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CASIA OpenIR
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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：341/30  |  提交时间：2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction  
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：435/108  |  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：489/124  |  提交时间：2019/07/12
Collaborative manufacturing  mass customisation  optimisation SM resources  
Parallel Vehicular Networks: A CPSS-Based Approach via Multimodal Big Data in IoV 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2019, 卷号: 6, 期号: 1, 页码: 1079-1089
作者:  Han, Shuangshuang;  Wang, Xiao;  Zhang, Jun Jason;  Cao, Dongpu;  Wang, Fei-Yue
浏览  |  Adobe PDF(1880Kb)  |  收藏  |  浏览/下载：423/72  |  提交时间：2019/07/12
Cyber-social-physical system (CPSS)  Internet of Vehicles (IoV)  parallel system  social networks  
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 6, 页码: 2395-2400
作者:  Lin, Yilun;  Dai, Xingyuan;  Li, Li;  Wang, Fei-Yue
浏览  |  Adobe PDF(623Kb)  |  收藏  |  浏览/下载：366/158  |  提交时间：2019/05/07
Traflic flow prediction  deep learning  generative adversarial network