用可学习的多延迟差分增强传感器信号，提升低成本气体传感器的抗漂移识别性能。
LDAC-Net: A Learnable Multi-Lag Differencing Attention-Convolution Network for Drift-Robust Recognition with Low-Cost MOX Gas Sensors
- 设计可学习的多延迟差分模块，自动优化时间差分策略并补偿偏移与尺度变化。
- 在SmellNet-Base上达到68.2%准确率，比固定差分方法高14个百分点，比原始Transformer高30点。
- 模型具备强泛化能力，可在漂移严重场景和跨数据集任务中保持高性能，适合实际部署。
基于低成本金属氧化物（MOX）气体传感器的便携式电子鼻系统在气体与气味识别中具有实用性，但其信号易受慢速化学漂移、传感器偏移、尺度变化及通道间相关性影响。现有方法多采用手动设定延迟的固定一阶时间差分（FOTD），可能丢弃有用响应信息。本文提出LDAC-Net，一种端到端可学习的多延迟差分注意力卷积网络，直接处理多通道MOX信号。其可学习差分增强前端结合窗口自适应统计仿射归一化，补偿窗口特异性偏移与尺度变化，并通过可学习多延迟差分加权融合多个时间滞后差分；紧凑的注意力-卷积主干网络建模局部瞬态与长程时序依赖。在50类SmellNet-Base任务中，LDAC-Net取得68.2%的top-1准确率，较最优FOTD预处理模型高出约14个百分点，较原始Transformer高出30余点。消融实验证实两组件有效性。该表示还可迁移至SmellNet-Mixtures，准确率从45.4%提升至50.5%；并在62通道eNose-Drift基准下，于强长期漂移条件下实现70.6% top-1准确率与69.6% macro-F1，分别优于最佳对比模型8.0与3.0点，表明可学习的传感器感知预处理优于固定手工差分。
原文摘要 · Abstract (English)
Portable electronic-nose systems based on low-cost metal-oxide (MOX) gas sensors offer a practical solution for gas and odour recognition, but their signals are affected by slow chemical transients, drifting sensor offsets, scale variation, and cross-channel correlations. Existing pipelines commonly use fixed first-order temporal differencing (FOTD), which requires a manually selected lag and may discard useful response information. We propose LDAC-Net, an end-to-end learnable multi-lag differencing attention-convolution network that operates directly on multi-channel MOX signals. Its learnable differential feature enhancement front-end combines window-conditioned statistical affine normalisation, which compensates for window-specific offset and scale variation, with learnable multi-lag differencing, which weights and combines temporal differences across multiple lags. A compact attention-convolution backbone subsequently models local transients and longer-range temporal dependencies. On the 50-class SmellNet-Base task, LDAC-Net achieves 68.2% top-1 accuracy, exceeding the best FOTD-preprocessed comparison model by approximately 14 percentage points and the raw-input Transformer by more than 30 points. Ablation studies confirm the contributions of both proposed components. The representation also transfers to SmellNet-Mixtures, improving accuracy from 45.4% to 50.5%, and generalises to the 62-channel eNose-Drift benchmark under strong long-term drift, achieving 70.6% top-1 accuracy and 69.6% macro-F1. These results outperform the best comparison model with dataset-retuned FOTD preprocessing by 8.0 and 3.0 points, respectively, demonstrating that learnable, sensor-aware preprocessing is more effective than fixed handcrafted differencing for low-cost MOX gas-sensor recognition.
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