一种新的导航星选取算法研究

在导航星表的建立过程中，由于恒星的数量太多，往往要进行筛选，通常这种选择复杂费时，而结果往往并不是最优的。本文引入了动态星等阈值分布函数，将传统星等阈值过滤算法中的静态阈值用动态星等阈值代替，建立了一种新的动态星等阈值过滤选择模式。而基于统计学习理论的支持向量机方法为求解高维非线性动态星等阈值分布函数提供了新的途径。本文讨论了这种基于支持向量机的导航星自动选择算法——回归选取算法。实验表明，用该算法所选取的导航星表，导航星数量少、分布均匀性好。同时它还能适应多种任务的导航星选取要求，具有很强的通用性。     

单脉冲雷达导引头抗压制式干扰时域仿真

为研究单脉冲主动雷达导引头在自卫干扰(SSJ)和非SSJ条件下的抗干扰性能,建立了单脉冲角跟踪系统对SSJ和非SSJ压制式噪声干扰源进行跟踪的时域模型。仿真结果表明,在SSJ条件下,角跟踪回路的抗压制式噪声干扰性能较佳,跟踪情况与目标视线角速度有关;在非SSJ条件下,当导引头接收天线处远距支援干扰(SOJ)强于目标回波信号时,SOJ会引入较大的天线指向偏差。所建模型可用于雷达导引头抗干扰性能的仿真研究。     

基于引力球结构支持向量机多类算法的涡轮泵故障诊断

在涡轮泵故障诊断中，多类故障诊断是经常出现的问题。为提高多类故障诊断速度，在球结构支持向量机的基础上，提出一种引力球结构的支持向量机多类算法，该算法充分考虑样本分布疏松程度，经过试验优化分析得到最佳分类引力公式。用该算法和其他常用算法对涡轮泵仿真故障进行分类比较，结果表明基于引力球结构的支持向量机故障诊断算法学习速度快，诊断效果好。     

改进布谷鸟搜索算法优化支持向量机的MEMS陀螺温度零偏补偿

针对微机械陀螺零偏受温度影响较大的问题，提出一种改进布谷鸟搜索算法(CS)和支持向量机(SVM)相结合的陀螺零偏温度补偿方法。首先，将平滑处理后的陀螺数据作为样本点，采用基于径向基核函数的支持向量机方法构建漂移模型，把数据从低维空间映射到高维空间进行线性拟合。然后，利用改进布谷鸟算法对支持向量机的惩罚参数、核函数参数以及不敏感系数进行优化，避免了人为选择参数的盲目性且提高了建立模型的精度。实验结果表明：经CS调节支持向量机算法补偿后，陀螺输出精度更高。与最小二乘分段拟合方法、BP神经网络方法相比，陀螺输出数据方差分别平均减小了63.2%、43.4%，最大误差分别平均减小71.63%、48.3%。     

Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines

A hybrid calibration approach based on support vector machines (SVM) is proposed to characterize nonlinear cross coupling of multi-dimensional transducer. It is difficult to identify these unknown nonlinearities and crosstalk just with a single conventional calibration approach. In this paper, a hybrid model comprising calibration matrix and SVM model for calibrating linearity and nonlinearity respectively is built up. The calibration matrix is determined by linear artificial neural network (ANN), and the SVM is used to compensate for the nonlinear cross coupling among each dimension. A simulation of the calibration of a multi-dimensional sensor is conducted by the SVM hybrid calibration method, which is then utilized to calibrate a six-component force/torque transducer of wind tunnel balance. From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix. Moreover, with the calibration matrix, the hybrid model can provide a basis for the design of transducers.     

Silkworms culture as a source of protein for humans in space

This paper focuses on the problem about a configuration with complete nutrition for humans in a Controlled Ecological Life Support System (CELSS) applied in the spacebases. The possibility of feeding silkworms to provide edible animal protein with high quality for taikonauts during long-term spaceflights and lunar-based missions was investigated from several aspects, including the nutrition structure of silkworms, feeding method, processing methods, feeding equipment, growing conditions and the influences on the space environmental condition changes caused by the silkworms. The originally inedible silk is also regarded as a protein source. A possible process of edible silk protein was brought forward in this paper. After being processed, the silk can be converted to edible protein for humans. The conclusion provides a promising approach to solving the protein supply problem for the taikonauts living in space during an extended exploration period.     

Low light intensity effects on the growth,photosynthetic characteristics,antioxidant capacity,yield and quality of wheat (Triticum aestivum L.) at different growth stages in BLSS

Minimizing energy consumption and maximizing crop productivity are major challenges to growing plants in Bioregenerative Life Support System (BLSS) for future long-term space mission. As a primary source of energy, light is one of the most important environmental factors for plant growth. The purpose of this study is to investigate the effects of low light intensity at different stages on growth, pigment composition, photosynthetic efficiency, biological production and antioxidant defence systems of wheat (Triticum aestivum L.) cultivars during ontogenesis. Experiments were divided into 3 intensity-controlled stages according to growth period (a total of 65 days): seedling stage (first 20 days), heading and flowering stage (middle 30 days) and grain filling stage (last 15 days). Initial light condition of the control was 420 μmol m⁻² s⁻¹ and the light intensity increased with the growth of wheat plants. The light intensities of group I and II at the first stage and the last stage were adjusted to the half level of the control respectively. For group III, the first and the last stage were both adjusted to half level of the control. During the middle 30 days, all treatments were kept the same intensity. The results indicated that low-light treatment at seedling stage, biomass, nutritional contents, components of inedible biomass and healthy index (including peroxidase (POD) activity, malondialdehyde (MDA) and proline content) of wheat plants have no significant difference to the control. Furthermore, unit kilojoule yield of group I reached 0.591 × 10⁻³ g/kJ and induced the highest energy efficiency. However, low-light treatment at grain filling stage affected the final production significantly.     

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.     

一种应用于着陆支撑系统中的锁紧机构设计与仿真

垂直起降运载器着陆过程是一种对支撑机构的结构强度、锁紧机构可靠性、缓冲器吸能特性要求极高的复杂瞬态碰撞吸能过程。针对现有着陆支撑机构构型单一等问题,提出一种全新的着陆支撑机构构型,并对该锁紧机构进行结构设计。由于支撑机构在展开过程中受到重力、空气阻力等多种环境力的作用,导致锁紧连杆相对速度在有限范围内变化。通过建立锁紧机构中移动构件力学方程并计算得到合理驱动弹簧参数,而后将锁紧相对速度作为输入变量,通过ADAMS建立虚拟样机模型完成工况模拟,并分析不同驱动弹簧参数对锁定可行性的影响,为锁紧机构可靠锁定提供技术支持。     

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.