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Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer 期刊论文
FRONTIERS IN ONCOLOGY, 2019, 卷号: 9, 页码: 1007
作者:  Xu, Xiaojuan;  Li, Hailin;  Wang, Siwen;  Fang, Mengjie;  Zhong, Lianzhen;  Fan, Wenwen;  Dong, Di;  Tian, Jie;  Zhao, Xinming
Adobe PDF(2786Kb)  |  收藏  |  浏览/下载：295/52  |  提交时间：2020/03/30
endometrial cancer  lymph node  metastasis  magnetic resonance imaging  radiomics  
Fine-Grained Parcellation of the Macaque Nucleus Accumbens by High-Resolution Diffusion Tensor Tractography 期刊论文
FRONTIERS IN NEUROSCIENCE, 2019, 卷号: 13, 页码: 12
作者:  Xia, Xiaoluan;  Fan, Lingzhong;  Hou, Bing;  Zhang, Baogui;  Zhang, Dan;  Cheng, Chen;  Deng, Hongxia;  Dong, Yunyun;  Zhao, Xudong;  Li, Haifang;  Jiang, Tanzi
收藏  |  浏览/下载：344/0  |  提交时间：2019/12/16
neuroimaging  connectivity  ventral striatum  medial shell  lateral shell  
Adaptive Optimal Control of Heterogeneous CACC System With Uncertain Dynamics 期刊论文
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2019, 卷号: 27, 期号: 4, 页码: 1772-1779
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Zhong, Zhiguang
Adobe PDF(1189Kb)  |  收藏  |  浏览/下载：284/12  |  提交时间：2019/09/30
Adaptive optimal control  cooperative adaptive cruise control (CACC)  heterogeneous platoon  string stability  sum-of-squares polynomial  
Interspecies Differences in the Connectivity of Ventral Striatal Components Between Humans and Macaques 期刊论文
FRONTIERS IN NEUROSCIENCE, 2019, 卷号: 13, 页码: 12
作者:  Xia, Xiaoluan;  Fan, Lingzhong;  Cheng, Chen;  Yao, Rong;  Deng, HongXia;  Zhao, Dongqin;  Li, Haifang;  Jiang, Tianzi
收藏  |  浏览/下载：325/0  |  提交时间：2019/09/30
ventral striatum  nucleus accumbens  comparative neuroimaging  connectivity fingerprint  parcellation  
Decision Controller for Object Tracking With Deep Reinforcement Learning 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 28069-28079
作者:  Zhong, Zhao;  Yang, Zichen;  Feng, Weitao;  Wu, Wei;  Hu, Yangyang;  Liu, Cheng-Lin
浏览  |  Adobe PDF(2984Kb)  |  收藏  |  浏览/下载：624/208  |  提交时间：2019/04/30
Computer vision  deep learning  object tracking  reinforcement learning