Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control

Rymarczyk, Tomasz; Niderla, Konrad; Kozłowski, Edward; Król, Krzysztof; Wyrwisz, Joanna; Skrzypek-Ahmed, Sylwia; Gołąbek, Piotr

doi:10.3390/en14238116

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…In order to image the interior of the object under study based on measurements collected from probes, the most common approach is to solve an inverse problem (IP), where an underdetermined set of linear equations is solved [19][20][21]. This can be described by the following equation:…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

“…The use of machine learning algorithms in industrial processes and tomography is common [2,9,21,24]. The authors decide to use a very simple approach that is a fundamental method used in super-resolution technique known as Super Resolution Convolutional Neural Networks (SRCNN) developed by [25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Effect of Measurement Noise on Reconstruction using Machine Learning with Electrical Tomography in the Case of the Abdominal Cavity

Baran¹,

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Rymarczyk³

et al. 2023

2023 International Interdisciplinary PhD Workshop (IIPhDW)

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Use of electrical impedance tomography for lung volume reconstruction

Tchórzewski,

Lalak - Dybała,

Przysucha

et al. 2024

JoMS

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The article presents a study of the application of electro-impedance tomography (EIT) in diagnosing lung capacity using the Tikhonov regularization method. The possibility of reconstructing the lungs to monitor the degree of air filling was investigated. The experiment included a series of tests using a torso phantom designed to simulate different states of the lungs - from fully inflated to fully deflated. Lung-filling states were manipulated in controlled scenarios to test nine main experimental conditions reflecting different lung-filling states. In addition, the quality of reconstruction was checked using various types of reference backgrounds. The results show significant differences in lung volume reconstructions depending on the lung filling state. The most successful reconstructions, which were obtained using the 'No phantom' background, provided the most explicit visualization of the lungs, reassuring the method's reliability. The experiments confirm the potential of EIT to distinguish between different lung states and reconstruct the degree of lung filling. The study also underscores the need to optimize the reference background to increase the precision of the images, especially for the left lung.

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Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control

Cited by 7 publications

References 31 publications

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

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

Effect of Measurement Noise on Reconstruction using Machine Learning with Electrical Tomography in the Case of the Abdominal Cavity

Use of electrical impedance tomography for lung volume reconstruction

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