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Volume 14
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Article

Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

by
Dariusz Majerek
1,*,
Tomasz Rymarczyk
2,3,*,
Dariusz Wójcik
3,
Edward Kozłowski
4,
Magda Rzemieniak
4,
Janusz Gudowski
2 and
Konrad Gauda
2
1
Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland
2
Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
3
Research & Development Center Netrix S.A., 20-704 Lublin, Poland
4
Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
*
Authors to whom correspondence should be addressed.
Energies 2021, 14(22), 7549; https://doi.org/10.3390/en14227549
Submission received: 10 October 2021 / Revised: 5 November 2021 / Accepted: 8 November 2021 / Published: 12 November 2021
(This article belongs to the Special Issue Engineering and Medical Aspects of the Use of Electromagnetic Field Energy 2021)
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Abstract

This paper describes the method developed using the Extreme Gradient Boosting (Xgboost) algorithm that allows high-resolution imaging using the ultrasound tomography (UST) signal. More precisely, we can locate, isolate, and use the reflective peaks from the UST signal to achieve high-resolution images with low noise, which are far more useful for the location of points where the reflection occurred inside the experimental tank. Each reconstruction is divided into two parts, estimation of starting points of wave packets of raw signal (SAT—starting arrival time) and image reconstruction via XGBoost algorithm based on SAT matrix. This technology is the basis of a project to design non-invasive monitoring and diagnostics of technological processes. In this paper, we present a method of the complete solution for monitoring industrial processes. The measurements used in the study were obtained with the author’s solution of ultrasound tomography.
Keywords: ultrasound imagining; machine learning; extreme gradient boosting ultrasound imagining; machine learning; extreme gradient boosting

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MDPI and ACS Style

Majerek, D.; Rymarczyk, T.; Wójcik, D.; Kozłowski, E.; Rzemieniak, M.; Gudowski, J.; Gauda, K. Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography. Energies 2021, 14, 7549. https://doi.org/10.3390/en14227549

AMA Style

Majerek D, Rymarczyk T, Wójcik D, Kozłowski E, Rzemieniak M, Gudowski J, Gauda K. Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography. Energies. 2021; 14(22):7549. https://doi.org/10.3390/en14227549

Chicago/Turabian Style

Majerek, Dariusz, Tomasz Rymarczyk, Dariusz Wójcik, Edward Kozłowski, Magda Rzemieniak, Janusz Gudowski, and Konrad Gauda. 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography" Energies 14, no. 22: 7549. https://doi.org/10.3390/en14227549

APA Style

Majerek, D., Rymarczyk, T., Wójcik, D., Kozłowski, E., Rzemieniak, M., Gudowski, J., & Gauda, K. (2021). Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography. Energies, 14(22), 7549. https://doi.org/10.3390/en14227549

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