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

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


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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.

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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.

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  4/2012 - 5

A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

AKTAS, M. See more information about AKTAS, M. on SCOPUS See more information about AKTAS, M. on IEEExplore See more information about AKTAS, M. on Web of Science
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Download PDF pdficon (1,037 KB) | Citation | Downloads: 723 | Views: 3,418

Author keywords
discrete wavelet transforms, wavelet packets, fault diagnosis, electric vehicles, permanent magnet motors

References keywords
motor(22), power(18), synchronous(14), permanent(14), control(14), magnet(13), electric(13), fault(12), wavelet(11), system(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 33 - 38
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04005
Web of Science Accession Number: 000312128400005
SCOPUS ID: 84872785446

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The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits) in the driving systems with Field Oriented Controlled Permanent Magnet Synchronous Motors (PMSM) of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 2,079 TCR
SCOPUS® Citations for all references: 3,165 TCR

Web of Science® Average Citations per reference: 51 ACR
SCOPUS® Average Citations per reference: 77 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-07-13 20:49 in 224 seconds.

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