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71.
近几年真空玻璃产业发展迅速,而支撑物作为真空腔体的支撑材料直接影响真空玻璃的透明度、受力、热导等性能.本文制备了一种弹性模量适中、热变形可调的玻璃-陶瓷基真空玻璃支撑点,研究了其制备工艺以及工艺对玻璃力学性能及热导的影响.结果 表明:制备的支撑物材料粉体粒径分布范围集中在0.5~10 μm之间,采用点胶工艺可制备高度在0.2~0.35 mm之间,直径在0.5~1.0 mm之间,尺寸可控的支撑点矩阵;支撑点在经过物理钢化后有晶体析出,为玻璃-陶瓷结构,提高了支撑点的强度;支撑点由“点接触”改为“面接触”可以有效避免支撑点的破碎和玻璃微裂纹的产生;适当调整支撑点的尺寸和间距可以在保证力学性能的基础上使玻璃同时具有低的热导. 相似文献
72.
某含锌锡多金属硫化矿石Zn、Sn、Fe、S含量分别为6.04%、1.05%、29.33%、19.08%,锌主要以铁闪锌矿、闪锌矿的形式存在,锡主要以锡石的形式存在,铁主要以黄铁矿、磁黄铁矿等形式存在,其中的金属矿物共生关系密切,相互包裹现象普遍。为确保不影响后续选锡,对锌浮选流程进行了试验研究。结果表明:矿石在磨矿细度为-0.074 mm占80%的情况下,采用预先脱硫—锌硫混浮再分离流程处理,在选择硫酸铜为锌矿物活化剂、丁基黄药为捕收剂、松醇油为起泡剂、石灰为黄铁矿抑制剂的情况下,经1段脱硫、2粗1扫锌硫混浮、1粗2精锌硫分离,锌硫分离精选尾矿与锌硫混浮扫选精矿2次精选后锌硫分离,最终获得锌精矿Zn品位47.06%、回收率90.76%,试验指标良好。 相似文献
73.
We investigate the effects of doping and annealing on the dielectric properties of metal ions doped TiO2 ceramics. Colossal permittivity (CP) above 104 was observed in single Nb ion doped TiO2, which was dominated by electron transport related interfacial polarization. Moreover, the CP can be dropped to 120 when simultaneously introducing Mn ion into the sample. The disappearance of CP behaviors maybe due to the multivalence of Mn which would inhibit the reduction of Ti4+ to Ti3+, and thus reduce delocalized electrons. Interestingly, the CP was recovered for the (Nb+Mn) co-doped TiO2 after post-sintering heat treatment in N2 atmosphere. The recovery of CP in the sample after annealing can be ascribed to the semiconducting grain and the insulating grain boundary, according to impedance spectroscopy. We therefore believe that this work can help us understand the mechanism of CP from a new perspective. 相似文献
74.
The application of deep learning in the field of object detection has
experienced much progress. However, due to the domain shift problem, applying an
off-the-shelf detector to another domain leads to a significant performance drop. A
large number of ground truth labels are required when using another domain to train
models, demanding a large amount of human and financial resources. In order to avoid
excessive resource requirements and performance drop caused by domain shift, this
paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our
approach improves the cross-domain vehicle detection model from image space and
feature space. We employ objectives of the generative adversarial network and cycle
consistency loss for image style transfer in image space. For feature space, we align
feature distributions between the source domain and the target domain to improve the
detection accuracy. Experiments are carried out using the method with two different
datasets, proving that this technique effectively improves the accuracy of vehicle
detection in the target domain. 相似文献
76.
Metallurgical and Materials Transactions B - In the past two decades, friction stir processing (FSP) technology has received considerable attention. FSP can be used to adjust and control the... 相似文献
77.
78.
As accessing computing resources from the remote cloud inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among cloudlets, the cloudlet placement aiming to minimize the deployment cost (i.e., consisting of both the cloudlet cost and average E2E delay cost) has not been addressed effectively so far. The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing (CAPABLE) strategy, where both the cloudlet cost and average E2E delay are considered in the cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations. 相似文献
79.
Yejun Wu Bowen Yan Juan Zhou Huizhang Lian Xiaojun Yu Jianxin Zhao Hao Zhang Wei Chen Daming Fan 《Journal of food science》2020,85(10):3282-3292
80.