某型航空发动机涡轮多级气动优化设计

采用将准三维设计和多级局部优化联合的多级涡轮气动优化设计流程,对某型航空发动机3级涡轮进行了多级气动三维优化设计;采用人工神经网络和遗传算法对各列叶栅进行了三维局部优化,流场计算采用全三维黏性流N-S方程求解。通过优化设计,调整了功率分配,改善了各列性能,并对各列间参数进行了优化匹配,使总体性能提高,达到了设计要求。     

不确定性非线性系统的自适应输出反馈控制

研究了一种不确定性非线性系统的自适应输出反馈控制方法.首先引入伪控制信号使系统反馈线性化,然后设计一个线性动态补偿器和在线神经网络,自适应消除不确定性和建模所引起的误差.仿真结果表明,该控制方法能够消除系统的稳态误差,提高系统动态性能,并且具有较好的鲁棒性.     

基于小波包变换的模拟电路电源电流故障诊断

针对模拟电路故障诊断这一难点,运用基于IDDT技术的故障诊断方法,即利用小波包变换提取电源电流各频率成分的能量,作为神经网络的输入特征矢量进行故障诊断。仿真结果表明,该方法可以快速高效地进行模拟电路故障诊断与定位。     

Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is significant for spacecraft safety. In this study, using GOES satellite data during 2011–2020, we propose two neural network models with two hidden layers to predict geosynchronous relativistic electron fluxes at two energy channels (>0.8 MeV and > 2 MeV). The number of input neurons of the two channels (>0.8 MeV and > 2 MeV) are determined to be 36 and 44, respectively. The > 0.8 MeV model has 22 and 9 neurons in the hidden layers, while the > 2 MeV model has 25 and 15 neurons in the hidden layers. The input parameters include the north–south component of the interplanetary magnetic field, solar wind speed, solar wind dynamic pressure and solar wind proton density. Through the analysis of different time delays, we determine that the optimal time delays of two energy channels (>0.8 MeV and > 2 MeV) are 8 days and 10 days, respectively. The training set and validation set (Jan 2011-Dec 2018) are divided by the 10-fold cross-validation method, and the remaining data (Jan 2019-Feb 2020) is used to analyze the model performance as a test set. The prediction results of both energy channels show good agreement with satellite observations indicated by low RMSE (～0.3 cm^-2sr^-1s^?1), high PE (～0.8) and CC (～0.9). These results suggest that only using solar wind parameters is capable of obtaining reasonable predictions of geosynchronous relativistic electron fluxes.     

基于扩展Petri网的飞机装配系统设备调度方法

针对飞机装配过程中设备调度的复杂性,提出了基于扩展Petri网构建设备调度模型的新方法。结合飞机装配的实际特点,论述了模型定义过程和建立方法。该模型以面向对象Petri网表达飞机装配过程,以赋时Petri网将装配过程与辅助设备相关联,利用混合算法以时间和设备利用率为目标对模型进行优化求解。以某模型飞机中机身装配过程为例,应用该方法进行建模并求解,实例应用与分析表明该方法对实际装配设备调度具有较大的指导意义。     

一种卷积神经网络非合作目标姿态测量方法

针对空间非合作目标姿态测量问题，提出一种基于卷积神经网络的非合作目标姿态视觉测量方法。该方法先设计特征提取网络并利用公开数据集进行预训练，用少量实际目标图像进行迁移学习，实现非合作目标图像高层抽象特征的自动提取；再设计基于回归模型的姿态映射网络，建立图像高层特征与三轴姿态角之间的非线性关系，实现非合作目标的姿态测量。实验验证了两类特征提取网络测量精度和参数量大小，测量精度可达 0.711° (1σ),表明了“单目相机+卷积神经网络”方法的可行性。     

卫星融合交换系统负载均衡异构路径算法

卫星交换系统中的光与分组两种异构业务需要进行融合交换，为了合理利用卫星异构交换系统中的异构路径资源，需要对异构交换路径采用一体化生成方法。基于卫星异构融合交换结构模型，定义了异构路径影响因子，用以定量衡量异构路径对整体交换性能的影响，并针对卫星交换结构特点，采用交换系统子路径个数以及路径转换参数作为约束条件，通过构建异构路径资源权值函数，准确评估卫星异构交换系统中路径的负载均衡能力。研究了在异构交换系统中采用不同算法对于网络负载均衡度和路径拥塞概率指标的影响，仿真结果表明，与传统路由与波长分配算法相比，该算法可以在特定网络业务负载下将系统平均负载平衡度降低约32%，平均路径阻塞概率降低约51%。     

利用BP神经网络对卫星无热敏设备温度的估测

卫星上测温资源有限，只有部分设备有测温点，难以准确获得其他无测温点设备的温度。基于反向传播（BP）神经网络对复杂非线性系统优秀的拟合能力，建立了估测卫星上无测温点设备温度的神经网络，以在轨有测温点设备温度为输入层，以在轨无测温点设备为输出层，并使用卫星热试验获得的星上温度遥测数据和在轨无测温点设备的热电偶温度数据进行训练和测试。测试结果表明，所建立的神经网络估测精度在1℃以内，可以用来精确估测卫星无测温点设备的温度。针对学习样本对估测误差之间关系进行了研究，计算表明，学习样本的多样性和大数据量能够显著减小估测误差。     

基于马氏距离的雷达网有源假目标干扰鉴别技术

针对雷达网有源假目标干扰问题,为了消除长基线雷达组网时地球曲率对数据处理的影响,通过坐标转换,将各雷达站的目标的量测数据转化到了地心直角(ECEF)坐标系内,得到统一坐标系下的目标量测信息,完成空间上的对准;在此基础上,基于虚假目标的量测与雷达站位置有关,真实目标量测与雷达站无关,因而依据卡方分布的性质,在ECEF坐标系内对各个量测进行卡方检验,鉴别出虚假目标;最后,通过仿真验证了分析的正确性,该算法与工程实际接轨,实用性较强。     

Automatic landing system using neural networks and radio-technical subsystems

The paper focuses on the design of a new automatic landing system(ALS) in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral(PI) controller and the instrumental landing system– the first phase of landing(the glide slope) and a proportional-integral-derivative(PID) controller together with a radio-altimeter – the second phase of landing(the flare); both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging(PCH) block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.