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.     

As the number falls,alternatives to the Hagberg–Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.     

Near-Field Drives Long-Lived Shallow Trapping of Polymeric C3N4 for Efficient Photocatalytic Hydrogen Evolution

Undesired photoelectronic dormancy through active species decay is adverse to photoactivity enhancement. An insufficient extrinsic driving force leads to ultrafast deep charge trapping and photoactive species depopulation in carbon nitride (g-C₃N₄). Excitation of shallow trapping in g-C₃N₄ with long-lived excited states opens up the possibility of pursuing high-efficiency photocatalysis. Herein, a near-field-assisted model is constructed consisting of an In₂O₃-cube/g-C₃N₄ heterojunction associated with ultrafast photodynamic coupling. This In₂O₃-cube-induced near-field assistance system provides catalytic “hot areas”, efficiently enhances the lifetimes of excited states and shallow trapping in g-C₃N₄ and this favors an increased active species density. Optical simulations combined with time-resolved transient absorption spectroscopy shows there is a built-in charge transfer and the active species lifetimes are longer in the In₂O₃-cube/g-C₃N₄ hybrid. Besides these properties, the estimated overpotential and interfacial kinetics of the In₂O₃-cube/g-C₃N₄ hybrid co-promotes the liquid phase reaction and also helps in boosting the photocatalytic performance. The photocatalytic results exhibit a tremendous improvement (34-fold) for visible-light-driven hydrogen production. Near-field-assisted long-lived active species and the influences of trap states is a novel finding for enhancing (g-C₃N₄)-based photocatalytic performance.     

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。     

Copy Move Forgery Detection based on double matching

Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.     

基于“炉机对应”原则的连浇优化方案

国内某炼钢厂为保证浇注周期较短的2号连铸机连浇，多座转炉需同时向该连铸机供应钢水，导致炉 机对应关系不明确，生产组织难度较大。通过对该厂炼钢 连铸过程的工序作业周期与生产运行模式进行解析，在保证连铸机连浇的基础上，探究合理的炉 机对应关系，改善该厂的生产顺行。结果表明，在“一一对应”运行模式下，2号连铸机最大连浇炉数仅为6炉；而在“定炉对定机”运行模式下，若适时接受3号转炉冶炼的钢水，2号连铸机最大连浇炉数可延长至19炉。在此基础上，通过对连铸机“柔性”的优化和钢水供应的合理调配，2号连铸机可满足实际生产的连浇要求。此外，运用“炉→机”匹配度进行评价，计算结果表明优化后的炉 机对应关系得到显著改善。     

物联网中基于Multi-Agent的物间动态关系发现

物联网中存在大量异构关系的实体，其信息间的交互造成了物联网的内在矛盾。针对这一问题，提出将物联网中实体抽象化为对应的Agent，并以个体人为中心，利用本体的语义匹配及改进的物间动态关系计算方法，计算出针对个体人的物间动态关系；将原本异构的实体关系转化为Agent间基于动态关系紧密度排序的网络关系。经实验验证，该方法构建的关系网络可明显改善异构实体间交互的矛盾，而且相比原有类似方法，该方法解决问题的准确率和误差率均有改善，说明了该方法的准确性和可行性。     

硅酸盐型胶磷矿单一反浮选工艺研究及工业化应用

云南某矿区产的硅酸盐型胶磷矿的SiO_2含量一般在30%以上,堆存数量巨大,使用常规的单一反浮选工艺难以达到要求的精矿指标,但是使用正反浮选工艺或者双反浮选工艺的生产成本又较高,经济效益不理想。从矿石的多元素分析发现,与CaO∶P_2O_5大于1.79的碳酸型磷块岩配矿后使用单反浮选工艺可以获得合格的精矿指标。在此思路上,对两种矿石进行配合比、解离度、磨矿细度和药剂制度的探索研究。当配合比为1∶3,磨矿细度为92%,药剂制度为抑制剂1.5kg/t、调整剂18kg/t、捕收剂3.0kg/t的情况下,最终可以获得P_2O_5品位为28.89%,MgO品位为0.91%,产率62.73%,回收率85.24%的精矿指标。