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119 results on '"Xu, Qianyi"'

5. Seismic reliability evaluation of spatially correlated pipeline networks by quasi-Monte Carlo simulation.

Author

Hou, Benwei, Xu, Qianyi, Zhong, Zilan, Han, Junyan, Miao, Huiquan, and Du, Xiuli

Abstract

The reliability assessment of pipeline networks under nature disasters helps to improve the disaster prevention capability and overall resilience of pipeline networks. For the earthquake hazard, the seismic damage of pipelines in the network is usually correlated due to the spatial correlation of critical parameters, including characteristics of pipeline, soil properties and ground motions. This paper proposes a framework to evaluate the seismic reliability of pipeline network under spatially correlated parameters based on the quasi-Monte Carlo (QMC) simulation. The influence of spatial correlated random variables on the seismic reliability of pipeline network is investigated with explicit consideration of the correlated random variables including the peak ground velocity, the wall thickness and the yield stress of pipe segment. The framework has been implemented in seismic reliability evaluation of two pipeline networks. The result shows that the QMC method has better simulation accuracy than the standard MC method under same sampling numbers. The spatial correlations have different influences on the seismic connectivity reliability of pipeline networks with different topological redundancy and parallelism of connective paths from sources to user nodes. For the pipeline network with greater redundancy and parallelism, the model without considering the spatial correlations overestimates the network connectivity reliability. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Assessing base-resolution DNA mechanics on the genome scale

Author

Jiang, Wen-Jie, primary, Hu, Congcong, additional, Lai, Futing, additional, Pang, Weixiong, additional, Yi, Xinyao, additional, Xu, Qianyi, additional, Wang, Haojie, additional, Zhou, Jialu, additional, Zhu, Hanwen, additional, Zhong, Chunge, additional, Kuang, Zeyu, additional, Fan, Ruiqi, additional, Shen, Jing, additional, Zhou, Xiaorui, additional, Wang, Yu-Juan, additional, Wong, Catherine C L, additional, Zheng, Xiaoqi, additional, and Wu, Hua-Jun, additional

Published
2023
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10. HEVC Double Compression Detection Based on SN-PUPM Feature

Author

Xu, Qianyi, Sun, Tanfeng, Jiang, Xinghao, Dong, Yi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kraetzer, Christian, editor, Shi, Yun-Qing, editor, Dittmann, Jana, editor, and Kim, Hyoung Joong, editor

Published
2017
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18. Serum Spermidine in Relation to Risk of Stroke: A Multilevel Study

Author

Zheng, Liqiang, primary, Xie, Yanxia, additional, Sun, Zhaoqing, additional, Zhang, Rui, additional, Ma, Yanan, additional, Xu, Jiahui, additional, Zheng, Jia, additional, Xu, Qianyi, additional, Li, Zhao, additional, Guo, Xiaofan, additional, Sun, Guozhe, additional, Xing, Fuguo, additional, Sun, Yingxian, additional, and Wen, Deliang, additional

Published
2022
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22. Assessing base-resolution DNA mechanics on the genome scale

Author

Jiang, Wen-jie, primary, Hu, Congcong, additional, Yi, Xinyao, additional, Xu, Qianyi, additional, Lou, Tianai, additional, Wang, Haojie, additional, Zhou, Jialu, additional, Zhu, Hanwen, additional, Li, Yong, additional, Wen, Yilin, additional, Fan, Ruiqi, additional, Shen, Jing, additional, Wong, Catherine CL, additional, Zheng, Xiaoqi, additional, and Wu, Hua-Jun, additional

Published
2022
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23. Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

Author

Zhonghan Zhang, Jie Yan, Xu Qianyi, Jiang Huiming, Minghui Sun, and Guihe Qin

Subjects
Artificial neural network, business.industry, Computer science, Applied Mathematics, Dynamic data, Frame (networking), Feature extraction, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Gesture recognition, Softmax function, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Key frame, 020201 artificial intelligence & image processing, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

Published
2020

28. Mixpred: Video Prediction Beyond Optical Flow

Author

Liang Yanhua, Guihe Qin, Xu Qianyi, Jie Yan, and Rui Zhao

Subjects
General Computer Science, Computer science, business.industry, video prediction, spatial subnet, General Engineering, Optical flow, 02 engineering and technology, Video prediction, 010501 environmental sciences, 01 natural sciences, parallel interaction, 0202 electrical engineering, electronic engineering, information engineering, End-to-end training, 020201 artificial intelligence & image processing, General Materials Science, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, lcsh:TK1-9971, 0105 earth and related environmental sciences, temporal subnet
Abstract

Video prediction is a meaningful task for it has a wide range of application scenarios. And it is also a challenging task since it needs to learn the internal representation of a given video for both appearance and motion dynamics. The existing methods regard this problem as a spatiotemporal sequence forecasting problem and try to resolve it in a one-shot fashion, which causes the prediction result being blurry or inaccurate. So, a more intuitional thought is to split this problem into two parts: model the dynamic pattern of the given video and learn the appearance representation of given video frames. In this paper, we develop a novel network structure named MixPred based on this idea to address this issue. We divide the prediction problem into two parts as mentioned above and build two subnets to solve these two parts separately. Instead of fusing the results of subnets at the final layer, we put forward a parallel interaction style through the whole process to merge the dynamic information and content information in a more natural way. Besides, we propose three different connection methods for exploring the most effective connection structure. We trained the model on UCF-101 and KITTI, and testing our model on UCF-101, KITTI, and Caltech. The results demonstrate that our method achieves state-of-the-art both quantitatively and qualitatively.

Published
2019

30. Blood Pressure Changes in a Chinese Population Have a Greater Impact on Short-Term Outcomes Rather Than Long-Term Outcomes of Major Adverse Cardiovascular Events

Author

Xu, Qianyi, primary, Wang, Yali, additional, Xie, Yanxia, additional, Zheng, Jia, additional, Guo, Rongrong, additional, Dai, Yue, additional, Sun, Zhaoqing, additional, Xing, Liying, additional, Zhang, Xingang, additional, Ruan, Shikai, additional, Zheng, Liqiang, additional, and Sun, Yingxian, additional

Published
2020
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36. Blood Pressure Changes in a Chinese Population Have a Greater Impact on Short-Term Outcomes Rather Than Long-Term Outcomes of Major Adverse Cardiovascular Events.

Author

Xu, Qianyi, Wang, Yali, Xie, Yanxia, Zheng, Jia, Guo, Rongrong, Dai, Yue, Sun, Zhaoqing, Xing, Liying, Zhang, Xingang, Ruan, Shikai, Zheng, Liqiang, and Sun, Yingxian

Subjects
BLOOD pressure, HYPERTENSION, EVALUATION of medical care, CARDIOVASCULAR diseases risk factors, STROKE, MULTIVARIATE analysis, CARDIOVASCULAR diseases, FISHER exact test, MYOCARDIAL infarction, RESEARCH funding, DESCRIPTIVE statistics, ODDS ratio, ARRHYTHMIA, DATA analysis software, LONGITUDINAL method, PROPORTIONAL hazards models, PREHYPERTENSION
Abstract

The purpose of our study was to explore the association of blood pressure (BP) changes on short-and long-term outcomes of major adverse cardiovascular events (MACEs) in rural China. This study was designed to learn the effects of BP changes (2004-2008) on short-term (2008-2010, within 2 years of the initial examination) and long-term (2008-2017) outcomes of MACE, including 24 285 and 27 290 participants, respectively. In this study, 423 (short-term) and 1952 (long-term) MACEs were identified. For prehypertension to hypertension, the risk of long-term stroke was increased (hazard ratio [HR] = 1.18 [1.00-1.39]). For hypertension to prehypertension, the short-term MACE risk (0.65 [0.47-0.90]), short-term stroke risk (0.45 [0.26-0.76]), and long-term stroke risk (0.83 [0.70-0.99]) all decreased. Short-term outcomes conferred a stronger impact than long-term outcomes (Fisher Z test, measured as the difference of β coefficients, all P <.05). [ABSTRACT FROM AUTHOR]

Published
2021
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