Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

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 imag...

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Autores principales:	Dariusz Majerek, Tomasz Rymarczyk, Dariusz Wójcik, Edward Kozłowski, Magda Rzemieniak, Janusz Gudowski, Konrad Gauda
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ultrasound imagining machine learning extreme gradient boosting Technology T
Acceso en línea:	https://doaj.org/article/2ca4a343b3ec43aaa026550f54f3481a
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spelling	oai:doaj.org-article:2ca4a343b3ec43aaa026550f54f3481a2021-11-25T17:26:32ZMachine Learning and Deterministic Approach to the Reflective Ultrasound Tomography10.3390/en142275491996-1073https://doaj.org/article/2ca4a343b3ec43aaa026550f54f3481a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7549https://doaj.org/toc/1996-1073This 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.Dariusz MajerekTomasz RymarczykDariusz WójcikEdward KozłowskiMagda RzemieniakJanusz GudowskiKonrad GaudaMDPI AGarticleultrasound imaginingmachine learningextreme gradient boostingTechnologyTENEnergies, Vol 14, Iss 7549, p 7549 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ultrasound imagining machine learning extreme gradient boosting Technology T
spellingShingle	ultrasound imagining machine learning extreme gradient boosting Technology T Dariusz Majerek Tomasz Rymarczyk Dariusz Wójcik Edward Kozłowski Magda Rzemieniak Janusz Gudowski Konrad Gauda Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
description	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.
format	article
author	Dariusz Majerek Tomasz Rymarczyk Dariusz Wójcik Edward Kozłowski Magda Rzemieniak Janusz Gudowski Konrad Gauda
author_facet	Dariusz Majerek Tomasz Rymarczyk Dariusz Wójcik Edward Kozłowski Magda Rzemieniak Janusz Gudowski Konrad Gauda
author_sort	Dariusz Majerek
title	Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
title_short	Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
title_full	Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
title_fullStr	Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
title_full_unstemmed	Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
title_sort	machine learning and deterministic approach to the reflective ultrasound tomography
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2ca4a343b3ec43aaa026550f54f3481a
work_keys_str_mv	AT dariuszmajerek machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT tomaszrymarczyk machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT dariuszwojcik machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT edwardkozłowski machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT magdarzemieniak machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT januszgudowski machinelearninganddeterministicapproachtothereflectiveultrasoundtomography AT konradgauda machinelearninganddeterministicapproachtothereflectiveultrasoundtomography
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Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography

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