Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
10
Pages from-to
24-33
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Salamanca
Event date
Sep 5, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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