稀土混合物水溶液中囊泡的形成

依据柠檬酸的螯合作用和乙二醇的空间位阻效应，获得了稀土混合物囊泡。并利用ＩＣＰ、ＴＥＭ、ＦＳ等仪器对囊泡的性能进行了研究，最后对囊泡的形成机理进行了讨论。     

吐哈油田葡北3-12井超长封固段固井技术

葡北3-12注气井位于吐哈盆地葡北构造带中断葡北1号构造．井深为3610m。针对该区块地层承压能力低，易漏失，油气水窜严重，常规固井方法无法满足固井要求的问题，该井采用三段三凝制、常规水泥浆和泡沫水泥浆体系固井，并采用重钻井液顶替技术．降低施工压力。该技术能保证固井候凝过程中井底压力平衡，施工安全。现场应用表明，采用常规水泥浆和泡沫水泥浆固井技术．提高了低压易漏井、长封固井段的固井质量，解决了水泥返高不足的问题；并通过一次常规固井工艺实现了全井封固，简化了施工工艺，节约了成本，经济效益和社会效益十分显著。该井封固优质段累计达2600m，优质率达85％，固井结果良好。     

浅谈电梯选型应注意的问题

电梯选型配置问题是个十分有实际意义的话题。本文结合作者多年来检测检验电梯的经验，浅谈几点选型配置应注意的问题。     

Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

基于多尺度空洞卷积神经网络的高光谱图像分类

郑姗姗¹,刘文¹,单锐¹,赵静一²,江国乾³,张智⁴

（1. 燕山大学理学院,河北 秦皇岛 066004;2. 燕山大学机械工程学院,河北 秦皇岛 066004;3. 燕山大学电气工程学院,河北 秦皇岛 066004;4. 北京航天研究所,北京 100094）

创新点说明：

1）将图像分割方法——空洞卷积用于卷积神经网络进行高光谱图像分类,提取更加广泛、抽象的图像特征。

2）构建基于多尺度空洞卷积神经网络的高光谱图像分类方法。搭建多尺度聚合结构,在每个通道中使用快捷连接和空洞卷积结构,有效提取图像特征,避免信息丢失。

研究目的：

针对图像信息丢失问题,得到高精度的高光谱图像分类方法。

研究方法：

在Indian Pines和Pavia University数据集上,与4个已有的高光谱图像分类方法进行对比实验,比较OA, AA和Kappa值。

研究结果：

多尺度空洞卷积神经网络在Indian Pines和Pavia University数据集上OA值分别达到了99.58%,99.92%。AA值分别达到了99.57%,99.90%。Kappa分别达到了99.52%,99.89%。

结论：

1）在卷积神经网络中引入空洞卷积,可以有效避免图像信息丢失。

2）多尺度空洞卷积神经网络能提取更佳的判别性特征,实现高分类性能。

关键词：多尺度聚合;空洞卷积;高光谱图像分类;快捷连接

     

风电叶片大梁用碳纤维拉挤板材组合式加载压缩(CLC)测试浅析

依据ASTM D6641/D6641M-16《采用组合加载方式(CLC)测定聚合物基复合材料层压板压缩性能试验方法》标准,对风电叶片大梁用碳纤维拉挤板材进行压缩性能测试,通过采用不同类型的加强片制备压缩试样,对所测得的压缩强度、压缩模量和压缩应变的试验结果与失效模式进行分析.结果 表明,采用文中C型加强片制备的压缩试样,测得的压缩强度、压缩模量、压缩应变以及失效模式均较为理想,试验结果重复性较好.     

配电变压器直流电阻测量误差因素及结果不确定度分析

配电变压器直流电阻测量是线圈绕制过程中半成品和成品测试、变压器出厂试验、配电工程安装、交接试验及电网检修预防性试验的必测项目,其测量结果准确与否决定了配电变压器各环节是否需返工检修,因此对测量结果的误差因素分析极为重要.而测量不确定度是衡量检测结果准确与否的量化指标,直接反映了测试结果的可靠性和准确性.以配电变压器直流电阻测量为例,介绍了电阻测量的测试方法和试验原理,根据影响测量不确定度的各因素,建立电阻测量数学模型,对测量结果进行了不确定度评定和分析,展示了变压器绕组直流电阻测量的不确定度评定流程,为配电变压器制造商的出厂试验分析和实验室检测、计量的不确定度分析提供参考.     

生态型高吸水性树脂的研究

从高吸水性树脂的化学结构出发，论述了树脂的降解机理和降解方法，归纳和评述了近期我国在可降解天然聚合物、丙烯酸接枝共混和丙烯酸（盐）合成聚合物方向的研究，并对未来作出展望。     

Know by a handful the whole sack: efficient sampling for top-k influential user identification in large graphs

Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most existing greedy algorithms mainly focus on computing the exact influence spread, leading to low computational efficiency and limiting their application to real-world social networks. While in this paper we show that through supervised sampling, we can efficiently estimate the influence spread at only negligible cost of precision, thus significantly reducing the execution time. Motivated by this, we propose ESMCE, a power-law exponent supervised Monte Carlo estimation method. In particular, ESMCE exploits the power-law exponent of the social network to guide the sampling, and employs multiple iterative steps to guarantee the estimation accuracy. Moreover, ESMCE shows excellent scalability and well suits large-scale social networks. Extensive experiments on six real-world social networks demonstrate that, compared with state-of-the-art greedy algorithms, ESMCE is able to achieve almost two orders of magnitude speedup in execution time with only negligible error (2.21 % on average) in influence spread.     

Modeling of photocatalytic oxidation of VOCs in a packed bed reactor under continuous and discontinuous illuminations

In this paper, a mathematical model is presented to describe the photocatalytic degradation of VOCs in a packed bed reactor. Here, the adsorption of VOCs on the wall of the reactor is taken into account and the diffusion of VOCs in the axial direction is neglected. First-order kinetics is used to describe the photocatalytic oxidation of VOCs. The analytical solution of the present model is obtained by traveling wave method. The solution shows that the reactor performance is totally dependent on the inlet concentration of VOCs when the time is large enough. The present model is validated through the experimental result of the photocatalytic oxidation of trichloroethylene in a packed bed and the predicted results accord well with the experimental data. The influence of flow rate and inlet concentration on the performance of the reactor is discussed in detail. High flow rate offers high reaction rate and low conversion efficiency. The different inlet conditions and different reaction patterns are also investigated. The model would be useful to estimate the rate constant and help to the design of the reactor.