Artificial Intelligence-powered Devices and Sensors
Permanent URI for this collectionhttps://hdl.handle.net/10125/112421
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Item type: Item , Railroad Track Defect Detection Using YOLO Models: A Comparative Study(2026-01-06) Zakaria, Abdul-Rashid; Walter, Charles; Lautala, Pasi; Oommen, Thomas; Xiao, HongDetecting substructure defects is critical to ensuring track stability, preventing derailments, and avoiding costly slow orders. This study explores the use of surficial thermal and optical imagery captured by uncrewed aerial vehicle (UAV)-mounted sensors to detect subsurface issues such as ballast contamination, mud pumping, and differential settlement. We evaluate real-time object detection models You Only Look Once (YOLO)v5 through YOLOv10 across optical, thermal, and combined datasets. Results show that YOLOv10 achieves state-of-the-art performance, reaching 0.915 in precision and 0.918 in recall on optical imagery, and leading across metrics on thermal and combined modalities as well. Zero-shot cross-modal inference experiments, however, reveal limited transferability: models trained on thermal imagery generalize poorly to optical data, and vice versa, with mAP@50 below 0.25 in both directions. Overall, our results establish both a strong baseline for UAV-based defect detection and a roadmap for future research into more generalizable multimodal railway inspection models.Item type: Item , AI Application in Semiconductor Manufacturing: A Patent-driven Approach(2026-01-06) Kim, Dohee; Zo, HangjungAs semiconductor structures continue to shrink, semiconductor manufacturers are facing increasing challenges, such as yield degradation, process complexity, and rising production costs. While artificial intelligence (AI) is expected to improve profitability, limited research has systematically investigated how AI is actually being used in this context. Therefore, this study explores the current application of AI technologies in semiconductor manufacturing using patent data and identifies promising application areas. A technology-function matrix analysis reveals that AI is primarily applied to process control and defect detection through image analysis. Additionally, a generalized linear mixed model (GLMM) analysis shows that technologies related to error correction, scheduling, and advanced metrology have recently demonstrated high growth rates. These findings offer practical implications for managers and practitioners seeking to leverage AI in semiconductor manufacturing.Item type: Item , Introduction to the Minitrack on Artificial Intelligence-powered Devices and Sensors(2026-01-06) Cesini, Daniele; Dell’Agnello, Luca; Ronchieri, Elisabetta