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Stefan cel Mare
University of Suceava
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ROMANIA

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


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FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

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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: 113 | Views: 161

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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References Weight

Web of Science® Citations for all references: 42,661 TCR
SCOPUS® Citations for all references: 62,409 TCR

Web of Science® Average Citations per reference: 836 ACR
SCOPUS® Average Citations per reference: 1,224 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 2024-07-13 12:54 in 308 seconds.




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