对传感器失效具有完整性的神经网络控制器设计

本文研究了对一些传感器失效控制系统仍保持稳定性的问题，将多层前传神经网络用于这一问题，通过对神经网络的训练，使训练后的神经网络构成的控制器对传感器故障具有完整性。该方法对达到完整性设计的要求条件较低，且不受线性系统方法的限制。文末的仿真实例说明了所给方法的有效性。     

Research of multi-path routing based on network coding in space information networks

A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks. On the basis of traditional multi-path routing, the algorithm uses a random linear network coding strategy to code data pack- ets. Code number is determined by the next hop link status and the number of current received packets sent by the upstream node together. The algorithm improves retransmission and cache mechanisms through using redundancy caused by network coding. Meanwhile, the algorithm also adopts the flow distribution strategy based on time delay to balance network load. Simulation results show that the proposed routing algorithm can effectively improve packet delivery rate, reduce packet delay, and enhance network performance.     

无人机中继平台覆盖区域统计模型简

 针对中继信号覆盖区域确定性模型没有考虑信道随机衰落影响的问题,提出了一种基于中断概率的无人机（UAVs）中继平台信号覆盖半径统计模型。通过将中继链路建模为包含路径损耗、阴影衰落和多径衰落的复合分布模型,推导获得中继信号覆盖半径的概率分布,并给出一种利用中断概率数值求解覆盖半径的方法。仿真结果表明,信道衰落对覆盖半径影响非常大,当中断概率小于10%时,覆盖半径仅为无衰落信道时的一半。研究结果对移动自组织网络（MANETs）中的中继网络的无人机最优布置、飞行策略以及网络性能评估等具有重要的参考价值。     

基于非线性神经网络的摆式列车检测系统动态补偿方法研究

针对零极点匹配动态补偿方法的不足 ,提出基于非线性神经网络的摆式列车检测系统动态补偿方法。根据陀螺平台实测超高数据 ,采用LM算法对所设计的非线性神经网络进行训练。仿真结果表明 ,所设计的神经网络动态补偿器能有效改善陀螺平台系统的动态特性 ,满足摆式列车倾摆控制实时性的要求。     

航天用时间触发网络系统同步性的故障分析

确定性网络的同步性在航天电子系统中至关重要。为保证确定性网络在发生故障的情况下也能正常地进行时间同步,采用spaceVPX标准中典型的双星拓扑交换结构,配置基于时间触发以太网协议的可容错系统。针对此架构,对使用以太网的航天系统中发生故障的单容错结构进行了仿真,并模拟了几种时间同步协议控制帧异常的情况：协议控制帧包括冷启动帧、冷启动应答帧、集成帧,从故障交换机的端口发送到网络中的一个终端系统后,对于时间触发网络的同步网络所产生的影响。仿真结果表明,故障交换机会逐渐延长处于正常工作的终端系统的启动时间,不利于整个确定网络系统的同步过程,但是,如果设置不同协议控制帧的周期超过一定的数值后,单点故障不会影响双冗余系统的启动时间。     

一种结合深度学习的运动去模糊视觉SLAM方法

针对高动态环境下视觉同步定位与地图构建(SLAM)系统的可靠性受运动模糊的限制,研究了一种基于生成对抗网络(GAN)和AKAZE特征点的运动去模糊视觉SLAM方案。首先,对因相机快速运动而产生的模糊图像进行AKAZE特征点的提取与检测,并根据特征点分布的丰富程度计算图像块权重,结合灰度图像的方差信息建立特征点与模糊程度之间的量化关系表;然后,将达到模糊分数阈值的图像同步输入至改进GAN模型,该网络以端对端的形式恢复中心模糊帧的纹理信息;最后,对输出的清晰图像重新进行位姿估计,以参与ORB-SLAM2后端优化过程。在公开数据集TUM上进行测试,对于受模糊影响较严重的数据集,该方案可以显著降低相机轨迹估计的整体误差,同时维持较好的鲁棒性。     

Force measurement using strain-gauge balance in shock tunnel based on deep learning

When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.     

基于神经网络的预膜雾化模型

为了实现对预膜雾化质量的快速预估，基于神经网络建立了以结构参数和工况参数为输入,SMD值为输出的预膜雾化模型。使用试验数据训练和测试网络模型,结果显示,模型在较宽的工作范围内具有较高的精度和较好的泛化能力。SMD预测值随输入参数的变化规律符合试验结论,可作为给定工况参数下预膜式雾化装置的结构参数优化依据。神经网络能够较好地学习预膜雾化过程中的隐含规律。随着供油压差的增大,预膜器逐渐出现阻雾化效应,导致其雾化效果比单喷嘴和直混式雾化均差。     

考虑移动目标不确定行为方式的轨迹预测方法

针对现有方法难以预测出符合飞行移动目标不确定行为方式轨迹的问题,提出基于逆强化学习的飞行移动目标轨迹预测方法,通过学习目标行为偏好以及模拟目标行为决策过程的方式预测目标的移动轨迹。首先基于深度神经网络建立目标的行为决策模型与行为偏好模型,然后通过最大熵逆强化学习方法交替地学习模型参数。为了有效地学习目标的不确定行为特征,采用监督学习的方法学习出目标示例轨迹概率分布模型,用于指导目标行为偏好模型的训练以及初始化目标行为决策模型,同时通过对目标行为偏好模型进行预训练的方式提高其训练质量。仿真结果表明,提出的飞行移动目标轨迹预测方法可通过学习到的目标行为决策模型较为准确地模拟目标的行为方式,预测的目标轨迹分布与真实的目标轨迹分布在Kullback Leibler(KL)散度下的相似度可达0.24。     

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew’s situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions, especially aircraft icing conditions. The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed, commanded bank angle, and commanded vertical velocity) is developed to provide the crew with practi...