电子鼻漂移的图神经网络小样本补偿模型OA

Objective To address the sensor drift issue in electronic noses caused by temperature/humidity fluctuations in medical and outdoor scenarios,which leads to detection failures,this study proposes a few-shot compensation model.It resolves the bottleneck of traditional methods that rely on extensive drift data and struggle with long-term nonlinear drift adaptation.Methods We constructed the GNNSD model,integrating deep residual convolution and graph neural networks,as well as employing data augmentation and relational reasoning mechanisms,and conducted few-shot classification experiments on a public sensor drift dataset.Results The GNNSD model achieved an average accuracy of 84.12%under the K=1 setting,representing a 9.93%improvement over the best comparative algorithm,FEDA.Ablation experiments demonstrated the rationality of the model architecture.Conclusion By synergizing multi-scale feature extraction and graph-structured relational reasoning,the model maintains high classification accuracy even with only one reference sample per category.This provides a low-sample-dependent drift compensation solution for biosafety applications such as medical monitoring and cross-border screening.