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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 37, Number 1(3) (2025)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 193-205
S&M3900 Research Paper of Special Issue https://doi.org/10.18494/SAM5257 Published: January 31, 2025 Mamba-based Multibranch State Space Iterative Fusion Algorithm for Multisource Power Grid Survey Data [PDF] Aiyuan Zhang, Jingguo Lv, Jiyong Zhang, Xiaohu Sun, Chunhui Zhao, Changjiang Yang, and Junjie Sun (Received July 26, 2024; Accepted January 20, 2025) Keywords: multisource remote sensing data fusion, power grid surveying, Mamba, iterative attention mechanism
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.
Corresponding author: Jingguo Lv  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Aiyuan Zhang, Jingguo Lv, Jiyong Zhang, Xiaohu Sun, Chunhui Zhao, Changjiang Yang, and Junjie Sun, Mamba-based Multibranch State Space Iterative Fusion Algorithm for Multisource Power Grid Survey Data, Sens. Mater., Vol. 37, No. 1, 2025, p. 193-205. |
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