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653 results on '"Zhao, Yingjie"'

5. Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

Author

Zhao, Yingjie, Liu, Yong, and Xu, Zhiping

Subjects
Computer Science - Machine Learning
Abstract

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.

Published
2023
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6. Research on hydraulic control system for shield type temporary support robot driving under pressure

Author

MA Hongwei, LI Lang, XUE Xusheng, WANG Chuanwei, WANG Saisai, ZHAO Yingjie, ZHOU Wenjian, and ZHANG Heng

Subjects
shield type temporary support robot, hydraulic control system, driving under pressure, support force control, driving displacement control, fuzzy pid control, Mining engineering. Metallurgy, TN1-997
Abstract

The shield type temporary support robot is an important component of the intelligent excavation robot system for large section roadways that adapts to the coexistence of dirt and debris. Its main function is to provide a safe and reliable workspace for achieving 'parallel excavation and support' operations. In order to enhance the safe and stable support of the shield type temporary support robot for surrounding rock during its pushing and driving process, based on the structure, working environment, and operational requirements of the shield type temporary support robot, a mathematical model of its pushing amount and support force during pressurized driving, as well as a dynamic model of pressurized driving, are established. A hydraulic control system for the shield type temporary support robot driving under pressure is designed. The system mainly consists of a support hydraulic system and a driving hydraulic system. During static support, the support hydraulic system needs to constantly output a support force greater than the weight of the upper shield body itself, and the driving hydraulic system is in standby mode. When driving under pressure, the support hydraulic system and the driving hydraulic system work simultaneously, ensuring that the temporary support robot 'reducing stress without leaving the roof' while steadily moving forward with the roof under pressure at all times. A precise control method for shield type temporary support robot driving under pressure based on fuzzy PID is proposed. The pressure and displacement signals of the temporary support robot are collected in real time by displacement sensors integrated on the displacement cylinder and pressure sensors in the hydraulic circuit. The signals are used to reflect the changes in support force and driving displacement during the temporary support robot's driving under pressure. Based on the error and error rate of the support force and displacement, the fuzzy PID algorithm is used to modify the control parameters of the support force and displacement, achieving reliable control of driving under pressure based on the fuzzy PID algorithm. Both simulation and experimental results show that the effect of fuzzy PID control is superior to traditional PID control. Under fuzzy PID control, the relative error of support force during the pushing and driving process of the shield type temporary support robot is less than 1%, and the driving displacement error is less than 2 mm. Moreover, the response speed of support force and pushing amount control is fast, ensuring the safe and stable support of the surrounding rock during the pushing and driving process.

Published
2024
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10. Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS

Author

Zhao, Yingjie, Wang, Yujue, Shi, Lijie, McDonald-McGinn, Donna M., Crowley, T. Blaine, McGinn, Daniel E., Tran, Oanh T., Miller, Daniella, Lin, Jhih-Rong, Zackai, Elaine, Johnston, H. Richard, Chow, Eva W. C., Vorstman, Jacob A. S., Vingerhoets, Claudia, van Amelsvoort, Therese, Gothelf, Doron, Swillen, Ann, Breckpot, Jeroen, Vermeesch, Joris R., Eliez, Stephan, Schneider, Maude, van den Bree, Marianne B. M., Owen, Michael J., Kates, Wendy R., Repetto, Gabriela M., Shashi, Vandana, Schoch, Kelly, Bearden, Carrie E., Digilio, M. Cristina, Unolt, Marta, Putotto, Carolina, Marino, Bruno, Pontillo, Maria, Armando, Marco, Vicari, Stefano, Angkustsiri, Kathleen, Campbell, Linda, Busa, Tiffany, Heine-Suñer, Damian, Murphy, Kieran C., Murphy, Declan, García-Miñaúr, Sixto, Fernández, Luis, Zhang, Zhengdong D., Goldmuntz, Elizabeth, Gur, Raquel E., Emanuel, Beverly S., Zheng, Deyou, Marshall, Christian R., Bassett, Anne S., Wang, Tao, and Morrow, Bernice E.

Published
2023
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29. An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Author

Huang, Jianping, Wang, Danfeng, Zhu, Yongguan, Yang, Zifeng, Yao, Maosheng, Shi, Xiaoming, An, Taicheng, Zhang, Qiang, Huang, Cunrui, Bi, Xinhui, Li, Jiang, Wang, Zifa, Liu, Yongqin, Zhu, Guibing, Chen, Siyu, Hang, Jian, Qiu, Xinghua, Deng, Weiwei, Tian, Huaiyu, Zhang, Tengfei, Chen, Tianmu, Liu, Sijin, Lian, Xinbo, Chen, Bin, Zhang, Beidou, Zhao, Yingjie, Wang, Rui, and Li, Han

Published
2024
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36. Rare coding variants as risk modifiers of the 22q11.2 deletion implicate postnatal cortical development in syndromic schizophrenia

Author

Lin, Jhih-Rong, Zhao, Yingjie, Jabalameli, M. Reza, Nguyen, Nha, Mitra, Joydeep, Swillen, Ann, Vorstman, Jacob A. S., Chow, Eva W. C., van den Bree, Marianne, Emanuel, Beverly S., Vermeesch, Joris R., Owen, Michael J., Williams, Nigel M., Bassett, Anne S., McDonald-McGinn, Donna M., Gur, Raquel E., Bearden, Carrie E., Morrow, Bernice E., Lachman, Herbert M., and Zhang, Zhengdong D.

Published
2023
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44. Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

Author

Xia, HanQing, Liu, XiaoMing, Ma, ZeChao, Zhu, Fang, Zhang, Lin, Zhao, YingJie, and Wang, YuanRong

Subjects
Sensors, Transportation industry
Abstract

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates., Author(s): HanQing Xia (corresponding author) [1,2]; XiaoMing Liu [1,2]; ZeChao Ma [1,2]; Fang Zhu [1]; Lin Zhang [1]; YingJie Zhao [1]; YuanRong Wang [3] 1. Introduction Vehicle trajectory information accurately [...]

Published
2023
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