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Faculté des Sciences appliquées
Faculté des Sciences appliquées
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Interactive machine learning in industrial control

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Vallot, Arnaud ULiège
Promotor(s) : Geurts, Pierre ULiège
Date of defense : 26-Jun-2019/27-Jun-2019 • Permalink : http://hdl.handle.net/2268.2/6739
Details
Title : Interactive machine learning in industrial control
Author : Vallot, Arnaud ULiège
Date of defense  : 26-Jun-2019/27-Jun-2019
Advisor(s) : Geurts, Pierre ULiège
Committee's member(s) : Marée, Raphaël ULiège
Louppe, Gilles ULiège
Greffe, Christophe 
Language : English
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] In industrial control, digital radiography is used to detect defects in parts. However visual inspection by a trained expert can take up to a few minutes to identify anomalies on one image, which creates a bottleneck. That is why projects like ADRIC (automatic defect recognition in industrial control) try to use machine learning to help the operators speed up the process.

However the manual defects identification is not fully reliable, which can confuse the machine learning algorithm tasked to discriminate those defects. This is why we want to design a method that will use data efficiently, in order to achieve the best results with less data than before.

In this document, we show that, using suitable data selection in the framework of active learning in conjunction with Bayesian deep learning, we achieved better results than random, for a patch based method, while we did not achieve convincing results for a more realistic image based method. More work need to be done in this direction to get similar results on full images.

We also developed post-processing techniques on heat-maps in order to enhance the performance metric, as well as the accuracy. We also noticed that the heat-maps production was not fast enough, only marginally surpassing the human experts time-wise. We thus designed a heat-maps production algorithm that offers speed ups, but with lower accuracy.

Finally, we found out that using weights trained on a synthetic dataset as a starting point, we can achieve better performance, without almost any additional cost, on a real dataset.


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Author

  • Vallot, Arnaud ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
  • Louppe, Gilles ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
    ORBi View his publications on ORBi
  • Greffe, Christophe X-RAY Imaging Solutions
  • Total number of views 79
  • Total number of downloads 11










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