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JCR Impact Factor: 0.700
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SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: May 2024
Next issue: Aug 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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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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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  2/2024 - 3

Workflow Detection with Improved Phase Discriminability

ZHANG, M. See more information about ZHANG, M. on SCOPUS See more information about ZHANG, M. on IEEExplore See more information about ZHANG, M. on Web of Science, HU, H. See more information about  HU, H. on SCOPUS See more information about  HU, H. on SCOPUS See more information about HU, H. on Web of Science, LI, Z. See more information about LI, Z. on SCOPUS See more information about LI, Z. on SCOPUS See more information about LI, Z. on Web of Science
 
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Download PDF pdficon (2,243 KB) | Citation | Downloads: 77 | Views: 95

Author keywords
intelligent manufacturing, workflow detection, self-attention mechanism, graph relation reasoning, transformer

References keywords
vision(24), recognition(24), action(23), temporal(20), pattern(15), networks(12), convolutional(12), network(10), iccv(10), cvpr(10)
No common words between the references section and the paper title.

About this article
Date of Publication: 2024-05-31
Volume 24, Issue 2, Year 2024, On page(s): 21 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.02003
SCOPUS ID: 85195645436

Abstract
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Workflow detection is a challenge issue in the process of Industry 4.0, which plays a crucial role in intelligent production. However, it faces the problem of inaccurate phase classification and unclear boundary positioning, which are not well resolved in previous works. To solve them, this paper develops a temporal-aware workflow detection framework (TransGAN) which takes advantage of the complementarity between Transformer and graph attention network to improve phase discriminability. Specifically, temporal self-attention is firstly designed to learn the relationship between different positions of feature sequence. Then, multi-scale Transformer is introduced to encode pyramid features, which fuses multiple context cues for discriminative feature representation. At last, contextual and surrounding relations are learned in graph attention network for refined phase classification and boundary localization. Comprehensive experiments are performed to verify the effectiveness of our method. Compared to the advanced AFSD, the accuracy is improved by 2.3 % and 2.1 % when tIoU=0.5 on POTFD and THUMOS-14 dataset, respectively. Empirical study of running speed indicates that the proposed TransGAN can be deployed to real-world industrial environment for workflow detection.


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


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