Adaptive Neuro-Fuzzy System For Quantitative Evaluation of Woven Fabrics? Pilling Resistance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F15%3A%230003700" target="_blank" >RIV/46747885:24410/15:#0003700 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24410/15:00002648

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.eswa.2014.10.013" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2014.10.013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2014.10.013" target="_blank" >10.1016/j.eswa.2014.10.013</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Neuro-Fuzzy System For Quantitative Evaluation of Woven Fabrics? Pilling Resistance

Popis výsledku v původním jazyce

Fabric pilling is considered a performance and aesthetic property of the woven products that determine its quality. The subjective evaluation of the fabric pilling results in misleading values that depend on the measurement standard even for the same sample. This work utilizes some textural features extracted from the fabric?s images to obtain better representative and quantitative values of the fabric?s surface. An algorithm for creating features dataset for training and testing the soft-computing classifier was described where random noise was added to the limited number of fabric?s pilling standard images. The objective pilling classification of the fabric samples was performed using an adaptive neuro-fuzzy system (ANFIS) which showed an ability toclassify the noised standard images with a correct classification rate of 85.8%. The ANFIS was also able to classify actual fabric samples with a Spearman?s coefficient of rank correlation at +0.985 when compared with the classification g

Název v anglickém jazyce

Adaptive Neuro-Fuzzy System For Quantitative Evaluation of Woven Fabrics? Pilling Resistance

Popis výsledku anglicky

Fabric pilling is considered a performance and aesthetic property of the woven products that determine its quality. The subjective evaluation of the fabric pilling results in misleading values that depend on the measurement standard even for the same sample. This work utilizes some textural features extracted from the fabric?s images to obtain better representative and quantitative values of the fabric?s surface. An algorithm for creating features dataset for training and testing the soft-computing classifier was described where random noise was added to the limited number of fabric?s pilling standard images. The objective pilling classification of the fabric samples was performed using an adaptive neuro-fuzzy system (ANFIS) which showed an ability toclassify the noised standard images with a correct classification rate of 85.8%. The ANFIS was also able to classify actual fabric samples with a Spearman?s coefficient of rank correlation at +0.985 when compared with the classification g

Druh

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

CEP obor

JS - Řízení spolehlivosti a kvality, zkušebnictví

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0065" target="_blank" >EE2.3.30.0065: Podpora tvorby excelentních výzkumných a vývojových týmů na Technické univerzitě v Liberci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

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Svazek periodika

42

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

2098-2113

Kód UT WoS článku

000347579500029

EID výsledku v databázi Scopus

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