scholarly journals Data Augmentation and Artificial Neural Networks for Eddy Currents Testing

2020 ◽  
Author(s):  
Romain Cormerais ◽  
Roberto Longo ◽  
Aroune Duclos ◽  
Guillaume Wasselynck ◽  
Gérard Berthiau
Keyword(s):  
Eddy Currents ◽  
Data Augmentation ◽  
Numerical Models ◽  
Simulated Data ◽  
Principal Component ◽  
Physical Models ◽  
Destructive Testing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Non Destructive

Eddy Currents (ECs) Non Destructive Testing (NDT) is widely used to determine the position and size of flaws in metal materials. Due to difficulties in estimating these parameters via inverse algorithms based on physical models, approaches focused on Artificial Neural Network (ANN) are nowadays of great interest. The main drawbacks of these techniques still reside in the complexity of the numerical models and the large number of simulated data needed to train and test the ANN, leading to a considerable amount of calculation time and resources. To overcome these limitations, this article proposes a new approach based on a data augmentation procedure via Principal Component Analysis (PCA) applied to numerical simulations.

scholarly journals Using artificial neural networks to extract the 21-cm global signal from the EDGES data

2021 ◽  
Vol 502 (2) ◽  
pp. 2815-2825
Author(s):  
Madhurima Choudhury ◽  
Atrideb Chatterjee ◽  
Abhirup Datta ◽  
Tirthankar Roy Choudhury
Keyword(s):  
Neutral Hydrogen ◽  
Simulated Data ◽  
Physical Models ◽  
Data Sets ◽  
First Stars ◽  
Global Signal ◽  
Reconstructed Signal ◽  
Radio Background ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT The redshifted 21-cm signal of neutral hydrogen is a promising probe into the period of evolution of our Universe when the first stars were formed (Cosmic Dawn), to the period where the entire Universe changed its state from being completely neutral to completely ionized (Reionization). The most striking feature of this line of neutral hydrogen is that it can be observed across an entire frequency range as a sky-averaged continuous signature, or its fluctuations can be measured using an interferometer. However, the 21-cm signal is very faint and is dominated by a much brighter Galactic and extragalactic foregrounds, making it an observational challenge. We have used different physical models to simulate various realizations of the 21-cm global signals, including an excess radio background to match the amplitude of the Experiment to Detect the Global EoR Signature (EDGES) 21-cm signal. First, we have used an artificial neural network (ANN) to extract the astrophysical parameters from these simulated data sets. Then, mock observations were generated by adding a physically motivated foreground model and an ANN was used to extract the astrophysical parameters from such data. The R2 score of our predictions from the mock observations is in the range of 0.65–0.89. We have used this ANN to predict the signal parameters giving the EDGES data as the input. We find that the reconstructed signal closely mimics the amplitude of the reported detection. The recovered parameters can be used to infer the physical state of the gas at high redshifts.

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

Review on Non-Destructive Testing Technique of Eddy Current Pulsed Thermography

2015 ◽  
Vol 742 ◽  
pp. 128-131 ◽  
Author(s):  
Jian Min Zhou ◽  
Jun Yang ◽  
Qi Wan
Keyword(s):  
Eddy Current ◽  
Signal Reconstruction ◽  
Infrared Image ◽  
Principal Component ◽  
Research Progress ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Pulsed Phase Thermography ◽  
Non Destructive

This paper introduces the theory of eddy current pulsed thermography and expounds the research status of eddy current pulsed thermography in application and information extraction. Thermographic signal reconstruction, pulsed phase thermography, principal component analysis were introuduced in this paper and listed some fusion multiple methods to acquire information from infrared image. At last, it summarizes research progress, existing problem and deelopment of eddy current pulsed thermography.

A New Approach to Image Reconstruction in Positron Emission Tomography Using Artificial Neural Networks

1998 ◽  
Vol 09 (01) ◽  
pp. 71-85 ◽  
Author(s):  
A. Bevilacqua ◽  
D. Bollini ◽  
R. Campanini ◽  
N. Lanconelli ◽  
M. Galli
Keyword(s):  
Positron Emission Tomography ◽  
Simulated Data ◽  
Emission Tomography ◽  
Source Distribution ◽  
New Approach ◽  
High Data ◽  
Positron Emission ◽  
Physical Effects ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels).

Electromagnetic Effect of Eddy Currents in Composites with Conductive Components: Magnetic Field Strengthening and its Simple Analytical Estimation

2018 ◽  
Vol 284 ◽  
pp. 37-42 ◽  
Author(s):  
R.R. Sattarov ◽  
T.A. Volkova ◽  
I.Z. Gubaydullin
Keyword(s):  
Magnetic Field ◽  
Electromagnetic Compatibility ◽  
Eddy Currents ◽  
Electromagnetic Effect ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Compatibility Analysis ◽  
Dynamic Materials ◽  
Analytical Estimation ◽  
Non Destructive

Composites and dynamic materials that include conductive components are becoming a suitable choice in different applications. The eddy currents are generated when the conductive components are placed in alternating magnetic field. The eddy currents decrease the primary field and this effect has been well studied and it is used for electromagnetic shielding. Besides, the magnetic field increases in small space near edges of the conductive components. While this effect of magnetic field strengthening is known, it is rarely examined. We will introduce a simple model that can be appropriate for the conductive components in form of long thin sheets. We analytically analyze the model and obtain expressions that give upper bounds for increasing of the net magnetic field. The electromagnetic effect of strengthening should be taken into account when considering an application of the composites. The results are useful for electromagnetic compatibility analysis, non-destructive testing and monitoring of composite and dynamic materials with conductive components.

scholarly journals Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 13 ◽  
Author(s):  
Yousefi ◽  
Ibarra-Castanedo ◽  
Maldague
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Computational Time ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive ◽  
Non Negative Matrix Factorization ◽  
Subsurface Defects

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

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