ST－4型节能全自动辊道双推板窑

ＳＴ－４型全自动辊道双推板窑是为永磁铁氧体规模生产而研制的一种节能型空气烧结窑。它根据永磁铁氧体烧结的工艺要求，设计了合理的温区，使规模生产烧结成品率达９５％以上；采用辊道推板相结合的窑床形式与复合保温结构，达到了永磁铁氧体能耗１．２ｋｗｈ／ｋｇ；采用优化设计，使设备性能先进，稳定可靠。     

美国的灭火与救援机器人研发综述

设想某一个城镇生产木材调色板的流水线车间里 ,由于电器原因引发了一场火灾 ,致使建筑物坍塌。没有人能够确切地说出有多少工人被埋压在里面 ,更糟的是 ,当毗邻墙体坍塌时 ,隔壁的旧轮胎储藏室也被埋压在下面。于是市消防局派遣一批机器人去现场救援。第一个到达的机器人进入现场调查情况 ,该机器人能忍耐80 0℃的高温炙烤 ,身上安装了摄像机 ,可以给 90 m外的控制室发送图片 ,控制室的指挥人员就可以根据图片决定是否派消防队员冒险进入现场。第二个机器人是无人驾驶卡车 ,进入后便从 75 m远的地方以2 6 gal/ s的速度往火场喷水 ,并用它所…     

复杂形体目标激光散射特征测量系统的研制

复杂形体目标激光散射特征的测量系统中，利用反射式离轴光路满足了可见到远红外波段的测量需要，通过大孔径（250mm）物方远心接收光路实现了对实际远场的模拟测量，以光滑镀金球体作为标准器，可直接获得任意目标散射截面的绝对值。在0．6328、1．06和10．6μm三个波段针对标准器的实测结果表明，平均误差小于4．95％。     

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.     

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。     

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.     

Double Medical Images Zero-Watermarking Algorithm Based on the Chaotic System and Ternary Accurate Polar Complex Exponential Transform

Journal of Mathematical Imaging and Vision - Different from the traditional watermarking schemes, zero-watermarking schemes are lossless embedding methods, which are applicable to be used in...