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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

  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
31 Mendeley
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 9 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 35%
Researcher 4 13%
Student > Ph. D. Student 3 10%
Student > Bachelor 1 3%
Lecturer 1 3%
Other 3 10%
Unknown 8 26%
Readers by discipline Count As %
Computer Science 10 32%
Engineering 5 16%
Physics and Astronomy 1 3%
Economics, Econometrics and Finance 1 3%
Social Sciences 1 3%
Other 3 10%
Unknown 10 32%

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
#7,966,704
of 15,371,078 outputs
Outputs from EURASIP Journal on Advances in Signal Processing
#180
of 355 outputs
Outputs of similar age
#158,456
of 385,841 outputs
Outputs of similar age from EURASIP Journal on Advances in Signal Processing
#6
of 27 outputs
Altmetric has tracked 15,371,078 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 355 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 385,841 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 58% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.