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Autor:
Ma, Yinji, Choi, Jungil, Hourlier-Fargette, Aurélie, Xue, Yeguang, Chung, Ha Uk, Lee, Jong Yoon, Wang, Xiufeng, Xie, Zhaoqian, Kang, Daeshik, Wang, Heling, Han, Seungyong, Kang, Seung-Kyun, Kang, Yisak, Yu, Xinge, Slepian, Marvin J., Raj, Milan S., Model, Jeffrey B., Feng, Xue, Ghaffari, Roozbeh, Rogers, John A., Huang, Yonggang
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2018 Oct . 115(44), 11144-11149.
Externí odkaz:
https://www.jstor.org/stable/26532419
Publikováno v:
Drones; Volume 7; Issue 7; Pages: 437
This article presents a novel approach for detecting and isolating faulty actuators in highly redundant Multirotor UAVs using cascaded Deep Neural Network (DNN) models. The proposed Fault Detection and Isolation (FDI) framework combines Long Short-Te