Asteroid and comet hazard: Identification problem of observed space objects with the parental bodies

This article focuses on the genetic identification of observed small cosmic bodies with alleged parental bodies; namely, comets, asteroids and meteoroid swarms. There is a problem of the upper D-value limit as a measure of proximity between the orbits of the bodies in the five-dimensional phase space (Southworth and Hawkins, 1963). In the study of genetic relationships of the comet and meteor complexes, the D value is usually taken as equal to 0.2 for all meteor showers. However, the upper D limit should be investigated for each meteoroid complex. For example, such investigation was performed for the Taurid meteor complex (Porub?an et al., 2006). In this paper, the upper D-criterion limit value was investigated for the Perseid meteor shower. The 1862 III Swift–Tuttle comet is its parental comet.     

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。     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

小积液情况下卧式转子的稳定性实验研究

盘腔积液现象在舰用燃气轮机工作过程中时有发生，汽化的滑油和水蒸气凝结形成积液。转子带动腔内的积液旋转，引发振动失稳，严重时会对发动机结构造成致命性破坏。为此，本文开展燃气轮机压气机盘腔积液的模拟实验研究，以双盘双支承的卧式柔性转子系统作为研究对象，重点关注小积液量的特点，对积液转子的动力学稳定性进行了研究。观测到转子的失稳现象，开展了不同积液体积、不同积液类型（水、植物油和滑油）等因素的影响规律研究，揭示了小积液的卧式转子动力学特征。研究结果表明：①对于积液失稳振动，存在失稳边界积液量，当积液量大于该边界值时，失稳将会发生。②失稳边界转速及恢复转速均高于临界转速。当转速高于失稳边界转速时，失稳将会发生。转速进一步增加，高于恢复转速时，失稳现象可能消失。③失稳时的振动特征为：出现幅值突增，次谐波成分也随之增加；基频和次谐波发生调制，表现出拍振特征。④随着积液体积的增加，失稳边界转速先减小后增加。⑤随着积液粘度系数增大，失稳边界转速和边界积液量均增大。     

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。     

Improving orbit prediction accuracy through supervised machine learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.     

我国高校无形资产经营管理分析

高等学校的无形资产经营管理是市场经济条件下各个高校需要解决的一个难题和重点。分析我国高校无形资产经营管理的现状及其问题,并给出相关政策建设。对加快相关的法规建设,规范高校无形资产经营管理具有现实意义。     

复合材料钻削刀具状态集成学习分类技术

刀具状态监测关系到工件加工质量,因此实时掌握刀具的磨损状态具有重要的意义。针对单一模式识别分类器的局限性,提出了基于异态集成学习模型的刀具状态监测系统。在该系统中,根据集成学习基分类器选择原则,选取了支持向量机(SVM)、隐马尔科夫模型(HMM)以及径向基神经网络(RBF)这3个单分类器作为基分类器。为了验证监测系统的有效性,进行了复合材料钻削实验,提取了加工过程中的钻削力信号、振动信号的时域特征并利用局部保持法(LPP)进行了特征选择。通过与单分类器和集成学习分类效果的对比,表明了集成学习模型相比于单一基分类器具有更高的分类精度和更好的稳定性。     

Learning Routes from Visualizations for Indoor Wayfinding: Presentation Modes and Individual Differences

Abstract

Participants (N = 78) studied a visualization of a route through a complex building and walked that route in the real building without further assistance. Erroneous turns on the route as well as indicators of uncertainty such as hesitations were assessed. Three types of route visualizations were compared: (1) an allocentric, map-based visualization with the route indicated in floor maps, (2) an ordered sequence of pictures of decision points shown from the egocentric perspective, and (3) an animation showing a virtual walk of the route from the egocentric perspective. In addition to the experimental variation, gender differences, differences in visual-spatial abilities and differences in self-reported wayfinding strategies were considered as predictor variables. Wayfinding performance did not differ between allocentric (map) and egocentric (decision point pictures and animation) visualizations. However, wayfinding performance was better with animated than with static egocentric visualizations. Individual differences in the ability to encode visual-spatial information from the visualization played a critical role for route learning. Self-reported sense of direction related to egocentric wayfinding strategies also predicted wayfinding performance. Gender differences were attributable to differences in visual-spatial abilities and egocentric wayfinding strategies. Interactions between visualizations and individual differences were not found. It is concluded that animations of virtual walks are suitable to convey route information in complex buildings. Successful acquisition of route knowledge from maps is possible but might depend on the comprehensibility of the structure of the building.