基于SAN构建高可用实时存储系统

介绍SAN网络存储技术及其拓扑结构,针对某飞控中心数据存储系统的实际需求,设计和实现一个SAN存储结构上的高可用实时可扩展集群存储方案,并详细描述通过软、硬件结合实现冗余数据路径、故障切换、实时存储和查询的方法。     

Performance improvement of licklider transmission protocol in complex deep space networks based on parameter optimization

A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol (LTP). Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol (BP). However, the research results are not applicable to communications in Complex Deep Space Networks (CDSNs) without the custody mechanism of BP that are more suitable for deep space communications with LTP. In this paper, we propose a model of file delivery time for LTP in CDSNs. Based on the model, we propose a Parameter Optimization Design Algorithm for LTP (LTP-PODA) of configuring reasonable parameters for LTP. The results show that the accuracy of the proposed model is at least 6.47% higher than that of the previously established models based on simple scenarios, and the proposed model is more suitable for CDSNs. Moreover, the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan. Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77% compared with configuring the other parameter configuration plans.     

运载火箭遥测数据智能判读算法应用

在运载火箭高发射密度、高判读需求、高数据量的背景下，现有自动化判读的判据覆盖率不全、判据编写门槛高、耗时多的问题日益凸显，缺少较通用的算法对传统判读算法未覆盖的判读任务进行判读补充，进而影响运载火箭效果评估与系统性能评定。为充分挖掘海量遥测数据中隐含的参数变化规律，设计智能判读算法作为传统算法的有益补充，提升传统判读的判读覆盖率和判读效率。以液体运载火箭长期加电试验产生的遥测数据为研究对象，设计集成神经网络智能判读算法，在给出的判读指标下研究得出，集成神经网络在频率异常、丢帧等五种现有判据难以描述的判读场景下，判读性能提升30%，提高了现有判据的覆盖率，后续可为判读体系完善和智能判读落地提供研究参考。     

Cooperative fault detection and recovery in the GNSS positioning of mobile agent swarms based on relative distance measurements

Relative measurements are exploited to cooperatively detect and recover faults in the positioning of Mobile Agent(MA) Swarms(MASs). First, a network vertex fault detection method based on edge testing is proposed. For each edge, a property that has a functional relationship with the properties of its two vertices is measured and tested. Based on the edge testing results of the network, the maximum likelihood principle is used to identify the vertex fault sources. Second, an edge distance testing...     

飞行器制导控制一体化编队控制器设计

针对主从式结构飞行器协同编队控制问题，以侧滑转弯飞行器为研究对象，采用制导控制一体化(Integrated guidance and control, IGC)方法设计编队控制器。首先在惯性坐标系中定义相对运动坐标系，建立相对运动模型，结合飞行器动力学模型，得到全状态制导控制一体化模型；然后采用反演方法，结合滑模变结构与神经网络自适应理论设计了编队控制器，并证明了控制系统稳定性；最后在高速情况下进行了六自由度数值仿真，对比了IGC设计方法与分离设计方法的控制性能。仿真结果表明所设计的IGC控制器能够快速精确地对期望编队队形进行构建与保持，并且较分离设计方法具有优越性。     

增强神经网络辨识模型泛化能力的研究

神经网络(Artificial Neural Network,ANN)辨识模型的泛化能力是其最主要的性能之一,增强ANN模型的泛化能力也是近年来国内外有关专家学者研究的重点问题。大量研究表明,ANN模型泛化能力的改善与很多因素相关联,其中恰当的性能指标函数设计是一个重要影响因素。文中在分析常见的基于均方误差最小原则的性能指标函数基础上,通过加入ANN辨识模型权值间的延迟信息,进而获得一种改进型性能指标函数。通过仿真,验证了所设计的改进型性能指标函数对增强ANN辨识模型的泛化能力是有效的。     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.     

6G service-oriented space-air-ground integrated network: A survey

As an indispensable component of the emerging 6G networks, Space-Air-Ground Integrated Networks (SAGINs) are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks, aerial networks, and terrestrial networks. In 6G SAGINs, a wide variety of network services with the features of diverse requirements, complex mobility, and multi-dimensional resources will pose great challenges to service provisioning, which urges the development of service-oriented SAGINs. In this paper, we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented network. First, we present the requirements of service-oriented networks, and then propose a service-oriented SAGINs management architecture. Two categories of critical technologies are presented and discussed, i.e., heterogeneous resource orchestration technologies and the cloud-edge synergy technologies, which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains, according to the service features and requirements. In addition, the potential future research directions are also presented and discussed.     

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.