Mamba-based Multibranch State Space Iterative Fusion Algorithm for Multisource Power Grid Survey Data

The effective integration of multisource survey data for power grids benefits designers by providing comprehensive and accurate analyses of the terrain and landforms surrounding the survey area. In this study, inspired by the Mamba concept, we propose an iterative attentional feature fusion Mamba (i...

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Veröffentlicht in:Sensors and materials Jg. 37; H. 1; S. 193
Hauptverfasser: Zhang, Aiyuan, Lv, Jingguo, Zhang, Jiyong, Sun, Xiaohu, Zhao, Chunhui, Yang, Changjiang, Sun, Junjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo MYU Scientific Publishing Division 01.01.2025
Schlagworte:
Algorithms
Direct current
Electric power transmission
Landforms
Transmission lines
ISSN:0914-4935, 2435-0869
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Zusammenfassung:The effective integration of multisource survey data for power grids benefits designers by providing comprehensive and accurate analyses of the terrain and landforms surrounding the survey area. In this study, inspired by the Mamba concept, we propose an iterative attentional feature fusion Mamba (iAFF-FMA) framework that constructs a multibranch state space for iterative fusion, reducing differences between data modalities and enhancing feature interaction within the same modality. Experiments conducted with actual engineering data from ultra-high-voltage direct current (UHVDC) transmission lines demonstrate the iAFF-FMA framework's superiority over six common fusion methods. This offers a novel technical approach to the integration of power grid survey data.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0914-4935
2435-0869
DOI:10.18494/SAM5257