300km/h高速铁路PC槽型梁动力试验研究

介绍北方交通大学与比利时鲁汶大学、布鲁塞尔自由大学、比利时铁路公司合作,在巴黎至布鲁塞尔之间高速铁路线上的Antoing大桥进行的二次高速铁路桥梁动力试验。试验桥梁由跨度50m的多跨预应力混凝土简支槽型梁构成,试验中列车速度达265-310km/h。通过现场试验和实验结果分析,得到了桥梁的频率、振型、阻尼等自振特性,以及桥梁在高速列车作用下的动挠度、梁和桥墩的横向和竖向加速度、橡胶支座的相对位移、梁体的动应变等动力响应特性。试验经验和测试结果对于充实高速铁路桥梁动力分析理论、改进数值分析模型、验证计算结果、提高高速铁路桥梁的动力设计水平、保证行车安全,具有重要的意义。     

193nm光刻技术进展和问题

光刻是制造大规模集成电路的主要方法。由于快速计算机和多功能处理器不断发展,并提出很多新的要求,促使半导体集成线路制造商力求增加单片上的元件密度。元件密度受最小光刻尺寸限制。虽然光刻工艺水平已能刻出准波长特征尺寸的集成线路的线宽,但是,连续发展几代的单片集成线路要求光源波长从436nm到365nm。用248nm波长光源(KrF准分子激光器).光刻的特征尺寸为0.25μm。期望能生产0.18μm特征尺寸的集成线路。为了生     

光学信号处理器

本专利阐述一种结构新颖的CCD信号处理器。采用CCD处理器,元件内的偏压可以直接抑制。CCD间以及读出放大器间的散射也可减少。(美国专利号:5170048)     

先进的微型器件和空间科学传感器

国际光学工程学会于1994年7月28—29日在美国圣迭戈召开了“先进微器件和空间科学传感器”学术交流会，会议共交流15篇论文。第一部分论文阐述了关于行星表面，大气和行星际间探测所用的器件和系统以及今后三年对火星表面和大气探测可能用的微型器件和小型系统。第二部分论文重点论述了新的传感器技术在现场探测和遥测领域的潜在应用。第三部分论文阐述了行星探测和地球遥感仪器整体小型化问题。     

引人注目的新的显示技术

信息显示学会1996年学术交流年会在美国加利福尼亚州圣地戈举行(5月14—16日).会上发表了有关液晶显示技术、主动式液晶显示器、发射式显示器、大型和小型显示器以及最传统的显示器—阴极射线管.会议还举办了成果展览和展示了有关技术论文,反映了显示器研究和应用方面的世界水平.     

Dynex: multiplex ELISA technology

Conventional enzyme-linked immunosorbent assay (ELISA) is a gold standard for screening antibodies and testing for protein or antigen presence. A significant limitation of this assay resides in the fact that only one analyte can be assessed per microplate well. Here, we describe and investigate a new technology consisting of an automated ELISA system in which up to 10 analytes can be measured within one single well, thus improving productivity, accuracy, and repeatability by reducing the amount of human labor required. Another strength of the platform is that a user can load any necessary sets/subsets of beads to perform required assays, with improved flexibility compared to manufactured-loaded arrays for multiplex analysis. We also demonstrate that this system can be used to determine the pathogenicity (i.e., presence of Shiga toxins) and serotype (i.e., Escherichia coli O157) of E. coli isolates.     

Using previous models to bias structural learning in the hierarchical BOA

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step toward the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in the future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.