Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

基于GIS单线图的配网停电单模拟操作应用研究

配电网停电会造成电力系统供配电可靠性以及服务质量下降,研究基于地理信息系统(GIS)单线图的配网停电单模拟操作应用。利用网格长度作为基本单位建立坐标系,以选取起始点与终止点为基础,通过四参数法将GIS坐标映射至图纸网格内,实现配网内设备初步布局,将杆塔、站房和整体均匀分布作为优化目标,设置多目标优化目标函数实现GIS单线图最终优化。选取某电力公司配网作为单模拟操作应用对象,模拟结果表明,单模拟操作配网停电后,该配网各负荷点年故障率、次平均停电时间以及年停电时间均有所减少,可有效提升配网的供配电可靠性。     

Towards type-selective carbon nanotube growth at low substrate temperature via photo-thermal chemical vapour deposition

Carbon nanotubes have been intensively researched for electronic applications, driven by their excellent electronic properties, where the goals are control and reproducibility of growth, semiconducting/metallic type selectivity and maintaining high quality of carbon nanotubes, in a process that is temperature-compatible with the electronics. Photo-thermal chemical vapour deposition can achieve these goals and, through a thorough investigation of the parameter space, we achieve very high nanotube-quality and growth rates, and produce a phase-diagram that reveals distinct regions for growing semiconducting and metallic single-walled nanotubes, as well as multi-walled. Correlation with the carbon-catalyst phase diagram allows for the development of a novel growth model. We propose that the temperature-gradient induces carbon diffusivity-gradient across the catalyst to yield the high growth rate. This is attributed to the increase of α-iron of catalyst. The growth control demonstrated here allows for integration of the nanotube growth process by photo-thermal deposition into mainstream electronics manufacture.     

Effect of in-app components,medium, and screen size of electronic textbooks on reading performance,behavior, and perception

Although many app-based textbooks are available for students, reading have not been thoroughly outlined. This study aimed to understand how changes from paper to electronic textbooks have affected the academic reading task, investigate student users’ perceptions of in-app components and screen sizes, and identify issues affecting in-app components and task requirements. A mixed factorial design experiment was employed. Results showed that there were no significant changes in comprehension and time spent reading between print text and the iPad. Yet, student highlighting, notetaking, and reading behavior and perception significantly changed based on condition. In addition, students struggled to use in-app components and found them frustrating especially when accounting for sentence splitting. The findings presented can assist in understanding the changes in student reading behavior, which can be used to improve interface design of future e-textbooks.     

混凝土构件承载力计算的Excel实现

运用Excel软件,高效解决了混凝土结构计算中较复杂的圆形截面偏压构件的承载力计算问题。该编程计算方法同样适用于土力学、水力学、钢结构等领域。对于解决工程中的各种学科计算问题有着广阔的应用前景。     

2010-2020年深圳市机动车发展和油气需求量预测

介绍了深圳市机动车发展现状,分别用趋势外推法、城市比较分析法和分类法预测了2010-2020年深圳市的机动车保有量,最后预测了机动车的油气需求量。     

Supercapacitors based on conducting polymers/nanotubes composites

Three types of electrically conducting polymers (ECPs), i.e. polyaniline (PANI), polypyrrole (PPy) and poly-(3,4-ethylenedioxythiophene) (PEDOT) have been tested as supercapacitor electrode materials in the form of composites with multiwalled carbon nanotubes (CNTs). The energy storage in such a type of composite combines an electrostatic attraction as well as quick faradaic processes called pseudo-capacitance. It has been shown that carbon nanotubes play the role of a perfect backbone for a homogenous distribution of ECP in the composite. It is well known that pure conducting polymers are mechanically weak, hence, the carbon nanotubes preserve the ECP active material from mechanical changes (shrinkage and breaking) during long cycling. Apart of excellent conducting and mechanical properties, the presence of nanotubes improves also the charge transfer that enables a high charge/discharge rate. For an optimal use of ECPs in electrochemical capacitors, a special electrode composition with ca. 20 wt.% of CNTs and a careful selection of the potential range is necessary. The capacitance values ranging from 100 to 330 F g⁻¹ could be reached for different asymmetric configurations with a capacitor voltage from 0.6 to 1.8 V. It is also noteworthy that such a type of ECP/CNTs composite does not need any binding substance that is an important practical advantage.     

云计算平台的虚拟安全

随着信息技术的发展和环保节能技术的普及和深入人心,越来越多的个人和组织对虚拟化技术感兴趣。然而,敏感数据传输和在线存储的安全是虚拟工作面临的重要挑战之一。在本文中,我们提供一个新的运行在云计算平台上的安全服务解决方案,该方案使用虚拟专有网络(VPN)和透明加密方式来保护虚拟工作的安全。它的优点是:按需使用,方便易用,成本效益高和管理简单。可以为中小型企业节省开支,不需要采购IT设备建立自己的虚拟工作的安全解决方案。     

3D human pose estimation in motion based on multi-stage regression

3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.