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Research on geometric dimension measurement system of shaft parts based on machine vision

Overview of attention for article published in EURASIP Journal on Image & Video Processing, October 2018
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Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
42 Mendeley
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Title
Research on geometric dimension measurement system of shaft parts based on machine vision
Published in
EURASIP Journal on Image & Video Processing, October 2018
DOI 10.1186/s13640-018-0339-x
Authors

Bin Li

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 14%
Student > Ph. D. Student 6 14%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Lecturer 2 5%
Other 5 12%
Unknown 17 40%
Readers by discipline Count As %
Engineering 15 36%
Computer Science 6 14%
Energy 1 2%
Mathematics 1 2%
Chemistry 1 2%
Other 1 2%
Unknown 17 40%

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 05 October 2018.
All research outputs
#10,824,443
of 13,603,158 outputs
Outputs from EURASIP Journal on Image & Video Processing
#104
of 173 outputs
Outputs of similar age
#198,971
of 264,359 outputs
Outputs of similar age from EURASIP Journal on Image & Video Processing
#2
of 3 outputs
Altmetric has tracked 13,603,158 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 173 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 17th percentile – i.e., 17% 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 264,359 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.