飞行器RCS计算前置处理中裁剪曲面剖分算法

在雷达散射截面(RCS)计算中,首先需要将物体的表面进行三角剖分,称为前置处理,三角片单元的边长通常为0.1个波长(一般1个波长取3 cm).飞行器外表形状复杂,在建立数学化模型中,使用了大量的裁剪曲面,曲面之间的关系非常复杂,而3 mm边长的三角片剖分,使得飞行器的三角片单元的数目巨大,给前置处理带来难度.对此,提出了一种裁剪曲面三角网格剖分的方法.利用飞行器外形设计的特点,在以拱高为逼近误差的前提下,把曲面离散一系列等参数线,等参数线上等弦长布点,两两参数值相邻的参数线生成网格单元.根据网格单元与裁剪区域的位置关系,将落在裁剪区域外的网格单元进行裁剪处理.然后对曲面间边界处的网格进行相容性处理,最终实现多张裁剪曲面的三角网格剖分.      

空间变化场景下卫星部组件域适应识别研究

针对小样本限制下卫星部组件识别域适应困难的问题,提出一种变化场景下自适应迁移的目标识别算法.卫星部组件的识别模型框架为YOLO,迁移算法包括3个策略:基于特征关联性的样本加权策略,基于模型的参数自适应策略和最优特征变换自适应迁移策略.基于以上策略,YOLO模型建立域特征空间的相似性,选择性地迁移源域知识,同时在适应过程...     

基于背景网格簇的动网格生成方法

提出了基于背景网格簇实现网格变形的新方法。背景网格簇由任一内边界节点和远场边界角点生成的背景网格构成,并随边界运动而变化。通过保持计算网格节点在每一个背景网格中所在单元的面积坐标不变(三维时保持体积坐标不变)而求出一组期望位置,加权平均该组期望位置,确定出该计算网格节点的新位置。其中权值与内边界节点和计算网格节点距离的倒数相关。新方法引入的背景网格簇可以有效地改进单个背景网格的不足之处。算例结果表明:基于背景网格簇的动网格生成方法实现简单,与弹簧近似法相比,新方法因不需要迭代求解方程组而非常高效,且拥有更强的网格变形能力;与Delaunay图映射法相比,该方法背景网格的单元个数极少,因此易于定位,且不会出现背景网格单元交叉的现象,网格变形能力更强,变形后的网格质量更好。     

三角网格模型的细分曲面拟合

提出一种用Loop细分曲面拟合三角网格模型的新算法.首先对网格模型进行特征识别,然后把经过简化和形状优化的网格作为拟合细分曲面初始控制网格.通过对控制网格顶点的循环修正、网格形状优化、局部自适应细分来求解拟合细分曲面控制顶点.该算法不仅避免了求解线性方程组,克服了拟合控制网格的扭曲现象,而且达到了用较少的控制网格拟合出反映物体细节特征并满足精度要求的分片光滑(片内除奇异点C1外其余C2连续)的Loop细分曲面的目的.实例表明,该算法用于测量数据的曲面重构是有效可行的.     

穿孔管式扩散器出口流速均匀性研究

为解决穿孔管式燃油扩散器内部压力分布严重不均匀,导致燃油扩散器出口流速不符合要求的问题对穿孔管式扩散器出口流速均匀性开展研究。以 Matlab 及 Fuent 软件为平台,通过 Sparlar-Allmaras 湍流模型来处理燃油在扩散器内的三维流动,建立穿孔管式扩散器内部燃油流动模型,并分析其内部压力分布及出口处的速度分布。多次选代计算后,发现网孔开设及内部构型设计可有效调节内部的流动方式和压力分布。其中网孔开设方面应考虑开孔总面积足够大:另外,对内部构型进行修型,在扩散器内部适当位置添加合适高度的孔板。详细给出了网孔的开设方法及扩散器内部需重点考虑的3个关键孔板的设计规律,对穿孔管式燃油扩散器的设计及优化提供了理论参考。     

Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.     

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.     

Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising are...     

Sea fog detection based on unsupervised domain adaptation

Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are datadriven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering t...     

Active,compact, wideband,receiving filtenna with power adaptation for space-limited wireless platforms

An active, compact, wideband, receiving filtenna with power adaptation for space-limited wireless platforms is presented. By organically combining a filtering antenna and a Variable Gain Amplifier (VGA), the proposed co-designed active filtenna is achieved. Its power adaptation is empowered by controlling gain of the VGA unit. A demonstrated prototype with a compact overall size is fabricated, assembled and measured. Good performances are achieved. Furthermore, in any condition, the proposed active filtenna displays outstanding impedance matching (|S₁₁| < ?10 dB) and high passband skirt selectivity at upper and lower stopbands (Selectivity ≥ 170 dB/GHz).