A Unified Deep-Learning Framework for Smart Gas Sensing

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.