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Article

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

1
Research and Development Center, Netrix S.A., 20-704 Lublin, Poland
2
Faculty of Administration and Social Sciences, University of Economics and Innovation, 20-209 Lublin, Poland
3
Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
4
Faculty of Transport and Computer Science, University of Economics and Innovation, 20-209 Lublin, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Álvaro Gutiérrez
Energies 2021, 14(23), 8116; https://doi.org/10.3390/en14238116
Received: 5 November 2021 / Revised: 27 November 2021 / Accepted: 1 December 2021 / Published: 3 December 2021
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. View Full-Text
Keywords: industrial tomography; sensors; numerical calculation; machine learning; elastic net; logistic regression; wavelet preprocessing industrial tomography; sensors; numerical calculation; machine learning; elastic net; logistic regression; wavelet preprocessing
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MDPI and ACS Style

Rymarczyk, T.; Niderla, K.; Kozłowski, E.; Król, K.; Wyrwisz, J.M.; Skrzypek-Ahmed, S.; Gołąbek, P. Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control. Energies 2021, 14, 8116. https://doi.org/10.3390/en14238116

AMA Style

Rymarczyk T, Niderla K, Kozłowski E, Król K, Wyrwisz JM, Skrzypek-Ahmed S, Gołąbek P. Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control. Energies. 2021; 14(23):8116. https://doi.org/10.3390/en14238116

Chicago/Turabian Style

Rymarczyk, Tomasz, Konrad Niderla, Edward Kozłowski, Krzysztof Król, Joanna M. Wyrwisz, Sylwia Skrzypek-Ahmed, and Piotr Gołąbek. 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control" Energies 14, no. 23: 8116. https://doi.org/10.3390/en14238116

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