IEICE TRANSACTIONS on Information
Clustering-Based Neural Network for Carbon Dioxide Estimation
Summary :
In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.829-832
- Publication Date
- 2023/05/01
- Publicized
- 2022/08/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLL0012
- Type of Manuscript
- Special Section LETTER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Intelligent Transportation Systems
Authors
Conghui LI
College of Geographical Science
Quanlin ZHONG
College of Geographical Science
Baoyin LI
College of Geographical Science
Keyword
Latest Issue
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Conghui LI, Quanlin ZHONG, Baoyin LI, "Clustering-Based Neural Network for Carbon Dioxide Estimation" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 829-832, May 2023, doi: 10.1587/transinf.2022DLL0012.
Abstract: In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLL0012/_p
Copy
@ARTICLE{e106-d_5_829,
author={Conghui LI, Quanlin ZHONG, Baoyin LI, },
journal={IEICE TRANSACTIONS on Information},
title={Clustering-Based Neural Network for Carbon Dioxide Estimation},
year={2023},
volume={E106-D},
number={5},
pages={829-832},
abstract={In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.},
keywords={},
doi={10.1587/transinf.2022DLL0012},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Clustering-Based Neural Network for Carbon Dioxide Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 829
EP - 832
AU - Conghui LI
AU - Quanlin ZHONG
AU - Baoyin LI
PY - 2023
DO - 10.1587/transinf.2022DLL0012
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.
ER -
English
