English | 繁體

Search in the results

UM

Browse/Search Results:  1-7 of 7 Help

Selected(0)Clear Items/Page:    Sort:
H-Bonds Enhanced Natural Polyphenols Bined Polysaccharide/Gelatin Composites with Controlled Photothermal Stimulation Phase Transition for Wound Care Journal article
Chen, Chonghao, Zhang, Junbo, Zhong, Guofeng, Lei, Pengkun, Qin, Xuhua, Zhang, Chen, Zeng, Rui, Qu, Yan. H-Bonds Enhanced Natural Polyphenols Bined Polysaccharide/Gelatin Composites with Controlled Photothermal Stimulation Phase Transition for Wound Care[J]. Biomaterials Research, 2024, 28, 0082.
Authors:  Chen, Chonghao;  Zhang, Junbo;  Zhong, Guofeng;  Lei, Pengkun;  Qin, Xuhua; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:8.1/0 | Submit date：2024/12/05
Semi-supervised 3D Object Detection with Proficient Teachers Conference paper
Yin, Junbo, Fang, Jin, Zhou, Dingfu, Zhang, Liangjun, Xu, Cheng Zhong, Shen, Jianbing, Wang, Wenguan. Semi-supervised 3D Object Detection with Proficient Teachers[C]:SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2022, 727-743.
Authors:  Yin, Junbo;  Fang, Jin;  Zhou, Dingfu;  Zhang, Liangjun;  Xu, Cheng Zhong; et al.
Favorite | TC[WOS]:45 TC[Scopus]:52 | Submit date：2023/01/30
3d Object Detection  Point Cloud  Semi-supervised Learning  
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection Conference paper
Yin, Junbo, Zhou, Dingfu, Zhang, Liangjun, Fang, Jin, Xu, Cheng Zhong, Shen, Jianbing, Wang, Wenguan. ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection[C]:SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2022, 17-33.
Authors:  Yin, Junbo;  Zhou, Dingfu;  Zhang, Liangjun;  Fang, Jin;  Xu, Cheng Zhong; et al.
Favorite | TC[WOS]:48 TC[Scopus]:60 | Submit date：2023/01/30
3d Object Detection  Unsupervised Point Cloud Pre-training  
Semi-supervised 3D Object Detection with Proficient Teachers Conference paper
Junbo, Yin, Jin, Fang, Dingfu, Zhou, Liangjun, Zhang, Cheng-Zhong, Xu, Jianbing, Shen, Wenguan, Wang. Semi-supervised 3D Object Detection with Proficient Teachers[C], 2022.
Authors:  Junbo, Yin;  Jin, Fang;  Dingfu, Zhou;  Liangjun, Zhang;  Cheng-Zhong, Xu; et al.
Favorite | TC[WOS]:45 TC[Scopus]:52 | Submit date：2023/08/08
3d Object Detection  Semi-supervised Learning  Point Cloud  
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection Conference paper
Junbo, Yin, Junbo, Yin, Liangjun, Zhang, Jin, Fang, Dingfu, Zhou, Cheng-Zhong, Xu, Jianbing, Shen, Wenguan, Wang. ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection[C], 2022.
Authors:  Junbo, Yin;  Junbo, Yin;  Liangjun, Zhang;  Jin, Fang;  Dingfu, Zhou; et al.
Favorite | TC[WOS]:48 TC[Scopus]:63 | Submit date：2023/08/08
3d Object Detection  Unsupervised Point Cloud Pre-training  
Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning Journal article
Wang, Zhaoyuan, Pan, Zheyi, Chen, Shun, Ji, Shenggong, Yi, Xiuwen, Zhang, Junbo, Wang, Jingyuan, Gong, Zhiguo, Li, Tianrui, Zheng, Yu. Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 35(5), 5282-5295.
Authors:  Wang, Zhaoyuan;  Pan, Zheyi;  Chen, Shun;  Ji, Shenggong;  Yi, Xiuwen; et al.
Favorite | TC[WOS]:2 TC[Scopus]:4  IF:8.9/8.8 | Submit date：2022/05/17
Metro Systems  Spatio-temporal Data  Neural Network  Deep Reinforcement Learning  Urban Computing  
Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements Journal article
Zhou, Kai, Sun, Yaoting, Li, Lu, Zang, Zelin, Wang, Jing, Li, Jun, Liang, Junbo, Zhang, Fangfei, Zhang, Qiushi, Ge, Weigang, Chen, Hao, Sun, Xindong, Yue, Liang, Wu, Xiaomai, Shen, Bo, Xu, Jiaqin, Zhu, Hongguo, Chen, Shiyong, Yang, Hai, Huang, Shigao, Peng, Minfei, Lv, Dongqing, Zhang, Chao, Zhao, Haihong, Hong, Luxiao, Zhou, Zhehan, Chen, Haixiao, Dong, Xuejun, Tu, Chunyu, Li, Minghui, Zhu, Yi, Chen, Baofu, Li, Stan Z., Guo, Tiannan. Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements[J]. Computational and Structural Biotechnology Journal, 2021, 19, 3640-3649.
Authors:  Zhou, Kai;  Sun, Yaoting;  Li, Lu;  Zang, Zelin;  Wang, Jing; et al.
Favorite | TC[WOS]:26 TC[Scopus]:29  IF:4.4/5.0 | Submit date：2021/12/07
Covid-19  Sars-cov-2  Severity Prediction  Machine Learning  Routine Clinical Test  Longitudinal Dynamics