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Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing

Overview of attention for article published in Journal of Intelligent Manufacturing, May 2013
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Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
15 Mendeley
Title
Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing
Published in
Journal of Intelligent Manufacturing, May 2013
DOI 10.1007/s10845-013-0785-3
Authors

Hui-Chun Yu, Kuo-Yi Lin, Chen-Fu Chien

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Student > Doctoral Student 2 13%
Researcher 2 13%
Professor 1 7%
Student > Bachelor 1 7%
Other 0 0%
Unknown 4 27%
Readers by discipline Count As %
Engineering 6 40%
Computer Science 1 7%
Chemical Engineering 1 7%
Materials Science 1 7%
Decision Sciences 1 7%
Other 0 0%
Unknown 5 33%
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 25 November 2014.
All research outputs
#20,243,777
of 22,771,140 outputs
Outputs from Journal of Intelligent Manufacturing
#393
of 552 outputs
Outputs of similar age
#171,473
of 196,633 outputs
Outputs of similar age from Journal of Intelligent Manufacturing
#7
of 7 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 552 research outputs from this source. They receive a mean Attention Score of 1.5. 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 196,633 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.