Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

Dariusz Majerek, Tomasz Rymarczyk, Dariusz Wójcik, Edward Kozłowski, Magda Rzemieniak, Janusz Gudowski and Konrad Gauda
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Dariusz Majerek: Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Rymarczyk: Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
Dariusz Wójcik: Research & Development Center Netrix S.A., 20-704 Lublin, Poland
Edward Kozłowski: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Magda Rzemieniak: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Janusz Gudowski: Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
Konrad Gauda: Institute of Computer Science and Innovative Technologies, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland

Energies, 2021, vol. 14, issue 22, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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