夹具质量对标准测力仪的影响

分析了用试验机进行力学性能检定过程中，夹具的质量对标准测力仪造成的影响，并通过分析原因，给出消除或减小这种影响的方法。     

一种激光长刻机的工作原理

介绍了一种静态激光长刻机的工作原理,并介绍了该光刻方法的关键技术和工艺.     

地面系留设施万向滑轮结构优化设计

基于系留缆绳换向及缆绳拉力和倾角的测量,提出了一种优化的机动式系留气球万向滑轮结构设计方案,描述了万向滑轮结构形式、材料选择及工艺实现等,采用三维建模及有限元分析相结合的方法,对万向滑轮结构进行校核分析。结果表明,该结构满足刚度和强度要求,具有较广泛的工程应用价值。     

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.     

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.     

发动机高模试验技术研究

基于ANSYS软件平台,建立了发动机高模试验系统传动轴强度和疲劳数值仿真模型,并进行了数值仿真与分析计算,研究了传动轴强度和疲劳与位移之间的关系,得出了传动轴设计准则.通过对发动机高模试验系统扩压器与氮气破空设备的研究、分析与计算,得出了储能气缸和氮气破空环管的设计准则.采用该设计准则设计的发动机高模试验系统解决了发动机试验启动过程压力过高、回火严重、发动机喷管变形等问题,试验系统满足发动机设计对高模试验的要求.通过该试验系统考核的发动机已成功应用于发射卫星的运载火箭系统.     

The future role of relay satellites for orbital telerobotics

Orbital robotics focuses on a variety of applications, as e.g. inspection and repair activities, spacecraft construction or orbit corrections. On-Orbit Servicing (OOS) activities have to be closely monitored by operators on ground. A direct contact to the spacecraft in Low Earth Orbit (LEO) is limiting the operational time of the robotic application. Therefore, geostationary satellites are desirable to relay the OOS signals and extend the servicing time window. A geostationary satellite in the communication chain not only introduces additional boundary conditions to the mission but also increases the time delay in the system. The latter is not very critical if the servicer satellite is operating autonomously. However, if the servicer is operating in a supervised control regime with a human in the loop, the increased time delay will have an impact on the operator’s task performance.     

影响聚四氟乙烯材料加工表面粗糙度因素的研究

在现代航空发动机的产品中,保护圈、密封件等零件广泛使用聚四氟乙烯材料制造.在加工中多采用车削加工,粗糙度一般为Ra1.6,本文通过选用不同车床、刀具、加工参数的方法,提高该类材料加工后的表面粗糙度,为后续加工提供经验和数据.     

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.