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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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 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 (en)
Title	Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation (en)
skos:prefLabel	Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation (en)
skos:notation	RIV/61989100:27360/08:00019531!RIV10-MSM-27360___
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM6198910015)
http://linked.open...iv/cisloPeriodika	8
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Schindler, Ivo Špička, Ivo Heger, Milan
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta metalurgie a materiálového inženýrství
http://linked.open...dnocenehoVysledku	400062
http://linked.open...ai/riv/idVysledku	RIV/61989100:27360/08:00019531
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	cooling down of materials; temperature prediction; artificial neural network (en)
http://linked.open.../riv/klicoveSlovo	cooling down of materials artificial neural network temperature prediction
http://linked.open...odStatuVydavatele	PL - Polská republika
http://linked.open...ontrolniKodProRIV	[38F6B7AB8785]
http://linked.open...i/riv/nazevZdroje	Hutnik-Wiadomośti Hutnicze
http://linked.open...in/vavai/riv/obor	JG
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...UplatneniVysledku	2008
http://linked.open...v/svazekPeriodika	LXXV
http://linked.open...iv/tvurceVysledku	Schindler, Ivo Špička, Ivo Heger, Milan Franz, Jiří
http://linked.open...n/vavai/riv/zamer	Structural potentiality and properties of intensively formed materials
issn	1230-3534
number of pages	3 (xsd:int)
http://localhost/t...ganizacniJednotka	27360
is http://linked.open...avai/riv/vysledek of	Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation

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