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

Author

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  • Tomasz Rymarczyk

    (Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
    Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Konrad Niderla

    (Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
    Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Edward Kozłowski

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Krzysztof Król

    (Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
    Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Joanna Maria Wyrwisz

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Sylwia Skrzypek-Ahmed

    (Faculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, Poland)

  • Piotr Gołąbek

    (Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland)

Abstract

The research presented here concerns the analysis and selection of logistic regression with wave preprocessing to solve the inverse problem in industrial tomography. The presented application includes a specialized device for tomographic measurements and dedicated algorithms for image reconstruction. The subject of the research was a model of a tank filled with tap water and specific inclusions. The research mainly targeted the study of developing and comparing models and methods for data reconstruction and analysis. The application allows choosing the appropriate method of image reconstruction, knowing the specifics of the solution. The novelty of the presented solution is the use of original machine learning algorithms to implement electrical impedance tomography. One of the features of the presented solution was the use of many individually trained subsystems, each of which produces a unique pixel of the final image. The methods were trained on data sets generated by computer simulation and based on actual laboratory measurements. Conductivity values for individual pixels are the result of the reconstruction of vector images within the tested object. By comparing the results of image reconstruction, the most efficient methods were identified.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8116-:d:694601
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    References listed on IDEAS

    as
    1. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jan Sikora & Tomasz Wołowiec & Paweł Tchórzewski & Stanisław Skowron, 2021. "Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls," Energies, MDPI, vol. 14(10), pages 1-22, May.
    2. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
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    Cited by:

    1. Tao Liu & Jiayuan Yu & Yuanjin Zheng & Chao Liu & Yanxiong Yang & Yunfei Qi, 2022. "A Nonlinear Multigrid Method for the Parameter Identification Problem of Partial Differential Equations with Constraints," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    2. Bartosz Przysucha & Dariusz Wójcik & Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Marcin Gąsior, 2023. "Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net," Energies, MDPI, vol. 16(3), pages 1-22, February.
    3. 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.
    4. Michał Styła & Bartłomiej Kiczek & Grzegorz Kłosowski & Tomasz Rymarczyk & Przemysław Adamkiewicz & Dariusz Wójcik & Tomasz Cieplak, 2022. "Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-20, December.

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