构建多模态虚拟传感统一评测平台，打破数据与方法孤岛。
MuViS: Multimodal Virtual Sensing Benchmark
- 设计统一接口整合多领域传感数据集，支持标准化处理与评估。
- 对比树模型与深度网络，发现无通用最优方案，凸显泛化需求。
- 开源可扩展平台，适合做传感建模、工业控制的研究者使用。
虚拟传感旨在通过易获取的测量值推断难以直接测量的物理量，是物理系统感知与控制的核心。尽管从机理模型到混合模型再到现代数据驱动方法进展迅速，研究仍呈碎片化，缺乏跨流程、跨模态和跨传感器配置的通用范式。本文提出 MuViS，一个面向多模态虚拟传感的领域无关基准测试套件，将多样数据集整合为统一接口，实现标准化预处理与评估。基于该框架，我们评测了涵盖梯度提升决策树与深度神经网络架构的多种主流方法，结果表明这些方法均无普遍优势，凸显对可泛化虚拟传感架构的需求。MuViS 作为开源、可扩展平台，支持可复现比较，并可集成新数据集与模型类别。
原文摘要 · Abstract (English)
Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a domain-agnostic benchmarking suite for multimodal virtual sensing that consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation. Using this framework, we benchmark established approaches spanning gradient-boosted decision trees and deep neural network (NN) architectures, and show that none of these provides a universal advantage, underscoring the need for generalizable virtual sensing architectures. MuViS is released as an open-source, extensible platform for reproducible comparison and future integration of new datasets and model classes.
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