Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography
Electrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles gro...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/21/7269 |
| _version_ | 1827600503340531712 |
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| author | Grzegorz Kłosowski Tomasz Rymarczyk Konrad Niderla Magdalena Rzemieniak Artur Dmowski Michał Maj |
| author_facet | Grzegorz Kłosowski Tomasz Rymarczyk Konrad Niderla Magdalena Rzemieniak Artur Dmowski Michał Maj |
| author_sort | Grzegorz Kłosowski |
| collection | DOAJ |
| description | Electrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high-resolution tomographic images is problematic because it involves solving the so-called ill-posed inverse problem. Noisy input data cause problems, too. Therefore, the use of appropriate hardware solutions to eliminate this phenomenon is necessary. An important cause of obtaining accurate tomographic images may also be the incorrect selection of algorithmic methods used to convert the measurements into the output images. In a dynamically changing environment of a tank reactor, selecting the optimal algorithmic method used to create a tomographic image becomes an optimization problem. This article presents the machine learning method’s original concept of intelligent selection depending on the reconstructed case. The long short-term memory network was used to classify the methods to choose one of the five homogenous methods—elastic net, linear regression with the least-squares learner, linear regression with support vector machine learner, support vector machine model, or artificial neural networks. In the presented research, tomographic images of selected measurement cases, reconstructed using five methods, were compared. Then, the selection methods’ accuracy was verified thanks to the long short-term memory network used as a classifier. The results proved that the new concept of long short-term memory classification ensures better tomographic reconstructions efficiency than imaging all measurement cases with single homogeneous methods. |
| first_indexed | 2024-03-09T04:38:12Z |
| format | Article |
| id | doaj.art-825b2602f3fb4e5c98256a043769b9d5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T04:38:12Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-825b2602f3fb4e5c98256a043769b9d52023-12-03T13:25:37ZengMDPI AGEnergies1996-10732021-11-011421726910.3390/en14217269Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical TomographyGrzegorz Kłosowski0Tomasz Rymarczyk1Konrad Niderla2Magdalena Rzemieniak3Artur Dmowski4Michał Maj5Faculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandFaculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandFaculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, PolandElectrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high-resolution tomographic images is problematic because it involves solving the so-called ill-posed inverse problem. Noisy input data cause problems, too. Therefore, the use of appropriate hardware solutions to eliminate this phenomenon is necessary. An important cause of obtaining accurate tomographic images may also be the incorrect selection of algorithmic methods used to convert the measurements into the output images. In a dynamically changing environment of a tank reactor, selecting the optimal algorithmic method used to create a tomographic image becomes an optimization problem. This article presents the machine learning method’s original concept of intelligent selection depending on the reconstructed case. The long short-term memory network was used to classify the methods to choose one of the five homogenous methods—elastic net, linear regression with the least-squares learner, linear regression with support vector machine learner, support vector machine model, or artificial neural networks. In the presented research, tomographic images of selected measurement cases, reconstructed using five methods, were compared. Then, the selection methods’ accuracy was verified thanks to the long short-term memory network used as a classifier. The results proved that the new concept of long short-term memory classification ensures better tomographic reconstructions efficiency than imaging all measurement cases with single homogeneous methods.https://www.mdpi.com/1996-1073/14/21/7269electrical tomographyindustrial tomographymachine learningneural networkslong short-term memory (LSTM) networks |
| spellingShingle | Grzegorz Kłosowski Tomasz Rymarczyk Konrad Niderla Magdalena Rzemieniak Artur Dmowski Michał Maj Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography Energies electrical tomography industrial tomography machine learning neural networks long short-term memory (LSTM) networks |
| title | Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography |
| title_full | Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography |
| title_fullStr | Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography |
| title_full_unstemmed | Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography |
| title_short | Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography |
| title_sort | comparison of machine learning methods for image reconstruction using the lstm classifier in industrial electrical tomography |
| topic | electrical tomography industrial tomography machine learning neural networks long short-term memory (LSTM) networks |
| url | https://www.mdpi.com/1996-1073/14/21/7269 |
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