Logistic Regression for Machine Learning in Process Tomography

The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasoun...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-08, Vol.19 (15), p.3400
Main Authors: Rymarczyk, Tomasz, Kozłowski, Edward, Kłosowski, Grzegorz, Niderla, Konrad
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Deep learning
Defects
Electrical impedance
electrical impedance tomography
image reconstruction
Image transmission
Innovations
International conferences
Inverse problems
Machine learning
Neural networks
Pixels
process tomography
Quality assessment
Regression analysis
Reinforced concrete
Sensors
Statistical methods
Tomography
Ultrasonic imaging
ultrasound tomography
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Summary:The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19153400