A recent study published in Automation in Construction by researchers from Incheon National University exploits a novel approach to improving excavator tracking performance under real-world conditions. By integrating deep learning-based instance segmentation with an automated, reliability-based multi-camera strategy, this study addresses one of the most persistent challenges in construction monitoring—frequent occlusions caused by dynamic site activities. In addition, the researchers propose a frame-level reliability estimation process that automatically identifies unreliable tracking results.

A research team led by Senior Researcher Dr. Jun Sae Han at the Nano Lithography and Manufacturing Research Center, Nano-Convergence Research Division, Korea Institute of Machinery and Materials (KIMM, President Seog-Hyeon Ryu), has proposed a roll-to-roll (R2R)-based direct roll lamination process. This method enable continuous material pressing allowing for efficient and scalable manufacturing.

Researchers at Concordia University have developed an AI-driven robotic system that can autonomously perform cardiac ultrasound scans without human guidance. Trained using realistic simulated images and reinforcement learning, the robot learns to position and adjust the probe to capture diagnostic views. Tests show it can match or outperform human operators, potentially expanding access to reliable heart imaging in underserved settings.