Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920965" target="_blank" >RIV/00216275:25530/23:39920965 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-42529-5_3" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-42529-5_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-42529-5_3" target="_blank" >10.1007/978-3-031-42529-5_3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction

Popis výsledku v původním jazyce

Glass transitions are an important phenomenon in amor phous materials with potential for various applications. The Tool-Narayanaswamy-Moynihan (TNM) model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challeng ing. To address this issue, a multi-scale convolutional neural model is pro posed that can accurately predict the TNM parameters directly from the set of differential scanning calorimetry curves, experimentally measured using the sample of the considered amorphous material. The resulting Mean Absolute Error of the model over the test set is found to be 0.0252, indicating a high level of accuracy. Overall, the proposed neural model has the potential to become a valuable tool for practical application of the TNM model in the glass industry and related fields.

Název v anglickém jazyce

Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction

Popis výsledku anglicky

Glass transitions are an important phenomenon in amor phous materials with potential for various applications. The Tool-Narayanaswamy-Moynihan (TNM) model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challeng ing. To address this issue, a multi-scale convolutional neural model is pro posed that can accurately predict the TNM parameters directly from the set of differential scanning calorimetry curves, experimentally measured using the sample of the considered amorphous material. The resulting Mean Absolute Error of the model over the test set is found to be 0.0252, indicating a high level of accuracy. Overall, the proposed neural model has the potential to become a valuable tool for practical application of the TNM model in the glass industry and related fields.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) : proceedings, vol. 1

ISBN

978-3-031-42528-8

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

10

Strana od-do

24-33

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Salamanca

Datum konání akce

5. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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