Searched for: author%3A%22Dong%2C+Y.%22
(1 - 7 of 7)
document
Xue, Chaozhong (author), Dong, Y. (author), Liu, Jiaqi (author), Liao, Yijun (author), Li, Lingbo (author)
Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste...
conference paper 2023
document
Zhang, Li (author), Dong, Y. (author), Farah, H. (author), van Arem, B. (author)
The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs' deployment and safe driving under various maneuvers. Previous research mostly focuses on...
conference paper 2023
document
Yuan, Henan (author), Li, Penghui (author), van Arem, B. (author), Kang, Liujiang (author), Farah, H. (author), Dong, Y. (author)
Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving...
conference paper 2023
document
Dong, Y. (author), Datema, T. (author), Wassenaar, V. (author), van de Weg, J.J. (author), Kopar, C.T. (author), Suleman, H.I. (author)
Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging maneuvers. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment,...
conference paper 2023
document
Dong, Y. (author), Chen, Kejia (author), Ma, Zhiyuan (author)
Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially...
conference paper 2023
document
Xue, Chaozhong (author), Dong, Y. (author), Liu, Jiaqi (author), Liao, Yijun (author), Li, Lingbo (author)
With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges,...
conference paper 2023
document
Dong, Y. (author), Chen, Kejia (author), Peng, Yinxuan (author), Ma, Zhiyuan (author)
As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security...
conference paper 2022
Searched for: author%3A%22Dong%2C+Y.%22
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