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DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending 期刊论文
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 卷号: 103, 页码: 142-157
作者:  Dai, Xingyuan;  Fu, Rui;  Zhao, Enmin;  Zhang, Zuo;  Lin, Yilun;  Wang, Fei-Yue;  Li, Li
Adobe PDF(5109Kb)  |  收藏  |  浏览/下载：343/31  |  提交时间：2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction  
Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts 期刊论文
IEEE Access, 2019, 卷号: 7, 期号: Sep. 2019, 页码: 136951- 136961
作者:  Shuai Wang;  Chenchen Huang;  Juanjuan Li;  Yong Yuan;  Fei-Yue Wang
Adobe PDF(2505Kb)  |  收藏  |  浏览/下载：377/111  |  提交时间：2019/09/25
Decentralized Knowledge Graph  Deep Recommender System  Crowdsourcing  Blockchain  Smart Contracts  
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
浏览  |  Adobe PDF(2273Kb)  |  收藏  |  浏览/下载：437/109  |  提交时间：2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
Moving from mass customization to social manufacturing: a footwear industry case study 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2019, 卷号: 32, 期号: 2, 页码: 194-205
作者:  Shang, Xiuqin;  Shen, Zhen;  Xiong, Gang;  Wang, Fei-Yue;  Liu, Sheng;  Nyberg, Timo R.;  Wu, Huaiyu;  Guo, Chao
浏览  |  Adobe PDF(2349Kb)  |  收藏  |  浏览/下载：491/125  |  提交时间：2019/07/12
Collaborative manufacturing  mass customisation  optimisation SM resources  
A novel hybrid share reporting strategy for blockchain miners in PPLNS pools 期刊论文
DECISION SUPPORT SYSTEMS, 2019, 卷号: 118, 期号: NA, 页码: 91-101
作者:  Qin, Rui;  Yuan, Yong;  Wang, Fei-Yue
浏览  |  Adobe PDF(2213Kb)  |  收藏  |  浏览/下载：485/154  |  提交时间：2019/07/12
Share reporting strategy  Blockchain mining  PPLNS reward  Pool mining  Computational experiments approach