← 研踪 PaperTrailarXiv:2611.16027 · v1
A Unified Interpretable Semantic Framework for Multi-Modal Fiber Sensing Under Data Scarcity
首次提交 2026-11-12 05:33
版本更新 2026-11-12 05:33
雷达观测 2026-11-13 14:16
摘要
We propose a unified interpretable semantic framework for multi-modal fiber sensing under data scarcity. The framework maps heterogeneous measurements from state of-polarization (SOP) monitoring, distributed acoustic sensing (DAS), and optical time-domain reflectometry (OTDR) into a shared set of physics-grounded semantic descriptors capturing impulsiveness, persistence, structural characteristics, and energy concentration. Inference is performed through reasoning over semantic class profiles, optionally assisted by retrieval of semantically similar examples to resolve ambiguity and support adaptation without model retraining. Experimental validation on real SOP, DAS, and OTDR datasets demonstrates robust performance across sensing modalities, achieving accuracies of 95.0%, 91.1%, and 94.6%, respectively, while providing transparent and physically interpretable decisions. The proposed framework establishes a unified and data efficient approach for optical fiber sensing in evolving and data scarce deployment environments.
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v1 · 2026-11-12 05:33 · 最新观测版本
A Unified Interpretable Semantic Framework for Multi-Modal Fiber Sensing Under Data Scarcity
We propose a unified interpretable semantic framework for multi-modal fiber sensing under data scarcity. The framework maps heterogeneous measurements from state of-polarization (SOP) monitoring, distributed acoustic sensing (DAS), and optical time-domain reflectometry (OTDR) into a shared set of physics-grounded semantic descriptors capturing impulsiveness, persistence, structural characteristics, and energy concentration. Inference is performed through reasoning over semantic class profiles, optionally assisted by retrieval of semantically similar examples to resolve ambiguity and support adaptation without model retraining. Experimental validation on real SOP, DAS, and OTDR datasets demonstrates robust performance across sensing modalities, achieving accuracies of 95.0%, 91.1%, and 94.6%, respectively, while providing transparent and physically interpretable decisions. The proposed framework establishes a unified and data efficient approach for optical fiber sensing in evolving and data scarce deployment environments.
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