车号定位识别与可信度估计

以车号的定位与识别为例，应用行投影法对车号图片进行倾斜行定位，证明了模拟与场景的二维互相关系数近拟等于一维投影互相关系数，在此基础上提列投影法快速行扫描识别算法及相应的可信度估计方法，这种方法对于室外拍摄的有复杂背景，光照变化，字体微倾，字符排列疏密不均及噪声干扰的车牌图像具有很好的识别效果。     

高效离心风机的设计研究

该文在总结国内外高效离心风机的设计经验基础上,掌握和利用已有的经验,遵循一系列离心风机优化准则,成功的完成了高效机车用牵引电机通风机的设计.性能样机经台架试验,比原风机的性能有较大幅度的提高,效率达到86%,在国内同样风机中效率是较高的.     

Conditions for the Acceptance of Deontic Conditionals.

Recent psychological research has investigated how people assess the probability of an indicative conditional. Most people give the conditional probability of q given p as the probability of if p then q. Asking about the probability of an indicative conditional, one is in effect asking about its acceptability. But on what basis are deontic conditionals judged to be acceptable or unacceptable? Using a decision theoretic analysis, we argue that a deontic conditional, of the form if p then must q or if p then may q, will be judged acceptable to the extent that the p & q possibility is preferred to the p & not-q possibility. Two experiments are reported in which this prediction was upheld. There was also evidence that the pragmatic suitability of permission rules is partly determined by evaluations of the not-p & q possibility. Implications of these results for theories of deontic reasoning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于遗传算法的图像边缘信息链表匹配技术

提出了一种基于遗传算法的图像边缘信息链表匹配方法。首先,采用CANNY算子的方法提取图像边缘;然后,将图像边缘信息链表化;最后,基于链表化结果利用遗传算法进行链表匹配。该方法能够减少匹配过程中的计算量,且能实现高效匹配。通过仿真实验,验证了该方法的性能。     

无基坑轨道衡的研究

本文对无基坑轨道衡的开发与研制,进行了可行性研究。     

浅谈天馈线电压驻波比对微波电路的影响

天馈线电压驻波比是微波电路运行的重要指标，通过典型事例来阐述天馈线电压驻波比的劣化对微波电路的影响，并根据多年实践经验提出天馈线维护需注意的事项。     

一种基于模糊理论的图像边缘提取

讨论了模糊理论在边缘提取中的应用，并给出了一种改进的基于模糊理论的图像边缘检测算法。实验结果表明，与该算法不需要加额外的滤波器或图像降噪预处理。在抗噪性能方面(80％以内的Gaussian噪声和10％以内的椒盐噪声Salt＆Pepper)明显优于其它方法。     

硬度匹配对40CrNiMo滚子接触疲劳性能的影响

通过对“软－软”、“中－软”、“中－中”、“中－硬”四组不同硬度匹配的滚子接触疲劳试验得出四条Ｐ－Ｎ曲线。并发现中硬度材料的接触疲劳寿命高于软材料，而当主试样为中硬时，与之相匹配的陪试件硬度越高，主试样的寿命越短，通过分析讨论解释了产生上述现象的原因及其失效机理。     

Approximating the problem, not the solution: An alternative view of point set matching

This work discusses the issue of approximation in point set matching. In general, one may have two classes of approximations when tackling a matching problem: (1) an algorithmic approximation which consists in using suboptimal procedures to infer the assignment, and (2), a representational approximation which involves a simplified and suboptimal model for the original data. Matching techniques have typically relied on the first approach by retaining the complete model and using suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple Graphical Models, an instance of the second class, can significantly outperform instances of techniques from the first class. We experimentally compare this method with well-known Spectral and Relaxation methods, which are exemplars of the first class. We have performed experiments with synthetic and real-world data sets which reveal significant performance improvement in a wide operating range.