Yelin An

About

Yelin An is a leading researcher in edge computing and intelligent fault detection, whose work bridges the gap between real-time industrial monitoring and resource-constrained environments. Her most influential contribution, "LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks" (2018), has garnered 167 citations and pioneered a novel approach to deploying deep learning directly on edge devices. By integrating Long Short-Term Memory (LSTM) networks with lightweight architectures, An demonstrated that accurate, real-time fault detection in smart factories is achievable without relying on cloud servers—a breakthrough that significantly reduces latency and computational overhead. This work addresses a critical challenge in Industry 4.0: enabling continuous monitoring of machinery status while managing massive sensor data streams. An's research has profound implications for manufacturing, IoT, and cyber-physical systems, offering scalable solutions that maintain high accuracy under tight resource budgets. Her achievements highlight her expertise in embedded AI, anomaly detection, and system optimization, making her a key figure in advancing practical, deployable intelligence for industrial edge computing.

Research Focus

Key Achievements

Top Papers

1. 1
   LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

   167 citations · 2018

Key Collaborators

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