Quantum dosimetry and online visualization of X-ray and charged particle radiation in commercial aircraft at operational flight altitudes with the pixel detector Timepix

We investigate the application of the hybrid semiconductor pixel detector Timepix for precise characterization, quantum sensitivity dosimetry and visualization of the charged particle radiation and X-ray field inside commercial aircraft at operational flight altitudes. The quantum counting capability and granularity of Timepix provides the composition and spectral-characteristics of the X-ray and charged-particle field with high sensitivity, wide dynamic range, high spatial resolution and particle type resolving power. For energetic charged particles the direction of trajectory and linear energy transfer can be measured. The detector is operated by the integrated readout interface FITPix for power, control and data acquisition together with the software package Pixelman for online visualization and real-time data processing. The compact and portable radiation camera can be deployed remotely being controlled simply by a laptop computer. The device performs continuous monitoring and accurate time-dependent measurements in wide dynamic range of particle fluxes, deposited energy, absorbed dose and equivalent dose rates. Results are presented for in-flight measurements at altitudes up to 12 km in various flights selected in the period 2006–2013.     

Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 − Band3)/(Band7 + Band3) ((B7 − B3)/(B7 + B3)), Band20 − Band31 (B20 − B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection.     

基于增量学习的高光谱图像目标检测

高光谱图像目标检测是高光谱图像分析中的重要研究内容之一。本文从经典有效的约束能量最小化算法出发,提出了一种基于增量学习的高光谱目标检测方法。当获得新的样本时,不需要重新计算所有样本的自相关矩阵即可对检测器模型进行更新,减轻了星上有限计算资源的负担。实验结果表明:本文提出的目标检测算法在压制背景光谱的同时可以更好地适应目标光谱,提高了算法的检测性能。     

Three-line structured light vision system for non-cooperative satellites in proximity operations

Adapter ring is a commonly used component in non-cooperative satellites, which has high strength and is suitable to be recognized and grasped by the space manipulator. During proximity operations, this circle feature may be occluded by the robot arm or limited field of view. Moreover, the captured images may be underexposed when there is not enough illumination. To address these problems, this paper presents a structured light vision system with three line lasers and a monocular camera. The lasers project lines onto the surface of the satellite, and six break points are formed along both sides of the adapter ring. A closed-form solution for real-time pose estimation is given using these break points. Then, a virtual structured light platform is constructed to simulate synthetic images of the target satellite. Compared with the predefined camera parameters and relative positions, the proposed method is demonstrated to be more effective, especially at a close distance. Besides, a physical space verification system is set up to prove the effectiveness and robustness of our method under different light conditions. Experimental results indicate that it is a practical and effective method for the pose measurement of on-orbit tasks.     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.     

面向飞行员疲劳状态监测的脑认知深度模型研究

通过分析飞行员脑电信号，构建了疲劳状态的彩色脑功率图，设计基于高斯牛顿在线变分方法的卷 积神经网络参数优化方法，形成一种新型脑功率图深度网络模型，有效实现脑功率图深度网络的模型分类识别 能力。相比于其它基于脑电信号的疲劳检测深度模型，疲劳状态认知的准确度提升了3%~5%。     

不同喷铝参数对复合材料雷击防护性能模拟

为了研究复合材料不同的雷击防护(lightning strike protection,LSP)系统在雷电流作用下的损伤规律,对雷击损伤过程和烧蚀机理进行分析,建立复合材料层合板雷击防护的能量平衡数学模型。在此基础上,在ABAQUS中建立复合材料基准件、全喷铝和局部喷铝防护系统的碳纤维增强复合材料(carbon fiber reinforced polymer,CFRP)层合板电-热耦合有限元模型,和实验结果对比验证模型的有效性,对雷击烧蚀损伤特征进行分析,并引入雷击烧蚀损伤指数DI,得出三种不同模型在不同铝涂层厚度、不同峰值雷电流作用下的烧蚀损伤规律,并对两种不同的局部喷铝防护系统下复合材料的损伤特征进行对比分析。结果表明:雷击防护系统下复合材料的烧蚀损伤面积和铝涂层厚度关系进行函数拟合,两者均满足幂函数关系。     

基于ADS-B的飞机冲突探测

面对飞行流量持续快速增长、民航空域资源日益短缺的状况,对飞机间的潜在冲突进行有效探测,是避免飞机间危险接近或碰撞发生的关键.对飞机周围区域进行合理建模,给出了一种基于广播式自动相关监视(ADS-B)技术的确定型冲突探测算法,充分利用ADS-B报文提供的经度、纬度、速度和航向等信息及其报文高更新率等特点,预先排除无威胁飞机,然后分别从水平和垂直两个方向对飞机之间的冲突进行探测.运用Matlab进行功能仿真,结果表明该算法能有效排除无威胁的飞机,并且能够对有威胁的飞机及时给出告警.该算法对保证自由飞行条件下的飞行安全具有积极意义.     

空间引力波探测微牛电推进寿命评估研究现状

阐述了空间引力波探测任务中无拖曳控制技术对推进系统的特殊要求，介绍了目前主要的备选推进类型：冷气推进、离子电推进、会切型霍尔电推进、胶体电推进。针对面向空间引力波探测任务的电推进系统的寿命评估工作进行了任务分析，报告了目前微牛级电推进系统寿命试验研究现状，举例说明了目前主要应用的电推进装置寿命预测方式，包括半经验模型预测、数值模拟预测、基于数据驱动的预测以及系统层面的可靠性评估方法。对目前微牛级电推进系统的寿命评估研究现状进行总结，给出了发展思路。     

碳纤维复合材料结构损伤的自检方法研究进展

监测碳纤维增强树脂基复合材料(CFRP)的结构损伤是一个关键问题。本文就目前国内外研究较热且应用前景较大的一类新型无损检测技术——基于CFRP导电网络的自检技术进行综述,总结了直流法和交流法的原理和技术特点,尤其是对各种检测方法适用的环境和损伤类型进行了评估。最后指出了发展出一套智能化的CFRP结构损伤自检体系所要进一步开展的研究工作。