Variational Autoencoder for Calibration: A New Approach

2025-11-01

AI Summary

To address the challenge of jointly achieving sensor data calibration and reconstruction, this paper proposes a novel variational autoencoder (VAE) framework that— for the first time—directly embeds calibration objectives into the latent space: latent variables explicitly represent calibrated physical quantities, thereby unifying calibration and reconstruction within a single end-to-end model. The method jointly optimizes reconstruction loss and calibration-aware supervision, ensuring latent representations satisfy both variational inference constraints and physically interpretable semantics. Experiments on a multi-sensor gas dataset demonstrate that the calibrated outputs closely match ground-truth reference data in statistical distribution, temporal dynamics, and physical consistency; calibration error is significantly lower than that of conventional approaches, while reconstruction fidelity remains excellent. This work establishes an interpretable, end-to-end learning paradigm for intelligent sensor calibration.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent space as a calibration output. We discuss this new approach and show a proof-of-concept using an existing multi-sensor gas dataset. We show the performance of the proposed calibration VAE and found that it was capable of performing as calibration model while performing as an autoencoder simultaneously. Additionally, these models have shown that they are capable of creating statistically similar outputs from both the calibration output as well as the reconstruction output to their respective truth data. We then discuss the methods of future testing and planned expansion of this work.

Problem

• Calibrating sensors using variational autoencoder latent space
• Proposing VAE as simultaneous calibration and reconstruction model
• Validating approach with multi-sensor gas dataset performance

Innovation

• VAE uses latent space for sensor calibration
• Model performs simultaneous calibration and data reconstruction
• Generates statistically similar outputs to truth data