基于模糊聚类原理确定光测数据处理最优基线的方法

应用模糊数学理论,提出了光测数据处理最优基线的确定方法,有效地解决了长期以来基线选择过程中难以建立数学模型的问题,通过仿真试验验证了该方法的正确性、合理性。     

全测速多站系统的最优布站问题与数值算法

从解算弹道的逐点多站最小二乘算法得到的弹道精度出发,给出了一个全测速多站系统的最优布站数学模型,并通过大量试算选定了具有良好收敛性的数值算法.初步的计算表明：对全测速多站系统,其最优布站方案与现有布站方案在弹道精度上有着显著的差别;在最优布站方案下可确保逐点多站最小二乘算法具有良好的数值稳定性,并得到满足精度要求的外测弹道.     

二维跨音速Euler方程分区并行算法

对二维跨音速流动的Euler方程分区算法、并行算法以及多区计算的有效内边界耦合条件进行了探讨,应用Van Leer矢通量分裂方法和一维数组方式,研究了多种区域分解数目的分区计算效率.并行计算中采用"先进先出"的同步控制等待机制,采用纯结点并行编程方式进行了单区、二区和四区并行计算对比,分析了影响并行效率和通讯过载比的主要因素.     

单星测频静态目标无源定位研究

提出了一种基于非线性最优化技术的单星测频无源定位算法,该方法利用单颗卫星单个通道在不同位置上测得的信号多普勒频率,实现对地面固定辐射源的无源定位,具有有效载荷简单、对卫星姿态无特殊要求、定位收敛快、精度较高等优点。同时,探讨了多普勒频率测量误差预处理技术,以及基于PAR模型的系统误差分离技术,通过对测量误差的分离与处理,可以大幅度提高频率测量数据的精度,从而保证后续测频定位精度。最后,通过仿真试验和工程实际,验证了单星测频定位技术的有效性和准确性。     

基于深度长短期记忆网络的发动机叶片剩余寿命预测

为了研究航空发动机转子叶片的剩余寿命预测问题,提出了一种基于多传感器信号融合的深度长短期记忆网络(DLSTM)预测模型。首先利用深度学习和长短期记忆的组合来构造DLSTM网络。然后,将多个传感器信号数据进行融合处理,从而通过深度学习发现各个传感器时序信号之间隐藏的长期依赖关系。进一步在给定网格搜索策略的情况下,通过自适应矩估计算法调整DLSTM的网络结构和参数,并且在DLSTM模型中引入了一种随机丢失策略,以缓解过度拟合问题并使预测模型规范化。最后利用CMAPSS涡扇发动机进行了实验验证,在一种故障模式和两种故障模式条件下,DLSTM网络预测模型相对于其他传统方法的评价指标Score分别下降了17.19%和14.37%,其他两个评价指标相对来说也较优,结果表明本文提出的方法具有更高的准确性以及稳定性。     

Effects of tube system and data correction for fluctuating pressure test in wind tunnel

The accuracy of fluctuating pressure test in wind tunnel is affected by the tube system. By employing contradistinctive experimental method, systematic study was conducted to investigate the effects of the tube system on fluctuating pressure. The analyzed tube system parameters include tube length, inside diameter, curvature, deflection angle, thickness, material, restrictor length, restrictor inside diameter, and restrictor place. It is found that all the tube system parameters mentioned above except tube curvature have non-negligible effects on fluctuating pressure. Based on the research results, test methods were presented for the fluctuating pressure measurement in low-speed wind tunnel, which can obviously improve the data accuracy but not lose test efficiency. The effectiveness of the method is verified by the wind tunnel test.     

MHD code using multi graphical processing units: SMAUG+

This paper introduces the Sheffield Magnetohydrodynamics Algorithm Using GPUs (SMAUG+), an advanced numerical code for solving magnetohydrodynamic (MHD) problems, using multi-GPU systems. Multi-GPU systems facilitate the development of accelerated codes and enable us to investigate larger model sizes and/or more detailed computational domain resolutions. This is a significant advancement over the parent single-GPU MHD code, SMAUG (Griffiths et al., 2015). Here, we demonstrate the validity of the SMAUG?+?code, describe the parallelisation techniques and investigate performance benchmarks. The initial configuration of the Orszag-Tang vortex simulations are distributed among 4, 16, 64 and 100?GPUs. Furthermore, different simulation box resolutions are applied: $1000\times 1000\text{,}2044\times 2044\text{,}4000\times 4000$ and $8000\times 8000$. We also tested the code with the Brio-Wu shock tube simulations with model size of 800 employing up to 10?GPUs. Based on the test results, we observed speed ups and slow downs, depending on the granularity and the communication overhead of certain parallel tasks. The main aim of the code development is to provide massively parallel code without the memory limitation of a single GPU. By using our code, the applied model size could be significantly increased. We demonstrate that we are able to successfully compute numerically valid and large 2D MHD problems.     

测控计算机系统系统软件驱动程序设计与开发

本文介绍了基于串口时统和PCICCP硬件设备的测控计算机系统系统软件中的驱动程序的设计与开发,介绍了驱动程序与硬件设备的接口,驱动程序模块设计以及主要模块的编程实现。这两个驱动程序模块划分清晰,代码执行高效,运行稳定可靠,在实践中得到了较好应用。     

基于数据挖掘技术的金融信用决策管理系统

论文首先介绍了数据挖掘的概念及相关技术,然后论述了数据挖掘技术在金融信用决策管理系统中的应用,通过对预测解析、内在理解、决策建模、策略优化和策略精调的介绍,对数据挖掘的实现进行了有效描述.