基于混合求解法的二维多段翼气动噪声特性分析

基于混合求解法,通过对声源区计算域进行分区,研究了二维多段翼不同部件对远场声压级的贡献,给出了声场分布和远场指向性,发现前缘缝翼噪声和主翼尾缘噪声对远场的贡献较小,襟翼后缘噪声的贡献在总声压级中占主导地位,是值得关注的噪声来源。     

基于流固耦合方法的柴油机排气管热机疲劳寿命研究

由于高功率大缸径柴油机的排气管处于高温、高压、强冲击的工作环境,承受复杂恶劣的热机载荷,使得疲劳失效问题日渐突出.为解决工程中的排气管疲劳失效故障,基于流固耦合仿真分析的方法,研究了多通排气管在热载荷、机械载荷和热机耦合载荷三种不同条件下的疲劳损伤机理以及低周疲劳寿命.结果表明,单纯交变热载荷不足以造成排气管低周疲劳失...     

基于回归分析的低空空域资源可用性评价方法

低空空域资源一直是通用航空产业发展的瓶颈,是制约我国通用航空发展的关键因素之一。基于理论研究和实地调研,构建低空空域资源可用性评价指标体系,运用多元线性回归法对影响低空资源的军/民航各项因素进行系统分析,确定各级指标权重;以江苏省为例,深入分析该省低空可利用资源的现状,总结各地区实际情况和大量数据,通过多元线性回归结果显示各项因素对该地区的影响值;最后得出低空空域资源可用性结果。结果表明:采用回归分析方法分析低空空域资源,所得结果是科学且有效的,可为我国低空空域资源合理利用以及通用航空活动空域申报提供参考。     

A compositional method to model dependent failure behavior based on PoF models

In this paper, a new method is developed to model dependent failure behavior among failure mechanisms. Unlike the existing methods, the developed method models the root cause of the dependency explicitly, so that a deterministic model, rather than a probabilistic one, can be established. Three steps comprise the developed method. First, physics-of-failure(PoF) models are utilized to model each failure mechanism. Then, interactions among failure mechanisms are modeled as a combination of three basic relations, competition, superposition and coupling. This is the reason why the method is referred to as ‘‘compositional method". Finally, the PoF models and the interaction model are combined to develop a deterministic model of the dependent failure behavior. As a demonstration, the method is applied on an actual spool and the developed failure behavior model is validated by a wear test. The result demonstrates that the compositional method is an effective way to model dependent failure behavior.     

基于MIMO圆阵雷达的二维DOA估计新算法

基于对均匀圆阵导向矢量的分析,将四元均匀圆阵作为多输入多输出(MIMO)雷达的发射阵,通过构造相应的二阶统计量并对其进行联合对角化获得了目标方位角和俯仰角的闭式解,以实现存在角度兼并时目标的二维到达方向(DOA)的估计,并保持原DOA矩阵方法无需二维谱峰搜索和参数配对等优点。仿真结果证明算法正确、可行。     

Multiple-constraint cooperative guidance based on two-stage sequential convex programming

An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target. The problem with time-varying Proportional Navigation (PN) gain is first formulated as a nonlinear optimal control problem, which is difficult to solve due to the existence of nonlinear kinematics and nonconvex constraints. After convexification treatments and discretization, the solution to the original problem can be approximately obtained by solving a sequence of Second-Order Cone Programming (SOCP) problems, which can be readily solved by state-of-the-art Interior-Point Methods (IPMs). To mitigate the sensibility of the algorithm on the user-provided initial profile, a Two-Stage Sequential Convex Programming (TSSCP) method is presented in detail. Furthermore, numerical simulations under different mission scenarios are conducted to show the superiority of the proposed method in solving the cooperative guidance problem. The research indicated that the TSSCP method is more tractable and reliable than the traditional methods and has great potential for real-time processing and on-board implementation.     

The effect of solar and geomagnetic parameters on total electron content over Ankara,Turkey

In this study, the relationship between total electron content (TEC) and solar and geomagnetic parameters for Ankara station (39.7 N, 32.76 E), Turkey located in the mid-latitude ionosphere is investigated. In this context, F10.7 solar flux and Interplanetary Magnetic Fields (IMF) from solar parameters and Kp and Dst indices from geomagnetic parameters affecting on TEC are considered. The relationship between the variables is investigated by means of the statistical multiple regression model at the universal time (UT) (Local Time = UT + 2 h) 1200 and 2400 in the years when the 24th solar cycle was minimum (2007–2009) and maximum (2015). As a result, it is found that explainable rates by solar and geomagnetic parameters of TEC changes in 2007–2009 are lower than in 2015 at daytime, while the explainable rates in the solar minimum years are higher than those the maximum year at nighttime. To be higher than the solar maximum of explainable rate in the solar minimum years at nighttime may be related to the fact that the dynamics of the ionosphere is significantly different than expected in this deep minimum period. As expected in 2015, the relationship between TEC and independent parameters is greater at daytime than at nighttime.     

复杂产品系统的风险管理实现研究

提出了一套跨研制单位持续项目风险管理机制.在其风险因素识别阶段.提出了以核对表和鱼刺图为基础的基于特征一因子法的风险因素识别法;在风险量化阶段,提出了先基于模糊综合评判进行单个风险量化、后基于改进PERT方法进行风险整体量化的实现方法:在风险应对阶段,提出了基于人工神经网络和框架知识表示及推理相结合的风险应对方法,建立了混合型专家系统.以实现风险应对智能化.最后,以具体实例说明了该项技术的可行性.     

服务化多工艺设计研究

提出了服务化的多工艺设计方法。建立了基于过程规范语言的完整多工艺信息模型,提供统一的工艺数据访问服务和工艺业务服务调用。最后,给出了服务化多工艺设计平台体系结构,采用开源的面向服务架构技术进行了系统实现。     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.