Study of complex dielectric properties of lunar simulants and comparison with Apollo samples at microwave frequencies

Laboratory characterization of dielectric properties of terrestrial analogues of lunar soil (JSC-1A) and comparison with lunar samples returned from various Apollo missions is made at different as well as normalized bulk density. Here measurements of dielectric constants and losses were made at four microwave frequencies such as 1.7 GHz, 2.5 GHz, 6.6 GHz and 31.6 GHz. Complex permittivity of lunar simulant was measured at temperature ranging from −190 °C to + 200 °C using Wave-Guide cell method. Comparison of permittivity of JSC-1A with Apollo sample also has been done at similar microwave frequencies. The investigations reveal that dielectric constant and loss factor of terrestrial analogues of lunar soil are temperature dependent. As temperature is gradually increased both these parameter (storage factor and loss factor) also gradually increases. These temperatures were chosen because the Moon undergoes at that extremes level of temperature. It is scorching heat at 110 °C during the day and freezing cold at −180 °C during night. The measured value of ε can be useful for designing passive as well as active sensors.     

基于数字信号特征提取的测频方法研究

传统的幂次谱特征能简便有效地提取信号特征并测量参数,但在样本数较少、信噪比较低的情况下,特征就变得不明显了。利用最佳采样点再进行幂次的非线性运算,能够完成特征提取及载频的精确测量。经仿真实验和实际工程验证,在信噪比不大于10dB、样本数不大于1000个符号时,该方法切实、有效。     

基于全脉冲的周期信号样本提取技术

现代电子战中,传统的辐射源分选识别方法无法在密集的信号环境中快速有效地对复杂体制雷达进行分选识别,造成系统漏警。基于上述情况,提出了一种基于脉冲样本序列自提取的分选算法。这种算法利用雷达信号脉冲序列的周期性和相关性,实现对模板序列的自动提取,从而可实现全脉冲序列中小样本数或脉间规律复杂的雷达信号的分选。仿真实验表明,在全脉冲数据量级适中的情况下,该算法可以有效提取目标模板序列。     

基于小样本的试验系统可信度评估方法

针对小样本试验数据信息匮乏、难以利用概率统计方法进行可信度评估的问题,将重抽样方法与小样本区间估计方法相融合,提出了小样本试验系统新型可信度评估方法。利用重抽样理论,获取多个具有相同样本量的相似小样本。根据灰色系统理论,提出了改进的灰色置信度及置信区间的定义及计算方法。并在此基础上通过求公共区间的方式计算原小样本数据的灰色置信区间。该置信区间在相同的置信度下,区间宽度更窄,精度更高。通过算例仿真,验证了上述方法的合理性、有效性,对工程试验中的小样本数据系统评估提供了一定的方法指导。      

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.     

基于研制信息的测试性验证试验方案研究

针对现有测试性验证试验方案需要较大故障样本量的问题,提出了一种利用研制信息制定测试性验证试验方案的新方法。首先,在二项分布经典抽样模型的基础上,运用证据理论方法,研究了基于测试性试验数据、测试性预计结果和专家经验等研制信息的基本信任分配函数的构造方法,建立了基于融合不同种类研制信息的测试性验证试验方案,并确定了该方案的存在性,给出了方案的确定方法,最后开展了案例应用研究。由于充分考虑了研制信息,因此在确保有较好的验证效果的条件下,与传统的试验方案相比,该方法可以明显减少故障样本量,或在样本量保持不变的情况下有效降低双方风险。     

一种数字滤波器组在动态风洞试验中的应用

在动态风洞试验中,由于机械振动、随机扰动等原因,原始试验数据均有较大扰动。为得到其中的有用信息,需利用数字滤波器对其处理。基于MATLAB实现了梳状和频率抽样两种数字滤波器,对其各自特性做了较为深入的研究和比较。最后,利用二者串行组成了滤波器组。该滤波器组已经应用于预研项目中,结果表明,该滤波器组低频和高频特性均能够有效减弱信号中的干扰和噪声,达到预期目标。在不进行降采样率处理的情况下,达到相似的滤波结果,滤波器组的滤波时间仅为频率抽样滤波器的2％左右。     

基于自助法的小样本数据分析方法研究

自助法（Bootstrap）和随机加权法（Bayes Bootstrap）都是较好的处理小样本数据的方法,其无先验性,以及计算过程中只需要实际观测数据的优越性,使其广泛地应用于实际数据处理之中,后者的估计精度要更好些。但对连续情况而言,自助法的计算特性使得重抽样本局限在原始样本范围内,无法渐进于真实情况。文章基于自助法研究了用改进的样本经验分布函数来解决这个问题,并通过仿真算例说明方法的有效性。     

航空发动机可靠性评估中故障样本时效性分析

通过故障数据建立可靠性模型反映系统可靠性状态的变化具有一定的滞后性,随着发动机可靠性水平的不断提高,对航空发动机进行可靠性评估需要将早期故障样本进行时效性处理.提出衰减因子 α 用于表示故障强度的衰弱程度,对Gompertz可靠性增长模型改进得到故障的衰减函数,来描述故障强度的衰弱过程;用Bayes方法合理利用发动机全部故障样本进行数据融合,可有效解决样本的时效性问题.对在研的某涡扇发动机进行故障统计分析,研究了典型故障模式的时效性变化,结果表明:考虑样本的时效性后进行可靠性评估对发动机当前的可靠性水平能给予更加科学的评价.     

基于混合验前分布的制导精度评定方法

如何合理有效地利用验前信息是小子样评定中的关键问题。针对导弹制导精度评定,提出了一种根据验前数据构造验前分布的方法,并结合验前信息可信度给出了一种混合验前分布。通过对参数验后估计的分析,该方法能够有效避免验前信息“淹没”现场信息的情况,而验前信息可信度则通过验后边缘分布得到。通过对验后估计均方差(MSE)的分析,给出了验前信息利用的一些基本原则,为工程应用提供了指导。