Field Trial of Partial Co-Cable Detection with a Self-Attention Deep Learning Model
A deep learning architecture incorporating a self-attention mechanism for detecting identical and partial co-cable scenarios is presented. Field trial shows that precise co-cable segment localization with a detection accuracy of 96.1% can be achieved.
Authors
- Yucong Liu (ORCID: https://orcid.org/0000-0002-3753-9670)
- Dechao Zhang (ORCID: https://orcid.org/0009-0000-4213-7951)
- Hui Yu Yang (ORCID: https://orcid.org/0000-0003-4035-8894)
- Dong Wang (ORCID: https://orcid.org/0000-0001-8102-9291)
- Zhiwei Wang (ORCID: https://orcid.org/0009-0000-2018-9339)
- Yunbo Li (ORCID: https://orcid.org/0009-0008-0642-3641)
- Yang Zhao (ORCID: https://orcid.org/0009-0007-2579-7836)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- China Mobile (China) (CN)
Publication Details
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/acp/ipoc63121.2024.10809575
- Primary Topic
- Integrated Circuits and Semiconductor Failure Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China