Abstract The nonlinear collision operator consumes a significant amount of computation time in tokamak whole-volume modeling, and in current numerical methods, the computational time grows O(n2), with n representing the number of plasma spe... Read More
Abstract Over the last five decades, Arctic sea ice has been shrinking in area and thickness. As a result, increased marine traffic has created a need for improved sea ice forecasting on seasonal to annual time-scales. In this study, we int... Read More
The ability to predict the feasibility of manufacturing parts significantly enhances production efficiency and reduces costs in the manufacturing industry. Leveraging this technology, the laboratory of the Department of Industrial Engineeri... Read More
Abstract Accurately predicting tool wear is crucial for intelligent machining process monitoring, control, and quality improvement. Recent studies on tool wear prediction predominantly apply deep learning-based data-driven approaches that u... Read More
Abstract 1 This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption... Read More
A research team, led by Professor Sungryul Baek from the Graduate School of Artificial Intelligence at UNIST has achieved remarkable success at the ‘ICCV 2023 Workshop on Observing and Understanding Hands in Action (HANDS),’ whi... Read More
Abstract Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near s... Read More
A UNIST graduate student, Jae Gyeong Choi (Advisor: Professor Sunghoon Lim) from the Department of Industrial Engineering has been recognized for outstanding poster presentation at the 2021 International Conference of Women Scientists and E... Read More
A team of researchers, affiliated with UNIST presented an artificial intelligence technology that can predict traffic conditions for the next 5 to 15 minutes at an error rate of less than four kilometers an hour. This breakthrough has been... Read More
UNIST signed an industry-academia partnership agreement with Samsung SDS to pledge mutual support for one another in the pursuit of excellence in big data analytics related research and education. The signing ceremony of MoU took place in t... Read More
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