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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 78 days
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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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
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2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  4/2022 - 9

Analog Circuit Fault Classification and Data Reduction Using PCA-ANFIS Technique Aided by K-means Clustering Approach

LAIDANI, I. See more information about LAIDANI, I. on SCOPUS See more information about LAIDANI, I. on IEEExplore See more information about LAIDANI, I. on Web of Science, BOUROUBA, N. See more information about BOUROUBA, N. on SCOPUS See more information about BOUROUBA, N. on SCOPUS See more information about BOUROUBA, N. on Web of Science
 
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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 (1,457 KB) | Citation | Downloads: 663 | Views: 853

Author keywords
analog integrated circuits, artificial neural networks, fault diagnosis, fuzzy logic, clustering methods

References keywords
analog(18), fault(17), diagnosis(14), circuits(13), circuit(9), fuzzy(7), electronic(6), method(5), classifier(5), approach(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-11-30
Volume 22, Issue 4, Year 2022, On page(s): 73 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.04009
Web of Science Accession Number: 000920289700009
SCOPUS ID: 85150155847

Abstract
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The paper work aims to extract effectively the fault feature information of analog integrated circuits and to improve the performance of a fault classification process. Thus, a fault classification method based on principal component analysis (PCA) and adaptive neuro fuzzy inference system classifier (ANFIS) preprocessed by K-means clustering (KMC) is proposed. To effectively extract and select fault features the traditional signal processing based on sampling technique conducts to different signature parameters. A stimulus pulse signal applied to the circuit under test (CUT) allowed us to get a reference output response. Respecting both specific sampling interval and step, the fault free and the faulty output responses are sampled to create amplitude sample features that will serve the fault classification process. The PCA employed for data reduction has lessened the computational complexity and obtaining the optimal features. Thus more than 75% of data volume decreased without loss of original information. The principal components extracted by this reduction data method have been input into ANFIS aided by KMC to obtain the best fault diagnosis results. The experimental results show a score of 100% diagnostic accuracies for the CUTs. Therefore, our approach has achieved best fault classification precision comparing to other research works.


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Cited-By Clarivate Web of Science

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Cited-By CrossRef

[1] An efficient method for faults diagnosis in analog circuits based on machine learning classifiers, Arabi, Abderrazak, Ayad, Mouloud, Bourouba, Nacerdine, Benziane, Mourad, Griche, Issam, Ghoneim, Sherif S.M., Ali, Enas, Elsisi, Mahmoud, Ghaly, Ramy N.R., Alexandria Engineering Journal, ISSN 1110-0168, Issue , 2023.
Digital Object Identifier: 10.1016/j.aej.2023.06.090
[CrossRef]

[2] Intermittent fault diagnosis for electronics-rich analog circuit systems based on multi-scale enhanced convolution transformer network with novel token fusion strategy, Wang, Shengdong, Liu, Zhenbao, Jia, Zhen, Zhao, Wen, Li, Zihao, Expert Systems with Applications, ISSN 0957-4174, Issue , 2024.
Digital Object Identifier: 10.1016/j.eswa.2023.121964
[CrossRef]

[3] Intermittent fault diagnosis of analog circuit based on enhanced one-dimensional vision transformer and transfer learning strategy, Wang, Shengdong, Liu, Zhenbao, Jia, Zhen, Zhao, Wen, Li, Zihao, Wang, Luyao, Engineering Applications of Artificial Intelligence, ISSN 0952-1976, Issue , 2024.
Digital Object Identifier: 10.1016/j.engappai.2023.107281
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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