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pro vyhledávání: '"Li, Minhan"'
Automatically configuring a robotic prosthesis to fit its user's needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Previously, we have successfully developed reinforcement learning (RL) so
Externí odkaz:
http://arxiv.org/abs/2101.03487
Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel
Externí odkaz:
http://arxiv.org/abs/2011.06116
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability and optimality at systems level. Existing approximate/adaptive dynamic
Externí odkaz:
http://arxiv.org/abs/2006.09008
Publikováno v:
IEEE Transactions on Robotics, 2021
Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is
Externí odkaz:
http://arxiv.org/abs/2006.06518
Autor:
Curtis, Frank E., Li, Minhan
Gradient sampling (GS) has proved to be an effective methodology for the minimization of objective functions that may be nonconvex and/or nonsmooth. The most computationally expensive component of a contemporary GS method is the need to solve a conve
Externí odkaz:
http://arxiv.org/abs/2005.07822
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of supervised
Externí odkaz:
http://arxiv.org/abs/1903.00359
Autor:
Li, Minhan1,2 (AUTHOR), Zhang, Fangzhou1 (AUTHOR), Kuang, Min1 (AUTHOR), Ma, Yuanyuan1 (AUTHOR) yyma@dhu.edu.cn, Liao, Ting3 (AUTHOR), Sun, Ziqi3 (AUTHOR), Luo, Wei1 (AUTHOR), Jiang, Wan1 (AUTHOR), Yang, Jianping1 (AUTHOR) jianpingyang@dhu.edu.cn
Publikováno v:
Nano-Micro Letters. 10/26/2023, Vol. 15 Issue 1, p1-15. 15p.
Akademický článek
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Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extens
Externí odkaz:
http://arxiv.org/abs/1803.10647
Publikováno v:
In Biochemistry and Biophysics Reports September 2022 31