Výsledky vyhledávání

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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

Autor: Xie, Kan, Zhang, Zhe, Li, Bo, Kang, Jiawen, Niyato, Dusit, Xie, Shengli, Wu, Yi

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-

Externí odkaz: http://arxiv.org/abs/2205.14315

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FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

Autor: Huang, Xumin, Li, Peichun, Yu, Rong, Wu, Yuan, Xie, Kan, Xie, Shengli

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking

Externí odkaz: http://arxiv.org/abs/2110.12876

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Akademický článek

Learning the continuous-time optimal decision law from discrete-time rewards

Autor: Chen Ci, Xie Lihua, Xie Kan, Lewis Frank Leroy, Liu Yilu, Xie Shengli

Publikováno v: National Science Open, Vol 3 (2024)

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge

Externí odkaz: https://doaj.org/article/fb870eb2ae62417a8c52317062af4a48

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Discrete Boltzmann Modeling of Plasma Shock Wave

Autor: Liu, Zhipeng, Song, Jiahui, Xu, Aiguo, Zhang, Yudong, Xie, Kan

Publikováno v: Proc IMechE Part C: J Mechanical Engineering Science 2023,237(11): 2532-2548

Plasma shock waves widely exist and play an important role in high-energy-density environment, especially in the inertial confinement fusion. Due to the large gradient of macroscopic physical quantities and the coupled thermal, electrical, magnetic a

Externí odkaz: http://arxiv.org/abs/2108.10590

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Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems

Autor: Chen, Ci, Xie, Lihua, Jiang, Yi, Xie, Kan, Xie, Shengli

In this paper, we investigate the optimal output tracking problem for linear discrete-time systems with unknown dynamics using reinforcement learning and robust output regulation theory. This output tracking problem only allows to utilize the outputs

Externí odkaz: http://arxiv.org/abs/2101.08706

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