丁腈橡胶硫化胶在乙二醇中的加速老化失效及寿命预测

为了研究丁腈橡胶硫化胶在乙二醇中老化过程中的性能变化,预测其在乙二醇中的寿命,采用了高温加速老化失效的方法,考察了丁腈橡胶硫化胶在70℃,90℃,110℃乙二醇中的失重、力学性能变化、断面形貌.并以扯断伸长率作为指标,根据阿伦尼乌斯方程,推出老化速度常数和温度的关系.结果表明:丁腈橡胶与乙二醇介质之间有物质交换,并且丁腈橡胶失重;丁腈橡胶在乙二醇中出现片状结构并脆化,产生孔洞和裂纹;可以采用加速老化失效的方法推测丁腈橡胶在乙二醇中的寿命.     

超声速拦射导弹的一种预测变结构中制导律

针对攻击超声速大机动目标的超声速拦射导弹的中制导律设计问题,基于变结构控制理论并结合预测导引的思想提出一种预测变结构中制导律。该制导律能够零化导弹速度前置角,交班时过载较小,从而很好地满足中末交班要求。通过调节预测步长,在目标作大机动的情况下,既能保证中末交班时几乎为零的导弹速度前置角,又能有效地减小交班时导弹法向过载。最后在Matlab/Simulink环境下对攻击超声速蛇形机动目标进行了仿真,仿真结果表明了所设计的制导律的正确性和攻击超声速大机动目标的有效性。     

流场特性预测的两类高效方法

在多因素多水平试验设计方法建立数据库的基础上,提出了预测流场特性的两类高效方法:一类是响应面方法;一类是小波神经网络方法.典型算例表明,两类方法都可以高效率的获取流场特性参数,而且在预测的精度与计算的效率上,小波神经网络方法较响应面方法更具有优势.      

太阳10.7 cm射电流量中期预报模型研究(Ⅱ)

太阳活动指数中期预报一直是空间环境业务预报的难点之一.本文在自回归方法模型的基础上,利用太阳活动区面积、位置等参数与10.7cm辐射流量之间的定量关系,根据活动区面积衰减规律,建立了一个基于活动区参数及演化规律的改进型太阳活动指数中期预报模型.通过对预报测试实例分析发现,在日面出现较大活动区导致F_(10.7)迅速增长并超过历史数据峰值的情况,在日面活动区消亡导致指数突然出现平静期的情况,新模型的预报准确性相比自回归模型有很大提高,预报的平均相对误差下降约5%～9%.由此可见,新模型在某些特定条件下提高了原有模型的精度.该研究为提高业务型太阳10.7cm射电流量中期预报模型的预报精度奠定了基础.     

Enhancement of solar wind low-energy energetic particles as precursor of geomagnetic disturbance in operational geomagnetic forecast

A study of the relationship between solar wind low-energy energetic particles using data from the Electron, Proton, and Alpha Monitor (EPAM) onboard the Advanced Compositional Explorer spacecraft (ACE) and geomagnetic activity using data from Canadian magnetic observatories in Canada’s polar cap, auroral zone, and subauroral zone was carried out for a period spanning 1997–2005. Full halo coronal mass ejections (CMEs) were used to gauge the initial particle enhancements and the subsequent geomagnetic activity. It was found that maximum geomagnetic activity is related to maximum particle enhancements in a non-linear fashion. Quadratic fit of the data results in expressions that can be easily used in an operational space weather setting to forecast geomagnetic disturbance quantitatively. A superposed epoch analysis shows increase in particle flux level starts hours before geomagnetic activity attains its peak, affirming the precursory nature of EPAM particles for the impending geomagnetic impact of CME. This can supplement the decision process in formulating geomagnetic warning after the launch of CME from the Sun but before the arrival of shock at Earth. The empirical relationships between solar wind low-energy energetic particles and geomagnetic activity revealed in this statistical study can be easily codified, and thus utilized in operational space weather forecast to appraise the geoeffectiveness of the CME and to provide a quantitative forecast for maximum geomagnetic activity in Canada’s polar cap, auroral zone, and subauroral zone after the occurrence of a CME.     

基于粗糙集和BP神经网络的飞航导弹备件消耗规律预测

结合BP神经网络对飞航导弹备件进行消耗预测,提出了基于粗糙集和BP神经网络的预测法。该方法充分发挥了粗糙集在处理冗余数据上的优势,提高了预测速度和有效性。     

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control (MSPC) and stream of variations (SoV) is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error (OSFE) is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.     

基于ISODATA模糊系统的飞船落点选优方法

针对如何从基于分布式并行计算获得的数千个飞船返回过程预报落点中优选出＂最可能＂落点这一问题,在充分考虑以往返回落点计算的先验知识和最新落点信息的基础上,设计了飞船返回的自适应聚类模糊系统,采用含有时间因子的迭代自组织数据分析算法研究了返回舱落点优选方法。最后,利用＂神舟八号＂返回落点实测数据进行模型检验。计算结果表明,该方法的落点预报精度较传统选优算法的预报精度高50%以上,同时具有较好的稳定性,可为飞船返回搜救提供技术支撑。     

一种基于航摄图像特性的H.263压缩算法的改进

研究了航摄视频图像的特性,并根据航摄视频图像的特性,从预测初始搜索点、搜索策略和半像素精度运动估值方面改进了H.263标准中的运动估计算法。实验结果表明,针对航摄图像特性改进的算法比全搜索算法和三步搜索算法具有更高的搜索效率。     

基于深度学习的太阳10.7 cm射电流量日值的中期预报

针对太阳10.7cm射电流量中期日值预报问题,采用深度学习方法,建立了一个典型的基于多层感知器模型的神经网络。该网络采用1个包含90个神经元的隐含层,实现了一种非参数的时间序列自回归模型。预报中不仅考虑历史日值,还考虑了历史预报误差。模型根据前27d的历史数据实现了未来27d的日值预报。通过对50多年数据的训练和试验分析,该方法在短期和中期预报上较传统方法的相对误差明显降低。特别是模型经一次训练后,参数可以完全固定,不同于以往研究参数需要每天滚动更新,大大简化了日常预报,同时极为有利于模型在其他相关应用中的推广。