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Bi-level deep reinforcement learning for PEV decision-making guidance by coordinating transportation-electrification coupled systems

Overview of attention for article published in Frontiers in Energy Research, January 2023
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
2 X users

Readers on

mendeley
8 Mendeley
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Title
Bi-level deep reinforcement learning for PEV decision-making guidance by coordinating transportation-electrification coupled systems
Published in
Frontiers in Energy Research, January 2023
DOI 10.3389/fenrg.2022.944313
Authors

Qiang Xing, Zhong Chen, Ruisheng Wang, Ziqi Zhang

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 25%
Professor > Associate Professor 2 25%
Researcher 1 13%
Student > Master 1 13%
Unknown 2 25%
Readers by discipline Count As %
Engineering 3 38%
Unspecified 2 25%
Social Sciences 1 13%
Unknown 2 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 January 2023.
All research outputs
#18,722,646
of 23,206,358 outputs
Outputs from Frontiers in Energy Research
#761
of 3,403 outputs
Outputs of similar age
#274,710
of 408,305 outputs
Outputs of similar age from Frontiers in Energy Research
#18
of 263 outputs
Altmetric has tracked 23,206,358 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,403 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 408,305 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 263 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.