High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold-Reference-Cited by-同舟云学术

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High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold

Published:2023-11 Issue:11 Volume:24 Page:1633-1646
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Front Inform Technol Electron Eng

Author:

Xu Zhenyi^ORCID,Wang Renjun,Cao Yang,Kang Yu^ORCID

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

https://link.springer.com/content/pdf/10.1631/FITEE.2300005.pdf

Reference32 articles.

1. Chandola V, Banerjee A, Kumar V, 2009. Anomaly detection: a survey. ACM Comput Surv, 41(3):15. https://doi.org/10.1145/1541880.1541882

2. Cho K, van Merriënboer B, Gulcehre C, et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proc Conf on Empirical Methods in Natural Language Processing, p.1724–1734. https://doi.org/10.3115/v1/D14-1179

3. Fang SW, Li Q, Karimian H, et al., 2022. DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting. Environ Sci Poll Res, 29(36):54150–54166. https://doi.org/10.1007/s11356-022-19574-4

4. Franco V, Kousoulidou M, Muntean M, et al., 2013. Road vehicle emission factors development: a review. Atmos Environ, 70:84–97. https://doi.org/10.1016/j.atmosenv.2013.01.006

5. Guo HF, Zeng J, Hu YM, 2006. Neural network modeling of vehicle gross emitter prediction based on remote sensing data. Proc IEEE Int Conf on Networking, Sensing and Control, p.943–946. https://doi.org/10.1109/ICNSC.2006.1673275

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1. Emission Evaluation for Diesel Vehicles Under Typical Operating Conditions;2023 International Conference on Advanced Robotics and Mechatronics (ICARM);2023-07-08

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