关于混合单调算子的耦合拟不动点

在Banach空间中引入强锥弱紧集等概念，讨论了不连续的混合单调算子的耦合拟不动点存在性问题，得到了更一般性的结果。     

Spectral line broadening by relativistic electrons in plasmas: Collision operator

In the present work we compute the real part of the impact collision operator for the electron broadening of ion lines in plasmas, taking into account relativistic effects in the dynamics of the perturbing electron. Specifically two relativistic effects are included: The modification of the trajectory due to non-Newtonian mechanics and the modification of the velocity distribution (Maxwell–Juttner). The results are compared to the non-relativistic case.     

基于空间算子代数的空间多体系统动力学递推计算

应用空间算子代数方法对空间多体系统进行动力学建模、分析与仿真。空间算子代 数 作为基于李群及旋量方法的算子理论,采用估计理论中卡尔曼滤波及平滑方法,实现了具有 明确物理含义的O(n)计算效率的动力学实时递推求解算法。首先使用空间算子代数理论 对多体系统动力学进行递推形式的描述、建模及分析,并通过与估计理论对比提出了质量矩 阵求逆的高效递推算法,而后基于面向对象技术编制了软件,并通过漂浮基双臂机器人典型 算例的求解,与商业动力学软件Simpack进行对比,验证了软件及算法的正确。结果表明, 空间算子代数方法概念清晰、推导方便,计算效率及精度能够满足工程需求,可用于空间多 体系统动力学实时仿真及控制系统设计。〖JP〗     

Turbo码在802.11b协议物理层中的应用研究

给出用 Turbo码代替现行 80 2 .11b协议中物理层的信道纠错码——卷积码的方法 ,通过性能仿真和译码复杂程度比较 ,验证在该协议的物理层上采用 Turbo码来提高无线局域网的性能是可行的。     

基于PD算子的InSAR干涉图滤波研究

讨论了InSAR干涉图的滤波问题，提出了基于PD算子的干涉图滤波算法。算法首先对干涉图作分块处理，并假定其中各数据块上的相位值符合多项式模型，通过PD算子分别估计各数据块的模型系数，由此重构出近似干涉图。然后根据近似干涉图对原始干涉图进行‘解调’，降低原始干涉图的条纹密度，并使用滑窗作低通滤波处理。最后将滑窗处理后的干涉图与近似干涉图相乘得到滤波后的干涉图。为避免因分块处理而产生干涉相位图的马赛克效应，算法通过处理两组错位的干涉图分块数据，并对滤波后的两幅干涉相位图作加权处理，有效地降低相邻数据块间相位值的不连续性。对X＿SAR重复轨道数据产生的干涉图的处理结果表明，该算法能够对干涉图作很好的滤波处理，特别是在低信噪比区域，算法具有较突出的滤波性能。     

基于模糊评价与GIOWA算子的技术风险评估方法

将不确定多属性决策方法应用到风险评估当中,给出了一种定性与定量相结合的飞行器型号研制项目技术风险评估方法.采用基于广义导出的有序加权平均(GIOWA,Generally Induced Ordered Weighted Averaging)算子的模糊综合评价系统去处理风险评估中的专家语言信息,通过对风险概率与后果的分别量化并结合风险矩阵确定单个技术风险事件等级,从而进一步获得整个项目的风险状况.结合一个航空研制项目分系统的风险评估实例,阐述了方法的详细实施步骤与实用性,为型号研制过程中技术风险评估工作的开展提供了一种方法.      

数字信号磁带录取机理

介绍数字信号磁带录取的调频体制和模式及其数据录取的基本原理。论述了录取系统信噪比的估算，以及系统录取信号数据的误码率，指出提高输入系统信噪比可降低信号录取的误码率。     

铝合金小直径薄壁管的全位置焊工艺研究

采用ＭＫ－２００全位置焊管机焊接小直径薄壁铝合金导管,必须解决好焊缝的表面成形和内部气孔问题。分析了气孔产生的原因,并提出了抑制气孔的措施。大量试验表明,采用加填充环和背保护气加压的方法可改善焊缝成形;通过严格的焊前清理、合理的参数设置等抑制气孔的工艺措施可获得理想的气孔合格率。     

含时滞结构线性时变系统伺服机构设计的PGOPO法

采用线性、连续、有界算子－分段广义正交多项式算子对含时滞结构的线性时变系统的线性伺服机构设计问题进行了研究,得到了一种形式简洁、计算精度较Ｗａｌｓｈ函数、块脉冲函数法更高的逼近方法。文中所列举的两个例子充分说明了分段广义正交多项式算子法在时滞系统伺服机构设计中的适用性和有效性。     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).