Effect of microstructural features on the corrosion behavior of severely deformed Al–Mg–Si alloy

The aim of this study was to determine the influence of severe plastic deformation processing and the changes in microstructure resulting therefrom on the corrosion resistance of an Al–Mg–Si alloy. The alloy was processed using incremental equal channel angular pressing, which caused a reduction in grain size from 15 to 0.9 µm. The grain refinement was accompanied by an increase in the number of grain boundaries and dislocations, and by changes in grain orientation. However, there was no change in the size and number of intermetallic particles, which presumably resulted in a constant number of galvanic couplings. Electrochemical experiments revealed only slight differences between the samples before and after processing. Higher potential transients/oscillations upon immersion and increased corrosion currents in the vicinity of corrosion potential point to slightly higher reactivity of the most refined material. This indicates that intermetallic particles are the most crucial microstructural elements in terms of corrosion resistance. Their impact exceeds that of grain boundaries, in particular, at the stage of corrosion initiation. The development of corrosion attack is controlled more by the microstructure of the matrix as the grain refinement resulted in a less pronounced corrosion attack in comparison with the coarse-grained sample.     

Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

结合句法依存信息的方面级情感分类

引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。     

基于光刀图像的机器人示教纠偏方法

为了解决机器人辅助在线检测系统示教过程中效率低、人为干预多的问题,针对孔类特征,提出一种通过处理光刀图像获取机器人位姿纠正参数的方法。在该方法中,纠偏过程按照将图像光刀线调整为水平方向、将被测特征调整至图像中央、根据不同特征调整最优扫描方向3个步骤进行。根据光刀图像可以求出测量坐标系与被测特征的位姿关系,并得到该位姿与理想测量位姿的偏差。经过坐标变换即可得到在机器人基坐标系下的调整参数。另外,可利用电脑控制机器人运动,从而实现了纠偏过程的自动化。实验结果表明,该方法可将初始的示教位姿修正为理想位姿,且比传统方式效率更高。     

某微细粒嵌布磁铁矿选矿工艺研究

针对某微细粒磁铁矿进行了全磁选流程和磁选-反浮选流程对比试验研究。结果表明,在最终磨矿细度相当的情况下,2种工艺流程都获得了产率48%左右、TFe品位66%左右、回收率80%左右的铁精矿指标,而采用磁选-反浮选流程的第三段磨矿量比全磁选流程减少了2/3。磁选-反浮选流程具有显著的节能降耗优势。     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

基于可供性的文化意象交互式智能音箱设计研究

目的 探索将文化民俗有效融入现代产品中的技术思路，保留并延续中华传统文化的生命力，有助于提升用户体验。方法 分析可供性与文化意象之间的主客观互通性;挖掘交互式智能音箱中的可供性触点;构建形态、感官、认知及情境四类可供性的设计框架;以楚文化意象为例进行设计实践，对智能音箱的产品造型与交互操作方式进行设计，并进行设计评价。结果 在可供性理论指导下的楚文化意象交互式智能音箱产品外观及交互体验设计较好地满足了设计目标和用户需求。结论 可供性有助于文化意象的正确提取与转化，可将意象抽象为具有实际意义的设计要素，帮助用户在生理与认知两个层面理解产品的使用方法，同时进一步回味、体验文化内涵和文化精髓。