网络防火墙系统的实施方案

介绍一种防火墙的有效实施方案。该方案是采用C和JAVA两种程序设计语言来实现的，能够有效的对进出网络的数据进行监听和控制。     

机床热误差的无限冲激响应网络动态模型

本文阐述了数控机床热误差补偿技术的基本概念，提出了一种基于无限冲激响应（ⅡR）网络的数控机床热误差预报模型。讨论了该模型的建立及相关技术问题，对智能预报补偿系统进行了研究，并给出了智能预报的结果和精度评价。     

Forecasting of DST variations from polar cap indices using neural networks

The 15-min averaged polar cap (PC) index was used as an input parameter for the Dst variation forecasting. The PC index is known to describe well the principal features of the solar wind as well as the total energy input to the magnetosphere. This allowed us to design a neural network able to forecast the Dst variations from 1 to 4 h ahead. 1998 PC and Dst data sets were used for training and testing and 1997 data sets was used for validation proposes. From the 15 moderate and strong geomagnetic storms observed during 1997, nine were successfully forecasted. In three cases the observed minimum Dst value was less than the predicted one, and only in three cases the neural network was not able to reproduce the features of the geomagnetic storm.     

基于旋转矩阵的预设时间航天器编队姿态协同控制

由多个航天器组成的编队系统对复杂的环境往往具有较高的适应性和容错性,能更高效率地完成单航天器难以完成的任务。因此主要针对多航天器系统的姿态协同控制问题,提出一种基于旋转矩阵的预设时间控制算法。首先,为了避免航天器姿态建模的奇异性和模糊性问题,采用旋转矩阵对航天器的姿态进行统一描述,同时结合有向的通信拓扑对航天器姿态协同控制系统进行建模。其次,为赋予系统可控的收敛速度,提出一种基于滑模的预设时间控制算法。该算法的引入使得航天器编队系统的收敛时间可以在合理的范围内任意给定。此外,为了实现系统对参数摄动和外部干扰的鲁棒性,采用神经网络和自适应算法对不确定性进行在线估计与补偿。最后,通过理论分析和数值仿真验证了所提预设时间控制算法的有效性。     

低轨巨型星座网络容量评估与分析

低轨卫星网络可以提供广覆盖、低时延、大容量的通信服务和随遇接入的网络服务,可有效弥补地面通信网络和高轨卫星网络的不足。近年来“星链”“一网”等星座项目推动了低轨巨型星座的飞速发展。卫星网络容量是网络性能评价的重要指标,传统方法计算复杂度高、耗时长,且计算开销随网络节点数迅速增加,在巨型星座网络评估中产生巨大计算开销。面向低轨巨型星座网络容量评估问题,提出了一种基于最大流的网络容量评估方法,与传统方法相比计算复杂度和计算耗时大大降低。以“星链”星座为例,分析了星座构型、星间链路拓扑连接方式、链路容量及用户需求量对巨型星座网络容量的影响。仿真分析结果对未来低轨巨型星座网络建设具有指导意义。     

Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network

The variations in gas path parameter deviations can fully reflect the healthy state of aero-engine gas path components and units; therefore, airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis. In the past, the airlines could not obtain deviations autonomously. At present, a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations. However, it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks. To obtain monitoring autonomy of each aero-engine model, it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models. This paper adopts the Residual-Back Propagation Neural Network (Res-BPNN) to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy (MK-MMD) adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space (RKHS) for discrepancy measurement. To further reduce the distribution discrepancy of each aero-engine model, the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible, and then the learned features can be confused. Through the above methods, domain-invariant features can be extracted, and the optimal adaptation effect can be achieved. Finally, the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.     

三维对流项在CVFEM中的一种质量加权迎风方案

对三维对流项在四面体单元上建立了一种各结点控制体相一致的一种质量加权迎风方案,它既保留了质量加权方案对大的Ｐｅｃｌｅｔ数及非结构式几何域的适用性,又简化了计算机程序的实现过程。     

故障诊断的神经网络多重模型自适应方法

将神经网络与故障诊断的多重模型自适应方法相结合,提出了故障诊断的神经网络多重模型自适应方法,并对某型航空发动机控制系统传感器故障进行诊断仿真。仿真表明,该方法能够用来解决具有模型不确定性系统的故障诊断问题,同时,对未知的故障模态具有自学习能力     

导航星座整网自主定轨的动力学短弧段伪逆平差法

针对传统静态自由网平差进行自主定轨存在的秩亏问题,提出一种卫星动力学短弧段定轨和自由网伪逆平差理论相结合的自主定轨算法。该算法以导航星座星间链路(ISL)双向测距为基础,利用伪逆平差法,解决整网位置解算起算数据不足带来的秩亏问题;同时,利用轨道动力学建立不同历元之间状态转移关系,解决单历元平差无法对速度分量进行有效估计的问题。仿真结果表明,自主运行90天,平均用户测距精度（URE）小于1 m（2σ）,优于导航星座自主运行指标要求,验证了本文算法的有效性。     

对失控翻滚目标逼近的神经网络自适应滑模控制

针对失控航天器在空间中自由翻滚的情况,研究追踪器对失控翻滚目标逼近的位置和姿态六自由度耦合控制问题。建立追踪器与目标器相对运动的姿轨一体化动力学模型,设计追踪器逼近过程的标称轨迹和标称姿态。综合考虑系统不确定性和外部干扰,设计无抖振的神经网络自适应滑模控制器。将滑模控制与神经网络逼近相结合,采用径向基函数(RBF)神经网络对系统未知部分进行自适应逼近。由Lyapunov方法导出神经网络自适应律,通过自适应权重的调节保证整个闭环系统的稳定性。数值模拟实例说明了所设计的标称轨迹和标称姿态的合理性,同时验证了神经网络自适应滑模控制器的有效性。