一种基于半导体氧化物传感器阵列短周期温度调制信号的气体浓度识别方法
By combining short-cycle temperature modulation signals from a semiconductor oxide sensor array with a Transformer network, path signature features are extracted and temporal correlations are modeled, solving the problem of low gas selectivity of the sensor in complex environments and improving the accuracy of gas concentration identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN UNIV
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing metal-oxide-semiconductor gas sensors exhibit low gas selectivity in complex environments and struggle to effectively utilize the long and short timescale characteristics of temperature-modulated signals, resulting in high model learning complexity and insufficient recognition accuracy.
By employing short-cycle temperature modulation signals from a semiconductor oxide sensor array, features are extracted through path signatures and modeled using a Transformer network to capture the temporal correlation and long-term dependency of different modulation cycles, thereby improving the accuracy of gas concentration identification.
It significantly improves the accuracy of gas concentration identification, especially in mixed gas scenarios, reducing prediction errors and achieving higher identification accuracy compared to traditional models.
Smart Images
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