一种小样本自适应的混合气体传感器动态响应建模方法
By constructing a gas sensor array and a GRU network, the accuracy and efficiency issues of dynamic response modeling for mixed gas sensors were resolved, enabling high-precision gas component identification with small samples and simplifying the testing scheme.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNIV OF ELECTRONICS SCI & TECH OF CHINA
- Filing Date
- 2025-05-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to effectively identify the dynamic response of multi-component mixed gases. Traditional modeling methods neglect the transient characteristics of gas diffusion and chemical reactions, resulting in insufficient model accuracy, cumbersome and heavy testing schemes, large testing volumes, and difficulty in adapting to the performance differences of different sensors.
A small-sample adaptive dynamic response modeling method for mixed gas sensors is adopted. By constructing a gas sensor array, simulating gas pulse signals and response signals, short time segments are generated. Using GRU network and transfer learning, a concise dynamic response model of the gas sensor is constructed to achieve data augmentation and feature extraction.
With limited data and resources, it can quickly adapt to environmental changes, improve the accuracy and efficiency of mixed gas component identification, reduce testing pressure, simplify model structure, and enhance the dynamic response accuracy of gas sensors.
Smart Images
Figure CN120609969B_ABST