基于引文索引的航空发动机咨询通告分析方法

引入了引文索引方法对整部航空规章中所有咨询通告进行整体上的分析,提出了一种基于引文索引的航空发动机咨询通告分析方法.首先,确定某部航空规章的咨询通告为研究对象,根据咨询通告间的相互引用关系画出引文网络图;然后,在引文网络图的基础上结合评价指标的定性分析和定量分析对咨询通告进行全面的研究分析;最后,应用此方法对美国某部航空规章的咨询通告进行实例分析,同时也说明了基于引文索引的航空发动机咨询通告分析方法的可行性.     

基于混沌遗传算法优化神经网络方法的救护直升机效能评估与指标优化研究

采用遗传算法与神经网络相结合的方法研究救护直升机的效能评估,建立了评估指标与效能评估值的非线性关系,克服了传统线性评估方法的不足,结果表明GA-BP算法能够很好地评估救护直升机效能,并保持了很高的精确度;同时,采用BP-GA算法对救护直升机的指标进行了优化,结果同样表明该方法可以有效地优化救护直升机的指标.     

情报研究成果的AHP-F评价

本文采用层次分析法与模糊数学综合译判法相结合的方法——AHP-F法,提出了综合情报评判研究成果指标体系和方法,旨在实现对情报研究成果的科学有效的管理。     

多元过程能力指数计算方法的比较研究

对有关多元过程能力指数的研究文献进行了简要梳理与分析,重点研究了4种多元过程能力指数的理论模型与计算方法,分析了4种方法各自的特点与不足,并利用一个经典实例对4种方法的测算结果进行了对比分析.     

星载光学遥感器消杂光技术现状与发展

通过对国内外杂散辐射理论发展状况的研究和消杂光技术的探讨，归纳出杂光技术领域目前的国际水平以及该项技术的发展趋势；分别从散射理论、分析软件、测试途径以及杂光抑制措施等７个方面，力图全面系统地阐述该项技术的诸方面内容。文章从国情出发，根据当前中国消杂光技术现状和未来高水平空间遥感器的要求，提出下一步中国消杂光技术发展的建议。     

九自由度运动模拟器相对位置精度分析与指标分解

摘要： 九自由度运动模拟器是标定航天器交会对接GNC子系统中交会对接光学成像敏感器（CRDS）的关键设备,具有较高的相对位置精度要求.以此为出发点,对影响系统综合指标的误差组成进行系统性分析.梳理各误差分量的关系及相互作用原理,建立误差模型,通过对系统综合指标进行合理的分解与分配,最终使九自由度运动模拟器系统精度达到设计要求.     

Sensor calibration impacts on dust detection based on MODIS and VIIRS thermal emissive bands

Dust detection using remotely sensed measurements has been one of the challenging problems encountered by atmospheric scientists. MODerate Resolution Imaging Spectroradiometer (MODIS) on the Terra (T) and Aqua (A) platforms have been a versatile sensor for well over 21 and 18 years respectively, and have been extremely useful in the retrieval of aerosol information over the entire globe. The MODIS radiances from the Level 1B in general are expected to be within 5% accuracy in the reflective wavelengths and within 1% in the thermal emissive wavelengths. In this paper, we evaluate the sensitivity of previously developed dust detection technique based on thermal emissive wavelengths, which correspond to MODIS bands 20, 29, 31, and 32 respectively. The Thermal Emissive Dust Index (TEDI) performed very comparably to the traditional Aerosol Optical Thickness (AOT) retrievals by MODIS reflective channels. Since the MODIS Thermal Emissive Bands (TEB) are well calibrated on-orbit using a BlackBody (BB) source, the calibration of these long wave infrared bands is quite robust. As A-MODIS continues to perform well beyond its designed lifetime of 6 years, the instrument has undergone various levels of degradation during its mission time. As a consequence, it is imperative to check the impacts of calibration on the higher-level retrievals. In this paper, we rigorously analyze the sensitivity of TEDI due to the impact of calibration by the afore-mentioned TEB. The perturbation of the dominant (linear) calibration term demonstrated the following: first, there was a correlation in the sensitivity of the TEDI due to the uncertainty in the linear calibration term. Based on a perturbation in the linear calibration term for all aforementioned bands over a range of ±5% yielded the TEDI sensitivity to vary from approximately ?3.2% to about ?3.6%. When considering the uncertainty in each individual band significant changes were observed. The least change was observed for the perturbation in the calibration of band 20 with the TEDI sensitivity and the largest sensitivity in TEDI was observed in the perturbation of band 31 calibration. Thus, in the case of TEDI, noticeable sensitivity due to calibration uncertainty was observed in bands 29, 31, and 32, reiterating the importance of the TEB calibration in these bands. Also, the dust detection scheme based on A-MODIS was successfully transferred to the follow-on sensors such as Suomi (SNPP) and NOAA 20 (N20) VIIRS. The results presented in this paper would be extremely helpful in understanding impacts of calibration on the higher-level products for both current and future missions based on the MODIS heritage. Finally, the work also identifies the importance of radiometric fidelity in maintaining the accuracy of the dust detection. Results presented will show drastic improvement of the Saharan dust detection after the reduction of the electronic crosstalk in the 8.5 µm channel of T-MODIS.     

改进暗通道遥感影像去雾方法及效果分析

遥感影像的预处理工作是遥感数据应用的基础。去除云雾是影像预处理工作的重要组成部分。针对遥感影像雾霾浓度分布不均匀的问题,提出一种改进的暗通道遥感影像去雾方法。以"高分一号"(GF-1)卫星为例,根据影像灰度图中的灰度值对影像雾霾浓度区域进行划分,对每个区域中暗原色值的获取方式进行改进,使用导向滤波优化大气传输率,以归一化植被指数(NDVI)为基础,设计用于评价影像去雾质量的定量指标。结果表明:所提出的方法能明显去除雾霾干扰,有效改善卫星影像数据的视觉效果,增强影像细节。该方法去雾处理后的遥感数据能应用于定量遥感,提高遥感影像的可用性及有效性。     

基于HyperFLOW平台的客机标模CHN-T1气动性能预测及可信度研究

近年来,针对实际飞行器外形的CFD气动性能预测及可信度研究逐步得到重视,国内也召开了第一届航空CFD可信度研讨会(AeCW-1)。本文首先基于自主研发的CFD软件平台HyperFLOW对NACA0012翼型低速绕流进行了网格收敛性研究,验证了软件对简单湍流问题的模拟能力且具备良好的网格收敛性。其次,针对AeCW-1提供的客机标模CHN-T1,选用其中的两个算例:(1)定升力系数的网格收敛性研究;(2)考虑模型支撑和模型静气动弹性变形的抖振特性研究,研究了计算结果的网格收敛性及模型支撑、静气动弹性变形和湍流模型等对气动特性预测精度的影响。结果表明:观测精度阶和网格收敛性指数显示数值结果具有良好的网格收敛性和可信度;是否考虑模型支撑对力矩的预测精度影响较大,引入尾撑和弹性变形后,数值结果与实验结果吻合较好;对于CHN-T1标模,采用QCR关系式对原始SA模型进行修正对标模力矩特性有一定影响。     

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.