90°分束偏光镜光强分束比研究

给出了90°分束偏光镜的e,o光光强分束比的计算公式,以及光强分束比随光波长的变化曲线;在6328Å测得的光强分束比与计算值是一致的。     

用线性状态观测器重构交流矢量控制系统中的负载扰动

在异步电动机磁场定向数学模型的基础上,给出了恒磁条件下异步电动机的解耦模型及传动系统的线性状态方程,并提出了重构负载扰动转矩的线性状态观测器。利用重构的负载转矩在矢量控制系统中进行扰动前馈,明显地提高了系统的抗负载扰动能力,从而为进一步提高矢量控制系统的动态性能开辟了一条有效的途径。     

砷化镓声电荷转移延迟线的设计与工艺研究

由于声电荷转移(ACT)这一新颖的高速缓冲抽样技术的发明,使得新一代高速高频信号处理器件的实现成为可能。本文研究了第二代平面结构的ACT器件转移沟道的电位分布特性,给出了平面结构ACT延迟线的设计,并研究了ACT器件的工艺实现。     

一种新型微处理器功能验证

文中介绍一种新型微处理器的验证方法，并对相关的一些验证策略进行深入的讨论。     

HDPE/CB-GP复合材料体系电性质的研究

当HDPE含量46％（质量分数，以下同）、其它添加剂（如抗氧剂、阻燃剂、交联剂和润滑剂等辅助填料）的含量11％，两种导电填料石墨（GP）和碳黑（CB）的总含量为43％时，研究结果表明石墨含量的变化对HDPE/CB-GP复合材料室温电阻率、阴C强度和后NTC强度有显著的影响．HDPE/CB-GP复合材料中GP的含量〈8．6％时，样品的室温电阻率〈30Ω·m，PTC强度〉7、后NTC强度〈1．3；电阻率．温度曲线经过17次冷热循环后重现性好，复合材料具有实际应用价值；GP在复合材料中为层片状结构。     

变频驱动在长运距带式输送机系统的应用

介绍了赵庄煤矿主斜井长运距带式输送机系统的构成,通过变频器在输送系统上的应用实践,表明该变频驱动技术具有先进性。     

Achievement of specificational information usage with true and false feedback in learning a visual relative-mass discrimination task.

Participants' usage of informational variables in learning visual relative-mass discrimination in collisions was tracked by means of PROBIT correlations. Four groups received feedback that was true or accorded with either of three nonspecificational cue variables. A majority in each group adopted the feedback, but several participants defied the false feedback. Unlike in previous research, the fit to data of the relative-mass invariant could not be bettered by post hoc linear combinations of the cues. Discriminability was lower in the use of the invariant. Analytic complexity was rejected as an explanation for discriminability differences. A "smart mechanism" for pickup of the relative-mass invariant was developed as an extension of G. Johansson's (1950/1994) vector model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

景像匹配区中稳定景物的分割方法研究

研究了景像匹配区中稳定景物的分割方法———强度和法及分形常数法 ,成功地分割了匹配区中的稳定景物 ,为评价区配区景物稳定性提供了依据 .其中分形常数法是分形理论应用在图像分割领域的一个新尝试     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.