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Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

Overview of attention for article published in Frontiers in Energy Research, February 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
15 Mendeley
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Title
Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism
Published in
Frontiers in Energy Research, February 2022
DOI 10.3389/fenrg.2022.810490
Authors

Yang Ge, Fusheng Zhang, Yong Ren

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 13%
Lecturer 1 7%
Student > Doctoral Student 1 7%
Student > Master 1 7%
Researcher 1 7%
Other 1 7%
Unknown 8 53%
Readers by discipline Count As %
Engineering 3 20%
Energy 1 7%
Physics and Astronomy 1 7%
Medicine and Dentistry 1 7%
Materials Science 1 7%
Other 0 0%
Unknown 8 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 February 2022.
All research outputs
#12,941,902
of 23,164,913 outputs
Outputs from Frontiers in Energy Research
#268
of 3,377 outputs
Outputs of similar age
#194,172
of 520,300 outputs
Outputs of similar age from Frontiers in Energy Research
#20
of 428 outputs
Altmetric has tracked 23,164,913 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,377 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done particularly well, scoring higher than 91% 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 520,300 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 428 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.