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Yili Fu
Researcher at Harbin Institute of Technology
Publications - 101
Citations - 948
Yili Fu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 13, co-authored 71 publications receiving 748 citations.
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Journal ArticleDOI
Brainstem atrophy in the early stage of Alzheimer's disease: a voxel-based morphometry study.
Xiaoxi Ji,Xiaoxi Ji,Hui Wang,Minwei Zhu,Yingjie He,Hong Zhang,Xiaoguang Chen,Wenpeng Gao,Yili Fu,Alzheimer’s Disease Neuroimaging Initiative +9 more
TL;DR: The present voxel-based morphometry (VBM) study was designed to investigate the brainstem differences between the AD-VM/AD-M groups and the NC group and revealed that brainstem atrophy occurs in the early stages of AD.
Journal ArticleDOI
3D stable biped walking control and implementation on real robot
Jianwen Luo,Yili Fu,Shuguo Wang +2 more
TL;DR: The feasibility of the combination of the control methods proved to be practical in keeping biped robot walking stable both in linear and rotation motion.
Proceedings ArticleDOI
A wheel-leg hybrid wall climbing robot with multi-surface locomotion ability
Yili Fu,Zhihai Li,Shuguo Wang +2 more
TL;DR: In this paper, a wheel-leg hybrid mobile robot that can move on both ground and wall surfaces is presented, which could be used for special tasks such as rescue, inspection, surveillance and reconnaissance.
Journal ArticleDOI
Variable stiffness control of series elastic actuated biped locomotion
TL;DR: Two online variable stiffness control algorithms, i.e., torque balance algorithm (TBA) and surface fitting algorithm (SFA) are proposed based on virtual spring leg to achieve compliant performance to improve stability and safety of walking biped robots.
Journal ArticleDOI
A novel deep LSTM network for artifacts detection in microelectrode recordings
TL;DR: A novel deep learning architecture based on long short-term memory (LSTM) network for automatic artifact detection in Microelectrode recording signals is proposed, which is the first study to use LSTM network for artifacts detection in MER signals.