An air-rail inter-modal strategy for aircraft recovery

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail(HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter...     

基于多层次特征的高分影像海岸带地物分类

针对高分辨率影像地物光谱丰富复杂的问题，文章提出了一种结合像元-对象-空间格局特征的水域、盐田等海岸带精细地物分类方法。首先基于像元和空间格局特征分别利用遗传算法优化随机森林参数进行分类；其次将两种分类结果采用D-S证据理论融合；然后基于不同地物斑块的空间上下文信息，采用粒子群算法优化支持向量机（Support Vector Machine，SVM）参数实现面向对象的二次分类。为验证方法的有效性，收集山东省莱州湾海岸带附近区域的“高分二号”卫星影像，开展了水域及水利设施用地、盐田、工矿用地、交通运输用地、住宅用地、沿海滩涂、海域与耕地8类地物的分类实验，总体精度达到97.21%，Kappa系数为0.964 2。实验结果表明，文章提出的高分影像海岸带地物分类方法兼顾像元、对象及空间格局特征优点，能够实现多层次细致分类，并同时兼具较短的训练时长与较高的分类精度。该研究成果可为中国海岸带生态环境监测与土地利用规划提供一定技术支撑。     

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.     

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...     

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.     

A new approach for pulse amplitude measurement using Lagrange’s interpolation for radiation or particle detectors

In radiation detector signal processing, usually, the charge-sensitive preamplifier converts the small charge signal coming from the semiconductor-based detector into voltage form and then the signal is further amplified to measure the energy of the incoming radiation. The voltage pulse from a charge-sensitive preamplifier (CSPA) is amplified using a shaping amplifier which reduces the signal bandwidth. To achieve better energy resolution, precise measurement of the peak amplitude of shaping amplifier output is required. The signal processing methods are available in which the signal from the charge-sensitive preamplifier can be directly digitized using high-speed Analog to Digital Converters (ADC), and then further signal processing such as gain and shaping is carried out inside the Field Programmable Gate Arrays (FPGA). For multiple detector systems, digital signal processing methods are quite difficult to implement in Field Programmable Gate Arrays (FPGA). In this context, The development of an alternative technique is initiated that uses a charge-sensitive preamplifier, shaping amplifier, low sampling analog-to-digital converter, and FPGA, where LaGrange’s interpolation technique is implemented in FPGA to precisely measure the peak of the analog pulse. In this paper, the comparison of the proposed method with other pulse amplitude measurement techniques is discussed. Results show that the implemented technique gives similar energy resolution compared to digital pulse processing and standard peak detector-based techniques.