An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments

Hu, Hao; Wang, Jiayue; Ai, Chen; Liu, Yang

doi:10.1016/j.net.2022.09.010

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Furthermore, neural networks can be leveraged for parameter optimization, a critical aspect of fusion energy research involving the optimization of numerous parameters [9][10][11]. Neural networks, such as deep reinforcement learning methods, can automatically adjust experimental or simulation parameters to identify optimal parameter combinations [12][13][14][15][16][17][18]. Additionally, neural networks are instrumental in establishing predictive models to forecast the reaction process and outcomes of fusion energy.…”

Section: Neural Network and Nuclear Fusionmentioning

confidence: 99%

Static performance prediction of long-pulse negative ion based neutral beam injection experiment

Li,

Hu,

Zhao

et al. 2024

Plasma Phys. Control. Fusion

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The mission of Negative Ion-Based Neutral Beam Injection (NNBI) is to conduct experiments with pulses lasting thousands of seconds. It is crucial to develop a simplified physical calculation model for the long-pulse negative ion source in the current NNBI device. This model will be used to evaluate the advantages and disadvantages of the selected parameters prior to the experiment, and to assist in adjusting and establishing the experimental parameters for the long-pulse ion source experiment. This paper presents the development of a static performance prediction model using a Back Propagation (BP) neural network. The model assesses the yield of negative hydrogen ions and the quantity of electrons in the ion source under specific parameter conditions, utilizing various experimental parameters as input. The experimental data used for this model are derived from historical data generated during the operation of the 2022 NNBI experiment. The test results indicate that under the current optimal hyperparameter condition, the prediction accuracy of I_H- is 80.84%, and the prediction accuracy of I_EG is 77.57%. This can effectively prevent invalid shots, accurately assess the advantages and disadvantages of the input parameters, and enhance the performance of the long-pulse NNBI device.

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Section: Neural Network and Nuclear Fusionmentioning

confidence: 99%

Static performance prediction of long-pulse negative ion based neutral beam injection experiment

Li,

Hu,

Zhao

et al. 2024

Plasma Phys. Control. Fusion

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show abstract

“…In [ 50 ], a two-tier approach that consists of exploration and localization was proposed. The former seeks to efficiently gather information, while the latter uses the information to determine the quickest path to the source.…”

Section: Literature Reviewmentioning

confidence: 99%

UAV Detection Using Reinforcement Learning

AlKhonaini,

Sheltami,

Mahmoud

et al. 2024

Sensors

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Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

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An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments

Cited by 2 publications

References 18 publications

Static performance prediction of long-pulse negative ion based neutral beam injection experiment

Static performance prediction of long-pulse negative ion based neutral beam injection experiment

UAV Detection Using Reinforcement Learning

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