Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment. To solve this problem, we propose a deep reinforcement learning based and maximum-entropy-based soft actor-critic (SAC) algorithm. In the proposed algorithm, we add an improved Wolpertinger architecture to the original SAC algorithm in order to reduce the number of interactions and improve the accuracy of the algorithm. The results show that the proposed algorithm shows excellent performance in various scenarios of jamming and achieves accurate, fast, and continuous jamming for both sides of the communication.

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.

In disaster scenarios, unmanned aerial vehicle (UAV) rescue path search missions have become an increasingly critical issue. This paper, proposes a Double Deep Q-Network State Splitting Q-Network (DDQN-SSQN) path planning algorithm, which planning rescue route guiding UAV to maximize the detection of ground-trapped devices by analyzing variations of communication signal strengths. However, in the post-disaster environment, communication signal strengths are vulnerable disturbed that severely compromise DDQN-SSQN reliability. To address this problem, in the first stage, we propose a novel State-and-Reward Joint Attack method (Adv-SR) that simulates electromagnetic perturbation to communication signal strengths while incorporating an action detection model to identify algorithmic vulnerabilities, realize attacks on both state space and reward space of DDQN-SSQN. In the second stage, to enhance the robustness of DDQN-SSQN against electromagnetic perturbation attacks, we propose a State-and-Reward Joint Defense method (Def-SR), using dual prediction networks to restore disturbed space. Simulation results demonstrate that compared with traditional attack methods, Adv-SR reduces the task success rate to 12.6% while decreasing attack frequency to 16.67%. Moreover, Def-SR significantly improves the robustness of DDQN-SSQN, restoring the task success rate to 98.4% under attack, showing superior performance compared to single-state or single-reward defense methods. Moreover, Def-SR significantly improves the robustness of DDQN-SSQN, restoring task success rates to 98.4%, showing superior performance compared to single-state or reward defense methods.