基于支持度的MEMS陀螺信息融合方法

为了解决微机械电子系统(MEMS, micro electronic mechanical system) 惯性器件测量准确度低、噪声大、长时间单独使用导航误差积累大等缺点,提出了一种MEMS陀螺阵列结构设计,在完成微型惯性测量单元(MIMU, micro inertial measurement unit)的标定后,采用基于支持度的信息融合方法对陀螺阵列输出进行信息融合。试验结果表明：该结构设计和信息融合方法能够有效的提高MIMU的测量准确度,具有一定的工程应用价值。     

离散拓扑业务模型中多媒体LEO网络性能分析

针对快速发展的多媒体卫星网络,提出一种新的低地球轨道卫星网络性能的定量研究模型。该模型考虑了星座动态拓扑变化和全球业务需求变化的复杂特点,建立了离散时间下的业务和拓扑映射关系;依据马尔科夫过程理论和卫星链路带宽约束条件,对实时和非实时两种类型业务采用概率分析方法,推导出定量计算阻塞概率、排队时延等网络性能参数的公式。大量仿真结果表明理论分析方法的正确性和有效性。该研究方法可用于多媒体低地球轨道卫星网络QoS路由管理和资源分配设计的参考。     

Behavior and functional modeling methods of doubly salient electromagnetic generators for aircraft electrical power system applications

The Doubly Salient Electromagnetic Generator(DSEG) is a promising candidate in aircraft generator application due to the simplicity, robustness and reliability. However, the field windings and the armature windings are strongly coupled, which makes the inductance characteristics non-linear and too complex to model. The complex model with low precision also leads to difficulties in modeling and analysis of the entire aircraft Electrical Power System(EPS). A behavior level modeling method based on modified inductance Support Vector Machine(SVM) is proposed. The Finite Element Analysis(FEA) inductance data are modified based on the experiment results to improve the precision. A functional level modeling method based on input–output characteristics SVM is also proposed. The two modeling methods are applied to a 9 kW DSEG prototype. The steady state and transient process precision of the proposed methods are proved by comparing with the experiment results. Meanwhile, the modeling time consumption, the application time consumption and the calculation resource demand are compared. The DSEG behavior and functional modeling methods provide precious results with high efficiency, which accelerates theoretical analysis and expands the application foreground of the DSEG in the aircraft EPS.     

基于SVM算法的虚假航迹识别

针对云雨杂波和主被动干扰导致多雷达传感器产生虚假目标航迹的问题,利用支持向量机(SVM)算法的自主学习能力,通过构建基于数据驱动的判别模型进行虚假航迹识别。针对航迹起始得到的目标潜在航迹,利用人工智能数据驱动、自学习的特点,设计了SVM算法。通过对已标记真假的目标航迹样本进行离线学习,形成虚假航迹识别的SVM分类器,实现了基于数据驱动的判别模型代替先验知识规则约束的固定模型,并在工程应用中,利用SVM分类器在线识别虚假航迹,完成实时剔除。通过实测雷达数据实验验证,该算法的目标虚假航迹准确率高达95%以上,完全满足实际的工程应用需求。相比基于阈值或规则进行硬性判断的传统虚假航迹识别方法,所提出的算法不仅提高了准确率,还具有较高的实时性,能够适应复杂多变的杂波环境,在实际应用中具有更强的适应性和实用性。因此,提出的基于SVM算法的虚假航迹识别方法对于密集杂波场景下的虚假航迹剔除问题具有显著的实际应用价值。     

CBERS-02B卫星HR相机遥感影像区域网平差技术

针对CBERS-02B卫星HR相机遥感影像的几何定位精度不高,不能满足有些用户的要求,提出了基于有理多项式（Rational Polynomial Coefficient,RPC）模型的区域网平差技术,并利用多幅CBERS-02B卫星HR相机遥感影像进行了区域网平差试验,结果表明该方法能有效地提高遥感影像的几何定位精度...     

结合邻域聚类分割的高光谱图像异常检测支持向量数据描述方法

支持向量数据描述方法在高光谱图像小异常目标检测中具有较好的检测性能,但是待检异常的几何形状受到约束和背景的选择具有盲目性影响检测效果,且检测需要对整幅图像进行遍历导致计算量大。提出邻域聚类分割和支持向量数据描述相结合的异常检测方法,首先利用邻域聚类方法分割图像,将几何尺寸小的分割块作为潜在异常目标;其次选择与潜在异常的形状和大小相适应的背景窗进行背景像元收集;最后采用SVDD方法从潜在异常中快速且准确地检测出异常目标。对HYMAP图像的实验结果表明,该算法提高了复杂地物背景下异常的检测性能,降低了SVDD用于高光谱图像异常检测的计算量。     

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.     

民用飞机通用地面支援设备选型程序浅析

为了满足航空公司/维修单位高效率、低成本的要求,在飞机的设计过程中,尽量减少专用地面支援设备的种类,以通用地面支援设备取代,成为了飞机主制造商及其供应商共同努力的目标,也成为了一种发展趋势。因此,在飞机运营及维护、维修过程中,通用地面支援设备的选型及程序显得越来越重要。     

飞机液压管路支撑的有限元建模方式对应力分析的影响

在飞机典型液压导管不同构型和负载模式下,做了应力试验测量,得到应力试验的数据,并在有限元环境MSC.PATRAN中,针对导管的不同构型建立有限元分析模型,对每种构型下支架与卡箍采用八种不同的简化方式,得到仿真结果。然后将不同简化方式的仿真结果与试验结果作对比分析,考虑不同因素(如支架刚度、卡箍橡胶刚度、导管与橡胶间的摩擦与滑动等)对分析结果的影响,找到了在工程上简单易行,精度上可以接受的支撑简化方法。