Grzegorz Kłosowski
ABSTRACT
This study presents an algorithmic concept that allows obtaining higher-quality tomographic images. The method solves the problem of imaging the interior of industrial tanks, reactors, or pipes. Research focuses on how to solve the inverse problem, which is converting measurements to images. Hybrid tomography combined electrical impedance tomography (EIT) and electrical capacitance tomography (ECT) measurements. The measurement vector was converted into images in two steps. In the first phase, the Long Short-Term Memory (LSTM) neural network was used, thanks to which raw reconstructions were obtained. A second network was then trained to convert the images obtained in the first step into enhanced images. The new method is effective and universal because its use is not limited to one type of tomography.
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Cross Ref
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- Grzegorz Kłosowski, Tomasz Rymarczyk, Konrad Kania, Antoni Świć, and Tomasz Cieplak. 2020. Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Eksploatacja i Niezawodnosc 22, 1.Google Scholar
- Grzegorz Kłosowski, Tomasz Rymarczyk, Konrad Niderla, Magdalena Rzemieniak, Artur Dmowski, and Michał Maj. 2021. Comparison of machine learning methods for image reconstruction using the LSTM classifier in industrial electrical tomography. Energies 14, 21.Google ScholarCross Ref
- Michał Majchrowicz, Paweł Kapusta, Lidia Jackowska-Strumiłło, Robert Banasiak, and Dominik Sankowski. 2020. Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System. Sensors 2020, Vol. 20, Page 391 20, 2: 391.Google Scholar
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