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FACTS & FIGURES

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
Avg accept to publ: 48 days
APC: 300 EUR


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
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.

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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  1/2015 - 8

 HIGHLY CITED PAPER 

Computer Vision Based Measurement of Wildfire Smoke Dynamics

BUGARIC, M. See more information about BUGARIC, M. on SCOPUS See more information about BUGARIC, M. on IEEExplore See more information about BUGARIC, M. on Web of Science, JAKOVCEVIC, T. See more information about  JAKOVCEVIC, T. on SCOPUS See more information about  JAKOVCEVIC, T. on SCOPUS See more information about JAKOVCEVIC, T. on Web of Science, STIPANICEV, D. See more information about STIPANICEV, D. on SCOPUS See more information about STIPANICEV, D. on SCOPUS See more information about STIPANICEV, D. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
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 (882 KB) | Citation | Downloads: 865 | Views: 3,563

Author keywords
image motion analysis, computer vision, computer aided analysis, virtual reality, pattern analysis

References keywords
smoke(16), detection(14), fire(8), wildfire(6), visual(5), computational(5), video(4), spatial(4), image(4), forest(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 55 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01008
Web of Science Accession Number: 000352158600008
SCOPUS ID: 84924804457

Abstract
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This article presents a novel method for measurement of wildfire smoke dynamics based on computer vision and augmented reality techniques. The aspect of smoke dynamics is an important feature in video smoke detection that could distinguish smoke from visually similar phenomena. However, most of the existing smoke detection systems are not capable of measuring the real-world size of the detected smoke regions. Using computer vision and GIS-based augmented reality, we measure the real dimensions of smoke plumes, and observe the change in size over time. The measurements are performed on offline video data with known camera parameters and location. The observed data is analyzed in order to create a classifier that could be used to eliminate certain categories of false alarms induced by phenomena with different dynamics than smoke. We carried out an offline evaluation where we measured the improvement in the detection process achieved using the proposed smoke dynamics characteristics. The results show a significant increase in algorithm performance, especially in terms of reducing false alarms rate. From this it follows that the proposed method for measurement of smoke dynamics could be used to improve existing smoke detection algorithms, or taken into account when designing new ones.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 5 [View]
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Updated 3 days, 17 hours ago


Cited-By SCOPUS

SCOPUS® Times Cited: 6
View record in SCOPUS®
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Updated 1 week, 5 days ago

Cited-By CrossRef

[1] An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data, NAMOZOV, A., CHO, Y. I., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 18, 2018.
Digital Object Identifier: 10.4316/AECE.2018.04015
[CrossRef] [Full text]

[2] 3d-reconstruction of destructive process models using remote sensing by a group of unmanned aerial vehicles, V, Sherstiuk, M, Zharikova, I, Dorovskaja, D, Chornyi, V, Romantsov, N, Kozub, V, Gusev, I, Sokol, Artificial Intelligence, ISSN 2710-1673, Issue jai2022.27(1), Volume 27, 2022.
Digital Object Identifier: 10.15407/jai2022.01.311
[CrossRef]

[3] Evaluation of Fire Intensity Based on Neural Networks in a Forest-Fire Monitoring System, Sherstjuk, Vladimir, Zharikova, Maryna, 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), ISBN 978-1-7281-2065-2, 2019.
Digital Object Identifier: 10.1109/ELNANO.2019.8783410
[CrossRef]

Updated 3 days, 17 hours ago

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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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