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Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks 期刊论文
KNOWLEDGE-BASED SYSTEMS, 2021, 卷号: 215, 页码: 12
作者:  Zhu, Yifan;  Lin, Qika;  Lu, Hao;  Shi, Kaize;  Qiu, Ping;  Niu, Zhendong
收藏  |  浏览/下载：286/0  |  提交时间：2021/04/21
Paper recommendation  Heterogeneous knowledge  Attentive recurrent neural networks  Recommendation systems  E-learning  
Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 3, 页码: 20
作者:  Liu, Shuang;  Li, Mei;  Zhang, Zhong;  Xiao, Baihua;  Durrani, Tariq S.
收藏  |  浏览/下载：250/0  |  提交时间：2020/04/07
ground-based cloud recognition  convolution neural network  feature fusion  
Towards Real-Time Advancement of Underwater Visual Quality With GAN 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 12, 页码: 9350-9359
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Fang, Xi;  Wen, Li
浏览  |  Adobe PDF(4984Kb)  |  收藏  |  浏览/下载：488/159  |  提交时间：2019/12/16
Generative adversarial networks (GAN) image restoration  machine learning  underwater vision  
Real-time segmentation of various insulators using generative adversarial networks 期刊论文
IET COMPUTER VISION, 2018, 卷号: 12, 期号: 5, 页码: 596-602
作者:  Chang, Wenkai;  Yang, Guodong;  Yu, Junzhi;  Liang, Zize
Adobe PDF(4584Kb)  |  收藏  |  浏览/下载：516/80  |  提交时间：2019/12/16
image segmentation  insulators  neural nets  power engineering computing  real-time pixel-level segmentation  generative adversarial networks  insulator segmentation algorithm  cluttered background  artificial thresholds  compact end-to-end neural network  visual saliency map  proposed two-stage training  segmentation quality  
GPR and SPSO-CG based gait pattern generation for subject-specific training 期刊论文
SCIENCE CHINA Information Sciences, 2019, 卷号: 64, 期号: 00, 页码: 00
作者:  weiqun wang;  Weiguo SHI;  Shixin REN;  zeng-guang hou;  Xu LIANG;  Jiaxin WANG;  Liang PENG
浏览  |  Adobe PDF(142Kb)  |  收藏  |  浏览/下载：323/106  |  提交时间：2019/11/07
Gait Pattern Generation  Subject-specific Training