Young‐Kuk Kim

Papers

Total Citations

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About

Young-Kuk Kim’s research bridges real-time computing and intelligent robotics, with a focus on enabling autonomous systems to operate reliably in dynamic, unpredictable environments. His early work on predictability and consistency in real-time database systems (1995, 37 citations) laid foundational principles for time-critical data management, addressing how computing systems can guarantee timely responses in applications like industrial monitoring and control. More recently, Kim has advanced robot vision and manufacturing automation. His highly cited 2021 paper on HSV color-space-based automated object localization (41 citations) introduced a simple yet robust algorithm that allows robots to grasp objects without prior knowledge or human intervention—a critical capability for high-variability manufacturing lines. This work directly supports the shift from mass production to personalized manufacturing services, as explored in his 2019 study on HSV-based robot grasping (3 citations). By combining theoretical rigor with practical, low-cost solutions, Kim’s contributions empower robots to adapt on the fly, reducing the need for extensive pre-programming. His research is particularly impactful for students and engineers working on flexible automation, computer vision, and real-time systems, demonstrating how elegant algorithms can solve complex industrial challenges.