ID 原文 译文
10324 运用最大似然估计的特征向量方法(eigenvector method for maximum likelihood estimation,EMMLE)实现了子孔径缺失数据的自聚焦,满足了压缩感知对图像的稀疏要求。 ‭Using maximum likelihood estimate of the eigenvector method (eigenvector method for maximum likelihood estimation, EMMLE) realize the self-focusing of subaperture missing data, meet the requirement of compression perception of image sparse.
10325 利用压缩感知恢复完整的相位误差信号,解决了子孔径补偿相位误差数据的拼接问题。 ‭Using compression perception to restore the complete phase error signal, the joining together of the subaperture phase error compensation data.
10326 最后通过对恢复的雷达回波数据成像并自聚焦校正了距离徙动,得到了聚焦良好的完整图像,提高了缺失数据的成像质量。 ‭Finally through to the recovery of radar echo data migration imaging and self-focusing correction distance, has been focused on the full image of the good, to improve the imaging quality of missing data.
10327 针对传统空战威胁评估方法难以直观地给出目标威胁状况及发展趋势的缺陷与不足,提出基于区间数雷达图的可视化空战威胁评估方法。 In view of the traditional air combat threat assessment method is difficult to intuitively given target threat situation and development trend of defects and deficiencies, proposed based on interval number of radar map visualization air combat threat assessment method.
10328 该方法首先通过区间数与反三角函数确定并归一化目标属性决策矩阵; The method firstly determined by interval Numbers and inverse trigonometric function and normalized target attribute decision matrix; ‭
10329 ‭然后运用区间交叉熵法和群组层次分析法确定目标属性权重,并对威胁进行分级; Then using the method of interval cross entropy and group analytic hierarchy process (ahp) to determine the target attribute weights, and the threat classification;
10330 ‭最后在改进雷达图方法基础上,建立可视化空战威胁评估数学模型。 ‭Finally, based on the improved radar map method in visualization of air combat threat assessment model.
10331 仿真分析表明,该方法能够使决策者在正确的评估结果前提下直观地分析出各目标威胁状况与趋势。 The simulation analysis show that the method can make decision makers under the precondition of correct evaluation results intuitively analyze the status and trend of target threat.
10332 针对非线性、非平稳情况下自确认气体传感器的故障诊断问题,提出了对传感器不同故障模式信号进行特征提取和智能识别的在线故障诊断方法。 For non-linear and non-stationary case since the confirmation of gas sensor fault diagnosis problem, put forward the different failure modes of sensor signal feature extraction and intelligent recognition of on-line fault diagnosis method.
10333 首先,该方法根据传感器信号的变化进行集合经验模态分解(ensemble empirical mode decomposition,EEMD),自适应地获得一组固有模态函数(intrinsic mode functions,IMFs),对每个IMF及残余分量进行样本熵分析,提取传感器输出信号的完备特征; ‭First, the collection according to the changes of the sensor signal is applied to empirical mode decomposition (ensemble empirical mode decomposition, the EEMD), adaptive to obtain a set of intrinsic mode function (the intrinsic mode functions provides, the IMFs), for each IMF and sample entropy analysis of residual component, sensor output signal of the complete feature extracting;