Data fusion and artificial neural networks for biomass estimation

1997 ◽  
Vol 144 (2) ◽  
pp. 69-72 ◽  
Author(s):  
R.R. Leal ◽  
P. Butler ◽  
P. Lane ◽  
P.A. Payne
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Biomass Estimation ◽  
Artificial Neural

Precipitation data fusion using vector space transformation and artificial neural networks

2010 ◽  
Vol 31 (10) ◽  
pp. 1184-1200 ◽  
Author(s):  
Anish C. Turlapaty ◽  
Valentine G. Anantharaj ◽  
Nicolas H. Younan ◽  
F. Joseph Turk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Vector Space ◽  
Precipitation Data ◽  
Space Transformation ◽  
Artificial Neural

scholarly journals A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2598
Author(s):  
Romain Cormerais ◽  
Aroune Duclos ◽  
Guillaume Wasselynck ◽  
Gérard Berthiau ◽  
Roberto Longo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Complex Structure ◽  
Fusion Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Aluminum Block ◽  
Artificial Neural ◽  
Non Destructive

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

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