Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F08%3A00019531" target="_blank" >RIV/61989100:27360/08:00019531 - 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

Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation

Popis výsledku v původním jazyce

The method exploits sufficient similarity between cooling down curves of individual specimens from the same material but when specimens vary in geometric shape. Time scale altering for individual specimens leads from practical point of view to coincidence of all curves with so called ?general curve? for given material which is calculated from measured values by means of statistic methods. This operation can be denoted as a definition of time transformation coefficient ( TTC ) (for known specimens). If an artificial neural network learns itself to assign time transformation coefficient to known dimensions of specimens, it is then with sufficient accuracy able to determine time transformation coefficient even for specimens with different shapes, for which it has not been learnt. By backward time transformation is then possible to predict probable time course of the cooling down curve and accordingly also the moment of accomplishment of given temperature

Název v anglickém jazyce

Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation

Popis výsledku anglicky

The method exploits sufficient similarity between cooling down curves of individual specimens from the same material but when specimens vary in geometric shape. Time scale altering for individual specimens leads from practical point of view to coincidence of all curves with so called ?general curve? for given material which is calculated from measured values by means of statistic methods. This operation can be denoted as a definition of time transformation coefficient ( TTC ) (for known specimens). If an artificial neural network learns itself to assign time transformation coefficient to known dimensions of specimens, it is then with sufficient accuracy able to determine time transformation coefficient even for specimens with different shapes, for which it has not been learnt. By backward time transformation is then possible to predict probable time course of the cooling down curve and accordingly also the moment of accomplishment of given temperature

Druh

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

CEP obor

JG - Hutnictví, kovové materiály

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název periodika

Hutnik-Wiadomośti Hutnicze

ISSN

1230-3534

e-ISSN

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Svazek periodika

LXXV

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

3

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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