Machine learning in image reconstruction by multi-sensor electrodes

• Autorzy: Rymarczyk Tomasz , Kozłowski Edward , Niderla Konrad , Rymarczyk Paweł , Bednarczuk Piotr , Sikora Jan

Identyfikatory

URI

10.15199/48.2019.12.42

Warianty tytułu

PL

Uczenie maszynowe w rekonstrukcji obrazu z użyciem elektrod wieloczujnikowych

• Języki publikacji: EN

Abstrakty

EN

The article presents a system that uses machine learning to reconstruct the image using multi-sensor electrodes based on electric tomography. It is an innovative approach to testing the properties of test areas, including levees. The measuring system was based on an electric tomography device, which assumes the use of two measuring methods and allows measurements to be made to 32 channels. The device based on electric impedance tomography measures the tested object based on the potential distribution measurements. The system collects measured data from the electrodes. In the process of image reconstruction, the elastic net method was used, where appropriate regularization methods help in choosing the optimal solution.

PL

W artykule przedstawiono system wykorzystujący uczenie maszynowe do rekonstrukcji obrazu za pomocą elektrod wieloczujnikowych oparty na tomografii elektrycznej. Jest to innowacyjne podejście do badania właściwości obszarów testowych, w tym wałów przeciwpowodziowych. System pomiarowy został oparty na urządzeniu do tomografii elektrycznej, który zakłada stosowanie dwóch metod pomiarowych i umożliwia wykonanie pomiarów do 32 kanałów. Urządzenie oparte na elektrycznej tomografii impedancyjnej mierzy badany obiekt w oparciu o pomiary rozkładu potencjału. System zbiera zmierzone dane z elektrod. W procesie rekonstrukcji obrazu zastosowana metodę elastycznej siatki, gdzie odpowiednie metody regularyzacji pomagają w wyborze optymalnego rozwiązania.

Słowa kluczowe

EN

electrical impedance tomography; machine learning; inverse problem

PL

elektryczna tomografia impedancyjna; uczenie maszynowe; zagadnienie odwrotne

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2019
• Tom: R. 95, nr 12
• Strony: 188--191
• Opis fizyczny: Bibliogr. 39 poz., rys.

Twórcy

autor

Rymarczyk Tomasz

• Research & Development Centre Netrix S.A.
• University of Economics and Innovation, Projektowa 4, Lublin, Poland

autor

Kozłowski Edward

• Lublin University of Technology, Nadbystrzycka 38A, Lublin, Poland

autor

Niderla Konrad

• Research & Development Centre Netrix S.A.

autor

Rymarczyk Paweł

• Research & Development Centre Netrix S.A.

autor

Bednarczuk Piotr

• University of Economics and Innovation, Projektowa 4

autor

Sikora Jan

• Research & Development Centre Netrix S.A.
• University of Economics and Innovation, Projektowa 4, Lublin, Poland

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)