Decision table for classifying point sources based on FIRST and 2MASS databases

With the availability of multi-wavelength, multi-scale and multi-epoch astronomical catalogues, the number of features to describe astronomical objects has increases. The better features we select to classify objects, the higher the classification accuracy is. In this paper, we have used data sets of stars and quasars from near-infrared band and radio band. Then best-first search method was applied to select features. For the data with selected features, the algorithm of decision table was implemented. The classification accuracy is more than 95.9%. As a result, the feature selection method improves the effectiveness and efficiency of the classification method. Moreover the result shows that decision table is robust and effective for discrimination of celestial objects and used for preselecting quasar candidates for large survey projects.     

统计模式识别方法在高压压气机振动监测中的应用

本文研究了统计模式识别方法在某高压压气机振动监测中的应用，主要涉及了主成分提取算法和距离判别算法。研究结果表明，该方法可以应用到该高压压气机振动监测中去，并且获得了较好的结果。     

高超声速飞行器底层性能评价指标的可行性分析

针对控制性能指标是否适用于高超声速飞行任务评价，研究了底层指标的可行性分析方法。首先分析了高超声速飞行器的模型特性，指出底层指标具备高超声速飞行器评价可行性的两点要求。根据上述要求将可行性分析转化为对于任务变量到底层评价指标的映射分析，并提出了一种基于统计实验分析的检验方法。结合高超声速飞行器模型特点，进一步设计了可行性分析流程。然后，分别以静态任务要求和速度、高度跟踪任务要求为例，对应提出频域性能指标和时域性能指标的可行性分析方法。最后针对频域性能指标评价静态任务的可行性设计仿真实验加以验证。仿真结果表明可行性分析的结论与军标观点一致，而且所提出的分析流程具有较好的可实现性。     

基于MATLAB下的磁性测量新方法

在简单介绍磁性测量冲击法基本原理的基础上,提出了一种将多元回归分析应用于磁性测量中的新方法。着重讲述了如何实现基于多元回归分析法下的磁性测量新方法,借助于MATLAB语言编辑可实现多元回归算法及磁测新方法的多个可嵌套调用的GUI界面。实践证明,使用MATLAB可实现磁性测量新方法,该方法大大缩短了测量周期,所编辑的GUI界面充分利用了MATLAB函数库,可快速精确的处理磁测数据,建立回归模型,保存并显示计算结果,且操作方便。     

统计过程控制在我国军工企业质量管理中的应用

统计过程控制是将数理统计方法与质量专业有机地结合起来,对现场生产过程进行及时监控检查的控制技术。通过介绍统计过程控制发展背景、原理及应用,阐述了在军工企业中推行统计过程控制的必要性及重要现实意义。     

Influence of stealth aircraft dynamic RCS peak on radar detection probability

For modern stealth aircraft, it is important to analyze the influence of Radar Cross Section (RCS) peak exposure on penetration for guiding stealth design and penetration trajectory planning, which needs to reflect the RCS statistical uncertainty and the RCS difference with the change of incident angle. Based on the RCS characteristics of typical stealth aircraft, this paper established a simplified RCS dynamic fluctuation statistical model with the parameters log mean and log standard deviation. According to the detection probability algorithm in radar signal processing field, this paper built the algorithm of radar detection probability based on the RCS dynamic fluctuation statistical model. The analysis of examples concluded that the key to successful penetration is to shorten the RCS peak exposure time, which can be reduced by decreasing the RCS peak width or increasing velocity. Based on the conclusion, this paper proposed the method of turning maneuvering to reduce RCS peak exposure time dramatically.     

Cv检验法的提出及其应用

在实践中发现产品性能参数的变差系数CV可当作常量，使用CV已知条件进行检验分析比较方便。于是，提出CV检验法，研究CV检验法的应用。     

疲劳裂纹扩展随机模型

 应用随机理论与实验数据相结合的方法建立了疲劳裂纹扩展参量与时间关系的随机模型,根据随机模型建立了相应的随机微分方程,求解了有关的统计分布函数,并应用随机模拟方法再现了疲劳裂纹扩展的时间历程。     

民用飞机维修间隔优化调整方法研究

航空公司对服役后飞机的初始维修大纲进行优化调整,是为了在保证安全性与可靠性的同时降低维修成本,而优化调整的实施往往基于航空公司针对每个MRB维修任务所报告的服役数据来进行.为了提高服役数据的评估质量,在理论研究和大量工程实践的基础上,建立了相应的分析流程及计算模型,用以辅助维修任务时间间隔调整的决策.     

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control (MSPC) and stream of variations (SoV) is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error (OSFE) is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.