A novel approach towards experimental parameters optimization in Laser-induced breakdown spectroscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137470" target="_blank" >RIV/00216305:26620/20:PU137470 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A novel approach towards experimental parameters optimization in Laser-induced breakdown spectroscopy

Popis výsledku v původním jazyce

Here we propose a novel and universal method of Laser-Induced breakdown spectroscopy (LIBS) experimental conditions optimization based on machine learning. The simple feedforward neural network (FNN) was trained by empirically measured data. The design of FNN was optimized using a genetic algorithm (GA). As the figure of merit of GA was utilized the signal to noise ratio of selected spectral lines. The input data for FNN can be divided in two groups, one group describing the sample and spectral lines of respective elements (e.g. sample density and hardness, content of selected element, energy levels of selected transitions etc.), and the other group describing the experimental conditions (e.g. laser wavelength and energy, gate delay, gate width etc.). The method is demonstrated and explained in a simple case of single pulse LIBS and two basal parameters – gate delay and laser pulse fluence. Afterwards, we present the optimization for more complex measurement three orthogonal laser pulse (3P LIBS), whe

Název v anglickém jazyce

A novel approach towards experimental parameters optimization in Laser-induced breakdown spectroscopy

Popis výsledku anglicky

Here we propose a novel and universal method of Laser-Induced breakdown spectroscopy (LIBS) experimental conditions optimization based on machine learning. The simple feedforward neural network (FNN) was trained by empirically measured data. The design of FNN was optimized using a genetic algorithm (GA). As the figure of merit of GA was utilized the signal to noise ratio of selected spectral lines. The input data for FNN can be divided in two groups, one group describing the sample and spectral lines of respective elements (e.g. sample density and hardness, content of selected element, energy levels of selected transitions etc.), and the other group describing the experimental conditions (e.g. laser wavelength and energy, gate delay, gate width etc.). The method is demonstrated and explained in a simple case of single pulse LIBS and two basal parameters – gate delay and laser pulse fluence. Afterwards, we present the optimization for more complex measurement three orthogonal laser pulse (3P LIBS), whe

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů