Future Internet, Volume 16, Issue 7 (July 2024) – 39 articles
Cover Story (view full-size image):
Digital twins, virtual replicas of physical systems, have revolutionized smart manufacturing by enabling advanced simulation and early fault diagnosis. However, these models deteriorate over time due to dynamic data streams. To address this issue, Ragini Gupta and her fellow researchers from Prof. Klara Nahrstedt's lab at the University of Illinois at Urbana-Champaign introduced TWIN-ADAPT, a continuous learning framework for real-time anomaly classification in IoT-driven semiconductor labs, cleanrooms. TWIN-ADAPT uses dual-window strategies to detect and adapt to concept drift and Particle Swarm Optimization for hyperparameter tuning. Tested on cleanroom datasets, it demonstrates superior performance, handling both abrupt and gradual data changes. View this paper
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