High Temperature Solid Oxide Fuel Cells(SOFC)

The history and the state of the art of solid oxide fuel cells(SOFC)is reviewed in this paper.Itdescribes mainly on the material selection,structure design and technological economics of SOFC.The appli-cation of great amount of rare earth materials in SOFC may well extend the market potential of rare earthmaterials in the world.A research project to be carried out as early as possible in China on the SOFC tech-nology will be far-reaching.     

聚酯－聚醚嵌段共聚物的合成及抗静电性能研究

研究和分析了ＰＥＧ分子量、加料次序、反应时间和温度、物料比例、分散剂和固相缩聚等因素对ＰＥＴ－ＰＥＧ嵌段共聚物分子量和耐水性的影响。合成的嵌段共聚酯醚与ＰＥＴ共混纺丝可以显著改善ＰＥＴ纤维的抗静电性能。     

真空阴极弧离子镀类金刚石碳（DLC）膜的碳弧稳定性研究

选用氩气、氩气加氢气、氩气加乙炔等气体作为介质，石墨作为靶材进行真空阴极弧离子镀来制备类金刚石碳膜。石墨电弧有其独特的电弧特性曲线，不同气体介质对碳弧特性的影响不同，磁场的大小对电弧的稳定性有很大作用，碳弧下基片偏流随电弧电压的增加而减小，试验得到表面光滑的类金刚石碳（DLC）膜，对膜的表面进行了SEM分析。     

动态毛吸法测定纤维表面能

本文用浸润测定仪对玻璃纤维和Kevlar-49芳纶纤维进行了表面能测定,讨论了纤维束柱末端沾湿量的影响,提出了浸润仪测试数据的正确处理方法,求得了Kevlar-49纤维的表面能并对其色散分量和非色散分量进行了解析.     

民国时期书刊封面设计的发展

民国时期[1]书刊封面中的汉字设计风格丰富多样,兼具了传达功能与艺术美感,具有鲜明的民族性与时代特征。如杉浦康平先生所说,"为了前进一步,回首过去。"[2]民国时期的书刊封面的设计,在中国书籍发展史及平面设计史上具有重要的历史地位,归纳与梳理这一时期的发展脉络,对于研究封面中的汉字设计有着重要意义。所以本文以时间为线索,归纳从1912年至1949年之间的民国书籍封面。     

两栖装甲装备故障诊断智能检测系统设计

针对目前两栖装甲装备故障检测时间长、检测效率低的缺点,研究设计了一种两栖装甲装备故障诊断智能检测系统。系统充分利用现代最新发展的大规模集成电路技术和数字处理技术,对多发、易发故障部位的故障信号进行采样、检测及处理,从而快速确定故障部位,便于及时排除故障。     

Response of Hydrodynamics and Water-quality Conditions to Climate Change in a Shallow Lake

Lake water resources operation and water quality management come up with higher challenges due to climate change. The frequency and intensity of extreme hydrological events are increasing under global warming, which may directly lead to more uncertainty and complexity for hydrodynamic and water-quality conditions in large shallow lake. However, studies about effects of climate change on lake hydrodynamic and water-quality conditions are not enough. Thus, a coupled model is es-tablished to investigate the potential responses of lake water level, flow field and pollutant migra-tion to the changing climatic factors. The results imply that water flow capacity and self-purification in the Hongze Lake can be improved by west, northwest, north, south and southeast winds indi-cating wind filed change has a great effect on the hydrodynamic and water-quality conditions in large shallow lake. It is further observed that both hydrodynamics and water quality are more sensitive to rainfall change than to temperature change; compared to the effect from temperature and rainfall, the effect from wind field appear to be more pronounced. Moreover, the results verify the feasibility of coupling basin hydrological model with lake hydrodynamic and water quality model. To the best of knowledge, the coupled model should not be used until independent calibra-tions and verifications for hydrodynamics and water quality modeling, the hydrological model and the coupled model.

     

基于互补原理确定黄土高原Budyko方程的流域特性参数

Budyko方程的流域特性参数一般根据植被覆盖度等下垫面特征进行确定,但相关资料不易获取。本文针对黄土高原地区29个小流域,通过分析流域特性参数、植被覆盖度和互补关系中大气湿润指数之间的关系发现,大气湿润指数能够间接反映植被覆盖度的大小,进而能够利用常规气象数据确定流域特性参数,并拟合出了经验公式。相比采用固定流域特性参数,这种方法能够提高Budyko方程对实际蒸散发量的模拟效果。相比于植被覆盖度等下垫面资料,常规气象数据时间序列长、且更易获取,因此本方法具有较好的应用前景。     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.