The Use of Artificial Neural Networks to Estimate Thermal Resistance of Knitted Fabrics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F15%3A00003200" target="_blank" >RIV/46747885:24410/15:00003200 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

The Use of Artificial Neural Networks to Estimate Thermal Resistance of Knitted Fabrics

Popis výsledku v původním jazyce

This study aims to develop a model for the prediction of thermal resistance of fleece fabric by using regression analysis and artificial neural network technique. Primarily fleece fabrics protect human body from heat loss during cold weather. Its secondpurpose is to absorb sweat from human skin. Fleece fabric is commonly used to make sweatshirts, trousers, and jackets for cold weather. Higher thermal resistance of fleece is one of the main demands of users. Many factors can influence the thermal resistance efficiency of fleece. We have used porosity, thickness of fabric, thermal conductivity of fabric, overall moisture management capacity, thermal absorptivity, percentage of cotton, and polyester and planner weight as independent variables for the prediction of thermal resistance of fleece fabric. We have found that there was a significant difference between regression and artificial neural network analysis in the selection of most significant factor. Nevertheless, both models are sig

Název v anglickém jazyce

The Use of Artificial Neural Networks to Estimate Thermal Resistance of Knitted Fabrics

Popis výsledku anglicky

This study aims to develop a model for the prediction of thermal resistance of fleece fabric by using regression analysis and artificial neural network technique. Primarily fleece fabrics protect human body from heat loss during cold weather. Its secondpurpose is to absorb sweat from human skin. Fleece fabric is commonly used to make sweatshirts, trousers, and jackets for cold weather. Higher thermal resistance of fleece is one of the main demands of users. Many factors can influence the thermal resistance efficiency of fleece. We have used porosity, thickness of fabric, thermal conductivity of fabric, overall moisture management capacity, thermal absorptivity, percentage of cotton, and polyester and planner weight as independent variables for the prediction of thermal resistance of fleece fabric. We have found that there was a significant difference between regression and artificial neural network analysis in the selection of most significant factor. Nevertheless, both models are sig

Druh

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

CEP obor

JJ - Ostatní materiály

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

TEKSTIL VE KONFEKSIYON

ISSN

1300-3356

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

TR - Turecká republika

Počet stran výsledku

9

Strana od-do

304-312

Kód UT WoS článku

000369622000004

EID výsledku v databázi Scopus

2-s2.0-84958699214