iconicon Uproszczony widok
 

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

cris.lastimport.scopus2024-09-19T01:30:54Z
dc.abstract.enThis 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.
dc.affiliationTransportu i Informatyki
dc.contributor.authorDariusz Wójcik
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorBartosz Przysucha
dc.contributor.authorMichał Gołąbek
dc.contributor.authorDariusz Majerek
dc.contributor.authorTomasz Warowny
dc.contributor.authorManuchehr Soleimani
dc.date.accessioned2024-04-22T11:20:04Z
dc.date.available2024-04-22T11:20:04Z
dc.date.issued2023
dc.description.abstract<jats:p>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.</jats:p>
dc.identifier.doi10.3390/en16031387
dc.identifier.issn1996-1073
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/199
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofEnergies
dc.rightsCC-BY
dc.subject.endeep learning
dc.subject.enmachine learning
dc.subject.eninverse problems
dc.subject.entomography
dc.subject.enIndustry 4.0
dc.subject.enenergy consumption
dc.subject.enenergy optimization
dc.titleEnergy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue3
oaire.citation.volume16