惯性推算误差抑制的神经网络自学习模型设计与验证

惯性推算误差抑制是提升复杂场景下组合导航定位性能的关键,现有采用运动约束或系统误差高阶建模的方法从运动学模型及传感器误差模型出发,通过经验确定参数及模型的最优解。深度学习隐式模型能够挖掘数据之间的隐含关系,进行自主化参数寻优,并在提升惯导误差建模精度方面具有一定优势。总结了现有主流网络模型设计的优缺点,通过对比不同的输入输出方案进行优选,最终利用卷积神经网络构建了一套惯性误差抑制的轻量化神经网络自学习模型,并利用实测车载数据验证了该模型的有效性。实验结果表明,在GNSS信号失锁300 s的路段I和失锁285 s的路段II,网络模型速度约束的算法相较于纯惯性推算和传统NHC算法均有一定提升,融合NHC及网络模型速度约束的算法在水平定位精度上分别改善了41.7%~47.4%和26.7%~36.6%,一定程度上抑制了惯性推算误差。     

Real-time mosaicking for infrared videos from an oblique sweeping camera

Image mosaicking is widely used in Geographic Information Systems (GISs) for large-scale ground surface analysis. However, most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation. In this paper, we propose a novel and practical algorithm for real-time infrared video mosaicking. To achieve this, a novel fast template matching algorithm based on Sum of Cosine Differences (SCD) is proposed to coarsely match the sequential images. The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform (FFT). We also propose a novel fast Least Squares Matching (LSM) algorithm for inter-frame fine registration, which can significantly reduce the computation without degrading the matching accuracy. In addition, the proposed fast LSM can effectively adapt for noise degradation and geometric distortion. Based on the proposed fast template matching algorithm and fine registration algorithm, we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.     

探讨培养高职学生语文职业能力的途径

从弱化传统语文重视知识传授的做法、注重语文教学与专业学习的结合及加强语文能力的实践训练等方面,探讨培养高职学生语文职业能力的途径。     

μ理论在柔顺力控制中的应用

研究μ理论在柔顺力控制系统中的应用.为模拟空间对接强制校正阶段的推出和拉近过程,提出基于6自由度并联机器人位置内环的柔顺力控制策略.综合考虑参数变化、模型变动和外来干扰等不确定性,利用μ综合控制理论设计鲁棒力控制器.通过μ分析比较鲁棒力控制器和经典力控制器的鲁棒稳定性和鲁棒性能.给出鲁棒力控制器和经典力控制器的实验结果,表明所设计鲁棒力控制器的有效性和优越性.     

钢板内部缺陷失效分析

采用120 mm厚的45#钢轧制板材经粗加工后进行调质处理,热处理后超声探伤发现钢板厚度方向中部存在缺陷。通过失效分析认为,钢板内部的缺陷为沿变形方向分布的带状组织,部分带状组织中存在沿晶裂纹。形成带状组织及裂纹的机理是原材料钢锭浇铸过程中心部形成枝晶偏析,轧制过程中偏析区被拉长因而形成偏析条带,条带区因富含Mn、S、P等元素形成硬度较高的马氏体组织,调质热处理过程中在组织应力及热应力作用下马氏体条带组织区域发生沿晶脆性开裂。     

基于产品-服务系统概念的商用飞机研发模式初探

我国商用飞机制造业正面临着市场化的趋势,飞机主制造商的关注点逐步向产品服务延伸,即可持续性发展。纵观飞机产品全生命周期,主制造商对于产品的研制应是面向包含飞机产品、服务产品及整个运营保障维护体系在内的整体化、系统化、综合性的研发工程,即商用飞机产品服务系统。本文基于产品服务系统概念,创造性地提出了有别于传统的、新型商用飞机研发模式的设想。本文介绍的新型商用飞机研发模式是基于系统工程方法,其研发过程主要包含捕获利益攸关方需要、建立产品服务系统功能架构、建立产品与服务功能关系矩阵以及产品服务系统技术解决方案定义共四个关键过程,文中开创性的提出了适用于该研发模式的、面向产品服务功能绩效实现的功能层级分解结构,作为整个研发过程的重要环节。     

带轴对称台阶的圆锥高超声速边界层转捩试验

在Ma 6风洞内,通过高频脉动压力测试技术和基于纳米粒子示踪的平面激光散射(NPLS)技术,分别对带前向、后向轴对称台阶的圆锥高超声速边界层转捩进行了试验研究。采用功率谱密度分析和互相关计算等方法对脉动压力数据进行分析,得到了边界层中扰动波的发展规律,定量分析了第二模态波的相关参数。结果显示：两种模型中第二模态波在沿流向向下游发展的过程中,其幅值均先增大再衰减、特征频率均逐渐减小;特征频率和传播速度整体上均随雷诺数的增大而增加(后台阶模型中特征频率由100 kHz增至196 kHz,前台阶中则由 97 kHz 增至174 kHz)、波长变化规律则与之相反(后台阶中由6.35 mm降至4.54 mm,前台阶由7.35 mm降至 4.66 mm );后台阶模型中第二模态波初次出现位置比前台阶中更靠近上游,边界层转捩位置较前台阶前移。将NPLS结果与高频脉动压力测试结果进行对比,两者吻合较好。     

上升段飞行器目标的视频图像跟踪

粒子滤波是一种基于贝叶斯估计理论和蒙特卡罗理论的实时目标跟踪方法,具有较为灵活的并行化跟踪方式,能够较好地维持跟踪目标的假设状态,具有较好的跟踪效果和鲁棒性。上升段飞行器目标飞行视频图像跟踪是火箭等目标飞行监控的重要阶段,但现阶段对飞行器上升段的视频图像跟踪主要依靠人工手动操作云台控制器,实现视频图像中的飞行器跟踪,跟踪图像存在跟踪滞后、画面抖动等现象,跟踪效果受人为因素影响较大。本文提出一种基于粒子滤波方法的上升段飞行器目标视频图像跟踪方法,建立飞行器目标粒子滤波跟踪模型实现对飞行器目标的识别和跟踪,在识别和跟踪的基础上建立云台控制模型,通过对云台的智能控制获得飞行器上升段的高质量图像。采用火箭发射的视频图像作为模型验证的实验数据,检验飞行器目标的跟踪效果。     

基于CMPSO算法的永磁同步电机转动惯量识别研究

永磁同步电机(PMSM)是一种具有很强非线性的动态系统,在工业驱动应用中发挥着重要作用。实时辨识转动惯量对于高精度PMSM系统控制和稳定性状态监测具有重要意义,但传统转动惯量识别方法精度较低。在传统离散模型参考自适应理论基础之上,用柯西变异粒子群优化(CMPSO)算法代替待辨识参数自适律设计环节,以实现PMSM转动惯量识别。该转动惯量辨识方法充分利用了CMPSO算法的快速高效收敛性,仿真结果和试验结果表明了可行性、正确性和准确性。     

Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine

Aero-engine gas path health monitoring plays a critical role in Engine Health Management(EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine(HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First,to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then,a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope.