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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Groves, Roger
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (29/29 displayed)
- 2024Shearography With Thermal Loading For Defect Detection Of Small Defects In Cfrp Composites
- 2024Towards hydrogen fueled aircraft
- 2024Advancing Hydrogen Sensing for Sustainable Aviationcitations
- 2023Towards safe shearography inspection of thick composites with controlled surface temperature heatingcitations
- 2022Shearography non-destructive testing of thick GFRP laminatescitations
- 2022Shearography non-destructive testing of a composite ship hull section subjected to multiple impacts
- 2021Optical Material Characterisation of Prepreg CFRP for Improved Composite Inspectioncitations
- 2021Spatially modulated thermal excitations for shearography non-destructive inspection of thick compositescitations
- 2021Modeling and imaging of ultrasonic array inspection of side drilled holes in layered anisotropic mediacitations
- 2020Simulation of ultrasonic beam propagation from phased arrays in anisotropic media using linearly phased multi-Gaussian beamscitations
- 2020A gaussian beam based recursive stiffness matrix model to simulate ultrasonic array signals from multi-layered mediacitations
- 2020Simultaneous temperature-strain measurement in a thin composite panel with embedded tilted Fibre Bragg Grating sensors (PPT)
- 2020Algorithm assessment for layup defect segmentation from laser line scan sensor based image datacitations
- 2019Systematic multiparameter design methodology for an ultrasonic health monitoring system for full-scale composite aircraft primary structurescitations
- 2018Experimental assessment of the influence of welding process parameters on Lamb wave transmission across ultrasonically welded thermoplastic composite jointscitations
- 2018Incorporating Inductive Bias into Deep Learning
- 2018Non-Destructive Testing for Detection, Localization and Quantification of Damage on Composite Structures for Composite Repair Applications
- 2018Full-scale testing of an ultrasonic guided wave based structural health monitoring system for a thermoplastic composite aircraft primary structure
- 2018EXTREME shearographycitations
- 20183.12 Inspection and Monitoring of Composite Aircraft Structurescitations
- 2017Online preventive non-destructive evaluation for automated fibre placement
- 2017Modelling of ultrasonic beam propagation from an array through transversely isotropic fibre reinforced composites using Multi Gaussian beams
- 2017Epoxy-hBN nanocompositescitations
- 2017Advanced signal processing techniques for fibre-optic structural health monitoring
- 2016Online Preventative Non-Destructive Evaluation in Automated Fibre Placement
- 2016Thermal strains in heated Fiber Metal Laminates
- 2016Monitoring chemical degradation of thermally cycled glass-fibre composites using hyperspectral imagingcitations
- 2016Experimental characterisation of Lamb wave propagation through thermoplastic composite ultrasonic welds
- 2016Perspectives on Structural Health Monitoring of Composite Civil Aircraft
Places of action
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document
Incorporating Inductive Bias into Deep Learning
Abstract
The near-term artificial intelligence, commonly referred as ‘weak AI’ in the last couple years was achieved thanks to the advances in machine learning (ML), particularly deep learning, which has currently the best in-class performance outperforming other machine learning algorithms. In the deep learning framework, many natural tasks such as object, image, and speech recognition that were impossible to be performed by classical ML algorithms in the previous decades can now be be done by typical home personal computer. Deep learning requires large amount of data that has to be rapidly collected (also known as ‘big data’) in order to create robust model parameters that are able to predict future occurrences of certain event. In some domains, a large dataset such as CIFAR-10, MNIST, or Kaggle exist already. However, in many other domains such as aircraft visual inspection, such a large dataset is not easily available and this clearly restricts deep learning to perform well to recognize material damage in aircraft structures. As many computer science researchers believe, we also think that in order to achieve a performance similar to human-level intelligence, AI could and should not start from scratch. Introducing an inductive bias into deep learning might be one solution to achieve that humanlevel intelligence. In this paper, we give an example how to incorporate aerospace domain knowledge into the development of deep learning algorithms. We performed a relatively simple procedure: we conducted fatigue testing of an aluminum plate that is typically used in aircraft fuselage and build a deep convolutional neural network that classifies crack length according to crack propagation curve obtained from fatigue test. The results of this network are then compared to the results of the same network that was not injected by domain knowledge