基于大数据的航空数据采集与处理系统研究与设计

提供了一种基于大数据的航空作战数据采集与处理系统架构技术,包括：多源异构数据采集层, 用于获取原始数据并发送给数据治理层;数据治理层,用于接收和对所述原始数据执行解析、清洗转换形成 干净数据,并进行预处理形成主题数据;数据管理层,用于对数据生命周期、元数据、数据库、文件等进行 管理及统计分析;数据服务层,用于提供数据智能缓存、事件分析、数据发布、数据挖掘等服务;数据应用 层,用于接收所述主题数据进行任务复盘和回放,以及基于元数据进行数据地图、商业智能（BI：Business Intelligence）等有价值的数据分析应用。该方法能够有效提高航空数据管理的智能化水平,挖掘数据价值。     

基于小波理论的液体火箭发动机试验数据噪声处理

1引言液体火箭发动机试验(也称发动机试车)的主要目的之一,就是为了获得发动机各类参数的大量数据,因为这些数据是验证发动机设计理论,研究发动机工作性能的重要依据[1]。稳态参数是液体火箭发动机试验的主要检测参数。在工程实践中,经常认为稳态参数是变化缓慢的参数,常采用降     

民机试飞规划与管理数据库设计研究

针对国内在民机试飞规划与管理工作中所涉及的各类数据及其之间的关联进行了深入地分析。所涉及的数据资源涵盖了从飞行试验的需求确认至试验规划编制这一完整过程。根据分析结果,以ER图的形式描述了民机试飞规划与管理数据库设计的概念数据模型。并结合关系型数据库模型,简述了数据库设计工作中由概念数据模型向逻辑数据模型转换的方式。     

Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance     

A Smart Low-Power Meteorological System

A new low-power instrument to measure meteorological parameters has been developed. The instrument is based on an intelligent data cruncher concept: Fast sensor data rates are stored and process to yield a variety of answers for each parameter, at slower data rates, as appropriate. Special methods are used to achieve these results with an average current drain of under one mA, including sensors. Sampling rates and processing algorithms are designed to correct for swaying ocean-deployed buoys. A modular approach to design allows many types of sensors to be accommodated and permits data dissemination to a variety of destinations; data is available for real-time transmission or for internal archiving. The Weather Station's high-capacity internal data storage system, coupled with its fast data acquisition rates, enable the instrument to be used for air turbulence measurements.     

基于XML语言和代理模型的翼型通用气动数据库技术

建立翼型气动数据库对翼型的选择和设计具有重要的意义。本文基于一种建立翼型通用气动数据库的思想,介绍该数据库的基本框架和基本功能;该数据库采用一种基于可扩展标记语言的通用数据格式进行存储,包含翼型几何特性数据、不同流动状态下力系数、物面压力分布等各种气动数据,并且还可提供灵活多样的数据处理功能;采用代理模型和变可信度模型技术,实现气动数据快速检索和插值功能。通过DU93-W-210翼型算例初步表明如何实现气动数据生成和快速插值,从而初步展示了所提出翼型气动数据库技术的可行性。     

Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

Imbalanced data classification is an important research topic in real-world applications, like fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used to solve this problem. It generates samples according to the distance between minority data. However, the traditional over-sampling method may change the original data distribution, which is harmful to the classification performance. In this paper, we propose a new method called Conditional Self-Attention Generative Adversarial Network with Differential Evolution (CSAGAN-DE) for imbalanced data classification. The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data. In CSAGAN-DE, the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class. Then, the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance. Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method. The results show that the new method can efficiently improve the classification performance compared with other related methods.     

一种对多传感器异步数据的融合处理方法

在传感器异步采样时刻情况下 ,对机动目标的跟踪滤波算法进行了探讨 ,基于细分时间片的方法 ,提出了一种将多传感器数据组合成类似于单传感器数据的异步数据处理方法 ,运用于目标点迹航迹的合成。通过对多传感器数据的利用 ,增大对目标观测的数据流数据率 ,来提高跟踪精度。仿真实验显示 ,各通道的均方根误差均相对减小 ,表明了算法的可行性。     

基于时钟驱动的循环调度

实时系统,诸如航空电子系统、空中交通控制系统(Air Traffic Control: ATC)等,从本质上说都是一种分布式实时系统,从理论上说,它们由三种类型的处理器组成,包括数据控制处理器、数据传输处理器和数据处理器.无论是数据控制处理器和数据处理器的数据计算任务,还是数据传输处理器的数据传输任务,它们的执行都必须满足任务的时间约束要求,从而正确完成系统设计的各项应用任务.本文将描述基于时钟驱动的循环调度策略,它不仅适用于对数据计算任务的实时调度,也适用于对数据传输任务的实时调度,最后,分析了此调度策略应用中存在的优势与劣势.     

数据库技术在多传感器信息融合系统中的应用综述

数据库技术在多传感器信息融合系统中占有非常重要的地位。论文以数据库对融合算法的支持为目标,以数据库模型和数据融合数据库的全面设计为基础,在全面考虑面向特征的数据表征形式和组织结构的基础上,详细讨论了多传感器信息融合系统中数据库设计的基本理论,讨论了融合算法对数据库的需求和数据库设计,设计了辐射源识别的知识数据库。     

A Low-Cost LAN Implementation for the Factory

As the factory environment becomes increasingly automated, the need for data organization, data integrity, and data access will be a requirement. A local-area network for the factory is an option capable of solving data requirements. In addition, factory operation complexity can be reduced by centralizing data and developing robust applications; these applications should fulfill data organization, integrity, and access requirements and be an operator's interface to the automated factory.     

基本维修作业工时的数据处理

根据基本维修作业时间的统计分析特点,提出了基本维修作业工时数据的处理方法。该方法明确了维修作业工时数据采集后的筛选和补充方法,按照数理统计方法处理数据的一般流程,给出了本处理方法中的样本数据常用统计量,阐述了基本维修作业工时数据分布的确定方法,提出了基本维修作业工时的计算方法和评价原则。该方法可以显著提高数据的有效性和可信性,处理后的采样数据主要用于建立基本维修作业工时数据库,奠定数字化维修作业平台建设的基础。     

A generative deep learning framework for airfoil flow field prediction with sparse data

Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments, deep learning models can be leveraged to somewhat mitigate such expenses with proper means. Nevertheless, effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity. In this paper, we present a novel data augmented Generative Adversarial Network(GAN), da GAN, for rapid and accurate fl...     

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion(MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion(...     

正态分布定时无失效寿命分散系数

针对现有分散系数计算公式只适用于完全数据,以及近年来在寿命试验中大量出现的无失效数据并不适用的情况,将完全数据的寿命分散系数的概念推广到定时无失效寿命数据,提出定时无失效数据寿命分散系数方法,给出了相应的寿命分散系数计算公式.理论分析和结算结果表明,定时无失效数据寿命分散系数L_f*明显小于完全数据寿命分散系数L_f,从而能有效提高被完全数据寿命分散系数过分压低的安全使用寿命.      

Data path synthesis in digital electronics. I. Memory allocation

A data path consists of memory elements (i.e., registers), data operators (i.e. ALUs) and interconnection units (i.e. buses) to control the data transfers in the digital system. Many approaches to hardware allocation for data path synthesis have been proposed in the literature; however, only single-port memory is considered for register allocation and no efficient synthesis approach for multiport memory synthesis. A novel design methodology for data path synthesis using multiport memories is proposed which can be applied to hardware allocation algorithms or to already synthesized data path as a postprocessor to achieve a better design. Illustrations of applying this method to different synthesis examples are presented. Results and improvements over previous techniques are demonstrated. Experiments on benchmarks show very promising results     

基于大数据分析方法的商用飞机MOC9试验项目管理可视化模型研究

实践表明,大数据技术可以对管理过程赋能,商用飞机MOC9 试验项目作为商用飞机研制复杂高端项目群中的重要项目,其在研制期间会产生大量项目管理数据,因此,研究如何利用大数据技术推动该类项目实现项目目标十分必要。基于商用飞机研制项目群下MOC9 试验项目的特点及其对大数据管理的需求,结合大数据技术处理流程和方法对MOC9 试验项目从架构策划、数据获取与存储、数据清洗处理、分析挖掘、可视化展示多方面开展研究。结果表明：通过大数据技术对MOC9 试验项目建立量化的可视化模型,对项目经理掌握项目整体情况、定位问题根本原因、识别项目潜在风险非常有帮助。     

大数据技术在航空发动机研发领域的应用探索

针对航空发动机研发领域内不同专业之间数据彼此孤立,相同专业的数据名称、符号、量纲不统一,无法应用大数据技术进行深层次分析的问题,通过对发动机数据进行系统有效的管理,建立了航空发动机大数据平台方案,从而挖掘数据间隐藏的规律,解决发动机复杂问题。通过系统梳理全寿命周期内发动机不同阶段、不同专业数据的现状及管理的不足,开展了航空发动机大数据平台建立的需求分析,并利用工程经验开展了数据种类分析,首次定义了航空发动机元数据的概念及组成,创新性地提出了一种多维度、多专业数据关联的命名方法,为后续科研院所及发动机承制厂商建立航空发动机大数据平台提供了数据间关联的支撑,摸索出大数据技术应用的途径,从航空发动机设计的专业角度提出了大数据平台实施方向,具有较好的工程应用价值。     

固定影响的平行数据模型的参数估计

以往研究经济问题时，人们大多采用单纯的时间序列数据或横截面数据作为样本观测值，而两者在实际应用中都存在一定的局限性。平行数据是时间序列数据和横截面数据的“综合信息”，因此可以用来解决许多单纯的时间序列数据或横截面数据无法解决的问题，特别是可以用来处理动态的经济问题。通过对一些应用范围比较广的固定影响的平行数据模型给出了参数估计的证明过程。该证明过程对进一步研究平行数据模型的种类及其模型参数的估计具有一定的参考价值。