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Title: NOx measurements in vehicle exhaust using advanced deep ELM networks
Authors: Ouyang, Tinghui
Wang, Chongwu
Yu, Zhangjun
Stach, Robert
Mizaikoff, Boris
Huang, Guang-Bin
Wang, Qijie
Keywords: Engineering::Electrical and electronic engineering::Applications of electronics
Issue Date: 2020
Source: Ouyang, T., Wang, C., Yu, Z., Stach, R., Mizaikoff, B., Huang, G. & Wang, Q. (2020). NOx measurements in vehicle exhaust using advanced deep ELM networks. IEEE Transactions On Instrumentation and Measurement, 70.
Journal: IEEE Transactions on Instrumentation and Measurement
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 substrate-integrated 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.
ISSN: 0018-9456
DOI: 10.1109/TIM.2020.3013129
Rights: © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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