Papers
5
Total Citations
210
H-Index
4
About
Yuting Bai is a dynamic researcher at the intersection of artificial intelligence, robotics, and autonomous systems, with a particular focus on state estimation, mobile intelligence, and smart agriculture. His most influential work, "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods" (2021, 125 citations), charts a compelling vision for the future of estimation techniques, bridging traditional model-based approaches with modern data-driven methodologies to advance IoT and unmanned systems. His 2018 paper on mobile intelligence (53 citations) demonstrated how AI can empower robots to navigate complex environments with human-like fluidity — a breakthrough with profound implications for autonomous vehicles and service robots. Bai has also made meaningful strides in precision agriculture, proposing deep learning systems for real-time vegetable recognition and developing LCA-Net, a lightweight neural network for fine-grained pest and disease identification in crop monitoring. His most recent work on inertial pose estimation using cascade networks reflects his continuing push toward safer, more accurate navigation for aerospace and robotic platforms. Across these contributions, Bai exemplifies the modern AI researcher — technically rigorous, application-driven, and committed to solving real-world engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods125 citations · 2021
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- 5Inertial Pose Estimation Method Based on Multi-Genre Cascade Networks2 citations · 2024