWT）的紧凑摘要。采用泄漏感知分组交叉验证，分别研究p型与n型传感区域的多类别浓度分类与连续浓度回归。在两个区域中，融合特征表现最优，而物理引导特征块仍具竞争力，表明浓度信息主要编码于物理可解释的瞬态动态中。p型分支在浓度分类上表现最佳，融合随机森林分类器准确率约96.5%；n型分支在定量估计上最优，融合随机森林回归器达到平均绝对误差≈1.48 ppm，决定系数R²≈0.992。结果揭示双模式特性：p型适合分类，n型适合高保真回归。研究还表明，泄漏感知、周期级、物理引导的机器学习能超越传统单响应指标分析，同时保持物理可解释性。
原文摘要 · Abstract (English)
This work presents a physics-guided machine-learning framework for carbon monoxide concentration inference from experimentally measured resistance transients of a mixed-phase SnO-SnO$_2$ material gas sensor exhibiting temperature-dependent p-n switching behavior. Cycle-level transient responses are represented through physically interpretable descriptors and complemented by compact fast Fourier transform (FFT) and discrete wavelet transform (DWT)-based summaries. Using leakage-aware grouped cross-validation, we study both multi-class concentration classification and continuous concentration regression for the p-type and n-type sensing regimes separately. Across both regimes, fused features provide the strongest overall performance, while the physics-guided descriptor block remains highly competitive, indicating that the dominant concentration information is already encoded in physically meaningful transient dynamics. The p-type branch shows the best concentration-class discrimination, with the fused Random Forest classifier reaching approximately $96.5\%$ accuracy, whereas the n-type branch yields the best quantitative concentration estimation, with the fused Random Forest regressor achieving an MAE$\approx 1.48$ ppm and an R$^2$ $\approx 0.992$. These results reveal a clear dual-regime behavior: p-type sensing is particularly favorable for classification, whereas n-type sensing is more favorable for high-fidelity regression. More broadly, the study demonstrates that leakage-aware, cycle-level, physics-guided machine learning can extend conventional gas-sensing analysis beyond single-response metrics while preserving physical interpretability
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。