Yuki Nagastu
Papers
1
Total Citations
2
H-Index
1
About
Yuki Nagatsu is a researcher whose work sits at the intersection of precision sensing, robotics, and intelligent control systems. His primary research areas include the development of high-accuracy magnetic encoders, motor control, and the application of machine learning to sensor systems. Nagatsu’s most notable contribution is his work on a magnetic absolute encoder that leverages an eccentric structure combined with Long Short-Term Memory (LSTM) networks. This innovative approach addresses a critical challenge in robotics: achieving high-resolution position sensing without losing absolute angle information, a problem that arises when using multi-pole magnets. By integrating deep learning, his method enhances the reliability and precision of encoders, which are essential for high-performance robotic control. While his most-cited paper currently holds 2 citations, reflecting a growing interest in this niche but impactful area, his work represents a meaningful step toward more intelligent and robust sensing systems. Nagatsu’s research is particularly relevant for students and engineers working on advanced robotics, automation, and mechatronics, offering a practical fusion of hardware design and machine learning to solve real-world engineering problems.
Research Focus
Key Achievements
Top Papers
- 1