浅论工卡管理的改进

以信息系统为基础,以精益管理为手段,提高工卡的各项功能,借此优化资源配置,提高效率,降低维修成本,更好地保障飞行安全和提高维修企业的效益。     

Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

This paper proposes a method for planning the three-dimensional path for low-flying unmanned aerial vehicle(UAV) in complex terrain based on interfered fluid dynamical system(IFDS) and the theory of obstacle avoidance by the flowing stream. With no requirement of solutions to fluid equations under complex boundary conditions, the proposed method is suitable for situations with complex terrain and different shapes of obstacles. Firstly, by transforming the mountains, radar and anti-aircraft fire in complex terrain into cylindrical, conical, spherical, parallelepiped obstacles and their combinations, the 3D low-flying path planning problem is turned into solving streamlines for obstacle avoidance by fluid flow. Secondly, on the basis of a unified mathematical expression of typical obstacle shapes including sphere, cylinder, cone and parallelepiped, the modulation matrix for interfered fluid dynamical system is constructed and 3D streamlines around a single obstacle are obtained. Solutions to streamlines with multiple obstacles are then derived using weighted average of the velocity field. Thirdly, extra control force method and virtual obstacle method are proposed to deal with the stagnation point and the case of obstacles’ overlapping respectively. Finally, taking path length and flight height as sub-goals, genetic algorithm(GA) is used to obtain optimal 3D path under the maneuverability constraints of the UAV. Simulation results show that the environmental modeling is simple and the path is smooth and suitable for UAV. Theoretical proof is also presented to show that the proposed method has no effect on the characteristics of fluid avoiding obstacles.     

从空战制胜机理演变看未来战斗机发展趋势

制空权是当代战争一切空中行动的前提条件,而承担空中优势重任的战斗机的研发工作长期以来为各军事大国所重视。数十年来,美军在空战理论、战斗机研发和空战实践等方面具有领先优势,已率先完成由能量机动制胜向信息机动制胜的空战能力转变。随着自主、人工智能、无人、通信、计算等技术的快速进步,美军正在塑造以下一代战斗机为核心,以复杂空战系统为基本空战单元的新空战形态,认知机动制胜将是未来空中对抗的制胜机理。本文将系统梳理空战制胜机理的演变历程,结合当前技术发展与布局,研判未来战斗机发展趋势。     

软判决Viterbi译码器硬件实现中的支路量度压缩与计算

以工程实现为目的,重点讨论(2,1,6)卷积编码8电平(3bits)软判决Viterbi 译码器硬件实现中的支路量度(Branch Metric)压缩与计算问题,给出了一种解决这一问题的方法以及用该法实现的译码器样机性能实验数据。     

一种软件质量度量模型的研究与应用

软件质量度量是软件工程方面的研究重点之一.通过量化的手段对软件产品进行度量和评价是最有效的质量保证手段.本文提出了软件质量度量模型,根据标准的评价过程模型对其进行了分析,从而确立了质量特性、质量子特性、度量特性和度量标准,并给出了这些特性的之间的关系以及它们的权值.然后运用一组公式对度量方法进行了表示并提出了该模型的应用.本文为软件质量度量提供了一套可行的定量评价方法.     

基于多态性的UML时序图度量研究

目前对于时序图度量的研究没有考虑到面向对象软件的多态性特点,度量不够准确。本文结合面向对象多态性的特点和UML(Unified mode ling language)类图、时序图,运用图论的方法构建了有向无环图,并在此基础上提出了相适应的度量集,最后通过实例分析说明时序图的多态性扩展和度量集的有效性。同时,本文的研究也为软件测试用例的设计提供了指导。     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.     

基于Markov的察打无人机数据链时延建模与补偿研究

为了改善在数据链时延情况下察打无人机的性能,对造成时延的因素及影响进行了分析,在此基础上对数据链时延进行了合理的假设和简化。并对无人机整个控制回路的各个节点的响应方式,提出了一种时间-事件-事件-时间驱动方式,使得无人机数据链的时延序列具有Markov性,同时给出了状态转移矩阵的求取方法。为了改善无人机的响应特性,采用最优控制的方法对时延进行了补偿,并基于某无人机平台验证了补偿方法的有效性。仿真结果表明,该方法的引入使得无人机的动态响应特性有了显著提高。     

红外多光谱扫描仪在发射段的结构可靠性

用Ｇ莱希纳和Ｂ贝尔契（德）提出的结构系统可靠性分析通用方法（以下简称ＧＢ法），详细分析了某多光谱扫描仪的结构特点、工作环境与受力情况，并根据航天器受载特点建立了遥感器部分在发射阶段的可靠度数学模型，以一次二阶矩法为主要算法对红外扫描仪主体在发射阶段的结构可靠性进行了分析和评估。