一个解析三维非结构网格生成方法

给出了一个三维非结构网格生成方法。提出弱区域指示函数描述三维封闭流场形状,并证明了该函数的存在不唯一性。利用该函数基于Delaunay方法依据流场尺度生成网格结构。其中,边界引力有效解决了流场边界恢复和内嵌边界问题,弹簧振子和正四面体趋进技术有效调整网格结构并提高网格质量。最后,弹簧振子和边界引力随着弱区域指示函数的演变与监测函数的指导成功应用到自适应网格生成。     

超声速条件下内埋式武器分离特性的数值分析

采用非结构动网格技术,耦合求解欧拉方程和六自由度(6-DOF)运动方程,对内埋式导弹在亚声速和超声速条件下的分离轨迹和姿态变化进行了比较,分析了不同马赫数下气动力对导弹分离特性的影响,重点讨论了超声速条件下导弹分离过程中相对于载机的位置和导弹的姿态变化情况,分析了超声速飞行环境下导弹安全分离的条件.     

低重环境下旋转轴对称贮箱内液体晃动研究

针对低重环境下旋转轴对称贮箱自由晃动和受侧向、纵向激励时的箱内液体晃动,用变分原理推导了基于低重环境下静液面形状的液体晃动的积分形式的动力学控制方程,由拉普拉斯方程得到用高斯超几何级数解析表达的速度势和波高,进而得到液体晃动的模态运动方程。分析了球形贮箱和Cassini贮箱内液体自由晃动基频,贮箱受侧向激励时液体晃动波高振幅、晃动对贮箱产生的晃动力和力矩、晃动的等效力学模型,以及贮箱受纵向激励时的晃动波高振幅、可能出现的参激共振问题。     

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...     

基于VBGMM-DCNN的列车卫星定位欺骗干扰检测方法

面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测，在卫星定位解算层次，运用深度学习建模学习方法的优势，提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络（variational Bayesian Gaussian mixture model-deep convolutional neural network, VBGMM-DCNN）的列车卫星定位欺骗干扰检测方法。该方法首先提取能够充分体现欺骗干扰对定位解算过程作用影响的卫星观测特征参数，构建干扰检测特征矢量；然后，采用VBGMM模型拟合经过预处理的特征向量的概率分布，得到二维概率密度图；最后，将概率密度图用于DCNN模型实施欺骗干扰的检测决策。结合现场实验所得运行场景数据，利用实验室搭建的欺骗干扰测试环境实施了干扰注入测试与检验，结果表明,欺骗干扰检测性能随着DCNN网络深度的增加而提升，相对于常规有监督决策方法F1值最高提升44.68%。基于VBGMM-DCNN的欺骗干扰检测能够适应测试验证中运用的列车运行特征及定位观测条件，所达到的检测性能优于对比算法。     

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).