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

Author

Listed:
  • Dariusz Wójcik

    (Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland
    Netrix S.A., Research & Development Centre, 20-704 Lublin, Poland)

  • Tomasz Rymarczyk

    (Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland
    Netrix S.A., Research & Development Centre, 20-704 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)

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.

Suggested Citation

  • Dariusz Wójcik & Tomasz Rymarczyk & Bartosz Przysucha & Michał Gołąbek & Dariusz Majerek & Tomasz Warowny & Manuchehr Soleimani, 2023. "Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography," Energies, MDPI, vol. 16(3), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1387-:d:1051512
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    References listed on IDEAS

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    1. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
    2. Dariusz Majerek & Tomasz Rymarczyk & Dariusz Wójcik & Edward Kozłowski & Magda Rzemieniak & Janusz Gudowski & Konrad Gauda, 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography," Energies, MDPI, vol. 14(22), pages 1-19, November.
    3. Adam Ryszard Zywica & Marcin Ziolkowski & Stanislaw Gratkowski, 2020. "Detailed Analytical Approach to Solve the Magnetoacoustic Tomography with Magnetic Induction (MAT-MI) Problem for Three-Layer Objects," Energies, MDPI, vol. 13(24), pages 1-17, December.
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    Cited by:

    1. Michał Styła & Edward Kozłowski & Paweł Tchórzewski & Dominik Gnaś & Przemysław Adamkiewicz & Jan Laskowski & Sylwia Skrzypek-Ahmed & Arkadiusz Małek & Dariusz Kasperek, 2024. "Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings," Energies, MDPI, vol. 17(11), pages 1-16, June.

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