Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

卷积神经网络和峭度在轴承故障诊断中的应用

针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。      

飞行器智能设计愿景与关键问题

未来飞行器正朝着多元化、无人化和智能化的方向发展，高超声速、超隐身和变体等新型飞行器不断涌现。而传统飞行器解耦分拆的设计方法越来越难以满足未来飞行器综合性能全面提升的要求，只有通过整体化设计才能充分发掘飞行器的潜能。通过分析传统飞行器设计中存在的问题，提出满足全生命周期要求的飞行器智能设计体系理念，利用知识库的构建将智能赋予飞行器平台系统设计、制造生产和运维这3个阶段，并通过数字孪生技术进行飞行器全生命周期的仿真、分析和预测，以对飞行器设计、运行等数据进行更新，使该体系形成闭环。就飞行器智能设计体系中需要的关键技术及涉及的科学问题等进行了讨论，并给出了未来发展方向以供参考。     

航空发动机限寿件疲劳可靠度计算新方法

针对目前的航空发动机限寿件（ELLP）疲劳可靠性分析中的小失效概率事件以及其极限状态函数具有较强非线性的特点，提出了一种具有自更新机制的半径外自适应重要抽样（AUMCROAIS）疲劳可靠性分析方法。该方法首先利用蒙特卡罗自适应重要抽样（MCAIS）快速逼近真实设计验算点（MPP）附近，随后以近似设计验算点为中心进行极坐标抽样，并依次构造主动学习函数，对近极限状态函数和抽样半径进行最优选取，从而实现最优抽样半径的更新，通过不断的更新确定出最优抽样半径，加速失效概率计算的收敛。本方法提高了设计验算点的收敛速度同时保证了计算精度，解决了小失效概率事件以及强非线性极限状态函数可靠度计算难题，最后以某型发动机压气机轮盘为对象应用本方法，并与传统的蒙特卡罗仿真（MCS）方法、蒙特卡罗半径外自适应重要抽样法（MCROAIS）和一阶可靠性方法（FORM）进行了对比，验证了本方法的高效率、鲁棒性和仿真精度。     

基于不确定性的飞行器分离可靠性建模与分析方法

为实现飞行器分离任务可靠性的定量分析和高效精确评估，研究了高超声速飞行器分离任务过程中各种不确定性因素对分离可靠性的影响，提出一种基于不确定性的飞行器分离可靠性建模与分析方法。面向高超声速飞行器分离任务需求，建立分离动力学仿真模型，综合考虑分离过程不确定性因素的影响，利用灵敏度分析方法识别主要不确定性因素，构建分离可靠性模型。针对此模型，提出一种改进主动学习Kriging(IAK)的分离可靠性分析方法，通过新的采样策略选取失效概率更大的采样点作为新增训练点，进行高效可靠性分析。实例结果表明，该方法能够准确描述不确定性因素对分离过程的影响，提升分离可靠性定量分析的精度和效率，为飞行器分离方案的精细化设计提供支撑。     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。     

基于核主成分分析的空域复杂度无监督评估

空域复杂度评估作为衡量空域运行态势、管制员工作压力的关键手段，是运行调控的基础。由于影响因素众多，不同因素间耦合关联复杂，且标定样本很难获取，空域复杂度的准确评估被公认为航空领域的一个挑战性问题。提出了一种空域复杂度的无监督评估方法。首先通过核主成分分析挖掘原始样本各维度的非线性耦合关系，准确提取能够最大化复杂度评估信息量的主成分，进一步设计了可按需定制的主成分聚类方法，实现了无监督条件下空域复杂度的准确评估，为空域划分、流量管理提供了有效的技术支撑。     

Stall flutter prediction based on multi-layer GRU neural network

The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model (ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit (GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation (LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.     

基于集成学习模型的飞行学员认知负荷研究

在飞行过程中,飞行员需要在短时间内接收大量信息,并做出正确的判断与决策,而过高的认知负荷会影响其感知、判断、决策等认知过程,进而影响飞行安全。首先通过飞行模拟实验获取飞行学员在执行不同飞行任务时的生理数据;然后通过时域、频域分析等方法提取呼吸和心电信号的特征,并通过统计学方法筛选出能够反映认知负荷水平的指标;最后结合支持向量机、K 最邻近、人工神经网络等方法建立集成学习模型,对飞行学员的认知负荷进行评估,并与单一算法进行对比。结果表明：本文建立的集成学习模型具有较高的准确率,能够更好地反映飞行学员认知负荷水平。