Optimizing performance of artificial neural network-based tomography model at golem tokamak: impact of training data quantity and quality

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F24%3A00616842" target="_blank" >RIV/61389021:_____/24:00616842 - isvavai.cz</a>
Výsledek na webu
<a href="https://ojs.cvut.cz/ojs/index.php/PPT/article/view/9980/7184" target="_blank" >https://ojs.cvut.cz/ojs/index.php/PPT/article/view/9980/7184</a>
DOI - Digital Object Identifier
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Jazyk výsledku
angličtina
Název v původním jazyce
Optimizing performance of artificial neural network-based tomography model at golem tokamak: impact of training data quantity and quality
Popis výsledku v původním jazyce
The paper presents an Artificial Neural Network (ANN)-based model for the tomography reconstruction of visible plasma radiation distribution at the GOLEM tokamak. To train the model, the training dataset is constructed using emissivity phantoms with associated synthetic measurements from one poloidal cross-section of the GOLEM tokamak. The trained model is validated by the test dataset. The performance optimization of the ANN-based model is investigated by considering the effect of the quantity and quality of the training data.
Název v anglickém jazyce
Optimizing performance of artificial neural network-based tomography model at golem tokamak: impact of training data quantity and quality
Popis výsledku anglicky
The paper presents an Artificial Neural Network (ANN)-based model for the tomography reconstruction of visible plasma radiation distribution at the GOLEM tokamak. To train the model, the training dataset is constructed using emissivity phantoms with associated synthetic measurements from one poloidal cross-section of the GOLEM tokamak. The trained model is validated by the test dataset. The performance optimization of the ANN-based model is investigated by considering the effect of the quantity and quality of the training data.

Projekt
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Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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