Dong‐Yun Kim
Papers
2
Total Citations
7
H-Index
2
About
Dong-Yun Kim is a researcher whose work bridges the critical intersection of robotics and human-technology interaction. His primary research areas include robotic process automation (RPA), object detection for robotic manipulation, and the behavioral acceptance of emerging technologies. Kim’s most cited work, “A Study on the Acceptance Intention of Robotic Process Automation Using Integrated Technology Acceptance Model” (2022, 4 citations), makes a significant contribution by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand how perceived value and innovative technology acceptance influence the adoption of RPA systems—a vital framework for organizations implementing automation. In parallel, his technical research in “Object Detection by Combining Two Different CNN Algorithms and Robotic Grasping Control” (2019, 3 citations) addresses a fundamental challenge in robotics: precisely predicting an object’s grasping parameters, including center coordinates, width, and yaw angle, to enable accurate manipulation. By integrating dual convolutional neural network algorithms, Kim advances the precision of robotic grasping control, a core capability for industrial automation. Though early in his citation trajectory, Kim’s work uniquely bridges behavioral models and computer vision, offering dual insights for researchers and practitioners seeking to both develop and deploy effective robotic systems.
Research Focus
Key Achievements
Top Papers
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