scholarly journals Thin-film neural networks for optical inverse problem

2021 ◽  
Vol 2 (4) ◽  
pp. 1-8
Author(s):  
Lingjie Fan ◽  
◽  
Ang Chen ◽  
Tongyu Li ◽  
Jiao Chu ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Inverse Problem

Approximation Approach to Solving The Inverse Problem of Geoelectrics Using Neural Networks

2021 ◽  
Author(s):  
M.I. Shimelevich ◽  
I.E. Obornev ◽  
E.A. Obornev ◽  
E.A. Rodionov
Keyword(s):  
Neural Networks ◽  
Inverse Problem ◽  
Approximation Approach

Experimental force reconstruction on plates of arbitrary shape using neural networks

2021 ◽  
Vol 263 (3) ◽  
pp. 3407-3416
Author(s):  
Tyler Dare
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Inverse Problem ◽  
Artificial Neural Network ◽  
Rectangular Plate ◽  
Arbitrary Shape ◽  
Impulsive Force ◽  
Force Reconstruction ◽  
Output Space ◽  
Arbitrary Shapes

Measuring the forces that excite a structure into vibration is an important tool in modeling the system and investigating ways to reduce the vibration. However, determining the forces that have been applied to a vibrating structure can be a challenging inverse problem, even when the structure is instrumented with a large number of sensors. Previously, an artificial neural network was developed to identify the location of an impulsive force on a rectangular plate. In this research, the techniques were extended to plates of arbitrary shape. The principal challenge of arbitrary shapes is that some combinations of network outputs (x- and y-coordinates) are invalid. For example, for a plate with a hole in the middle, the network should not output that the force was applied in the center of the hole. Different methods of accommodating arbitrary shapes were investigated, including output space quantization and selecting the closest valid region.

Applying theoretical spectra to artificial neural networks for real-time estimation of thin film thickness

2016 ◽  
Vol 55 (12) ◽  
pp. 125106 ◽  
Author(s):  
Tzong-Daw Wu ◽  
Jiun-Shen Chen ◽  
Ching-Pei Tseng ◽  
Cheng-Chang Hsieh
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Film Thickness ◽  
Real Time ◽  
Time Estimation ◽  
Thin Film Thickness ◽  
Real Time Estimation ◽  
Artificial Neural

Ultrasound Tomography Imaging of Defects Using Neural Networks

1992 ◽  
Vol 4 (5) ◽  
pp. 758-771 ◽  
Author(s):  
Denis M. Anthony ◽  
Evor L. Hines ◽  
David A. Hutchins ◽  
J. T. Mottram
Keyword(s):  
Neural Networks ◽  
Inverse Problem ◽  
Artificial Neural Networks ◽  
Longitudinal Wave ◽  
Time Of Flight ◽  
Simplified Model ◽  
Gray Scale ◽  
Ultrasound Tomography ◽  
Tomography Imaging ◽  
Ultrasound Propagation

Simulations of ultrasound tomography demonstrated that artificial neural networks can solve the inverse problem in ultrasound tomography. A highly simplified model of ultrasound propagation was constructed, taking no account of refraction or diffraction, and using only longitudinal wave time of flight (TOF). TOF data were used as the network inputs, and the target outputs were the expected pixel maps, showing defects (gray scale coded) according to the velocity of the wave in the defect. The effects of varying resolution and defect velocity were explored. It was found that defects could be imaged using time of flight of ultrasonic rays.

scholarly journals Inverse problem of photoelastic fringe mapping using neural networks

2007 ◽  
Vol 18 (5) ◽  
pp. 1361-1366 ◽  
Author(s):  
Gurtej S Grewal ◽  
Venketesh N Dubey
Keyword(s):  
Neural Networks ◽  
Inverse Problem

Inverse problem of aircraft structural parameter estimation: application of neural networks

2006 ◽  
Vol 14 (4) ◽  
pp. 351-363 ◽  
Author(s):  
P. M. Trivailo ◽  
G. S. Dulikravich ◽  
D. Sgarioto ◽  
T. Gilbert
Keyword(s):  
Neural Networks ◽  
Inverse Problem ◽  
Parameter Estimation ◽  
Structural Parameter

Determination of probabilistic parameters of concrete: solving the inverse problem by using artificial neural networks

2000 ◽  
Vol 78 (1-3) ◽  
pp. 497-503 ◽  
Author(s):  
E.M.R. Fairbairn ◽  
N.F.F. Ebecken ◽  
C.N.M. Paz ◽  
F.-J. Ulm
Keyword(s):  
Neural Networks ◽  
Inverse Problem ◽  
Artificial Neural Networks ◽  
Artificial Neural
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