航天测控中时空数据的可视化技术研究

多维时空数据的可视化是信息可视化领域一个深受关注的问题.首先分析了时空数据的构成要素和特点,在此基础上通过在空间模型中引入时间的概念,研究了基于事件的时空数据模型,并进一步提出了几种时空数据的可视化方法,包括基于空间时间线的方法、基于三维z轴时间线的方法和将屏幕时间线与几何空间相关联的方法,最后进一步探讨了时空可视化方法在航天测控领域的应用.     

一种有效的自适应边信息帧内插算法

分布式视频编码是一种新的编码结构,它主要是在解码端利用信源的统计特性获得有效的压缩.在分布式视频编码中,边信息质量对整个系统编码效率的提高起着至关重要的作用.在研究了现有边信息构造方法基础上,提出了一种有效的自适应边信息帧内插方法.利用相邻关键帧与当前帧的时间相关性,按一定的准则计算相应的帧间和块同的时间相关系数,然后根据时间相关性不同采用不同加权系数的运动补偿帧内插算法提高边信息的质量.实验结果表明在几乎不增加编码器负担的情况下重建边信息的质量平均提高了2.47dB.     

双应力恒加试验Weibull分布型产品置信可靠性评估模型

为了有效快速评估高可靠性长寿命产品在正常应力条件下的可靠性,提出了一种双应力恒加试验Weibull分布型产品置信可靠性评估模型。首先,采用加速寿命试验(accelerated life test,ALT)技术,建立广义Eyring-Weibull可靠性模型,假定试验各应力水平组合下Weibull分布的形状参数相同,且尺度参数与各应力水平组合间呈对数线性关系。其次,给出了Weibull分布定时截尾双应力恒加试验的极大似然估计(maximum likelihood estimation,MLE)方法、与似然函数相关的Fisher信息矩阵以及模型参数的渐近协方差矩阵,构造了模型参数和一些可靠性指标的渐近置信区间。最后,通过仿真算例证明了所提方法的可行性。     

国际油价冲击对汇率体系稳定性影响的研究综述与展望

油价冲击对汇率体系稳定性的影响是国际金融领域研究的重要课题,本文系统梳理和评价了国际汇率制度中间化倾向发展对油价冲击传导的非对称性和传导强度差异化影响的文献,市场风险的信息传导和汇率波动非线性化现象研究的最新成果. 在文献研究基础上,本文指出油价冲击对汇率体系稳定性影响的未来研究的突破口是：不同汇率制度安排缓冲油价冲击的内在规律、 油价冲击影响汇率的传导模式和强度、油价冲击和汇率的非线性研究范式.     

基于QHSME的装备危险耦合传导GERT分析

针对装备活动中危险耦合传导关系描述不清楚、分析不具体的问题,构建考虑任意分布的危险耦合传导图形评审技术（graph evaluation and review technique,GERT）改进模型。提出了系统危险度、危险耦元重要度、危险路径隶属度等扩展分析参数,进一步刻画装备活动中的微观危险信息;采用系统耦合理论和极大熵方法（maximum entropy,ME）配置GERT模型变量,并设计量子和声算法（quantum harmony search algorithm,QHS）求解危险度概率密度极大熵模型。最后,以飞机大表速低空俯冲科目为例进行分析。结果表明,与传统GERT模型比较,该改进模型能充分反映危险耦元对装备活动随机过程的交互影响关系,以及其他相关微观信息,且具有较好的求解精度与适用性。     

Optimized LDPC differential-encoded 16QAM scheme for high-speed transmission systems

The 16-ary quadrature amplitude modulation （16QAM） is a high spectral efficient scheme for high-speed transmission systems. To remove the phase ambiguity in the coherent detection system, differential-encoded 16QAM （DE-16QAM） is usually used, however, it will cause performance degradation about 3 dB as compared to the conventional 16QAM. To overcome the performance loss, a serial concatenated system with outer low density parity check （LDPC） codes and inner DE-16QAM is proposed. At the receiver, joint iterative differential demodulation and decoding （ID） is carried out to approach the maximum likelihood performance. Moreover, a genetic evolution algorithm based on the extrinsic information transfer chart is proposed to optimize the degree distribution of the outer LDPC codes. Both theoretical analyses and simulation results indicate that this algorithm not only compensates the performance loss, but also obtains a significant performance gain, which is up to 1 dB as compared to the conventional non-DE-16QAM.     

Information identification in different networks with heterogeneous information sources

Traditional cheaptalk game model with homogeneous information sources provided a con clusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous in formation sources into an agentbased artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffu sion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.     

Measuring and locating zones of chaos and irregularity

The new measures computed here are the spectral detrended fluctuation anatysls （sDFA） and spectral multi-taper method （sMTM）. sDFA applies the standard detrended fluctuation analysis （DFA） algorithm to power spectra, sMTM exploits the minute increases in the broadband response, typical of chaotic spectra approaching optimal values. The authors chose the Brusselator, Lorenz, and Duffing as the proposed models to measure and locate chaos and severe irregularity. Their series of chaotic parametric responses in short time-series is advantageous. Where cycles have only a limited number of slow oscillations such as for systems biology and medicine. It is difficult to create, locate, or monitor chaos. From 50 linearly increasing starting points applied to the chaos target function （CTF）; the mean percentage increases in Kolmogorov-Sinai entropy （KS-Entropy） for the proposed chosen models; and p-values when the models were compared statistically by Kruskal-Wallis and ANOVA1 test with distributions assumed normal are Duffing （CTF： 31%： p 〈0.03）; Lorenz （CTF： 2%： p 〈0.03）, and I3russelator （CTF： 8%： p 〈0.01）. Principal component analysis （PCA） is applied to assess the significance of the objective functions for tuning the chaotic response. From PCA the conclusion is that CTF is the most beneficial objective function overall delivering the highest increases in mean KS-Entropy.     

Consensus design for continuous-time multi-agent systems with communication delay

This paper investigates a consensus design problem for continuous-time first-order multiagent systems with uniform constant communication delay.Provided that the agent dynamic is unstable and the diagraph is undirected,sufficient conditions are derived to guarantee consensus.The key technique is the adoption of historical input information in the protocol.Especially,when agent＇s own historical input information is used in the protocol design,the consensus condition is constructed in terms of agent dynamic,communication delay,and the eigenratio of the network topology.Simulation result is presented to validate the effectiveness of the theoretical result.     

基于敏感等级的云租户数据安全保护模型研究

为云计算提供有效的租户数据安全保护,提出一个第三方租户数据安全保护模型,解决租户数据的存储和检索安 全问题. 模型采用隐藏数据间关系的方法,对租户数据进行切片,利用单向函数混淆切片关系;结合租户对数 据的隐私需求和安全等级定义,给出租户数据的分级方法;依据安全等级对分割后的子表内容进行局部加密或匿名,实 现云端数据存储安全;结合私有信息检索技术,利用云端加密数据的关键字检索算法,隐藏租户搜索请求,保护云端数据 的检索安全. 实验表明,该模型能满足租户数据在未知云环境中的存储和检索安全要求.