Zhi-Jie Xu

A Calibrated Hybrid Edge Framework for Elevator Safety Warning with Statistical Anomaly Detection

This paper presents a calibrated hybrid edge framework for elevator safety warning. The framework combines interpretable safety rules, robust statistical anomaly scoring, one-class anomaly-detection baselines, temporal persistence, and structured event logging on a Raspberry Pi platform. The prototype integrates accele...

CLARiF: clue alignment and retrieval-informed fusion for fine-grained pest recognition

Mamba-Guided Diffusion for Prediction-Based Industrial Video Anomaly Detection

DEPT-BT: Learning Parameterized and Revisable Behavior Trees From a Single Demonstration

Learning from Demonstration (LfD) combined with Behavior Trees (BTs) aims to lower the programming burden required to construct robot task programs from demonstrations. However, existing approaches have two structural limitations: skills are bound to specific object instances with no mechanism for runtime rebinding, an...