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About

Kejie Dai is a researcher at the forefront of intelligent systems, specializing in the fusion of signal processing, pattern recognition, and robotic control. His work spans two critical domains: high-voltage equipment diagnostics and human-robot interaction. In the field of electrical engineering, Dai made a significant contribution with his 2022 paper on GIS partial discharge pattern recognition, which has garnered 24 citations. He pioneered a multi-feature information fusion method for Phase-Resolved Partial Discharge (PRPD) images, overcoming the limitations of single-feature extraction to dramatically improve diagnostic accuracy for gas-insulated switchgear insulation—a vital advancement for power grid reliability. Simultaneously, Dai is advancing dexterous robotics. He proposed a novel hybrid system combining Surface Electromyography (SEMG) and Kinect sensors, using an adaptive directed acyclic graph to identify complex human in-hand motions, enabling more intuitive control of multi-fingered robotic hands. His work also extends to bipedal locomotion, where he developed an adaptive ankle impedance control strategy for upright balance, moving beyond fixed torque models to achieve more robust, human-like stability. Through these contributions, Dai is bridging the gap between sensor intelligence and real-world robotic and industrial applications.