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Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

Dariusz Wójcik (dariusz.wojcik@netrix.com.pl), Tomasz Rymarczyk, Bartosz Przysucha, Michał Gołąbek, Dariusz Majerek, Tomasz Warowny and Manuchehr Soleimani
Additional contact information
Dariusz Wójcik: Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland
Tomasz Rymarczyk: Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland
Bartosz Przysucha: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Michał Gołąbek: Netrix S.A., Research & Development Centre, 20-704 Lublin, Poland
Dariusz Majerek: Department of Applied Mathematics, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Warowny: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Manuchehr Soleimani: Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK

Energies, 2023, vol. 16, issue 3, 1-14

Abstract: This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.

Keywords: deep learning; machine learning; inverse problems; tomography; Industry 4.0; energy consumption; energy optimization (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: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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