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

Tomasz Rymarczyk, Konrad Niderla, Edward Kozłowski, Krzysztof Król, Joanna Maria Wyrwisz, Sylwia Skrzypek-Ahmed and Piotr Gołąbek
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Tomasz Rymarczyk: Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
Konrad Niderla: Research and Development Center, Netrix S.A., 20-704 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
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

Energies, 2021, vol. 14, issue 23, 1-21

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.

Keywords: industrial tomography; sensors; numerical calculation; machine learning; elastic net; logistic regression; wavelet preprocessing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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