基于矢量测量和UKBF的飞越小天体自主导航方法研究

以估计航天器和小天体在日心惯性系中的位置和速度为目的,提出了基于视线矢量测量的飞越小天体自主导航方法。该方法利用导航相机和激光高度计构建航天器到目标天体的视线矢量,同时为避免系统方程线性化和离散化所带来的模型误差,采用UKBF (Unscented Kalman\|Bucy Filter)算法对相应位置和速度进行估计。分析了导航系统的能观性,并以坦普尔一号为目标天体进行仿真验证,理论分析和仿真结果皆表明了方法的有效性和可行性。
     

基于欠采样的零阶优化算法

非平衡学习吸引了许多研究者的关注。一般情况下,少数类是更值得关注的,并且其误分类代价要远高于多数类。由于非平衡数据分布的非均衡性,标准的分类算法将难以适用。为了解决非平衡数据分类问题,给出了基于欠采样的零阶优化算法。首先,为了降低数据非平衡分布的影响,针对不同非平衡比的数据集给出了不同的两种采样策略。然后,采用了一种引入间隔均值项的支持向量机(Support vector machine,SVM)优化模型进行分类,并使用带有方差减小的零阶随机梯度下降算法进行求解,提高了算法的精度。在非平衡数据上进行了对比实验,实验证明提出的方法有效提高了非平衡数据的分类效果。     

微射流技术的原理及其应用

介绍了微射流技术的原理及其初步应用。基于非线性系统的演化对初始条件具有敏感依赖这个事实发展起来的微射流技术，由于与常规持续喷射射流控制方式相比具有许多优点，因而在流动和换热控制应用方面取得明显的效果。并指出，随着研究工作的深入，该项技术还可望用于高马赫数飞行体表面气体流动控制、火箭发动机推力方向控制等。     

基于灰色近似支持向量机的加速度计参数预测

温度、振动等环境载荷使得石英挠性加速度计参数产生漂移,直接影响了惯导系统的测量精度和性能,石英挠性加速度计参数变化趋势为非线性的,很难用常规的建模方法进行趋势预测。基于灰色理论适合进行小样本、贫信息不确定型系统建模以及近似支持向量机不需要求解二次规划就能求得非线性模型参数的优点,提出了基于灰色近似支持向量机进行石英挠性加速度计参数预测的方法。为了验证该方法的有效性,针对自然贮存的加速度计进行了固定周期的参数标定,结果表明灰色近似支持向量机具有很好的预测效果。     

钢板数控下料排样的一种优化算法

讨论了毛坯需求需要精确满足的不规则型毛坯数控气割下料排样问题。采用组块剪切排样法,排样时首先形成矩形组块,然后使组块在板材上优化排列。本文对两维约束排样算法进行修改,使之能生成所述排样问题的切割方式。最后应用所述算法给出变压器生产中钢板数控下料实例的优化排样方案,并与手工排样结果进行了对比     

先进的短距起飞垂直降落(ASTOVL)升力风扇飞机的动态逆控制

研究了推力矢量喷管、升力风扇以及气动舵面共同作用下 ASTOVL升力风扇飞机的飞控问题 ,采用非线性动态逆理论进行控制系统的设计。针对 ASTOVL升力风扇飞机的俯仰控制设计了动态逆控制律 ,并将所设计的控制律进行了仿真。结果表明 ,该方法能够满足控制系统性能的要求 ,具有工程应用前景。     

基于非连续算子广义变分不等式强解的存在性

在算子不连续的情况下,利用FKKM定理和Weierstrass定理及标量化方法得到了广义向量变分不等式的强解存在性定理.     

A straw-soil co-composting and evaluation for plant substrate in BLSS

Material closure is important for the establishment of Bioregenerative Life Support System, and many studies have focused on transforming candidate plant residues into plant culture medium. For the limitations of using wheat straw compost as substrate for plant cultivation, a straw-soil co-composting technique was studied. The changes of pH, C/N value, germination index, cellulose, lignin and so on were monitored during the co-composting process. The maturity was evaluated by the C/N value and the germination index. The result showed that after 45 days’ fermentation, the straw-soil final co-compost with inoculation (T1) became mature, while the co-compost without inoculation (T0) was not mature. In the plant culture test, the T1 substrate could satisfy the needs for lettuce’s growth, and the edible biomass yield of lettuce averaged 74.42 g pot⁻¹ at harvest. But the lettuces in T0 substrate showed stress symptoms and have not completed the growth cycle. Moreover, the results of nitrogen (N) transformation experiment showed that about 10.0% and 3.1% N were lost during the T1 co-composting and plant cultivation, respectively, 23.5% N was absorbed by lettuce, and 63.4% N remained in the T1 substrate after cultivation.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.     

铁基金刚石节块的热处理及其耐磨性能研究

首次提出了在烧结型金刚石工具的制造中引入热处理的新工艺，并系统地研究了淬火温度、淬火时间及回火温度等热处理工艺参数对热压烧结型铁基金刚石圆盘锯节块及其胎体材料机械性能的影响，同时对经热处理的铁基金刚石圆盘锯节块在石材旺苍红上进行磨损试验研究。试验结果表明，热压烧结型铁基金刚石圆盘锯节块胎体热处理的最佳工艺参数为：淬火温度为７６０～７９０℃、淬火时间为３．５ｍｉｎ、回火温度为２４５～３１５℃；经热处理后铁基金刚石圆盘锯胎体及节块的强度、硬度等机械性能有显著的提高，热处理也能明显改善铁基金刚石圆盘锯节块的耐磨性能。这说明将热处理工艺用于烧结型金刚石工具的制造中是切实可行的。