Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of ‘Low’ prediction at the same time. The ‘Low’ prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events.     

Study of the Enhancement Events of Relativistic Electron at the Geosynchronous Orbit

The high flux of energetic electron on geostationary orbit can induce many kinds of malfunction of the satellite there, within which the bulk-charging is the most significant that several broadcast satellite failures were confirmed to be due to this effect. The electron flux on geostationary orbit varies in a large range even up to three orders accompanied the passage of interplanetary magnetic cloud and the following geomagnetic disturbances. Upon the investigation of electron flux enhancement events, two types of events were partitioned as recurrent events and random ones. Both of the two kinds of events relate to the interplanetary conditions such as solar wind parameters, IMF etc and their evolution characters as well. As for the recurrent events, we found that, (1) all of the events exhibits periodic recurrence about 27 days, (2) significant increase of electron flux relates to interplanetary index and characters of their distribution, (3) the electron flux also has relation to solar activity index. An artificial neural network was constructed to estimate the flux I day ahead. The random electron flux enhancement events are rare and present different distribution figures to the recurrent ones. The figure of the random events and the conditions of their occurrence is also discussed in this paper.      

第2009卡林顿周太阳活动的初步分析

介绍了第 2 0 0 9卡林顿周大黑子群AR484,AR486和AR488产生的太阳活动以及这些太阳活动对地球空间环境造成的影响。依据对太阳活动的统计研究 ,简要分析了三个大黑子群活动的差异。分析结果指出 ,关注某一时期太阳活动的活动经度带以及太阳活动在某一半球的主次是解决太阳活动短期预报非常重要的思路     

民用客机研制阶段签派可靠度分配和预计

讨论了民用飞机航班延误的主要原因,着重分析了因技术原因导致航班延误和取消的三类设备,并结合飞机的构型和主最低设备清单(建议书)(P)MMEL给出了在民用客机研制阶段签派可靠度的分配和预计的初步模型和计算方法,为签派可靠度的定量分配与预计以及设计构型方案的改进和权衡提供指导。     

航空发动机飞行载荷谱的预测

提出了基于飞机的设计飞行任务剖面、飞行力学及发动机原理的发动机飞行载荷的预测方法,即首先将飞机的设计飞行任务剖面通过飞行力学的基本原理转化为发动机的推力（或油门）剖面,然后通过发动机性能计算获得发动机的其它工作状态参数,从而获得发动机的飞行载荷谱。      

固体火箭发动机通用药柱的一维准定常内弹道计算

本文提出对任何药型都通用的一维准定常内弹道计算方法和与其相适用的药柱计算方法.解决了药柱和一维准定常内弹道计算的配合问题.用本程序对某实际发动机的内弹道性能进行了计算,将计算结果和用零维燃烧模型计算结果及热试车结果进行了比较.     

非线性不平衡转子轴承系统周期解的预测

本文提出一种对非线性不平衡转子轴承系统周期解进行预测的新型算法,它利用系统周期解的稳态及瞬态信息,反解雅可比矩阵,实现对系统周期解的预测追踪,并利用反解得出的雅可比矩阵,求得系统周期解的Floquet乘子以判别其非线性稳定性。文中以刚性不平衡转子轴承系统为例,实现了周期解的预测追踪及非线性稳定性判别,说明了新算法的有效性。      

防空导弹任务规划与协同控制技术研究

针对防空导弹拦截群目标的任务需求，提出了多弹协同作战框架。考虑到实际拦截过程中弹目相对速度和角度较大的问题，建立了导弹拦截目标的能力预测模型，以确保分配结果处于导弹攻击能力范围内，并采用了改进的粒子群优化算法进行任务分配。然后为了兼顾协同制导和命中精度的要求，基于弹道成型和偏置比例导引的思想，结合一致性理论，设计了多弹时间/角度协同制导律。最后通过4枚导弹拦截3个目标的典型场景仿真，证明了方案的有效性。     

航天产品非金属材料/ 制品贮存寿命评估技术

从非金属材料老化机理与寿命预测模型、老化行为表征技术、贮存环境试验与寿命评估技术等方面,概述了国内外的技术发展和现状,介绍了航天产品用非金属材料及制品贮存寿命评估技术的发展及其应用进展。     

数值模拟技术在热压罐工艺中的应用

随着复合材料和计算机技术的发展,数值模拟技术在复合材料工艺仿真中的应用也日益明显.总结分析了数值模拟技术在复合材料热压罐工艺中的研究进展,包括热压罐内流场仿真、固化放热控制、固化变形预测,以及热隔膜成型过程仿真的应用前景.随着大数据和复合材料自动化、智能化的发展,发展数值模拟技术是大势所趋.