Artificial neural network analysis of optical measurements of glasses based on Sb2O3

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F16%3A86098528" target="_blank" >RIV/61989100:27350/16:86098528 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27360/16:86098528

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial neural network analysis of optical measurements of glasses based on Sb2O3

Popis výsledku v původním jazyce

In the paper we present application of artificial neural network (ANN) on relation between glass composition versus optical transmittance of the chosen glass systems of Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O, where M was Na, K and Li, respectively. The excellent prediction ability of special ANN program developed for this study demonstrates the possibility to influence the glass composition to obtain asked optical properties. The measurements of the temperature dependencies of the direct electric conductivity show the strong influence of the concentration of the individual glass compounds of systems Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O (M is Na, K, Li) on their electric and dielectric properties. Glasses own the same mechanism of the electric conductivity with activation energy, which goes to the value 3.75 eV when temperature is higher than 250oC. Similarly optical transmittance T of systems Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O strongly depends on the glass composition and the amount of defects, too. The glass 70Sb2O3 - 30PbCl2 reached the highest value of T. The minimal content of defects in its volume makes these glasses very perspective for next searching. The measurements of the complex modulus Mô of mentioned glasses showed their high sensitivity to the changes of glass structure connected with the creation of different sort and the amount of defects. The sensibility of the used methods is comparable with the usual exploited methods (X-ray analysis, optical microscopy) and makes possible to assess partially the quantitative occurrence of defects in the glass volume. A model of neural network for prediction of the optical transmittance was created. Model enables to predict the transmittance with sufficiently small error. After evaluation of results we can state that exploitation of neural networks is advantageous, if it is necessary to express complex mutual relations among sensor-based data.

Název v anglickém jazyce

Artificial neural network analysis of optical measurements of glasses based on Sb2O3

Popis výsledku anglicky

In the paper we present application of artificial neural network (ANN) on relation between glass composition versus optical transmittance of the chosen glass systems of Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O, where M was Na, K and Li, respectively. The excellent prediction ability of special ANN program developed for this study demonstrates the possibility to influence the glass composition to obtain asked optical properties. The measurements of the temperature dependencies of the direct electric conductivity show the strong influence of the concentration of the individual glass compounds of systems Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O (M is Na, K, Li) on their electric and dielectric properties. Glasses own the same mechanism of the electric conductivity with activation energy, which goes to the value 3.75 eV when temperature is higher than 250oC. Similarly optical transmittance T of systems Sb2O3 - PbCl2 and Sb2O3 - PbO - M2O strongly depends on the glass composition and the amount of defects, too. The glass 70Sb2O3 - 30PbCl2 reached the highest value of T. The minimal content of defects in its volume makes these glasses very perspective for next searching. The measurements of the complex modulus Mô of mentioned glasses showed their high sensitivity to the changes of glass structure connected with the creation of different sort and the amount of defects. The sensibility of the used methods is comparable with the usual exploited methods (X-ray analysis, optical microscopy) and makes possible to assess partially the quantitative occurrence of defects in the glass volume. A model of neural network for prediction of the optical transmittance was created. Model enables to predict the transmittance with sufficiently small error. After evaluation of results we can state that exploitation of neural networks is advantageous, if it is necessary to express complex mutual relations among sensor-based data.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JJ - Ostatní materiály

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Journal of optoelectronics and advanced materials

ISSN

1454-4164

e-ISSN

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Svazek periodika

18

Číslo periodika v rámci svazku

3-4

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

8

Strana od-do

240-247

Kód UT WoS článku

000375964800009

EID výsledku v databázi Scopus

2-s2.0-84979746822