Name
Abstract | ||
|---|---|---|
Considering that vehicle exhaust contributes to the majority of nitrogen oxides (NOx), which is harmful to environment and climate, it is important to measure NOx concentrations in sustainable developments. This article proposes to apply spectroscopic gas sensing methods and an innovative deep learning network algorithm for obtaining high-precision NOx data. The adopted mid-infrared sensor technology is based on mid-infrared spectroscopy combined with an advanced substrateintegrated hollow waveguide (iHWG) sensing interface. Using extreme learning machine (ELM) algorithms with an exceptionally fast learning speed when dealing with big data problems next to excellent generalization abilities, a deep learning network for regressing NOx concentrations was implemented. Moreover, to further improve the regression performance the proposed deep ELM was provided with features derived from supervised learning improving its ability to address target constituents. Finally, experiments with gas mixtures containing three species relevant in exhaust emission monitoring have confirmed the utility of the developed approach. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIM.2020.3013129 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Extreme learning machine (ELM), deep learning, gas sensing, midinfrared sensors, substrate-integrated hollow waveguides (iHWGs), vehicle exhaust | Journal | 70 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tinghui Ouyang | 1 | 6 | 3.82 |
| Chongwu Wang | 2 | 0 | 0.68 |
| Jun Yang | 3 | 1 | 4.01 |
| Robert Stach | 4 | 0 | 0.68 |
| Boris Mizaikoff | 5 | 2 | 2.05 |
| Guang-Bin Huang | 6 | 8 | 1.04 |
| Qi-Jie Wang | 7 | 0 | 0.34 |