Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation.
ACS Sens
; 9(4): 1927-1937, 2024 04 26.
Article
in En
| MEDLINE
| ID: mdl-38513127
ABSTRACT
Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Gases
Language:
En
Journal:
ACS Sens
Year:
2024
Document type:
Article