Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Piezoelectric properties and microstructure of ceramicrete-based piezoelectric composites

Inorganic piezoelectric ceramic composite is the potential sensing element for long-term structural health monitoring due to its excellent durability and compatibility. In this study, a Ceramicrete-based piezoelectric composite is proposed preliminarily, in which the magnesium potassium phosphate cement is used as the matrix and the lead zirconate titanate particle is utilized as the functional phase. Piezoelectric properties test and microstructure analysis are performed to evaluate the testing samples. Results show that the piezoelectric performance of the composite increase with the increase of piezoelectric ceramic particle size. The value of the piezoelectric strain factor (d₃₃) can reach 83.8 pC/N, while the corresponding piezoelectric voltage factor (g₃₃) is 50.1 × 10^-3 V•m/N at the 50th day after polarization. Microstructure analysis illustrates that the interfacial transition zone (ITZ) between the matrix and the particles is dense. Moreover, the influence of aging on the composite is attributed to the continuous hydration after polarization. It indicates that the composites have a higher piezoelectric performance, which can be regarded as a promising sensing element material.     

Upregulated Expression of Activation-Induced Cytidine Deaminase in Ocular Adnexal Marginal Zone Lymphoma with IgG4-Positive Cells

Immunoglobulin G4-related disease (IgG4-RD) is a systemic disorder characterized by tissue fibrosis and intense lymphoplasmacytic infiltration, causing progressive organ dysfunction. Activation-induced cytidine deaminase (AID), a deaminase normally expressed in activated B-cells in germinal centers, edits ribonucleotides to induce somatic hypermutation and class switching of immunoglobulin. While AID expression is strictly controlled under physiological conditions, chronic inflammation has been noted to induce its upregulation to propel oncogenesis. We examined AID expression in IgG4-related ophthalmic disease (IgG4-ROD; n = 16), marginal zone lymphoma with IgG4-positive cells (IgG4+ MZL; n = 11), and marginal zone lymphoma without IgG4-positive cells (IgG4- MZL; n = 12) of ocular adnexa using immunohistochemical staining. Immunohistochemistry revealed significantly higher AID-intensity index in IgG4-ROD and IgG4+ MZL than IgG4- MZL (p < 0.001 and = 0.001, respectively). The present results suggest that IgG4-RD has several specific causes of AID up-regulation in addition to inflammation, and AID may be a driver of oncogenesis in IgG4-ROD to IgG4+ MZL.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

基于FLAC3D的破碎围岩巷道支护效果分析

为了解破碎围岩分别采用锚杆支护、锚喷支护以及锚喷+锚索耦合三种支护方式下的支护效果,进而为破碎围岩巷道选择合理的支护方式提供参考。通过借助FLAC^3D软件建立数值模型,分析不同支护条件下的破碎围岩巷道位移量、应力分布以及塑性区的时空演化特征。结果表明,采用锚喷+锚索耦合支护时,可以较好的控制巷道围岩的位移量、减小应力集中效应、缩小塑性区的影响范围。     

基于虚拟元-有限元耦合的隧道内道床干缩裂缝细观研究

为解释道床与盾构隧道管片产生脱空的现象，研究新混凝土在浇筑后由干燥收缩产生的裂缝机理。基于虚拟元推导任意多边形骨料的刚度矩阵格式，利用UEL子程序实现有限元与虚拟元的耦合计算，纯弯梁数值试验结果表明耦合法较有限元法计算效率明显提升，且具有较好的稳定性。在此基础上，建立细观尺度下新老混凝土的干缩数值模型，并通过圆盘干缩模型验证了模型参数。研究骨料和砂浆的应力分布、内部湿度与裂缝发展的对应关系以及新老混凝土表面和界面的应力发展。结果表明：①收缩过程中，骨料主要承受压应力作用，砂浆沿骨料边界的切线方向受骨料约束作用易形成收缩裂缝，在新混凝土表面首先出现大量微小裂缝，随后界面两端发生剥离; ②受表面下骨料的约束作用，新混凝土表面拉应力呈现“驼峰”分布。开裂后表面被划分为多个收缩区，收缩区边界受拉应力，内部受压应力；③界面的拉伸和剪切应力由两侧开始增加，并且随着界面剥离的增加不断内移。在界面内部未剥离区域内存在由界面附近骨料约束导致的局部压应力和剪切应力增大。     

异种铁素体耐热钢接头界面组织及其影响

综述了铁素体与铁素体异种金属焊缝（dissimilar metal welds，DMWs）接头界面组织及其影响。结果表明，在焊后热处理或运行温度下的铁素体钢DMWs接头的不均匀界面组织中，通常会形成脱碳层和增碳层。在铁素体钢DMWs焊接接头界面组织影响因素中，焊缝金属的化学成分有重要影响；焊后热处理规范（温度和时间）、工作温度下运行时间的影响较为突出；焊接工艺参数的影响亦不可忽略。异种钢接头界面处近缝区裂纹的产生，以及接头的蠕变强度随Larson Miller 参数增大而下降等不利影响，均为异种钢界面碳迁移行为所导致。焊缝成分控制法是接头界面组织控制或改善的必要条件，而脱碳层部位转移法能有效防止裂纹发生，亦是接头安全运行的重要工艺措施之一。     

High quality 3D reconstruction based on fusion of polarization imaging and binocular stereo vision

Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the polarization surface by correcting the azimuth angle errors on the basis of registered binocular depth, to solve the azimuthal ambiguity in the polarization imaging. Then we propose a joint 3D reconstruction model for depth fusion, including a data fitting term and a robust low-rank matrix factorization constraint. The former is to transfer textures from the polarization surface to the fused depth by assuming their relationship linear, whereas the latter is to utilize the low-frequency part of binocular depth to improve the accuracy of the fused depth considering the influences of missing-entries and outliers. To solve the optimization problem in the proposed model, we adopt an efficient solution based on the alternating direction method of multipliers. Extensive experiments have been conducted to demonstrate the efficiency of the proposed method in comparison with state-of-the-art methods and to exhibit its wide application prospects in 3D reconstruction.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.