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Your search keyword '"Xu, Yanyan"' showing total 19 results
Start Over Author "Xu, Yanyan" Publisher hindawi limited
19 results on '"Xu, Yanyan"'

1. AlGaN/GaN Heterostructure Schottky Barrier Diodes with Graded Barrier Layer

Author

Liu, Honghui, primary, Liang, Zhiwen, additional, Yan, Chaokun, additional, Liu, Yuebo, additional, Wang, Fengge, additional, Xu, Yanyan, additional, Shen, Junyu, additional, Xiao, Zhengwen, additional, Wu, Zhisheng, additional, Liu, Yang, additional, Wang, Qi, additional, Wang, Xinqiang, additional, and Zhang, Baijun, additional

Published
2022
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3. No Evidence for a Causal Link between Serum Uric Acid and Nonalcoholic Fatty Liver Disease from the Dongfeng-Tongji Cohort Study

Author

Tang, Yuhan, primary, Xu, Yanyan, additional, Liu, Peiyi, additional, Liu, Cheng, additional, Zhong, Rong, additional, Yu, Xiao, additional, Xiao, Lin, additional, Du, Min, additional, Yang, Ling, additional, Yuan, Jing, additional, Wang, Youjie, additional, Chen, Weihong, additional, Wei, Sheng, additional, Liang, Yuan, additional, Zhang, Xiaomin, additional, Wu, Tangchun, additional, He, Meian, additional, Miao, Xiaoping, additional, and Yao, Ping, additional

Published
2022
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5. The Establishment of Esophageal Precancerous Lesion Model by Using p53 Conditional Knockout Mouse in Esophageal Epithelium

Author

Zhu, Lili, primary, Xu, Yanyan, additional, Chen, Xinhuan, additional, Qin, Jiace, additional, Niu, Tingting, additional, Zhu, Yanyan, additional, Jiang, Yanan, additional, Zhao, Simin, additional, Liu, Kangdong, additional, Lu, Jing, additional, Jin, Ge, additional, Ma, Junfen, additional, Dong, Ziming, additional, and Zhao, Jimin, additional

Published
2020
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17. Urban traffic flow prediction: a spatio-temporal variable selection-based approach.

Author

Xu, Yanyan, Chen, Hui, Kong, Qing-Jie, Zhai, Xi, and Liu, Yuncai

Subjects
*TRAFFIC flow, *CITY traffic, *TRAFFIC density, *HIGHWAY capacity, *TRAFFIC surveys
Abstract

Short-term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio-temporal-based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high-density road networks. Therefore, we propose a spatio-temporal variable selection-based support vector regression (VS-SVR) model fed with the high-dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two-stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high-dimensional spatio-temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real-world traffic volume collected from a sub-area of Shanghai, China, demonstrate that the proposed spatio-temporal VS-SVR model outperforms the state-of-the-art. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2016
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19. Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Author

Yanyan Xu, Xiaoqing Dai, Lijun Sun, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Program in Media Arts and Sciences (Massachusetts Institute of Technology), Dai, Xiaoqing, Sun, Lijun, and Xu, Yanyan

Subjects
Economics and Econometrics, Schedule, Article Subject, Computer science, Strategy and Management, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 02 engineering and technology, computer.software_genre, Data-driven, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, 050210 logistics & transportation, Ensemble forecasting, Artificial neural network, Mechanical Engineering, 05 social sciences, lcsh:TA1001-1280, Probabilistic logic, Statistical model, lcsh:HE1-9990, Computer Science Applications, Random forest, Support vector machine, Automotive Engineering, 020201 artificial intelligence & image processing, Data mining, lcsh:Transportation engineering, lcsh:Transportation and communications, computer
Abstract

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Published
2018

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