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Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks

Overview of attention for article published in International Journal of Automation and Computing, November 2019
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1 tweeter

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1 Mendeley
Title
Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks
Published in
International Journal of Automation and Computing, November 2019
DOI 10.1007/s11633-019-1203-x
Authors

Chang-Hao Zhu, Jie Zhang

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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Engineering 1 100%

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 06 November 2019.
All research outputs
#11,101,753
of 13,989,129 outputs
Outputs from International Journal of Automation and Computing
#72
of 221 outputs
Outputs of similar age
#195,432
of 271,665 outputs
Outputs of similar age from International Journal of Automation and Computing
#8
of 23 outputs
Altmetric has tracked 13,989,129 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 221 research outputs from this source. They receive a mean Attention Score of 1.3. 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 271,665 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 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.