In response to the problems of long training time,slow convergence,and poor performance in some complex situa-tions in deep reinforcement learning in dynamic environments,a combination of multi-step bootstrap,perturbation fluid algorithm(IFDS),and deep deterministic policy gradient algorithm(DDPG)is proposed.Firstly,N-step Bootstrap is added to DDPG to en-dow the model with the ability to combine multiple future time steps.Secondly,the perturbation fluid algorithm is introduced to joint-ly construct a potential field with the velocity information of obstacles in the environment,solving the problem of high-dimensional continuous action space and improving training efficiency.Finally,an environment consisting of a single obstacle and multiple obsta-cles is constructed to simulate and validate the algorithm.The simulation results show that the improved DDPG algorithm has higher training stability and speed compared to traditional DDPG algorithms in the simulation environment,and it can successfully achieve dynamic obstacle avoidance in more complex environments.At the same time,the success rate of training is improved.
The calculation of target distribution area is a prerequisite for anti-ship missile tactical decision-making and target acquisition probability calculation. On the basis of studying the target pursuit model of anti-ship missile, based on the relaxation variable method, the calculation equation for the target distribution is established, sufficient and necessary conditions are proved, the analytical model of target distribution area is obtained. Under the assumption that the target movement direction is arbitrary, and the calculation form under general conditions is given. Through the simulation example, the variation of distribution area under different conditions is compared, which can provide intuitive reference and estimation basis for the decision-making and use of the battle commanders.
In order to improve the professionalism and accuracy of machine translation in the field of notice to air navigation,a two-way machine translation model of notice to air navigation based on transfer learning and back translation is proposed to solve the problem of the parallel corpus of machine translation in the field of air notice.On the basis of training the machine translation of People's Daily,the parameters obtained from the training are transferred to the encoding end and the decoding end of the machine translation model of the partly back-translated announcements to initialize the parameters at both ends,and adjust the parameters at both ends through experiments.The two-way machine translation model of the notice of air travel is obtained.Experiments show that the introduction of the data enhancement strategy of transfer learning and partial back translation improves the robustness of the mod-el and improves the translation quality.The BLEU value of the translation evaluation index of the Chinese-to-English model has in-creased by 2.08%.The BLEU value of the translation evaluation index increased by 3.12%.
ECG is one of the most effective medical tests for heart disease diagnosis, and R-peak detection is the first step in ECG interpretation. For wearable ECG signals, the difficulty of R-peak detection mainly lies in the interference of dynamic strong noise, and the limited hardware computational resources limit the use of some complex algorithms. Therefore, an adaptive threshold R-peak detection algorithm based on Brown's exponential smoothing model is proposed in this paper. The algorithm selects features based on the morphological characteristics and occurrence law of R-peaks, updates the threshold parameters using the Brown exponential smoothing model, optimizes the smoothing coefficients in it using the relative error least squares method, corrects the smoothing ability of the observation error and the response speed to the change of the observation, so that the updated threshold can be more consistent with the R-peak detection. Finally the algorithm achieves 99.6% precision, 99.7% recall and 99.65% F1 score on the self-constructed ECG dataset, and compares with other R-peak detection algorithms to determine the superiority of the proposed algorithm in some performance metrics. It is demonstrated experimentally that the algorithm can adapt well to the strong noise environment and obtain satisfactory R-peak detection accuracy.
无人潜航器协同作战效能评估对于提升潜航器自身作战效能有着重要意义,针对无人潜航器的作战编队,基于DoDAF构建无人潜航器协同作战体系框架,分析作战活动与能力之间的关系,建立协同作战能力指标体系;对于不同类型的指标进行差异化处理,结合ADC模型,考虑综合性指标,减少单项指标的局限性的影响;以对海作战背景下的无人潜航器协同作战为场景,对不同作战方案进行效能评估和计算验证其有效性.
