Supported Molybdenum Carbides Lie Between Metallic and Sulfided Catalysts for Deep HDS

Hydrodesulfurization (HDS) of 4,6-dimethyldibenzothiophene on alumina-supported Mo₂C has been studied. These catalysts are stable and active under deep HDS conditions (0-250 wt ppm S). However, although they are well known to have hydrogenation properties, they lead preferentially to a non-hydrogenated product of the HDS reaction: dimethylbiphenyl. For the same reaction, supported platinum and sulfided molybdenum oxide lead to the hydrogenated products dimethyldicyclohexyl and methylcyclohexyltoluene, respectively. The ranking of HDS activity is as follows: MoS₂/Al₂O₃ < Mo₂C/Al₂O₃ < Pt/SiO₂.     

Volatile compounds in oils after deep frying or stir frying and subsequent storage

Soybean oil (900 g) was heated by deep frying at 200°C for 1 h with the addition of 0, 50, 100, 150 and 200 mL water, and then stored at 55°C for 26 weeks. Soybean oil, corn oil and lard were heated by stir frying and then stored at 55°C for 30 weeks. The volatiles and peroxide values of these samples were monitored. All samples contained aldehydes as major volatiles. During heating and storage, total volatiles increased 260-1100-fold. However, aldehyde content decreased from 62–87% to 47–67%, while volatile acid content increased from 1–6% to 12–33%; especially hexanoic acid which increased to 26–350 ppm in the oils after the storage period was completed. Water addition to the oils heated by deep frying tended to retard the formation of volatile compounds. The total amount of volatile constituents of lard heated by stir frying increased more during storage than that of corn oil or soybean oil. Peroxide values did not reflect the changes of volatile content in the samples.     

一种基于深度卷积网络的鲁棒头部姿态估计方法

针对头部姿态估计方法受特征提取限制导致姿态估计效果不佳的问题,提出使用深度卷积网络自动学习有效特征并进行分类的头部姿态估计方法。首先,利用DCNN非线性映射和自动提取图像结构信息的能力,设计一个深度卷积网络实现对姿态鲁棒特征的提取;然后再将提取的特征用于分类器训练并最终实现头部姿态估计。在Pointing’04和FacePix数据库上的测试结果表明,本文设计的深度卷积网络能有效的进行特征学习,避免了人工设计特征的不足,与现有的基于人工设计特征方法相比,本文方法在两个数据库上达到的预测平均绝对误差分别为4.05〫和2.04〫,充分证实了本文算法的稳定性和可靠性。     

过硼酸钠在有机合成中新的应用

介绍了过硼酸钠在有机合成中的一些新的应用。综述了过硼酸钠与芳胺、芳酰胺、有机硼化物、醛等化合物的氧化,取代、酯化反应。     

基于相位测量的微波位移测量方法

为满足建筑结构全天候、高精度及低成本的位移监测要求,提出了基于相位测量的微波位移测量方法。该方法以微波作为全天候测量信号,利用高精度相位测量实现高精度位移监测,并采用低成本的标准频率器件搭建位移测量系统。实验结果表明:系统位移测量精度可达0.12 mm,并验证了系统具有全天候、高精度、低成本的位移监测特性。     

霍尔传感器在气动健身器材中的应用

为了能更好地掌握运动者的运动力度,尽可能地减小运动中由于运动过剧造成的损伤,设计了一种气动健身器材,通过实际的气缸压力与霍尔传感器检测出气缸活塞的位移来计算器材使用者所承受的拉力,并结合记录气缸活塞的运动次数来计算运动者消耗的热量,更直观地显示出运动者的运动情况。经过测试,霍尔传感器反应灵敏,检测结果准确,系统稳定性良好,有较高的实用性。     

图表示学习方法研究综述

针对图表示方法的相关解析任务进行了研究,从形式化定义出发,首先以不同核心技术作为分类标准将图表示学习方法划分为五大类,其包括基于降维解析、矩阵分解、随机游走、深度学习和其他表示学习方法。其次通过归纳与对比分析梳理各类技术发展脉络,进而深层次展现各类图表示方法的优劣。随后结合图表示学习的常用数据集、评估方法和应用领域的归纳分析,展开动态性、可扩展性、可解释性和可解析性的四维剖析。最后总结并展望了图表示学习的未来研究趋势与发展方向。     

局部自适应复合绝缘子检测与憎水性分类网络

憎水性等级(Hydrophobicity Class,HC)是衡量绝缘子性能的重要指标之一。在实际环境的多种因素作用下绝缘子伞裙表面存在局部憎水性差异,为了准确识别绝缘子的性能,本文提出了一种基于深度学习的局部自适应绝缘子检测与憎水性分类模型。首先,通过绝缘子分割模块分离绝缘子与背景区域,为后续针对绝缘子区域的操作提供分割信息;然后将绝缘子区域划分为固定大小的图像块,在缩小分辨率减小运算难度的同时保留了绝缘子表面的细节信息;最后通过憎水性分类模块分析图像块内绝缘子的憎水性。实验使用巡检维护现场的绝缘子图片作为样本集,分阶段构建模型,分别对分割阶段和憎水性分类阶段的准确性进行评估。实验结果显示分割阶段模块能有效识别绝缘子和背景区域,交叉验证的测试集准确率均大于97.21%,并且憎水性分类阶段模块能准确分析绝缘子憎水性,对140幅测试图片的识别准确率达到98.65%。经过实验证明本文提出的模型在复杂自然环境中检测绝缘子性能是一种有效的解决方案。     

An Improved Procedure for Determining the Work of Adhesion for Polymer-Solid Contact

Force-balance experiments in conjunction with the Johnson-Kendall-Roberts (JKR) theory have been used to determine the work of adhesion between solid systems. It has been shown that deficiencies in understanding the deformation behavior can lead to erroneous results. A modified procedure for determining the work of adhesion by force-balance experiments and JKR theory using normal displacement behavior has been introduced to address these deficiencies. This procedure involves improved experiental and data analysis protocols and has been applied to give more precise work of adhesion values for PDMS poly(dimethylsiloxane)-PDMS, PDMS-F(fluorinated silicon) and PDMS-Si (silicon) systems. The work of adhesion determinations are consistently less than those estimated by contact angle measurements.     

地铁深基坑分区开挖监测及数值模拟

对于狭长的地铁车站深基坑,围护结构区域内岩土体在实际施工时经常采用分区的方式进行开挖。为了分析分区开挖工况下深基坑的稳定性规律,选取合肥市某地铁车站深基坑作为研究对象,运用有限差分软件对实际工程进行数值模拟。比较模型计算结果与现场实测结果,得到了基坑分区开挖过程中围护结构侧向位移、横向支撑轴力,以及地表沉降的变化规律。研究结果表明:现场实测结果与数值模拟结果较为接近且均小于规范限值,数值模型能较好地反映基坑变形特性;随着开挖的进行,围护结构变形出现明显的"鼓肚"状,最大侧向位移点向基坑中段移动;支撑轴力前期发展较快,之后趋于平稳;邻近地表沉降呈现为凹槽状,基坑开挖影响范围约在40 m内。研究结果可为其他类似工程设计或施工决策提供参考。