基于EMD能量占比的海面漂浮小目标特征检测

针对传统的时频分析方法对海杂波分析有限的问题，提出一种基于经验模态分解(empirical mode decomposition, EMD)能量占比的海面漂浮小目标特征检测方法。首先，采用EMD将接收回波分为独立不同尺度的若干个固有模态(intrinsic mode function, IMF)分量，实现对接收回波的频率从高频到低频的分解。然后，分别建立IMF分量与接收回波数据的相关系数，并利用平均均值-标准差之比作为筛选IMF分量的准则，自动筛选出能量较大且波动平稳的低阶IMF分量。最后，提取IMF分量在原始信号中的平均能量占比作为特征，利用蒙特卡罗方法设置门限，进行海面目标异常检测。实测数据的结果显示，所提算法的性能优于对比算法。     

改进模型估计与平差的多幅异源影像匹配方法

针对多幅异源影像匹配准确性不高的问题，提出了一种改进模型估计与平差的多幅异源影像匹配方法。首先利用基于快速自适应鲁棒性尺度不变的特征检测子与鲁棒性交叠的标准特征描述子，来增强特征匹配的鲁棒性；然后提出改进的随机抽样一致性算法，来提高模型估计的执行效率，同时保证鲁棒性；最后提出针对多幅异源影像匹配的平差方法，来优化异源匹配结果。实验结果表明，在异源影像存在较大差异的情况下，改进模型估计与平差的多幅异源影像匹配方法具有精度高的优势。      

亚纯函数系数微分方程解的复振荡性质

综合运用Nevanlinna值分布的理论,Wiman-Valiron的理论及其它复分析中的常用方法研究了复域上高阶微分方程解带有小函数时复振荡的性质,该文的结果将二阶情形推广到高阶情形。     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

单相PWM整流器电压外环模糊自适应控制

为了提高单相PWM整流器输出电压的质量,解决传统控制策略存在参数整定困难的问题,改善小型能源路由器中间级的输入电压,提出了电压外环模糊自适应控制策略。首先,根据电路特征,建立单相PWM整流器的最优数学模型;其次,将模糊控制思想引入电压外环控制策略中,与传统的PI控制相结合,针对传统PI参数的影响,构建模糊规则,计算模糊因子;最后,与比例谐振控制结合,给出了单相PWM整流器电压外环模糊自适应控制。结果表明,所提出的控制策略具有良好的动态特性,可以在一定程度上减少负载波动时输出电压的波动,且在单位功率因数条件下,能够实现整流器的整流或逆变。改进策略可为小型能源路由器能量双向传输方面的研究提供参考。     

基于GPC的环肋耐压圆柱壳结构失稳概率分析

为评估具有小失效概率特性的深潜环肋耐压圆柱壳结构的失稳概率，提出了一种新的基于高斯过程分类和重要抽样的自适应分析方法.该方法通过引入马尔科夫链蒙特卡洛法和欧式距离，开发了一种新的考虑预测不确定性和取样均匀性的自适应试验设计策略，以便更高效地构造高斯过程分类器；采用核密度估计构造准最优重要抽样密度函数；基于失效概率估计的稳定性，提出了一种更精确的迭代停止准则.通过某一分段函数验证了所提分析方法的准确性及高效性.应用所提方法得到某深潜环肋耐压圆柱壳结构的失稳概率约为8.242×10^-5.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

论新信息环境下中小型图书馆信息资源建设

论述了新信息环境下中小型图书馆信息资源建设的定位,探讨了新信息环境下中小型图书馆信息资源建设的有效对策.     

基于后验风险确定故障样本量的Bayes方法

针对目前测试性故障样本量的确定方法过于粗糙和试验样本量过大的问题,提出了合理运用信息熵方法对装备系统各单元的测试性先验信息进行信息融合,得到装备系统级测试性试验数据。在此基础上得到测试性指标的先验分布,并进一步通过Bayes后验风险准则确定故障样本量及试验方案。以某型电动舵机系统各模块的试验数据为例,以故障检测率为测试性指标,经过分析和计算,发现运用所提方法得到的故障样本量相比传统方法明显减少,从而减少了试验成本,同时得到的测试性指标相对误差较小,保证了可信度。