USING NEURAL NETWORKS AND DEEP LEARNING ALGORITHMS IN ELECTRICAL IMPEDANCE TOMOGRAPHY
Grzegorz Kłosowski
g.klosowski@pollub.plLublin University of Technology, Faculty of Management, Department of Organization of Enterprise (Poland)
Tomasz Rymarczyk
Research and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin (Poland)
Abstract
This paper refers to the cases of the use of Artificial Neural Networks and Convolutional Neural Networks in impedance tomography. Machine Learning methods can be used to teach computers different technical problems. The efficient use of conventional artificial neural networks in tomography is possible able to effectively visualize objects. The first step of implementation Deep Learning methods in Electrical Impedance Tomography was performed in this work.
Keywords:
Imaging tomography, Multilayer Perceptron, Deep Learning, Convolutional Neural NetworksReferences
Bladt E. et al.: Electron tomography based on highly limited data using a neural network reconstruction technique. Ultramicroscopy 158/2015, 81–88.
Google Scholar
Buduma N., Locascio N.: Fundamentals of Deep Learning. Designing Next-Generation Machine Intelligence Algorithms. O'Reilly Media, 2017.
Google Scholar
Durairaj D. C., Krishna M. C., Murugesan R.: A neural network approach for image reconstruction in electron magnetic resonance tomography. Computers in biology and medicine 37(10)/2007, 1492–1501.
Google Scholar
Egmont-Petersen M., Ridder de D., Handels H.: Image processing with neural networks – a review. Pattern Recognition 35/2002, 2279–2301.
Google Scholar
Minnett R. C. J. et al.: Neural network tomography: Network replication from output surface geometry. Neural Networks 24(5)/2011, 484–492.
Google Scholar
Pelt D. M., Batenburg K. J.: Fast tomographic reconstruction from limited data using artificial neural networks. IEEE Trans. Image Process. 22/2013, 5238–5251.
Google Scholar
Rybak G., Chaniecki Z., Grudzień K., Romanowski A., Sankowski D.: Non–invasive methods of industrial process control. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 4(3)/2014, 41–45.
Google Scholar
Rymarczyk T.: New Methods to Determine Moisture Areas by Electrical Impedance Tomography. International Journal of Applied Electromagnetics and Mechanics 37(1-2)/2016, 79–87.
Google Scholar
Stasiak M. et al.: Principal component analysis and artificial neural network approach to electrical impedance tomography problems approximated by multi-region boundary element method. Engineering Analysis with Boundary Elements 31(8)/2007, 713–720.
Google Scholar
Tapson J.: Neural Networks and Stochastic Search Methods Applied to Capacitive Tomography. IFAC Proceedings Volumes 30(7)/1997, 631–634.
Google Scholar
Tapson J.: Neural networks and stochastic search methods applied to industrial capacitive tomography. Control Engineering Practice 7(1)/1999, 117–121.
Google Scholar
Tchorzewski P., Rymarczyk T., Sikora J.: Using Topological Algorithms to Solve Inverse Problem in Electrical Impedance Tomography. International Interdisciplinary Phd Workshop 2016, 46–50.
Google Scholar
Wang J. et al.: Neural-network approach for optical tomography. Signal processing, 86(9)/2006, 2495–2502.
Google Scholar
Authors
Grzegorz Kłosowskig.klosowski@pollub.pl
Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise Poland
Authors
Tomasz RymarczykResearch and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin Poland
Statistics
Abstract views: 613PDF downloads: 224
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Tomasz Rymarczyk, Tomasz Cieplak, Grzegorz Kłosowski, Paweł Rymarczyk, DESIGN OF DATA ANALYSIS SYSTEMS FOR BUSINESS PROCESS AUTOMATION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 3 (2018)
- Tomasz Rymarczyk, Michał Gołąbek, Piotr Lesiak, Andrzej Marciniak, Mirosław Guzik, CONSTRUCTION OF AN ULTRASONIC TOMOGRAPH FOR ANALYSIS OF TECHNOLOGICAL PROCESSES IN THE FIELD OF REFLECTION AND TRANSMISSION WAVES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 9 No. 4 (2019)
- Tomasz Rymarczyk, Grzegorz Kłosowski, THE USE OF ARTIFICIAL INTELLIGENCE IN AUTOMATED IN-HOUSE LOGISTICS CENTRES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 1 (2018)
- Tomasz Rymarczyk, Bartek Przysucha, Marcin Kowalski, Piotr Bednarczuk, ANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 9 No. 4 (2019)
- Tomasz Rymarczyk, Krzysztof Polakowski, Jan Sikora, A NEW CONCEPT OF DISCRETIZATION MODEL FOR IMAGING IMPROVING IN ULTRASOUND TRANSMISSION TOMOGRAPHY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 9 No. 4 (2019)
- Konrad Niderla, Tomasz Rymarczyk, Jan Sikora, MANUFACTURING PLANNING AND CONTROL SYSTEM USING TOMOGRAPHIC SENSORS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 3 (2018)
- Tomasz Rymarczyk, Jan Sikora, SINGULAR INTEGRATION IN BOUNDARY ELEMENT METHOD FOR HELMHOLTZ EQUATION FORMULATED IN FREQUENCY DOMAIN , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 11 No. 4 (2021)
- Grzegorz Kłosowski, Tomasz Rymarczyk, APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 1 (2022)
- Tomasz Rymarczyk, Jan Sikora, SOME MORE ON LOGARITHMIC SINGULARITY INTEGRATION IN BOUNDARY ELEMENT METOD , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 1 (2024)
- Tomasz Rymarczyk, Grzegorz Kłosowski, SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 4 (2017)
