Application of neural network for real-time measurement of electrical resistivity in cold crucible

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F17%3A43949854" target="_blank" >RIV/49777513:23220/17:43949854 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.degruyter.com/view/j/jee.2017.68.issue-4/jee-2017-0042/jee-2017-0042.xml?rskey=lqGNcE&result=1" target="_blank" >https://www.degruyter.com/view/j/jee.2017.68.issue-4/jee-2017-0042/jee-2017-0042.xml?rskey=lqGNcE&result=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1515/jee-2017-0042" target="_blank" >10.1515/jee-2017-0042</a>

Jazyk výsledku
angličtina
Název v původním jazyce
Application of neural network for real-time measurement of electrical resistivity in cold crucible
Popis výsledku v původním jazyce
The article describes use of an Induction furnace with cold crucible as a tool for real-time measurement of a melted material electrical resistivity. The measurement is based on an inverse problem solution of a 2D mathematical model, possibly implementable in a microcontroller or a FPGA in a form of a neural network. The 2D mathematical model results has been provided as a training set for the neural network. At the end, the implementation results are discussed together with uncertainty of measurement, which is done by the neural network implementation itself.
Název v anglickém jazyce
Application of neural network for real-time measurement of electrical resistivity in cold crucible
Popis výsledku anglicky
The article describes use of an Induction furnace with cold crucible as a tool for real-time measurement of a melted material electrical resistivity. The measurement is based on an inverse problem solution of a 2D mathematical model, possibly implementable in a microcontroller or a FPGA in a form of a neural network. The 2D mathematical model results has been provided as a training set for the neural network. At the end, the implementation results are discussed together with uncertainty of measurement, which is done by the neural network implementation itself.

Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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