Xuna Wang
Papers
2
Total Citations
9
H-Index
2
About
Xuna Wang is a researcher at the forefront of intelligent control systems and computer vision, with a focus on advancing robotics and video understanding. Her work bridges hardware innovation and algorithmic learning, addressing critical challenges in both domains. In robotics, Wang proposed a novel motor structure—an optimized stepped rotor bearingless switched reluctance motor (BLSRM)—integrated with an extended particle swarm optimization method. This design directly tackles the poor self-starting ability and severe torque fluctuations that have long plagued traditional BLSRMs, offering a more reliable solution for space robot control. Her 2023 paper on this topic has garnered 6 citations, signaling early recognition in the field. In computer vision, Wang introduced a view-to-scene joint learning framework for recognizing video activities in the wild. Her 2024 work tackles the formidable challenge of identifying actions never seen in training data, captured from diverse angles and scenes, without relying on overly complex spatiotemporal architectures. With a total of 9 citations across her most-cited works, Wang is establishing herself as a rising talent, demonstrating a rare ability to solve practical engineering problems—from motor optimization to robust video analysis—that are essential for next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2