一种在SAR图像中进行目标识别的多尺度模型

本文基于SAR相干成像的原理，利用当图像分辨率改变时，自然景物和人工目标班点模式变化方式的不同，提出了一种从自然景物中识别人工目标的多尺度模型，并研究了此模型在不同分辨率图像中的性质及应用。     

Coronal mass ejection detection using wavelets,curvelets and ridgelets: Applications for space weather monitoring

Coronal mass ejections (CMEs) are large-scale eruptions of plasma and magnetic field that can produce adverse space weather at Earth and other locations in the Heliosphere. Due to the intrinsic multiscale nature of features in coronagraph images, wavelet and multiscale image processing techniques are well suited to enhancing the visibility of CMEs and suppressing noise. However, wavelets are better suited to identifying point-like features, such as noise or background stars, than to enhancing the visibility of the curved form of a typical CME front. Higher order multiscale techniques, such as ridgelets and curvelets, were therefore explored to characterise the morphology (width, curvature) and kinematics (position, velocity, acceleration) of CMEs. Curvelets in particular were found to be well suited to characterising CME properties in a self-consistent manner. Curvelets are thus likely to be of benefit to autonomous monitoring of CME properties for space weather applications.     

A novel surrogate modeling strategy of the mechanical properties of 3D braided composites

In this paper, a surrogate-based modeling methodology is developed and presented to predict the elastic properties of three dimensional (3D) four-directional braided composites. Using this approach, the prediction process becomes feasible with only a limited number of training points. The surrogate models constructed using Finite Element (FE) method and Diffuse Approximation, reduce the computational time and cost for preparing experimental samples. In the FE model, multiscale method is applied to couple the computations of elastic properties at microscale and mesoscale. Subsequently, a parametric study is performed to analyze the effects of the three design parameters on the elastic properties. Satisfactory results are obtained via the surrogate-based modeling predictions, which are compared with the experimental measurements. Moreover, the predictions obtained from surrogate models concur well with the FE predictions. This study orients a new direction for predicting the mechanical properties based on surrogate models which can effectively reduce the sample preparation cost and computational efforts.     

改进多尺度Canny算子在SAR图像边缘提取中的应用

着重讨论了在原始Ｃａｎｎｙ算子基础上提出的一种改进算法。其主要特点在于将多尺度分析的概念引入到基本Ｃａｎｎｙ算子中，从而改善了Ｃａｎｎｙ算子的检测性能，使其更加适应合成孔径雷达（ＳＡＲ）图像受噪声影响较大的特点。最后在原始ＳＡＲ图像基础上，将改进的算法与基本Ｃａｎｎｙ算子及其它几类边缘检测算子作了性能对比实验。实验结果显示了改进Ｃａｎｎｙ算子的良好检测性能。     

反舰导弹末制导多尺度多传感器数据融合方法

提出了一种实用有效的反舰导弹末制导多尺度多传感器数据融合方法。该方法可充分地利用捷联惯导和双星系统信息,对雷达导引头的测量信号进行有效的校正补偿,同时又可根据系统对校正算法实时性与精度的要求,通过合理地调整滤波算法节点数来满足系统的设计要求,从而大大地提高反舰导弹末制导的导引精度。该方法的有效性在某型超音速反舰导弹末制导信号校正仿真中得到验证。     

Multiscale eigenelement method for periodical composites: A review

Based on the eigenvector expansion idea, the Multiscale Eigenelement Method (MEM) was proposed by the author and co-workers. MEM satisfies two equivalent conditions, one condition is the equivalence of strain energy, and the other is the deformation similarity. These two equivalent conditions character the structure-preserving property of a multiscale analysis method. The equivalence of strain energy is necessary for achieving accurate macro behaviors such as lower order frequencies, while the deformation similarity is essential for predicting accurate micro behaviors such as stresses. The MEM has become a powerful multiscale method for the analysis of composite structures because of its high accuracy and efficiency. In this paper, the research advances of MEM are reviewed and all types of eigenelement methods are compared, focusing on superiorities and deficiencies from practical viewpoint. It is concluded that the eigenelement methods with smooth shape functions are more suitable for the analysis of macro behaviors such as lower order frequencies, and the eigenelement methods with piecewise shape functions are suitable for the analysis of both macro and micro behaviors.     

Multiscale topology optimization using feature-driven method

This paper presents a multiscale design method for simultaneous topology optimization of both macrostructures and microstructures. Geometric features are extended as design primitives at both macro and micro scales and represented by Level Set Functions (LSFs). Parameters related to the locations, sizes, and orientations of macro and micro features are considered as design variables and optimized simultaneously. In the overlapping areas of different macro features, embedded microstructures are optimally figured out as the solution of the corresponding sub-optimization problem. In this study, the eXtended Finite Element Method (XFEM) is implemented for structural and sensitivity analyses with respect to design variables. This method has the advantage of using a fixed grid independent of the topology optimization process. The homogenization procedure is applied to calculate the effective properties of considered microstructures in each macro feature. Numerical examples are presented to illustrate the effectiveness of the proposed method. Results depict that the multiscale design cannot obviously improve structural stiffness compared with a solid-material design under the linear elastic condition.     

基于材料微结构的高周疲劳性能定量表征及关键技术问题

长寿命、高可靠性已成为飞机结构设计的主要目标之一,疲劳失效对材料结构的完整性构成严重威胁.介绍了航空铝合金微结构对高周疲劳失效机制的作用及定量表征的研究现状,重点对材料微结构对多裂纹萌生的作用机制与定量表征、小裂纹转变为长裂纹的临界尺寸的数学描述和高周疲劳裂纹多尺度扩展行为定量表征等方面的研究状况进行了分析,指出了制约微结构对高周疲劳作用的定量表征的关键技术问题.     

Modified micro-mechanics based multiscale model for progressive failure prediction of 2D twill woven composites

To consider fiber random distribution at the microscale for the multiscale model based on the micro-mechanics failure (MMF) theory, clustering method is used for the extraction of amplification factors. As the clustering method is a kind of unsupervised machine learning method, the elements with similar mechanical behavior under external loading can be included in a cluster automatically at the microscale. With this modification, the fiber random distribution model can be used for multiscale damage analysis in the framework of MMF theory. To validate the modified multiscale analysis method, progressive damage analysis of a kind of 2D twill woven composites is conducted based on different microscale models. The stress values for microscale models with fiber hexagonal and random distribution patterns are compared first. Much higher stress concentration is generated in the fiber random distribution model due to the smaller inter-fiber distance especially under longitudinal shear loading. The obtained cluster distribution results exhibit the characters of the stress distribution in the two microscale models. Thereafter, tensile and compressive responses of the 2D twill woven composite are predicted with the modified multiscale analysis method and accuracy of the method is verified through comparison with published experimental results. From the simulation results, it can be found that the matrix damage initiation from the model based on the fiber random distribution model is premature compared with that from the model based on the fiber hexagonal distribution model. Besides, under tensile loading, the damage all initiates from the fill tows and propagates to the wrap tows. However, under compressive loading, the matrix damage initiates from the wrap tows in the model based on the fiber random distribution model.     

基于超像素与多尺度残差U-Net相结合的遥感图像飞机检测方法

遥感图像中存在飞机很小、角度和位置不确定且背景复杂等问题,从遥感图像中检测飞机是一项重要且具有挑战性的任务,因此,提出一种基于超像素与多尺度残差U-Net(Multi-scale Residual U-Net,MSRU-Net)相结合的遥感图像飞机检测方法。首先对遥感图像进行超像素预分割,将位置相邻且像素特征相似的像素点组成若干个超像素,保持图像进一步分割的有效特征;然后构建多尺度残差U-Net,学习其多尺度判别特征。与传统的飞机检测方法相比,该方法用少量的超像素代替大量像素表达图像特征,降低了图像分割的复杂度,再利用MSRU-Net分割遥感超像素图像,有效检测不同尺度的飞机图像。在公共飞机遥感图像数据集上实验,结果表明,该方法能够有效的检测遥感图像不同尺度的飞机图像,检测精确率达到91.2 %。