未来智能空战发展综述

随着装备战斗力生成模式逐渐向机械化、信息化、智能化"三化融合"发展演变,未来航空主战装备的定位、形态及运用将可能发生根本性变革。为应对新时期空战任务所面临的环境高复杂性、博弈强对抗性、响应高实时性、信息不完整性、边界不确定性等一系列挑战,交叉融合人工智能理论与空战对抗技术,研发智能空战系统,将有望在下一代无人制空装备谱系中构建不对称"智能代差",成为制胜未来空天战场的核心关键。本文完整梳理了智能空战研究的发展脉络,总结了以专家机动逻辑、自动规则生成、规则演进、机器学习等方法为代表的智能空战基础理论。从体系、应用及技术视角全面剖析了智能空战的发展趋势,以智能空战的不确定性、安全性、解释性、迁移性、协同性为切入点阐述了智能空战应用落地的若干问题,以期为未来智能空战技术研究勾勒出一条新的探索路径,为人工智能理论与航空科学技术的跨领域交叉融合提供新的发展思路。     

Smart BIT: a plan for intelligent built-in test

A major factor influencing the readiness of today's highly electronics-driven weapons systems is the amount of time spent maintaining them. The attainment of a better understanding of the causes for intermittent and other ill-defined failure modes and development of a greater appreciation for the effect of both external and internal environmental factors on the fault behavior of electronic systems will help to improve the BIT (built-in test) performance. Smart BIT is the name applied to a program of research and development sponsored by Rome Air Development Center (RADC) to investigate, develop, and apply artificial-intelligence (AI) techniques to effect BIT improvement. An overview of that program, its contents and purposes is provided. Deficiencies in current BIT implementations and potential benefits of the Smart BIT program are presented. Components of this approach are discussed and supporting technology areas highlighted. The current plan for implementing this approach is given along with a scenario describing its potential form     

Low observable target motion analysis using amplitude information

In conventional passive and active sonar system, target amplitude information (AI) at the output of the signal processor is used only to declare detections and provide measurements. We show that the AI can be used in passive sonar system, with or without frequency measurements, in the estimation process itself to enhance the performance in the presence of clutter where the target-originated measurements cannot be identified with certainty, i.e., for “low observable” or “dim” (low signal-to-noise ratio (SNR)) targets. A probabilistic data association (PDA) based maximum likelihood (ML) estimator for target motion analysis (TMA) that uses amplitude information is derived. A track formation algorithm and the Cramer-Rao lower bound (CRLB) in the presence of false measurements, which is met by the estimator even under low SNR conditions, are also given. The CRLB is met by the proposed estimator even at 6 dB in a cell (which corresponds to 0 dB for 1 Hz bandwidth in the case of a 0.25 Hz frequency cell) whereas the estimator without AI works only down to 9 dB. Results demonstrate improved accuracy and superior global convergence when compared with the estimator without AI. The same methodology can be used for bistatic radar     

Identifying and managing risks of AI-driven operations: A case study of automatic speech recognition for improving air traffic safety

In this work, the primary focus is to identify potential technical risks of Artificial Intelligence (AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience. The case study is performed to evaluate the AI-driven techniques and applications using objective metrics, in which several risks and technical facts are obtained to direct future research. Considering the safety–critical specificities of the air traffic control system, a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview. In this procedure, the potential risks obtained from the case study are confirmed, and the impacts on human working are considered. Both the case study and the evaluation of user experience provide compatible results and conclusions: (A) the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance; (B) the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers. Finally, a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.     

民用飞机智能飞行技术综述

人工智能已成为民用飞机技术创新发展与竞争的新赛道。在面向民用飞机全生命周期的多种人工智能融合应用中,智能飞行致力于改变传统飞行驾驶模式,重构未来飞行的人机交互模式与空中交通管理架构,成为行业特征最为显著、最具备颠覆性变革的方向,是民用飞机智能化竞争的新焦点。阐述了智能飞行理念,规划辅助智能、增强智能、完全智能三阶段实施路线,以25部、23部、轻型运动类飞机为对象,制定智能飞行推进路线,构建技术体系,提炼影响智能飞行在应用落地过程中需要解决的可信适航与人为因素两项关键应用基础技术,分享对于智能飞行的思考。     

Hybrid connectionist systems in research and teaching

There is a tremendous world-wide interest in the development of the theory and application of artificial intelligence (AI) and related techniques both in tertiary institutions and in industry. This paper presents a brief survey of overseas AI teaching programs together with a review of the teaching program in the University of South Australia. In addition, post-graduate projects undertaken by the Knowledge-Engineering Systems Group (KES), and sponsored by the Defence, Science and Technology Organisation (DSTO), are discussed. These involve expert systems, neural networks, fuzzy logic and genetic algorithms. It is shown by using a classical exclusive-OR problem that it is useful to use hybrid techniques which offer fast convergence in optimization problems. Although much of this work is still at an early stage, sufficient progress has been made to demonstrate the merit of using AI and related techniques to help create solutions to these problems     

粗集理论在航空产品质量诊断中的应用研究

分析了航空产品制造的复杂性,针对目前生产方式的转变以及人工智能方法用于过程质量智能化诊断的不足,提出利用粗集理论进行航空产品生产的过程质量诊断以满足产品复杂性对质量提出的要求,通过实例讨论了该方法的优越性.     

Artificial intelligence in test

This paper discusses the subject of applying artificial Intelligence (AI) techniques to automatic test. It has been the experience of the author that AI is not a “magic bullet”, and that its successful use requires an understanding of both the technology and the repair process to which it is being applied     

AI techniques in uninhabited aerial vehicle flight

This describes the development of an application of artificial intelligence (AI) for unmanned aerial vehicle (UAV) control. The project was part of the requirements for a class in AI at NOVA Southeastern University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an artificial intelligence method, fuzzy logic, to aspects of control logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.     

Morphology and Frictional Characteristics Under Electrical Currents of Al203/Cu Composites Prepared by Internal Oxidation

Two AhO3/Cu composites containing 0.24 wt.% A1203 and 0.60 wt.% A1203 separately are prepared by internal oxidation.Effectsof sliding speed and pressure on the fi-ictional characteristics of the composites and copper against brass are investigated and compared.The changes in morphology of the sliding surface and subsurface are examined with scanning electron microscope (SEM) and energy dispersive X-ray spectrum (EDS).The results show that the wear resistance of the AI203/Cu composites is superior to that of copperunder the same conditions.Under a given electrical current,the wear rate of AI203/Cu composites decreases as the AleO3-content increases.However,the wear rates of the Al203/Cu composites and copper increase as the sliding speed and pressure increase under drysliding condition.The main wear mechanisms for AleO3/Cu composites are of abrasion and adhesion;for copper,it is adhesion,although wear by oxidation and electrical erosion can also be observed as the speed and pressure rise.     

面向航空智能制造的DT与AI融合应用

针对航空装备复杂制造场景下制造过程管控维度多尺度大、制造资源组成复杂性高、质量问题跟踪定位难度大等问题，结合数字孪生（DT）与人工智能（AI）技术特点，开展了面向航空智能制造的DT与AI融合应用研究。基于数字孪生与人工智能应用现状，系统性地阐述了数字孪生与人工智能融合机理，分析了支撑数字孪生与人工智能融合驱动航空智能制造的关键技术和数字孪生与人工智能融合驱动的AI控制中心构建涉及的关键问题，在此基础上重点讨论了加工制造过程自适应控制、智能车间生产过程智能管控、制造过程资源调度与优化决策、产品智能质量控制等应用场景，为数字孪生与人工智能在航空智能制造融合应用提供参考。     

结构优化设计专家系统ESOPD的设计与应用

介绍了结构优化专家系统ESOPD的原理与应用,包括知识库的建立、推理机的选择、用户接口的设计等。对知识库中的知识作了重点介绍。     

测绘导航高精度定位关键技术及应用

人工智能(AI)、卫星导航及室内定位三大技术的快速发展持续推动测绘科技向智能化、学科交叉、信息融合的方向发展。指出了装备虚拟化与数据处理多样化将成为测绘导航的基本形态，拓展了测绘学科的内涵与外延。智能滤波是未来测绘导航定位理论发展的重要方向，AI芯片将成为测绘导航装备的关键部件，从而提高滤波效率与产品的实用性。给出了车载导航模组研制的技术流程，提出了模组原始信息同步采集与延时补偿、自适应定位算法及嵌入式固件时间分片优化等关键技术，研制了覆盖分米、厘米及毫米级定位精度的无缝组网定位、车载导航及变形监测模组，并研发了相应的终端系统，针对相应的应用领域，给出了各自应用的解决方案，以及具体的应用案例。     

计算机兵力生成在空空对抗仿真建模中的应用

研究了计算机兵力生成在空空对抗仿真建模中的应用。首先,介绍了计算机兵力生成在空战中的体系结构;然后,详细地举例分析了体系结构中的实体行为模型和人工智能行为模型。最后,结合兵力生成理论在一个空空对抗实例中的应用,把整个理论体系串连起来。该仿真系统可以完成系统建模,系统设计和可视化演示,具有一定工程实用性。     

OODA智能赋能技术发展思考

随着科学技术的飞速发展,信息化支持下的体系作战将是未来战争的一种主要样式,"基于感知-判断-决策-行动（OODA）以快吃慢"成为未来战争的重要制胜机理。由于战场环境日趋复杂、对抗多域多维,从战场态势到作战策略的映射关系复杂,给OODA环快速解算带来了新的挑战。为确保OODA环解算满足任务需求,将人工智能（AI）技术赋能OODA各环节,驱动各环节高效运行,缩短环路解算时间,为打赢战争提供关键支撑。首先综述了人工智能在军事领域的应用进展,分析了导弹OODA智能化赋能面临的挑战,初步提出了智能赋能OODA环涉及的相关技术的思考,以支撑导弹智能化的发展。     

磨粒分析技术及其在发动机故障诊断中的应用

基于显微形态学方法的磨损微粒显微分析技术是近年来出现的解决发动机磨损故障诊断问题的新方法之一。应用形态学分析方法建立一套磨粒显微特征描述体系提取磨粒特征信息,作为磨粒识别的信息基础,可以准确地完成磨粒自动识别。基于发动机润滑系统磨粒分析的结果和颗粒摩擦学理论,比较准确地评估出发动机接触磨损部件的磨损状态,进而诊断或预测此类磨损故障;另外,此项分析诊断技术在发动机气路磨损故障诊断中也具有潜在的应用前景。      

智能赋能流体力学展望

人工智能（AI）是21世纪的前沿科技,流体力学如何在智能化时代焕发青春是值得本领域研究者思考的话题。从智能赋能流体力学角度,就其研究内涵、研究内容、近期研究及难点进行了总结,并对智能流体力学未来的发展进行了展望。研究指出,流体力学计算或试验中所产生的数据是天生的大数据,如何通过深度神经网络、随机森林、强化学习等机器学习方法来利用这些数据,缓解甚至替代理论和方法层面对人脑的依赖,挖掘新的知识,成为一种新的研究范式;相关研究将涵盖流动控制方程的机器学习、湍流模型的机器学习、物理量纲分析与标度的智能化以及数值模拟方法的智能化;借助人工智能技术,发展流动信息特征提取与多源数据融合的智能化是流体力学发展的迫切需求;研究内容应至少涵盖海量数据挖掘方法以及多源气动数据的智能融合;发展数据驱动的流体力学多学科、多物理场耦合建模与控制是工程应用的迫切需求,相关工作涉及多场耦合建模、气动外形智能优化设计以及流动智能自适应控制等方面。     

Visual Prolog的DDE机制研究

对Visual Prolog的DDE机制进行了研究.首先介绍了动态数据交换技术及其工作原理,简明概括了Visual Prolog的功能、特点以及在专家系统、规划和其它AI相关问题求解方面的优势,其次阐述了Visual Prolog的DDE机制,列出了Visual Prolog中18个DDE谓词和13个DDE事件,分别介绍了作为客户机时,一个典型的请求从其他应用程序输入数据的Visual Prolog程序的关键步骤和作为服务器时,一个典型的响应其他应用程序请求数据的Visual Prolog程序的关键步骤.最后给出了一个Visual Prolog DDE机制应用的实例.     

Spacecraft Application of Expert Systems

Artificial Intelligence (AI) will play a major role in future spacecraft operation. Because the technology has not matured, knowledge-based systems will be incorporated in an evolutionary manner, with increasing responsibility as their performance is proven. Internal research at Boeing Aerospace Company has demonstrated that AI software development techniques, knowledge-based systems in particular, can be used to provide limited spacecraft subsystem automation. This capability represents a first step toward an evolutionary path to spacecraft automation. A likely progression will proceed to integrated subsystem control, automated planning and scheduling, plan execution, anomoly handling, and eventually to autonomous spacecraft operation. Although this paper is written in the context of Space Station the ideas and techniques identified should be easily transferable to spacecraft automation in general.     

面向军事应用的航空人工智能技术架构研究

通过全面梳理国内外面向军事应用的航空装备智能化发展现状,紧密围绕未来智能化空中作战的任务场景,给出航空军用人工智能技术的概念和内涵,并依据智能系统信息运行模型,提出不同运行层级的航空装备智能升级的途径和方法。在此基础上,形成包含基础使能、关键技术群、智能系统、运行层级的航空人工智能技术架构,对关键技术群按面向运行层级、面向功能需求、面向基础使能进行分析,得到重点技术发展方向,并给出提升我国航空装备智能化水平的具体措施建议,为制定相关航空人工智能技术规划提供参考。