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FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Feb 2019
Next issue: May 2019
Avg review time: 79 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  3/2013 - 18

Estimating Probability of Gas Breakdown Using Grey-Fuzzy Logic

CHEN, M.-J. See more information about CHEN, M.-J. on SCOPUS See more information about CHEN, M.-J. on IEEExplore See more information about CHEN, M.-J. on Web of Science, WU, Y.-C. See more information about  WU, Y.-C. on SCOPUS See more information about  WU, Y.-C. on SCOPUS See more information about WU, Y.-C. on Web of Science, WEN, K.-L. See more information about  WEN, K.-L. on SCOPUS See more information about  WEN, K.-L. on SCOPUS See more information about WEN, K.-L. on Web of Science, TSAI, M.-T. See more information about TSAI, M.-T. on SCOPUS See more information about TSAI, M.-T. on SCOPUS See more information about TSAI, M.-T. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (736 KB) | Citation | Downloads: 326 | Views: 404

Author keywords
grey system theory, fuzzy theory, gas breakdown, probability estimation

References keywords
grey(25), system(12), fuzzy(12), chen(7), systems(6), deng(6), control(5), applications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 113 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03018
Web of Science Accession Number: 000326321600018
SCOPUS ID: 84884935729

Abstract
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Designing the protection system of high voltage power systems involves elucidating the properties of gas breakdown. This work presents a simplified mathematical approach based on grey and fuzzy methods to estimate the probability of gas breakdown, which differs from other grey-fuzzy methods. With the help of the data normalization process and a linear fuzzy membership function in determining the proper value of the distinguishing coefficient, the proposed approach outperforms other grey-fuzzy-based methods, the curve fitting method, and the neural network method in terms of estimation accuracy. The average relative error of test cases 1 and 2 with the normalization processing is 6.43%, which is 2.53 lower than that of the neural network method, 4.35% lower than that of the curve fitting method, 17.64 lower than that of the 5-interval max-max fuzzy-grey method, 1.51% lower than that of the 5-interval min-min fuzzy-grey method, 13.87% lower than that of the 3-interval max-max method, and 2.72% lower than that of the 3-interval min-min method.


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Cited-By CrossRef

[1] The quantized transformation in Deng’s grey relational grade, Wen, Kunli, Grey Systems: Theory and Application, ISSN 2043-9377, Issue 3, Volume 6, 2016.
Digital Object Identifier: 10.1108/GS-06-2016-0012
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Copyright ©2001-2019
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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