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Integrated Tracking Control of an Underwater Bionic Robot Based on Multimodal Motions 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024, 卷号: 54, 期号: 3, 页码: 1599-1610
作者:  Wang, Jian;  Wu, Zhengxing;  Zhang, Yang;  Kong, Shihan;  Tan, Min;  Yu, Junzhi
Adobe PDF(5090Kb)  |  收藏  |  浏览/下载：42/3  |  提交时间：2024/03/27
Disturbance observer (DOB)  fuzzy system  model predictive control (MPC)  tracking control  underwater bionic robot  
Target-Following Control of a Biomimetic Autonomous System Based on Predictive Reinforcement Learning 期刊论文
BIOMIMETICS, 2024, 卷号: 9, 期号: 1, 页码: 19
作者:  Wang, Yu;  Wang, Jian;  Kang, Song;  Yu, Junzhi
Adobe PDF(1553Kb)  |  收藏  |  浏览/下载：29/0  |  提交时间：2024/03/26
biomimetic motion  biomimetic autonomous system  target following  deep reinforcement learning  predictive control  
Autonomous Recovery Control of Biomimetic Robotic Fish Based on Multi-Sensory System 期刊论文
IEEE ROBOTICS AND AUTOMATION LETTERS, 2024, 卷号: 9, 期号: 2, 页码: 1420-1427
作者:  Qiu, Changlin;  Wu, Zhengxing;  Wang, Jian;  Tan, Min;  Yu, Junzhi
Adobe PDF(4262Kb)  |  收藏  |  浏览/下载：32/1  |  提交时间：2024/03/26
Robots  Wires  Tail  Robot sensing systems  Task analysis  Springs  Sensors  Autonomous recovery  motion control  robotic fish  underwater perception  
DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging 期刊论文
PHYSICS IN MEDICINE AND BIOLOGY, 2024, 卷号: 69, 期号: 1, 页码: 15
作者:  Peng, Zhengyao;  Yin, Lin;  Sun, Zewen;  Liang, Qian;  Ma, Xiaopeng;  An, Yu;  Tian, Jie;  Du, Yang
Adobe PDF(1035Kb)  |  收藏  |  浏览/下载：79/2  |  提交时间：2024/02/22
magnetic particle imaging  end-to-end reconstruction  deep learning  image reconstruction  
Reparameterizing and dynamically quantizing image features for image generation 期刊论文
PATTERN RECOGNITION, 2024, 卷号: 146, 页码: 11
作者:  Sun, Mingzhen;  Wang, Weining;  Zhu, Xinxin;  Liu, Jing
Adobe PDF(3612Kb)  |  收藏  |  浏览/下载：127/15  |  提交时间：2023/12/21
Vector quantization  Variational auto-encoder  Unconditional image generation  Text-to-image generation  Autoregressive generation