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Volume 44 Issue 4
Apr.  2022
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DAI Jin, HU Biao, WANG Guoyin, ZHANG Lei. The Uncertainty Similarity Measure of Cloud Model Based on the Fusion of Distribution Contour and Local Feature[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1429-1439. doi: 10.11999/JEIT210033
Citation: DAI Jin, HU Biao, WANG Guoyin, ZHANG Lei. The Uncertainty Similarity Measure of Cloud Model Based on the Fusion of Distribution Contour and Local Feature[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1429-1439. doi: 10.11999/JEIT210033

The Uncertainty Similarity Measure of Cloud Model Based on the Fusion of Distribution Contour and Local Feature

doi: 10.11999/JEIT210033
Funds:  The National Natural Science Foundation of China (61936001, 61772096), The Natural Science Foundation of Chongqing (cstc2019jcyj-cxttX0002)
  • Received Date: 2021-01-11
  • Rev Recd Date: 2021-07-15
  • Available Online: 2021-08-26
  • Publish Date: 2022-04-18
  • In view of the fact that the current uncertainty similarity measurement based on cloud model is either the quantitative calculation of accurate local data, or the measurement only through its overall geometric features, which leads to the result with great one sidedness. In this paper, considering the overall geometric features of cloud model and the contribution of micro cloud droplets distribution, an uncertainty similarity measurement method based on the fusion of distribution contour and local features is proposed, i.e. the Envelope Area of the Contribution based on Cloud Model (EACCM). In this method, the envelope (the area between the inner envelope curve and the outer envelope curve) which reflects the geometric characteristics of the cloud model is used as the basis of similarity measurement, and a comprehensive measurement model is established based on the contribution of cloud droplets contained in the overlapped part. The simulation results show that the method is more scientific and reasonable, and can effectively avoid the problem of abnormally similarity caused by the large difference or very close of the same digital features.
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