基于因子图的状态估计方法运算实时性研究CSCD

对因子图算法及其理论基础进行研究,使用消元算法分析了一元观测因子图模型的稀疏结构,得到了一种适用于该模型的增量推断算法;并对固定滞后平滑算法进行了研究,实现了滑窗在该增量推断算法中的应用;最后通过仿真验证了基于因子图的状态估计方法的估计能力、该增量推断算法在求解一元观测因子图模型的状态估计问题时运算速度的提升能力,以及滑窗维持该增量推断算法运算速度稳定的能力。     

微晶玻璃及其抛光

讨论了微晶玻璃的内部结构,低温抛光及抛光后的表面缺陷和表面完整性.     

固化工艺影响L形复合材料制件固化变形研究

通过数值模拟方法,利用ABAQUS有限元仿真软件,模拟含R角的复合材料构件的固化过程.根据已有的固化工艺,比较不同工艺条件对复合材料构件固化过程中的温度场和固化度场及变形的影响.结果表明,合理地降低固化工艺温度和固化冷却速率可以减少残余应力的积累,缓解固化变形.     

基于故障机理和伪失效寿命的 电子产品剩余寿命预测

针对传统电子产品可靠性评估中存在的不足,提出了一种基于故障机理和伪失效寿命的电子产品剩余寿命预测方法。首先,基于电子产品的故障模式、故障机理分析,确定产品敏感性能参数;然后,对敏感参数退化量进行监测,建立电子产品退化轨迹模型,利用最大似然法估计其参数;最后,根据电子产品退化轨迹,设定故障阈值,得到电子产品寿命分布。通过仿真表明,该方法评估精度较高,对电子产品的可靠性评估有一定的参考价值。     

起重船用高强钢大厚板焊接残余应力的试验研究

以某型起重船结构为研究对象,采用焊接后热作为焊接残余应力消除措施,研究结构的残余应力情况及消除措施的有效性。首先采用焊态和经过焊接后热的两组试板进行焊接残余应力检测并对比,结果表明焊态的试板焊接纵向残余应力最高达552.5 MPa,接近材料的屈服强度;经后热的试板纵向残余应力最大为385.3MPa,与焊态相比降低了167.2 MPa,下降约30%;同时获取了残余应力分布情况。进一步对起重船导向基座结构的焊缝进行后热并进行应力检测,其纵向焊接残余应力最大值为310.8 MPa,远低于母材屈服强度。本文研究表明：起重船高强钢大厚板在焊态时焊接残余应力较大,需采取措施消除残余应力;采用焊接后热可以大幅降低高强钢大厚板的焊接残余应力水平,焊接后热具有较好的经济性和操作便捷性,在起重船建造过程中建议考虑对大厚板焊缝进行后热以消除焊接残余应力消除。     

高速端面铣削加工引入残余压应力场的试验研究

为研究高速铣削加工表面残余应力分布,在统计试验设计指导下,使用X射线衍射/电解抛光方法对试样表层、次表层残余应力分布进行测量,得出残余应力分布曲线经验预报公式,探讨切削参数对残余压应力场特征指标的影响规律。研究结果表明,合理的高速切削工艺将在加工表面内引入类似喷丸强化效果的残余压应力场;(3,1)有理式模型与三阶多项式模型是描述高速铣削引入残余应力分布曲线的有效拟合公式;高速切削工艺参数对加工表面残余应力场的产生有直接影响,每齿进给量是决定残余压应力分布的关键因素,每齿进给量的变化将引起残余应力场形成机理的改变。     

激光冲击强化提高外物打伤钛合金模拟叶片高周疲劳性能

为指导钛合金叶片抗外物打伤激光冲击强化工艺设计,根据真实叶片叶型特征设计了刃口型模拟叶片,采用两种激光冲击强化工艺对模拟叶片进行预处理,并采用空气炮系统进行外物打伤模拟试验,最后通过疲劳试验和应力场预测进行疲劳性能影响规律及机理分析.试验结果表明:模拟叶片外物打伤后疲劳强度由518.45MPa降为290.72MPa,而...     

嫦娥一号卫星CCD立体相机月表图像镶嵌

鉴于嫦娥一号CCD相机获取图像的特殊性, 选用投影的方法, 完成了Apollo12, Apollo14和Apollo15登月点区域的月表正视图像镶嵌. 在分析圆柱投影表象的基础上, 推导出了月球割35º Mercator投影公式. 首先读取单轨数据的经纬度和灰度, 进而根据四个角点的经纬度建立空图框, 并根据分辨率、长度比与月球投影理论分别在纵向和横向上进行网格划分, 实现图框的像元化. 通过单轨图像和图框的坐标匹配完成灰度镶嵌, 匹配误差控制在一个像元以内. 对重叠区域的灰度值进行平滑处理, 最终图像还原来完成月表图像的镶嵌. 该方法有普遍适用性, 可以用于其他轨道图像的镶嵌.     

Model based Computerized Ionospheric Tomography in space and time

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.