纵磁场真空开关管的技术工艺

本文介绍了研究真空电弧和改进真空开关管电极的新方法。电极表面温度测量揭示了电流开断时阳极表面熔化状况及其对开断能力的影响。数字式图像处理工艺技术使我们清楚了解燃弧现象。测量与试验结果表明纵磁场电极在提高真空开关管能力方面的优势。纵磁场电极优于其它类型电极已得到证实。我们相信用这些新的研究方法会加速优化电极结构，提高真空开关管的电压及额定电流。     

Policy Analysis, Peer Reviewed: Evaluation of Bioaccumulation Factors in Regulating Metals

Regulations.     

液晶光阀用ZnSSe薄膜的光电特性研究

用分子束外延法(MBE),在铟锡氧化物(ITO)导电玻璃衬底上生长了ZnSSe薄膜,详细研究了薄膜的光电特性.通过控制反应时的生长参数,制备出了符合紫外液晶光阀设计要求的光导层薄膜.室温下,该薄膜光谱响应截止边的响应度为0.01A/W,紫外/可见光响应对比度大于103.薄膜的暗电阻率随薄膜晶粒增大而减小,在衬底温度为2900C时,所获得的ZnSSe薄膜具有4.3×1011Ω@cm的暗电阻率.频率从40Hz到4000Hz的交流特性测试,也证实该薄膜符合器件紫外成像的工作要求.     

第23届APCOM会议上有关矿业计算机模拟、自动化、优化和争论(一)

本文内容取材于第23届国际APCOM会议论文集,它对矿业计算机模拟、自动化、优化以及争论要点进行了综述。     

聚焦离子束技术最近进展

本文评述了聚焦离子（ＦＩＢ）技术的最新进展。这些技术包括离子植入，离子束辅助蚀刻和沉积以及就地制造，由于ＦＩＢ的无掩模能力和高电流密度，发现ＦＩＢ作为一种显微外科手术工具的在超大规模集成（ＵＬＳＩ）器件或材料分析以及就地制造方面有重要应用，最近的聚焦离子束系统能产生直径小于１０ｎｍ的聚焦离子束，而且作为纳米制造工具的重要性也日益增加，使用ＦＩＢ主要在ＧａＡｓ／ＧａＡｌＡｓ异质结构中制造了各种小尺寸     

On the method-independent lower bound (MILB) to the variance of attenuation estimation in random reflection

Based on the Cramer-Rao inequality, the MILB is calculated for the problem of estimating the frequency slope of the attenuation coefficient of tissue from random reflections of ultrasonic waves. Under typical signal conditions, this bound for a 1-cm x 1-cm region was found to be about 0.08 db/(MHz-cm), rather close to the clinical requirement of 0.1 dB/(MHz-cm). Comparison to existing methods (including an autoregressive deconvolution method) shows room for further improvement.     

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.