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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Bibliographic Details
Published in:Sensors and materials Vol. 37; no. 1; p. 193
Main Authors: Zhang, Aiyuan, Lv, Jingguo, Zhang, Jiyong, Sun, Xiaohu, Zhao, Chunhui, Yang, Changjiang, Sun, Junjie
Format: Journal Article
Language:English
Published: Tokyo MYU Scientific Publishing Division 01.01.2025
Subjects:
Algorithms
Direct current
Electric power transmission
Landforms
Transmission lines
ISSN:0914-4935, 2435-0869
Online Access:Get full text
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Summary: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.
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ISSN:0914-4935
2435-0869
DOI:10.18494/SAM5257