无人机载SAR一体化显控系统的设计研究

阐述了无人机载SAR一体化显控系统的设计,对系统实现中应用到的一些关键技术进行了详细说明,就多位面技术、多线程技术及内存映射技术进行了探讨。     

Periodic ambiguity functions of EQC-based TFHC

The time-frequency hop codes based upon extended quadratic congruences (TFHC-EQC) were developed previously (1988, 1991). The known results about the ambiguity functions of the TFHC-EQC are extended from aperiodic cases to periodic cases. Precisely, the following new results are proved: (1) The tight upper bound for the periodic auto-ambiguity function of the TFHC-EQC is 6; (2) the periodic cross-ambiguity upper bound of the TFHC-EQC is at least (N-1)/2; (3) there exists a subset TFHC-EQC of volume (N-1)/2 such that its periodic cross-ambiguity is tightly upper bounded by 8     

100 mm一等量块自动测量仪测量不确定度评定

分析了100mm一等量块自动测量仪的误差来源，确定了测量量块的合成标准不确定度和扩展不确定度。     

适用于柔性工艺规划的分层规划法

提出了基于分层约束的柔性工艺规划方法,并用实例说明了规划的设计方法;通过引入AOS树作为特征加工顺序的组织方法,为后续的在线设计提供了多种可选方案路线,为非线性工艺设计提供了必需的技术保障.     

飞机配电系统中交流模拟固态功率控制器设计

主要介绍了一种交流模拟固态功率控制器（SSPC）的设计和实现。它主要使用了在嵌入式系统中广泛应用的单片机，来实现SSPC的基本功能。从核心器件到对负载电流通断控制以及对采入信号的调理都进行了详细的介绍与说明。     

基于UG和ADAMS的新型夹持器虚拟设计与仿真

采用UG和ADAMS研究了一种新型夹持器机构BHG-1的虚拟设计和仿真方法,运用UG来实现夹持器三维模型的虚拟设计和虚拟装配;运用ADAMS对夹持器夹持不同形状物体进行运动学和动力学仿真.通过对夹持器机构的虚拟设计和仿真,不但可以直观地了解夹持物体的具体过程,还可以通过仿真曲线了解其速度、加速度以及接触力的变化情况.     

双边界代数法在喷管内流场网格生成中的应用

用一种双边界代数方法生成二维网格，通过在超限插值函数中引入边界的一阶导数实现网格与边界是的正交，采用几何一致性条件保证物理区域不变形，发展一系列拉伸方程控制网格在边界或给定位置的间距。应用此方法生成了满意的喷管内流场网格，网格生成效率明显优于微分方程网格生成方法。     

整体级发动机用含硼BAMO／THF推进剂的探索研究

采用最小自由能法（ｗｈｉｔｅ法），计算研究了增塑剂与粘合剂质量比（Ｐ１、Ｐ０）、固体含量、硼含量、ＨＭＸ含量对用于整体级发动机的含硼ＢＡＭＯ／ＴＨＦ（１：１）推进剂能量的影响。实验研究制得的６５％固体含量的ＢＡＭＯ／ＴＨＦ（１：１）含硼固体推进剂的基础燃速、压强指数较低；φ６５ｍｍ发动机的试车结果表明：实验制得的６５％固体含量的含硼ＢＡＭＯ／ＴＨＦ（１：１）推进剂的比冲效率为８４．４５％。     

三点五阶优化广义紧致格式

根据两种差分格式色散误差目标函数对三点五阶广义紧致格式进行了优化。理论分析及数值实验证明了优化方法的正确性与有效性。该方法具有普遍意义，可以推广用来确定其它各阶精度的广义紧致格式的系数。并且利用类似的思想，可以通过其它限制条件对紧致格式进行优化。     

Practical Fusion of Quantized Measurements via Particle Filtering

Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.