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Dong Sui, Chenyu Ma and Chunjie Wei
To assist air traffic controllers (ATCOs) in resolving tactical conflicts, this paper proposes a conflict detection and resolution mechanism for handling continuous traffic flow by adopting finite discrete actions to resolve conflicts. The tactical confl...
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Hui Xiang, Guoce Zhang, Po Cheng, Jun Hu, Zhixin Wang and Dongling Zeng
There are stringent requirements on the vertical movement of a ground surface when using artificial ground freezing method to reinforce a shield tunnel in a city. This paper focused on the tunnel of Nanjing Metro Line Two between Yixianqiao and Daxinggon...
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Shao Xuan Seah and Sutthiphong Srigrarom
This paper explores the use of deep reinforcement learning in solving the multi-agent aircraft traffic planning (individual paths) and collision avoidance problem for a multiple UAS, such as that for a cargo drone network. Specifically, the Deep Q-Networ...
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Khuong Tran, Maxwell Standen, Junae Kim, David Bowman, Toby Richer, Ashlesha Akella and Chin-Teng Lin
Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that res...
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Jue Ma, Dejun Ning, Chengyi Zhang and Shipeng Liu
Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to mea...
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