Flue Gas Monitoring System With Empirically-Trained Dictionary

Hui Cao; Yajie Yu; Panpan Zhang; Yanxia Wang

doi:10.1109/JAS.2019.1911642

Volume 7 Issue 2

Mar. 2020

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(2): 606-616

Hui Cao, Yajie Yu, Panpan Zhang and Yanxia Wang, "Flue Gas Monitoring System With Empirically-Trained Dictionary," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 606-616, Mar. 2020. doi: 10.1109/JAS.2019.1911642

Citation:

Hui Cao, Yajie Yu, Panpan Zhang and Yanxia Wang, "Flue Gas Monitoring System With Empirically-Trained Dictionary," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 606-616, Mar. 2020. doi: 10.1109/JAS.2019.1911642

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Flue Gas Monitoring System With Empirically-Trained Dictionary

doi: 10.1109/JAS.2019.1911642

Funds: This work was supported by the National Natural Science Foundation of China (61375055), the Program for New Century Excellent Talents in University (NCET-12-0447), the Natural Science Foundation of Shaanxi Province of China (2014JQ8365), the State Key Laboratory of Electrical Insulation and Power Equipment (EIPE16313), and the Fundamental Research Funds for the Central University

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Author Bio:
Hui Cao (M’11) received the B.E., M.E., and Ph.D. degrees in electrical engineering from Xi’an Jiaotong University, in 2000, 2004, and 2009, respectively. He is a Professor with the School of Electrical Engineering, Xi’an Jiaotong University. He was a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, from 2014 to 2015. He has authored or coauthored over 30 scientific and technical papers in recent years. His current research interests include industrial intelligent control and data mining. Dr. Cao was a recipient of the Second Prize of National Technical Invention Award

Yajie Yu received the B.E. degree in electrical engineering and automatization from Xi’an University of Technology, in 2014. She is a Ph.D. candidate at the State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University. Her current research interests include data mining and image processing

Panpan Zhang received the B.E. degree in measurement and control technology and instrumentation from Anhui University, in 2014. She received the M.E. degree in electrical engineering from Xi’an Jiaotong University, in 2017. She is a Ph.D. candidate in electrical engineering with the School of Electrical Engineering in Xi’an Jiaotong University. Her current research interests include data mining and complex networks

Yanxia Wang received the B.E. degree in electrical engineering from Harbin Institute of Technology of Harbin, China, in 2011. She received the Ph.D. degree in electrical engineering from Xi’an Jiaotong University, in 2017. She was a Research Fellow with the Department of Electronics, Electrical Engineering and Computer Science in Queens University Belfast from Aug. 2017 to Apr. 2018. Now she is a Research Fellow with the School of Electronic and Electrical Engineering, University of Leeds. Her current research interests include industrial energy efficiency management and battery management system
Corresponding author: H. Cao is with Shaanxi Key Laboratory of Smart Grid and the State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China (e-mail: huicao@mail.xjtu.edu.cn)
Received Date: 2018-07-06
Accepted Date: 2018-08-21

Available Online: 2019-05-27

Abstract

Abstract

The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection. Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components. This paper proposes flue gas monitoring system with empirically-trained dictionary (ETD) to deal with the complexity and biases brought by the uninformative spectral data. Firstly, ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation. D1, D2 and D3 are three types of ETD obtained by different methods. Then, the predictive model of component concentration is constructed on the ETD. In the experiments, two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares, the background propagation neural network and the support vector machines are performed. Moreover, the optimal parameters are chosen according to the 10-fold root-mean-square error of cross validation. The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.
- Dictionary learning,
- empirically-trained dictionaty (ETD),
- flue gas monitoring system,
- quantitative analysis

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References(34)

References

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Empirically-trained dictionary.
Sparse representation.
Quantitative analysis.
Dictionary learning.

Flue Gas Monitoring System With Empirically-Trained Dictionary

doi: 10.1109/JAS.2019.1911642

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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