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Mendeley readers
Attention Score in Context
| Title |
Empirical mode decomposition with missing values
|
|---|---|
| Published in |
SpringerPlus, November 2016
|
| DOI | 10.1186/s40064-016-3692-1 |
| Pubmed ID | |
| Authors |
Donghoh Kim, Hee-Seok Oh |
| Abstract |
This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient when missing values are present. This paper proposes a modified EMD procedure based on a novel combination of empirical mode decomposition and self-consistency concept. The self-consistency provides an effective imputation method of missing data, and hence, the proposed EMD procedure produces stable decomposition results. Simulation studies and the image analysis demonstrate that the proposed method produces substantially effective results. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Greece | 1 | 33% |
| Switzerland | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 20% |
| Student > Ph. D. Student | 3 | 15% |
| Student > Doctoral Student | 2 | 10% |
| Professor | 2 | 10% |
| Lecturer | 2 | 10% |
| Other | 5 | 25% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 6 | 30% |
| Environmental Science | 2 | 10% |
| Earth and Planetary Sciences | 2 | 10% |
| Agricultural and Biological Sciences | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 5 | 25% |
| Unknown | 3 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 December 2016.
All research outputs
#14,282,319
of 22,903,988 outputs
Outputs from SpringerPlus
#773
of 1,850 outputs
Outputs of similar age
#224,591
of 415,675 outputs
Outputs of similar age from SpringerPlus
#46
of 84 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,850 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
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 415,675 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.