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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Vydané v:Sensors and materials Ročník 37; číslo 1; s. 193
Hlavní autori: Zhang, Aiyuan, Lv, Jingguo, Zhang, Jiyong, Sun, Xiaohu, Zhao, Chunhui, Yang, Changjiang, Sun, Junjie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tokyo MYU Scientific Publishing Division 01.01.2025
Predmet:
Algorithms
Direct current
Electric power transmission
Landforms
Transmission lines
ISSN:0914-4935, 2435-0869
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0914-4935
2435-0869
DOI:10.18494/SAM5257