IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8116-d694601.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8116/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8116/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
    2. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    3. Xiaxuan He & Qifeng Yuan & Yinghong Qin & Junwen Lu & Gang Li, 2024. "Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones," Land, MDPI, vol. 13(10), pages 1-34, October.
    4. Sascha O. Becker & Hans-Joachim Voth, 2023. "From the Death of God to the Rise of Hitler," CESifo Working Paper Series 10730, CESifo.
    5. Ahmet Faruk Aysan & Bekir Sait Ciftler & Ibrahim Musa Unal, 2024. "Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking," JRFM, MDPI, vol. 17(3), pages 1-19, March.
    6. Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
    7. Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
    8. Maria A. F. Silva Dias & Yania Molina Souto & Bruno Biazeto & Enzo Todesco & Jose A. Zuñiga Mora & Dylana Vargas Navarro & Melvin Pérez Chinchilla & Carlos Madrigal Araya & Dayanna Arce Fernández & Be, 2024. "Reduction of Wind Speed Forecast Error in Costa Rica Tejona Wind Farm with Artificial Intelligence," Energies, MDPI, vol. 17(22), pages 1-12, November.
    9. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    10. Jialing Zhang & Zhanxu Chen & An Wang & Zhenzhang Li & Wei Wan, 2023. "Intelligent Personalized Lighting Control System for Residents," Sustainability, MDPI, vol. 15(21), pages 1-12, October.
    11. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    12. Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Tomasz Wołowiec & Marta Cholewa-Wiktor & Piotr Bednarczuk, 2021. "Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks," Energies, MDPI, vol. 14(23), pages 1-35, December.
    13. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    14. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    15. David Simon & Aaron Sojourner & Jon Pedersen & Heidi Ombisa Skallet, 2024. "Financial Incentives for Adoption and Kin Guardianship Improve Achievement for Foster Children," Upjohn Working Papers 24-401, W.E. Upjohn Institute for Employment Research.
    16. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    17. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
    18. Hillebrecht, Michael & Klonner, Stefan & Pacere, Noraogo A., 2020. "Dynamic Properties of Poverty Targeting," Working Papers 0696, University of Heidelberg, Department of Economics.
    19. Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.
    20. Kang, Lili & Zhao, Guangchuan, 2022. "Financial support for unmet need for personal assistance with daily activities: Implications from China's long-term care insurance pilots," Finance Research Letters, Elsevier, vol. 45(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8116-:d:694601. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.