Organic mesh template-based laminated object manufacturing to fabricate ceramics with regular micron scaled pore structures

A new aqueous slurry-based laminated object manufacturing process for porous ceramics is proposed: firstly, an organic mesh sheet is pre-paved as a pore-forming template before slurry layer scraping; secondly, the 2D pattern is built with laser outline cutting of the dried mesh–ceramic composite layer; finally, the pore structure is formed after degreasing and sintering. Alumina parts with porosities of 51.5 %, round hole diameters of 80 ± 5 μm were fabricated using 70 wt. % solid content slurry and 100 mesh nylon net. Using an organic mesh as the framework and template not only reduces the risk of damage of the green body but also ensures the regularity, uniformity and connectivity of the micron scaled pore network. The layer-by-layer drying method avoids the delamination phenomenon and improves the paving density. The new method can realize the flexible design of the pore structure by using various organic mesh templates.     

2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。     

Structure-sensitive semantic matching for aggregate question answering over knowledge base

Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets.     

Synthesis of Hollow Three-Dimensional Channels LiNi_(0.5)Mn_(1.5)O_4 Microsphere by PEO Soft Template Assisted with Solvothermal Method

A appropriate size with three-dimension(3 D) channels for lithium diffusion plays an important role in constructing highperforming LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode materials, as it can not only reduce the transport path of lithium ions and electrons, but also reduce the side effects and withstand the structural strain in the process of repetitive Li~+ intercalation/deintercalation. In this work, an e fficient method for designing the hollow LNMO microsphere with 3 D channels structure by using polyethylene oxide(PEO) as soft template agent assisted solvothermal method is proposed. Experimental results indicate that PEO can make the reagents mingle evenly and nucleate slowly in the solvothermal process, thus obtaining a homogeneous distribution of carbonate precursors. In the final LNMO products, the hollow 3 D channels structure obtained by the decomposition of PEO and carbonate precursor in the calcination can provide abundant electroactive zones and electron/ion transport paths during the charge/discharge process, which benefits to improve the cycling performance and rate capability. The LNMO prepared by adding 1 g PEO possesses the most outstanding electrochemical performance, which presented an excellent discharge capacity of 143.1 mAh g~(-1) at 0.1 C and with a capacity retention of 92.2% after 100 cycles at 1 C. The superior performance attributed to the 3 D channels structure of hollow microspheres, which provide uninterrupted conductive systems and therefore achieve the stable transfer for electron/ion.     

基于PolyWorks虚拟装配间隙面差计算分析研究

随着当今社会汽车制造业的迅猛发展,高标准和高需求已经是消费者对汽车制造凯歌的主旋律。而这其中,汽车钣金件的间隙面差就是其中一个重要因素。快速检测间隙面差是否满足设计要求直接关系到汽车白车身的生产精度和企业的工作效率。传统计算间隙面差大多利用量具,易划伤被测物且在零件实际装配后才可测量,降低生产效率,而非接触测量造价昂贵。本课题基于实物的汽车钣金件虚拟装配,对间隙面差的计算方法进行分类与参数调整,提出一种高效率、高标准的间隙面差计算流程。     

Nitrogen-doped hierarchical porous carbon from polyaniline/silica self-aggregates for supercapacitor

In this paper, nitrogen-doped hierarchical porous carbon (N-HPC) was prepared from polyaniline (PANI)/silica self-aggregates. H-bonding between N-H groups in aniline/PANI and -OH groups in nano silica template led to a self-assembly type, which enabled the formation of uniform N-HPC nanoparticles. Silica self-aggregates provided macroporous channels resulted in a decreased diffusion distance. After removing the hard template, the N-HPC had a high surface area (899 m²·g^-1). Owing to two co-existed synergetic energy-storage mechanisms and the hierarchical porous structure, the obtained N-HPC exhibited a high specific capacitance of 218.75 F·g^-1 at 0.5 A·g^-1, compared with the nonporous nitrogen-doped carbon (N-C) derived from pure PANI. Moreover, the N-HPC electrode demonstrated excellent cycle life, retaining 99% of its initial specific capacitance after 1000 cycles.     

利用平面约束的月球车立体匹配方法

为了满足月球车视觉系统对检测障碍物检测的时效性和可靠性的需求，提出了一种基于平面约束和自适应惩罚参数的半全局立体匹配算法。首先，对极线校正后的两幅图像进行SIFT特征点提取与匹配，同时提取边缘特征；然后，利用匹配的SIFT特征点拟合空间平面，并根据平面估计左右图像所有像素点的视差搜索范围；最后，基于传统的半全局匹配算法，采用自适应惩罚参数的策略对左右图像进行立体匹配。实验结果表明：所提出的算法有效地降低了计算复杂度，其计算复杂度只有传统方法的19.9%，对于视差不连续区域以及遮挡区域都能够获得正确的匹配结果。较传统半全局匹配方法无论在速度还是匹配精度上都得到明显提高，为对立体匹配的实际应用奠定了基础。     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

Controlled galvanic decoration boosting catalysis: Enhanced glycerol electro-oxidation at Cu/Ni modified macroporous films

We report on glycerol electro-oxidation in alkaline medium at macroporous Ni electrodes decorated with Cu particles. Macroporous Ni film is electrodeposited, using hydrogen bubbles as dynamic templates, atop of a Cu substrate. This film shows good electrocatalytic activity towards glycerol oxidation reaction (GOR). The Ni film is further decorated with Cu via spontaneous deposition from CuSO₄ solution. This is done to enhance the catalytic activity of the film towards GOR. The morphology of the Cu-decorated Ni film is controlled using various additives such as KCl and (NH₄)₂SO₄ which are added to the Cu deposition bath. The as-prepared Cu-decorated Ni films are characterized by electrochemical measurements, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). It is found that these additives have tremendous effects on the morphology and the electrocatalytic activity of the decorating Cu particles.The decorated Ni foam showed superior electrocatalytic activity towards the GOR, as confirmed by the negative shift in the onset oxidation potential (ca. 100 mV) together with an increase in oxidation current that is up to 1.5-fold during the cyclic voltammetry (CV) measurements, compared to the undecorated Ni foam.