高频大电压标准器

主要阐述了该标准器的基本原理,从理论上对结构补偿法进行了分析和数学推导,当R/Z_o=1.9～2.0之间时,具有最小的交直流差。设计了匹配的同轴热电转换标准器;进行了误差分析;给出了试验结果,说明该标准器交直流差的重复性和复现性是好的。     

传感器故障诊断方法研究

对近年来传感器故障诊断和信号恢复的方法，包括硬件冗余、分析冗余和人工神经网络方法作了总结，并指出了它们的优缺点便为进一步研究传感器故障诊断和信号恢复方法提供启示。     

固体发动机喷管烧蚀和温度场测量

介绍了含铝复合推进剂发动机试验时,对其喷管进行的烧蚀和温度场测量,获得了复合喷管结构中的烧蚀和瞬时温度分布.     

燃速仪压强的自动控制方案

本文介绍了一种燃速仪压强的自动控制方案.这种方案结构简单,工作可靠.经过燃速实验测定,在4MPa压强时,其压强波动相对误差小于±1%.该方案应用于固体推进剂中止实验时,没有废气回流污染中止表面,也不影响降压速率的测量.     

风洞模拟近地面大气边界层

本文通过大气边界层风洞的模拟实验，介绍一种在风洞中模拟近地面大气边界层的方法。根据大风地区的现场实测资料，对大气的风剖面及湍流度等有关参数，提出了一些特殊的要求。常规的模拟方法难以保证与实际风环境的相似，为此专门研制了一种在保证平均风剖面相似的前提下，可使气流产生大湍流度的装置，简称湍流度调节器。通过对模拟边界层的平均风速，湍流度及风速谱的测量分析，证明与实际风区大风环境的风结构相当一致。为解决实     

超音速燃烧室燃烧效率数学模型及气流状态参数的计算

本文分析及提出了超音速燃烧冲压发动机燃烧室燃烧效率的数学模型.该模型综合了氢气喷射方式、燃烧室进口气流参数以及燃烧室结构的影响因素.用这一数学模型求解一组一元流方程.计算出通过燃烧室的气流状态参数,计算结果与试验数据对比,证明这个模型是适用的.     

Ll-CFAR: a flexible and robust alternative

A new family of constant false alarm rate (CFAR) processors is introduced. An Ll-CFAR forms its noise power estimate by linearly filtering ranked samples from the reference set; the weights of this combination, however, depend not only on the rank, but also on the relative proximity of the sample to the cell under test. From the class of Ll-CFARs may be chosen members which effectively censor spurious targets; members which exhibit impressive control of false alarm in the presence of a clutter edge; and members which are robust against both such inhomogeneities. While the design of such schemes is involved, their implementation is not significantly more burdensome than that of plain ordered statistic CFAR (OS-CFAR). After a discussion of the stochastic training of Ll-CFAR, the performance is thoroughly assessed under the most commonly encountered instances of environmental conditions, and compared with those of classical CFAR techniques     

FAST FEATURE RANKING AND ITS APPLICATION TO FACE RECOGNITION

A fast feature ranking algorithm for classification in the presence of high dimensionahty and small sample size is proposed. The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations, whereas neighboring points of different classes are no longer to stick to one an- other. Applying this assumption, an optimization problem weighting each feature is derived. The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time. Extensive exper- iments are conducted to validate the significance of selected features using the Yale, Extended YaleB and PIE data- sets. The thorough evaluation shows that, using one-nearest neighbor classifier, the recognition rates using 100-- 500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms, while using support vector machine features selected by the algorithm show less prominent improvement. Moreover, the experiments demonstrate that the proposed algorithm is particularly effi- cient for multi-class face recognition problem.     

计算机在光栅和线纹检测中的应用

介绍了一个将PC机和8031单片机组成的计算机系统应用于光栅尺、线纹尺检测的实例。     

Angle estimation for two unresolved targets with monopulse radar

Most present-day radar systems use monopulse techniques to extract angular measurements of sunbeam accuracy. The familiar "monopulse ratio" is a very effective means to derive the angle of a single target within a radar beam. For the simultaneous estimation of the angles of two closely-spaced targets, a modification on the monopulse ratio was derived in (Blair and Pearce, 2001), while (Sinha et al., 2002) presented a maximum likelihood (ML) technique via numerical search. In this paper it is shown that the ML solution can in fact be found explicitly, and the numerical search of ((Sinha et al., 2002) is unnecessary. However, the ML solution requires the signal to noise ratio (SNR) for each target to be known, and hence we generalize it so it requires only the relative SNR. Several versions of expectation maximization (EM) joint angle estimators are also derived, these differing in the degree to which prior information on SNR and on beam pattern are assumed. The performances of the different direction-of-arrival (DOA) estimators for unresolved targets are studied via Monte Carlo, and it is found that most have similar performance: this is remarkable since the use of prior information (SNR, relative SNR, beam pattern) varies widely between them. There is, however, considerable performance variability as a function of the two targets' off-boresight angles. A simple combined technique that fuses the results from different approaches is thus proposed, and it performs well uniformly.