Xuejiao Zhang
Papers
3
Total Citations
16
H-Index
3
About
Xuejiao Zhang is a researcher advancing the intersection of robotics, artificial intelligence, and intelligent systems. Her primary research areas include robotic grasping detection, multi-object matching in smart environments, and automated inspection using deep learning. Zhang’s most notable contribution is her work on small-sample multi-target grasping, where she proposed the CBAM-ASPP-SqueezeNet model—a novel architecture integrating attention mechanisms and atrous spatial pyramid pooling to improve robot grasping detection with limited training data. This work, published in 2023, has garnered 10 citations and addresses a critical bottleneck in robotic manipulation. She has also pioneered the dynamic three-sided matching model for personnel–robot–position matching, a framework that optimizes human-robot collaboration in intelligent production systems. More recently, Zhang has explored foreign object detection in cloud server centers using separable self-attention mechanisms, demonstrating her versatility in applying deep learning to industrial safety. Her research, though early in its citation trajectory, shows strong potential for impact in manufacturing automation and human-robot interaction. Zhang’s work exemplifies how transfer learning and attention-based models can solve real-world engineering challenges.
Research Focus
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