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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：63/10  |  提交时间：2024/03/26
biomimetic motion  biomimetic autonomous system  target following  deep reinforcement learning  predictive control  
GAN-Based Facial Attribute Manipulation 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 12, 页码: 14590-14610
作者:  Liu, Yunfan;  Li, Qi;  Deng, Qiyao;  Sun, Zhenan;  Yang, Ming-Hsuan
Adobe PDF(15297Kb)  |  收藏  |  浏览/下载：86/23  |  提交时间：2024/02/22
Generative adversarial networks  image translation  facial attribute manipulation  
A Survey on Reinforcement Learning Methods in Bionic Underwater Robots 期刊论文
BIOMIMETICS, 2023, 卷号: 8, 期号: 2, 页码: 29
作者:  Tong, Ru;  Feng, Yukai;  Wang, Jian;  Wu, Zhengxing;  Tan, Min;  Yu, Junzhi
Adobe PDF(1260Kb)  |  收藏  |  浏览/下载：123/12  |  提交时间：2023/11/17
bionic underwater robot  reinforcement learning  robotic fish  intelligent control  
Robotic Automatic Drilling for Craniotomy: Algorithms and In Vitro Animal Experiments 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023, 页码: 12
作者:  Bian, Gui-Bin;  Wei, Bing-Ting;  Li, Zhen;  Ge, Peicong;  Chen, Zhang;  Qian, Chen;  Wang, Jie;  Fu, Pan;  Zhao, Jizong
Adobe PDF(2827Kb)  |  收藏  |  浏览/下载：127/10  |  提交时间：2023/11/17
Drilling  Robots  Force  Robot sensing systems  Skull  Feeds  Service robots  Autonomous drilling  craniotomy robot  feed velocity and force constraint control  in vitro experiment  skull-dura boundary detection  
MsIFT: Multi-Source Image Fusion Transformer 期刊论文
REMOTE SENSING, 2022, 卷号: 14, 期号: 16, 页码: 19
作者:  Zhang, Xin;  Jiang, Hangzhi;  Xu, Nuo;  Ni, Lei;  Huo, Chunlei;  Pan, Chunhong
Adobe PDF(4788Kb)  |  收藏  |  浏览/下载：335/86  |  提交时间：2022/11/14
transformer  multi-source image fusion  non-local  
Task-aware adaptive attention learning for few-shot semantic segmentation 期刊论文
NEUROCOMPUTING, 2022, 卷号: 494, 页码: 104-115
作者:  Mao, Binjie;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(3903Kb)  |  收藏  |  浏览/下载：308/71  |  提交时间：2022/09/19
Few-shot semantic segmentation  Adaptive feature learning  Attention mechanism  Task-aware  
Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images 期刊论文
JAMA NETWORK OPEN, 2022, 卷号: 5, 期号: 6, 页码: 11
作者:  Zhang, Liwen;  Dong, Di;  Sun, Yongqing;  Hu, Chaoen;  Sun, Congxin;  Wu, Qingqing;  Tian, Jie
Adobe PDF(1293Kb)  |  收藏  |  浏览/下载：264/8  |  提交时间：2022/07/25
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma 期刊论文
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 页码: 11
作者:  Zhao, Xun;  Liang, Yu-Jing;  Zhang, Xu;  Wen, Dong-Xiang;  Fan, Wei;  Tang, Lin-Quan;  Dong, Di;  Tian, Jie;  Mai, Hai-Qiang
Adobe PDF(1720Kb)  |  收藏  |  浏览/下载：347/49  |  提交时间：2022/06/10
Recurrent nasopharyngeal carcinoma  Survival analysis  Radiomics  Deep learning  
An Efficient Sampling-Based Attention Network for Semantic Segmentation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 2850-2863
作者:  He, Xingjian;  Liu, Jing;  Wang, Weining;  Lu, Hanqing
Adobe PDF(3252Kb)  |  收藏  |  浏览/下载：384/78  |  提交时间：2022/06/10
Stochastic processes  Sampling methods  Semantics  Image segmentation  Computational complexity  Pattern recognition  Convolution  Semantic segmentation  stochastic sampling-based attention  deterministic sampling-based attention  
HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5
作者:  Cao, Yong;  Huo, Chunlei;  Xu, Nuo;  Zhang, Xin;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(1478Kb)  |  收藏  |  浏览/下载：260/4  |  提交时间：2022/06/06
Head  Computational modeling  Semantics  Image segmentation  Feature extraction  Correlation  Mathematical models  Cooperative learning (CL)  ensemble learning  semantic segmentation