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.     

RATAN-600 and RadioAstron reveal the neutrino-associated blazar TXS 0506+056 as a typical variable AGN

The possible association with the high-energy neutrino event IceCube-170922A has sparked interest in the blazar TXS 0506+056. We present 72 instantaneous 1–22 GHz spectra measured over the past 20 years with the RATAN-600 telescope and compare them with the results of observations of 700 variable Active Galactic Nuclei (AGN) studied within the same program. The recent radio flare of TXS 0506+056 started from a minimum in 2013 and reached its first peak in December 2017 and a second peak in May-June 2018. This was the third strong radio flare in this source since 1997. The spectrum remains nearly flat during the flares. The spectral shape and variability pattern observed in TXS 0506+056 are typical for variable AGN. RadioAstron Space VLBI observations in 2013–2015 did not detect TXS 0506+056 on space-ground baselines of more than 9 Earth diameters. However, an observation on 23 September 2015 resulted in the detection of interferometric signal on 6 Earth diameter baselines at 18 cm close to the detection limit. We consider the possibility that TXS 0506+056 and other AGN may accelerate relativistic protons more efficiently than electrons. Relativistic protons are necessary to produce both the high-energy neutrinos observed in the IceCube Observatory and the high AGN brightness temperatures implied by the RadioAstron detection. They may also provide the main contribution to the observed synchrotron radiation of parsec-scale AGN jets. This supports the suggestion that relativistic protons may play a much more important part in extragalactic astrophysics than earlier anticipated.     

Assessment study of ionosphere correction model using single- and multi-shell algorithms approach over sub-Saharan African region

It is a known fact that ionosphere is the largest and the least predictable among the sources of error limiting the reliability and accuracy of Global Navigation Satellite Systems (GNSS) and its regional augmentation systems like Satellite Based Augmentation System (SBAS) in a safety-of-life application. The situation becomes worse in the Equatorial Ionization Anomaly (EIA) region, where the daytime ionization distribution is modified by the fountain effect that develops a crest of electron density at around ±15° to ±20° of the magnetic equator and a trough at the magnetic equator during the local noon hours. Related to this phenomenon is the appearance of ionosphere irregularities and plasma bubbles after local sunset. These may degrade further the quality of service obtained from the GNSS/SBAS system of the said periods. Considering the present operational augmentation systems, the accuracy and integrity of the ionosphere corrections estimate decreases as the level of disturbances increases. In order to provide a correct ionosphere correction to the user of GNSS operating in African EIA region and meet the integrity requirements, a certified ionosphere correction model that accurately characterizes EIA gradient with the full capacity to over-bound the residual error will be needed. An irregularities detector and a decorrelation adaptor are essential in an algorithm usable for African sub-Saharan SBAS operation. The algorithm should be able to cater to the equatorial plasma vertical drifts, diurnal and seasonal variability of the ionosphere electron density and also should take into account the large spatial and temporal gradients in the region. This study presents the assessment of the ionosphere threat model with single and multi-layer algorithm, using modified planar fit and Kriging approaches.     

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。     

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。     

基于 Kriging与多点取样算法的压气机特性建模

针对航空燃气轮机流量系数插值建模过程中,获取样本数据时的实验成本较高以及插值模型精度偏低这 2个问题,根据样本点在设计空间中的分布与模型精度的关系,在 Kriging插值模型的基础上,采用把垂距作为设计变量取值标准的取样算法。首先,将样本点与相邻 2点之间连线的垂距与垂距阈值进行比较,筛选出符合条件的数据点,形成候选点集;其次,选用高斯函数判定基础点集和候选点集的相关性;最后,使用该算法筛选后的样本点构建 Kriging插值模型。实验结果表明,该方法使用的样本点为原来数量的 70%左右,并且预测精度得到了提高,该方法是有效可行的。     

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T) has changed the way that network services are developed, deployed, and operated. Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and t...     

一种模块级可降级重组双机系统的系统管理程序实现方法

分析了模块级可降级重组双机系统的有效组态数,并与双机系统级备份进行了比较。分析结果表明,随着冗余级别的降低,系统的可靠性有所改善,但系统的有效组态数将大大增加,这将导致系统管理程序的复杂化。文章介绍了该系统管理程序的设计准则,并对其中几个有特点的子程序作了简要说明。