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A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics

Overview of attention for article published in arXiv, May 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
67 Mendeley
Title
A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics
Published in
arXiv, May 2019
DOI 10.1186/s13634-019-0619-3
Authors

Lia Ahrens, Julian Ahrens, Hans D. Schotten

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 25%
Student > Ph. D. Student 12 18%
Researcher 8 12%
Lecturer 3 4%
Student > Doctoral Student 2 3%
Other 8 12%
Unknown 17 25%
Readers by discipline Count As %
Computer Science 21 31%
Engineering 12 18%
Economics, Econometrics and Finance 3 4%
Physics and Astronomy 2 3%
Medicine and Dentistry 2 3%
Other 8 12%
Unknown 19 28%
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 03 July 2019.
All research outputs
#14,396,821
of 25,385,509 outputs
Outputs from arXiv
#170,611
of 915,125 outputs
Outputs of similar age
#173,647
of 363,257 outputs
Outputs of similar age from arXiv
#5,070
of 21,489 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 915,125 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 80% 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 363,257 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 51% of its contemporaries.
We're also able to compare this research output to 21,489 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.