Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

Topological homogenization of metamaterial variability

With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.     

Study on ice-melting performance of gradient gas diffusion layer in proton exchange membrane fuel cell

In this study, a three-dimensional model was established using the lattice Boltzmann method (LBM) to study the internal ice melting process of the gas diffusion layer (GDL) of the proton exchange membrane fuel cell (PEMFC). The single-point second-order curved boundary condition was adopted. The effects of GDL carbon fiber number, growth slope of the number of carbon fibers and carbon fiber diameter on ice melting were studied. The results were revealed that the temperature in the middle and lower part of the gradient distribution GDL is significantly higher than that of the no-gradient GDL. With the increase of the growth slope of the number of carbon fiber, the temperature and melting rate gradually increase, and the position of the solid-liquid interface gradually decreases. The decrease in the number of carbon fibers has a similar effect as the increase in the growth slope of the number of carbon fibers. In addition, as the diameter of the carbon fiber increases, the position of the solid-liquid interface gradually decreases first and then increases.     

Thermal properties of hierarchical YSZ and LZO ceramic microspheres with multi-scaled voids investigated by using theoretical,experimental and simulation methods

Heat transfer within ceramic feedstock powders is still unclear, which impedes optimization of the thermal and mechanical properties of the thermal sprayed coatings. The microspheres (yttria-stabilized zirconia YSZ and lanthanum zirconate LZO) were prepared via the electro-spraying assisted phase inversion method (ESP). The thermal properties of the two ESP microspheres and a commercial hollow spherical powder (HOSP) were investigated by using theoretical, experimental, and simulation methods. Thermal conductivity of the single microsphere was estimated via a novel nest model that was derived from the Maxwell-Eucken 1 and the EMT model. Thermal conductivity of a single YSZ/LZO-ESP microsphere prepared at 1100–1200 °C was within 0.36–0.75 W/m K, which was ～ 20 % lower than that of a single YSZ-HOSP microsphere with a similar porosity. Heat flux simulation showed that high tortuosity around the multi-scaled voids of the ESP microsphere led to a more efficient decrease in thermal conductivity compared with total porosity.     

Effect of lattice structure evolution and stacking fault energy on the properties of Cu–ZrO2/GNP nanocomposites

A hybrid nanocomposite comprising nanosized ZrO₂ and graphene nanoplatelet (GNP)-reinforced Cu matrix was synthesised via powder metallurgy. The influence of sintering temperature and GNP content on the electrical and mechanical behaviour of the Cu–ZrO₂/GNP nanocomposite was investigated. The ZrO₂ concentration was fixed at 10% for all the composites. Upon increasing the GNP concentration up to 0.5%, a significant improvement was observed in the compressive strength, microhardness, and electrical conductivity of the composite. Furthermore, the properties were significantly improved by increasing the sintering temperature from 900 to 1000 °C. The compressive strength, hardness, and electrical conductivity of Cu–10%ZrO₂/0.5%GNP were higher than those of the Cu–ZrO₂ nanocomposite by 60, 21, and 23.8%, respectively. This improvement in the mechanical properties is because of the decrease in the crystallite size and dislocation spacing, which increases the dislocation density, thereby increasing the impedance towards dislocation movement. The lower stacking fault energy of the hybrid nanocomposites enables easier electron transfer within and between the Cu grains, resulting in an improved electrical conductivity. The enhancement in strength and electrical conductivity were aided by the GNPs and ZrO₂ nanoparticles that were dispersed widely in the Cu matrix.     

基于归一化水体指数及其阈值自适应算法的水体遥感反演效果分析

归一化水体指数（NDWI）是水体遥感反演的一种重要指标，其阈值及修正直接影响反演结果的精度。基于Landsat-8与GF-1光学多光谱影像，使用归一化水体指数法阈值0（TH₀）、最大类间方差法（OTSU）自适应阈值（TH_otsu）与均值漂移聚类算法（Mean-Shift）自适应阈值（TH_MS）分别对典型正常水体、云雾覆盖水体、富营养化水体、高含泥沙水体进行水体遥感提取与效果分析，结果表明：正常水体以TH₀为阈值提取精度最高，THMS提取精度次之，TH_otsu提取精度最差；而云雾覆盖水体、富营养化水体以及含泥沙水体使用THMS提取精度最高，尤其少量云雾覆盖下的水体，TH_MS具有更明显的优势，TH₀提取精度次之，TH_otsu提取精度最差；对于不同的阈值，Land?sat-8比GF-1总体表现出更高的水体提取精度。Mean-Shift算法应用于NDWI阈值修正与水体遥感反演具有快速、水质适应性强、效果稳定的优势，对尤其是复杂条件下的水体信息遥感反演具有较好的提取效果。     

Revealing the dimensional stability mechanisms of SiC/Al composite under long-term thermal cycling

Long-term thermal cycling causes irreversible dimensional changes of the material, which in turn affects the reliability of precision instruments. In this paper, dimensional stability mechanisms of SiC/Al composites during thermal cycling were revealed using high-precision thermal dilatometer, XRD and spherical aberration correction transmission electron microscope (Cs-TEM). First, how the factors including dislocations, internal stress and precipitates affect the dimensional change of SiC/Al composites were separately introduced. Then, the integrated effect of these factors affecting the dimensional stability of SiC/Al composites was further discussed. Among them, the integrated effect of dislocation-internal stress in SiC/pure Al composites leads to an increase in dislocation density and average lattice constant, which leads to an increase in dimensional change. The integrated effect of dislocation-internal stress-precipitates in SiC/2024Al composites leads to a decrease in the average lattice constant and some changes in the precipitation behavior (including the type, density and lattice constant of the precipitates), which ultimately leads to a decrease in dimensional change. The dimensional change of the two types of composites was semi-quantitatively estimated. Finally, the reasons for the significantly higher dimensional stability of the SiC/2024Al composites were given.     

基于SAOR的Massive MIMO系统信号检测算法

大规模多输入多输出(Massive multiple input multiple output, Massive MIMO)系统采用最小均方误差(Minimum mean square error, MMSE)接收检测方法时存在矩阵求逆复杂度高的问题，已有较多降低复杂度的研究。在降低检测算法复杂度的同时，如何提高算法收敛速度和检测性能一直是人们关注的焦点。本文将对称加速超松弛(Symmetric accelerated over-relaxation， SAOR)迭代算法应用于Massive MIMO系统信号检测中，避免了复杂的矩阵求逆计算，实现了复杂度较最小均方误差算法降低了一个数量级。仿真结果表明，基于SAOR的检测方法通过较少的迭代次数就能逼近最小均方误差（Minimum mean square error, MMSE）算法的检测性能，为Massive MIMO系统中接收信号的快速检测提供了较好的实现方法。     

煤系地层致密砂岩气甜点区地震逐级预测——以鄂尔多斯盆地东南缘下二叠统山西组2~3亚段为例

鄂尔多斯盆地东南部下二叠统山西组2~3亚段(以下简称山2~3亚段)为该盆地重要的天然气勘探目的层,但该亚段储层薄、厚度变化快、非均质性强,储层预测和勘探目标优选难度大。为了准确预测该亚段煤系地层致密砂岩气甜点区、提高天然气勘探成功率,针对该套储层的特征和预测难点,提出了90°相移技术识别河道外形、模型约束波阻抗反演刻画砂体厚度和子波衰减梯度属性识别含气砂体的地震逐级预测技术。研究结果表明:①山2~3亚段上覆5号煤地震强反射层,下伏储层地震反射能量弱,加之为稀疏二维地震测网、井控程度低,致使致密砂岩气甜点区预测难度大;②所提出的技术方法通过地震逐级预测约束,可以有效地刻画河道砂体分布并识别有效含气储层,提高了对勘探开发目标预测的精度;③基于该技术方法指导部署的勘探开发目标实钻效果好,地震预测结果横向分辨率高,真实地反映了河道及河道砂体的变化特征。结论认为,采用该方法可以有效地解决二维地震勘探区煤系地层强非均质性、薄储层致密砂岩气甜点区预测的地质难题。