scholarly journals Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 114261-114270 ◽  
Author(s):  
Yili Xia ◽  
Bin Zhang ◽  
Wenjiang Pei ◽  
Danilo P. Mandic
Keyword(s):  
Image Fusion ◽  
Empirical Mode Decomposition ◽  
Multivariate Empirical Mode Decomposition ◽  
Multi Scale ◽  
Mode Decomposition

scholarly journals Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

Sensors ◽  
2015 ◽  
Vol 15 (5) ◽  
pp. 10923-10947 ◽  
Author(s):  
Naveed Rehman ◽  
Shoaib Ehsan ◽  
Syed Abdullah ◽  
Muhammad Akhtar ◽  
Danilo Mandic ◽  
...  
Keyword(s):  
Image Fusion ◽  
Empirical Mode Decomposition ◽  
Multivariate Empirical Mode Decomposition ◽  
Multi Scale ◽  
Mode Decomposition

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

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