Ziwei Pan | Searcharxiv

arXiv2025

UniLabOS: An AI-Native Operating System for Autonomous Laboratories

Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embodied experimentation through typed, stateful abstractions and transactional safeguards. UniLabOS unifies laboratory elements via an Action/Resource/Action&Resource (A/R/A&R) model, represents laboratory structure with a dual-topology of logical ownership and physical connectivity, and reconciles digital state with material motion using a transactional CRUTD protocol. Built on a distributed edge-cloud architecture with decentralized discovery, UniLabOS enables protocol mobility across reconfigurable topologies while supporting human-in-the-loop governance. We demonstrate the system in four real-world settings -- a liquid-handling workstation, a modular organic synthesis platform, a distributed electrolyte foundry, and a decentralized computation-intensive closed-loop system -- showing robust orchestration across heterogeneous instruments and multi-node coordination. UniLabOS establishes a scalable foundation for agent-ready, reproducible, and provenance-aware autonomous experimentation.

cs.CE↗

arXiv2017

Sigmoid-Based Refined Composite Multiscale Fuzzy Entropy and t-Distributed Stochastic Neighbor Embedding Based Fault Diagnosis of Rolling Bearing

Multiscale fuzzy entropy (MFE) has been a prevalent tool to quantify the complexity of time series. However, it is extremely sensitive to the predetermined parameters and length of time series and it may yield an inaccurate estimation of entropy or cause undefined entropy when the length of time series is too short. In this paper the Sigmoid-based refined composite multiscale fuzzy entropy (SRCMFE) is introduced to improve the robustness of complexity measurement of MFE for short time series analysis. Also SRCMFE is used to quantify the dynamical properties of mechanical vibration signals and based on that a new rolling bearing fault diagnosis approach is proposed by combining SRCMFE with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension and variable predictive models based class discrimination (VPMCD) for mode classification. In the proposed method, SRCMFE firstly is employed to extract the complexity characteristic from vibration signals of rolling bearing and t-SNE for feature dimension reduction is utilized to obtain a low dimensional manifold characteristic. Then VPMCD is employed to construct a multi-fault classifier to fulfill an automatic fault diagnosis. Finally, the proposed approach is applied to experimental data of rolling bearing and the results indicate that the proposed method can effectively distinguish different fault categories of rolling bearings.

physics.data-an↗