爆炸排淤填石法中淤泥的本构模型

以爆炸排淤填石法为背景 ,对不同应变率阶段淤泥的本构模型进行了分析 ,利用ANSYS/LS-DYNA动态有限元分析程序对所选择的模型进行验证和确认。计算和分析结果表明 :在形成爆炸空腔的高应变率阶段 ,淤泥表现为理想不可压缩流体的性质 ;在小药量小抵抗线条件下 ,甚至在淤泥自重作用下的较低应变率变形阶段 ,其黏性效应也可以忽略不计。     

A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.      

基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Analytical study of surface wave multiple refraction in boundary of a scalar impedance surface with a tensor impedance surface

In this paper, the multiple refraction phenomenon is investigated on the boundary of a scalar impedance surface (SIS) and a tensor impedance surface (TIS). When a surface wave (SW) propagates on the SIS and radiates to the boundary of the TIS, the propagation direction of it is changed and the refraction phenomenon is accrued. The method that is proposed in this paper can predict the multiple refraction for the SW. Moreover, another analytical method is introduced for designing the proposed structure which the double refraction (DR) occurs at arbitrary angles on it. Using it, a sample of the structure is designed by printed circuits in 15.2GHz and the results are verified by the full‐wave simulation and measurement. The results are shown that in the structure, DR is occurred in 2° and 22° as predicted. The proposed method can provide many applications such as design of SW power dividers based on the TISs, impedance surface based waveguides, holographic antennas, and feeding of array antennas.     

利用不连续性的各向异性扩散滤波方法识别断层

在提高地震数据信噪比的同时保护断层等不连续的边界信息，对于地震精细解释具有非常重要的意义。为此，在前人的研究基础上，提出了基于不连续性的三维各向异性扩散滤波方法，分析了结构张量特征值与三维地震图像的局部结构特征之间的关系，并利用断层置信度参数重新设计扩散张量的特征值，可以更合理地控制地震数据沿不同方向的扩散滤波强度：在地震同相轴连续性较好之处，断层置信度参数趋于0，滤波强度大；在断层等地质体的边缘处，断层置信度参数趋于1，扩散滤波强度弱，可以有效保护断层等不连续边界的信息。模型测试和实际资料的应用证明，所提方法在提高地震资料信噪比的同时，可有效保护断层等不连续的边界信息，并在连续地层区域增强同相轴的连续性，为断层解释提供良好的基础数据。     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.     

温度饱和水下的裂隙岩体力学特性研究*

在理论上，利用O‘Connell的饱和水裂隙岩体等效弹性模量与岩石模量的关系式，并由式中的裂隙密度概念意义，建立了温度作用下的裂隙密度与Oda提出的裂隙张量之间的关系式；在试验的基础上，给出了温度、饱和水下的单裂隙岩体应力－应变、抗压强度回归拟合关系表达式。并通过试验与拟合结果对以加以验证，其裂隙岩体力学模型可为裂隙岩体THM耦合分析提供合理的依据。     

裂隙的工程地质调查与损伤效应

本文将岩体中的宏观裂隙体视为岩体的一种初始损伤，给出了岩体的各向异性宏观损伤张量与裂隙间断位移引起的损伤应变张量，建立了以岩体体平均应力应变为基本变量的等效连续模型。同时，还讨论了如何通过现场工程地质调查，以获取确定岩体宏观损伤性质的工程地质信息。