基于Markov过程的卫星星座备份策略研究

如何在满足星座可用性指标的同时降低卫星星座备份成本,是当前研究的热点问题。为了选出一种兼顾星座性能与经济效益的备份策略,研究不同备份策略对某低轨卫星星座性能的影响,建立考虑降级运行的星座可用度模型,利用Markov状态转移过程对星座可用度模型进行求解,并基于真实低轨卫星星座在轨运行数据对采用不同备份策略的星座在不同星座等级下的可用度进行计算。结果表明：在每条轨道上部署两颗备份卫星可以使该星座在满足可用度要求的情况下备份费用最低,该备份策略对低轨卫星星座的建设有一定的参考价值,可以在合理降低成本的前提下,为精确地配置备份卫星提供指导。     

区间不确定信息下可修系统基于幂律过程的贝叶斯可靠性评估方法研究

贝叶斯方法常用于小子样场合下航空航天等领域中可修系统的可靠性评估,由于一些不确定因素影响致使无法精确收集先验信息和/或可修系统的故障信息,但却可获得其上下界信息。针对区间不确定信息情形,提出多台可修系统当其失效过程服从幂律过程（PLP）时的贝叶斯可靠性分析方法,将信息先验下PLP模型基于区间信息的贝叶斯分析转化为所求目标函数恰是该先验下PLP模型传统贝叶斯分析的约束优化问题;通过具体工程实例对本文所提方法的可行性、有效性进行验证。结果表明：本文所提出的贝叶斯可靠性分析方法能够为小子样场合下,考虑不确定性因素影响时可修系统的可靠性评估问题研究提供一种值得参考的方法。     

运用双重环境模型的移动机器人主动探测策略

针对移动机器人基于超声波传感器的地图创建,提出了一种双重环境模型。该模型同时包涵以直线段表示的几何模型和以概率形式表示的栅格模型,结合了几何模型与栅格模型的优点。给出了该模型的建立及更新算法,并基于该模型提出了一种主动探测策略,在保证探测效率的同时减小了超声波传感器的镜面反射产生的不良影响。在室内环境下进行移动机器人实验,验证了基于双重环境模型的探测策略的有效性。     

基于企业集群理论的中小企业发展战略研究

本文简要介绍了企业集群的概念、特征,分析了企业集群对于提升集群内企业竞争力的作用,在分析我国中小企业的特点的基础上,提出了我国中小企业应该走一条集群化发展战略之路.     

Surface elements and landing strategies for small bodies missions – Philae and beyond

The investigation of small bodies, comets and asteroids, can contribute substantially to our understanding of the formation and history of the Solar System. In-situ observations by Landers play a prominent role in this field.The Rosetta Lander – Philae – is currently on its way to comet 67P/Churyumov–Gerasimenko. It will land in November 2014 and perform numerous experiments with a suite of 10 scientific instruments.Philae has been designed to cope with a wide range of possible comet properties. The considerations taken during its development are relevant for future Lander missions to small bodies in the Solar System.In addition the paper provides a review of alternative concepts, studied or developed for various missions like Phobos, Hayabusa/Minerva or Géocroiseur/Leonard.Various missions to small bodies in the Solar System, including Landers, are currently studied (e.g., Marco Polo). The paper will address the mission options and compare applicable technologies with the solutions chosen for Philae.     

一种基于实时性考虑的对称多处理机任务调度策略的设计

针对机载设备强实时性的特点,设计了一种对称多处理机任务调度策略.重点需要解决的问题是:就绪队列的设计和任务的分配.同时考虑多机带来的任务绑定,cache有效利用等问题.为了实现良好的系统性能,采取多种技巧提高系统的实时性,利用线程代替进程提高程序的并行度,运用代码的可重入性实现内核数据的完整性.     

非线性反演控制律在航空发动机多变量控制中的应用

主要研究了非线性反演控制规律设计方法在航空发动机控制中的应用.首先基于小偏差线化模型建立了航空发动机的非线性仿射模型, 讨论了非线性反演控制规律的设计方法.最后, 针对某型涡扇发动机运用非线性反演控制规律设计方法设计了双转子转速控制规律, 并给出了仿真结果, 仿真结果表明这种控制规律的设计方法在航空发动机控制中是可行的.      

基于变形力数据的结构件残余应力场重构方法

残余应力是引起零件加工变形和失效的重要因素,残余应力场的重构有助于消除和控制零件在设计、制造、评价和使用过程中的残余应力。针对已有方法难以准确重构残余应力场的问题,提出了一种通过零件加工过程中的变形力数据实现零件残余应力场重构的方法。该方法首先提取了由于材料去除引起的应力重新分布带来的装夹力变化数据,即变形力。残余应力到变形力之间通过协方差矩阵自适应进化策略(CMA-ES算法)反复迭代求解出映射关系,从而构建出零件的残余应力场,最后在仿真环境下对该方法进行了验证。验证结果表明:该方法能较好地预测残余应力场,重构出的零件的初始残余应力场与原始施加的应力场平均误差值约0.38 MPa,为零件残余应力场的重构提供了一种新思路。     

A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.     

An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.