碳纳米管/HTPB复合粒子的制备及其催化性能研究

用HTPB对碳纳米管（CNTs）进行改性,改善其对AP热分解的催化性能,并使用TEM和FTIR对其进行表征。热重分析表明,CNTs／HTPB复合粒子中HTPB的含量为28．57％。采用差示热分析（DTA）研究了CNTs／HTPB复合粒子对AP热分解的催化性能。结果表明,CNTs／HTPB复合粒子对AP的热分解具有一定的催化性能,与纯AP相比,高温分解峰温提前了111．9℃,表观分解热由309．92J／g提高到987．18J／g,优于CNTs与AP的简单混合样,表明经过HTPB改性后CNTs对AP具有较好催化性能,同时AP与CNTs／HTPB复合可使其高温分解峰温进一步降低9．8℃。     

Investigation of PMSE dependence on high energy particle precipitation during their simultaneous occurrence

This paper is based on the observations of Polar Mesosphere Summer Echoes (PMSE) with the EISCAT VHF 224?MHz radar during the summer month 08–12 July 2013. The effect of high energy particle precipitation on PMSE intensity, particularly during their simultaneous occurrence for longer time interval (longer than or equal to 3-h) has been investigated. The correlation between the two phenomena has been computed using the Spearman rank and Pearson linear correlation coefficient. The variations in high energy particle precipitation reaching down to altitude of 91?km and PMSE intensity in the altitude range of 80–90?km are positively correlated. The electron density irregularity due to ionization caused by precipitating particles might be one of the possible reasons for this positive correlation. Moreover, some other background parameters i.e. K-indices (proxy of high energy particle precipitation) and electron fluxes during the simultaneous occurrence of the two phenomena also support one of the possible reasons given for explanation of the observed positive correlation. The X-rays and proton fluxes have no noticeable effect on PMSE echoes in this study.     

MCMC-Particle-based group tracking of space objects within Bayesian framework

With the intense increase in space objects, especially space debris, it is necessary to efficiently track and catalog the extensive dense clusters of space objects. As the main instrument for low earth orbit (LEO) space surveillance, ground-based radar system is usually limited by its resolution while tracking small space debris with high density. Thus, the obtained measurement information could have been seriously missed, which makes the traditional tracking method inefficient. To address this issue, we conceived the concept of group tracking. For group tracking, the overall tendency of the group objects is expected to be revealed, and the trajectories of individual objects are simultaneously reconstructed explicitly. According to model the interaction between the group center and individual trajectories using the Markov random field (MRF) within Bayesian framework, the objects’ number and individual trajectory can be estimated more accurately in the condition of high miss alarm probability. The Markov chain Monte Carlo (MCMC)-Particle algorithm was utilized for solving the Bayesian integral problem. Furthermore, we introduced the mechanism for describing the behaviors of groups merging and splitting, which can expand the single group tracking algorithm to track variable multiple groups. Finally, simulation of the group tracking of space objects was carried out to validate the efficiency of the proposed method.     

基于OpenFlow的AFDX网络虚拟链路动态优先级接纳控制

为了在航空电子全双工交换式以太网(AFDX)中更加灵活地对虚拟链路(VL)实行接纳控制,将OpenFlow引入AFDX网络中,建立了相应的网络模型、消息模型和流量模型。利用网络演算方法分析了优先级VL在基于OpenFlow的AFDX网络中的端到端延迟上界,结合粒子群优化算法,提出了动态优先级接纳控制算法。理论分析结果表明:与动态非优先级接纳控制方法和传统静态优先级分配方法相比,本算法的消息延迟分别降低了49.2%和26.4%,并且本算法能够更加灵活地对VL实行接纳控制,提高网络资源的利用率。最后通过仿真对理论分析的结果进行了验证。本算法为提高接纳控制方法的性能提供了参考,增强了AFDX网络的通信效率与实时性能。     

Hybrid Multipopulation Cellular Genetic Algorithm and Its Performance

The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm（HCGA）is proposed,which combines population segmentation with particle swarm optimization（PSO）.The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.     

Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts

In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts. The system state, measurement output and filter parameters are all subject to stochastic uncertainties or multiplicative noises, where the measurement noises are finite-step autocorrelated. When there exist multiple packet dropouts in the system output, the original system is converted into an auxiliary stochastic uncertain system by the augmentation of system states and measurements. The process noises and measurement noises of the auxiliary system are shown to be finite-step autocorrelated and cross-correlated. Then, a robust non-fragile Kalman-type recursive filter is designed that is optimal in the minimum-variance sense. The proposed filter is not only robust against the uncertainties in the system model and measurement model, but also non-fragile against the implementation error with the filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed method.     

Robust adaptive filtering method for SINS/SAR integrated navigation system

This paper presents a new robust adaptive filtering method for SINS/SAR (Strap-down Inertial Navigation System/Synthetic Aperture Radar) integrated navigation system. This method adopts the principle of robust estimation to adaptive filtering of observational data. A robust adaptive filter is developed to adaptively determine the covariance matrix of observation noise, and adaptively adjust the covariance matrix of system state noise according to the adaptive factor constructed based on predicted residuals. Experimental results and comparison analysis demonstrate that the proposed method cannot only effectively resist disturbances due to system state noise and observation noise, but it can also achieve higher accuracy than the adaptive Kalman filtering method.     

散乱点云自适应滤波算法

提出一种散乱点云自适应滤波算法,该算法采用改进的R*-树组织散乱点云的拓扑近邻关系,基于该结构快速准确获取局部型面参考数据,自适应调节二维高斯分布的数字特征计算滤波权值,计算局部型面参考数据对原始型面数据的影响因子,以此作为权值计算各点滤波后的坐标,采用加权平均方法实现散乱点云的自适应滤波.实验证明该算法可有效提高点云的滤波效率,在保留原始型面特征的基础上,减小点云的随机误差,提高光顺性.     

基于协同进化粒子群和Pareto最优解的卫星编队队形重构方法

针对卫星编队自主队形重构问题,提出了基于协同进化粒子群优化(CPSO)和Pareto最优解的求解方法.首先,使用Legendre伪谱法(LPM)将队形重构问题离散化为非线性规划(NLP)问题;其次,根据卫星编队的特点及碰撞规避的需要,使用CPSO算法对重构问题采用既独立又集中的求解方式,避免了传统优化方法对梯度的求解;...