Jingxi Zhu

Shinan Huang, Jingxi Zhu, Fotis Kopsaftopoulos

In this work, a stochastic time series-based framework is introduced for multicopter propeller damage diagnosis using functionally pooled autoregressive (FP-AR) models. The framework addresses damage detection, motor-level identification, and damage magnitude estimation using only standard inertial measurement unit data, without requiring additional sensors. Functional pooling provides a compact representation of system dynamics across varying operating conditions and supports reliable model estimation from short data records. Damage detection is performed through statistical testing of prediction residuals, damage identification through model selection, and damage quantification through a Bayesian inference scheme that provides posterior estimates and uncertainty bounds. The framework is experimentally evaluated through outdoor flight tests of a custom-built hexacopter following figure-eight trajectories under ambient wind disturbances. Six IMU channels, including three-axis acceleration and angular velocity, are analyzed across multiple motors and propeller damage levels. The results demonstrate consistent cross-flight performance without case-specific retuning. Compared with conventional batch-based quantification, the Bayesian approach provides more stable estimates and explicit uncertainty characterization. Overall, the proposed framework offers a data-efficient, interpretable, and statistically rigorous solution for multicopter structural health monitoring.

Weijieying Ren, Jingxi Zhu, Zehao Liu et al.

Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based modeling systems. Within each dimension, we review representative methods addressing data quality enhancement, structural and temporal representation, self-supervised learning, and integration with clinical knowledge. We further highlight emerging trends such as foundation models, LLM-driven clinical agents, and EHR-to-text translation for downstream reasoning. Finally, we discuss open challenges in benchmarking, explainability, clinical alignment, and generalization across diverse clinical settings. This survey aims to provide a structured roadmap for advancing AI-driven EHR modeling and clinical decision support. For a comprehensive list of EHR-related methods, kindly refer to https://survey-on-tabular-data.github.io/.