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A Unified Deep-Learning Framework for Smart Gas Sensing
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AI
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F
杨
DOI：10.1021/acssensors.6c00575.png)
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Abstract
En 中文
Smart perception systems are essential for detecting complex physical and chemical stimuli in diverse environmental monitoring and clinical diagnostic applications. However, the escalating demands for multi-functional inference, cross-scenario deployment, and long-term stability remain difficult to satisfy simultaneously within existing sensing frameworks. This work proposes a unified and computationally efficient deep-learning framework that integrates multi-task learning, transfer learning, and domain adaptation under a shared backbone to resolve these fragmented reliability bottlenecks. Using gas sensing as a representative modality, a lightweight, task-aligned model is developed to concurrently predict sensor working status, gas identity, and gas concentration from transient responses while maintaining a minimal parameter footprint. To bridge the gap between black-box decision logic and physical sensing mechanisms, SHapley Additive exPlanations (SHAP) analysis is employed to quantify multi-scale attributions, elucidate multi-task synergy, and guide sensor-array lightweighting. For cross-scenario scalability, a few-shot structural transfer strategy utilizing parameter-efficient fine-tuning is introduced to facilitate rapid adaptation to heterogeneous domains. To ensure cross-period robustness under baseline drift, a semi-supervised adversarial domain-adaptation scheme with dual statistical alignment is implemented to mitigate distribution shifts. Across diverse datasets, the framework achieves high accuracy (>0.98 in the source domain and >0.91 in adaptation settings) with minimal fine-tuning overhead (trainable parameters <2%) and significantly enhanced robustness against sensor drift (up to 24.7% gain). This work provides an interpretable and resource-efficient methodological foundation for deployable intelligent sensing systems, enabling cohesive cross-task, cross-scenario, and cross-period reliability.
Keywords:
smart sensing
electronic nose
multi-task learning
interpretability
transfer learning
domain adaptation
Journal
IF:
9.1
Papers:
975
Citations:
2.6W
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Cited Papers