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General value functions for fault detection in multivariate time series data

Overview of attention for article published in Frontiers in Robotics and AI, March 2024
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Title
General value functions for fault detection in multivariate time series data
Published in
Frontiers in Robotics and AI, March 2024
DOI 10.3389/frobt.2024.1214043
Pubmed ID
Authors

Andy Wong, Mehran Taghian Jazi, Tomoharu Takeuchi, Johannes Günther, Osmar Zaïane

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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 13 March 2024.
All research outputs
#20,742,377
of 25,482,409 outputs
Outputs from Frontiers in Robotics and AI
#1,440
of 1,777 outputs
Outputs of similar age
#107,354
of 159,665 outputs
Outputs of similar age from Frontiers in Robotics and AI
#13
of 30 outputs
Altmetric has tracked 25,482,409 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,777 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one is in the 1st percentile – i.e., 1% 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 159,665 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.