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

Author:

Wójcik Dariusz12ORCID,Rymarczyk Tomasz12ORCID,Przysucha Bartosz3ORCID,Gołąbek Michał2ORCID,Majerek Dariusz4ORCID,Warowny Tomasz3ORCID,Soleimani Manuchehr5ORCID

Affiliation:

1. Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland

2. Netrix S.A., Research & Development Centre, 20-704 Lublin, Poland

3. Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland

4. Department of Applied Mathematics, Lublin University of Technology, 20-618 Lublin, Poland

5. Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK

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.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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