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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 EURASIP Journal on Advances in Signal Processing, May 2019
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About this Attention Score

  • Among the highest-scoring outputs from this source (#47 of 313)
  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
10 tweeters

Readers on

mendeley
30 Mendeley
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Title
A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics
Published in
EURASIP Journal on Advances in Signal Processing, May 2019
DOI 10.1186/s13634-019-0619-3
Authors

Lia Ahrens, Julian Ahrens, Hans D. Schotten

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 27%
Student > Ph. D. Student 5 17%
Researcher 4 13%
Student > Bachelor 2 7%
Other 2 7%
Other 2 7%
Unknown 7 23%
Readers by discipline Count As %
Computer Science 12 40%
Engineering 4 13%
Physics and Astronomy 2 7%
Medicine and Dentistry 1 3%
Economics, Econometrics and Finance 1 3%
Other 2 7%
Unknown 8 27%

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
#4,247,259
of 14,076,191 outputs
Outputs from EURASIP Journal on Advances in Signal Processing
#47
of 313 outputs
Outputs of similar age
#142,867
of 371,740 outputs
Outputs of similar age from EURASIP Journal on Advances in Signal Processing
#6
of 26 outputs
Altmetric has tracked 14,076,191 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 313 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 53% 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 371,740 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 60% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.