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JCR Impact Factor: 0.800
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SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
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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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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
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

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.

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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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  3/2014 - 13

Development of Precision Information Measuring System for Ultraviolet Radiation

ROSHCHUPKIN, O. See more information about ROSHCHUPKIN, O. on SCOPUS See more information about ROSHCHUPKIN, O. on IEEExplore See more information about ROSHCHUPKIN, O. on Web of Science, SMID, R. See more information about  SMID, R. on SCOPUS See more information about  SMID, R. on SCOPUS See more information about SMID, R. on Web of Science, SACHENKO, A. See more information about  SACHENKO, A. on SCOPUS See more information about  SACHENKO, A. on SCOPUS See more information about SACHENKO, A. on Web of Science, KOCHAN, V. See more information about KOCHAN, V. on SCOPUS See more information about KOCHAN, V. on SCOPUS See more information about KOCHAN, V. on Web of Science
 
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Download PDF pdficon (720 KB) | Citation | Downloads: 701 | Views: 3,804

Author keywords
information-measuring system, multisensor, neural network, photodiode, individual conversion function, temperature dependency

References keywords
sensors(9), neural(9), networks(7), sachenko(6), kochan(6), systems(5), measurement(5), link(5), intelligent(5)
No common words between the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 101 - 106
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03013
Web of Science Accession Number: 000340869800013
SCOPUS ID: 84907348487

Abstract
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Results of studying the neural network method are presented to reduce the amount of calibration points for the multisensor (MS), in particular for the photodiode. This allows transmitting to the MS individual conversion function and provides the high accuracy of measurement. The structure of synthesized information-measuring system and its measuring channel has created for implementing of the proposed approach. A structural scheme is proposed as well for values transmitting the etalon measures to measuring systems. Its used to determine the errors of photodiodes, as those which are produced for customers. This assures the interchangeability of sensors when using the individual conversion function.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Data acquisition system with low-accuracy sensors, Jun, Su, Roshchupkin, Oleksiy, Kochan, Volodymyr, Sachenko, Anatoliy, Roshchupkina, Nataliia, 2016 International Conference on Development and Application Systems (DAS), ISBN 978-1-5090-1993-9, 2016.
Digital Object Identifier: 10.1109/DAAS.2016.7492577
[CrossRef]

[2] Improving the adaptive neuro-fuzzy method to intellectualize multisensor signals processing, Jun, Su, Roshchupkina, Nataliia, Roshchupkin, Oleksiy, Kochan, Volodymyr, 2018 International Conference on Development and Application Systems (DAS), ISBN 978-1-5386-1493-8, 2018.
Digital Object Identifier: 10.1109/DAAS.2018.8396097
[CrossRef]

[3] Methods for improving the accuracy of sensors with a significant influence of non-informative factors, Su, Jun, Roshchupkina, Nataliia, Kochan, Volodymyr, Roshchupkin, Oleksiy, Sachenko, Anatoliy, 2016 IEEE Sensors Applications Symposium (SAS), ISBN 978-1-4799-7250-0, 2016.
Digital Object Identifier: 10.1109/SAS.2016.7479894
[CrossRef]

[4] Method of ensuring an interchangeability of the ultraviolet radiation sensors during a transition to its individual conversion function, Roshchupkin, Oleksiy, Smid, Radislav, Sachenko, Anatoliy, Kochan, Volodymyr, Roshchupkina, Nataliya, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), ISBN 978-1-4673-8359-2, 2015.
Digital Object Identifier: 10.1109/IDAACS.2015.7340710
[CrossRef]

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


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