考虑不同制氢方法的高速公路加氢站布局规划

针对高速公路加氢设施不完善的问题,提出一种基于改进自适应遗传算法的高速公路加氢站布局方法。根据高速公路特性及电子收费记录,建立高速公路网络和加氢判断模型。基于多层次-模糊综合评价方法,从经济性、环保性以及安全性角度,提供适合高速公路加氢站的制氢方法和制氢地点的组合。再结合分布式光伏的投入,以经济效益最优为目标,构建高速公路站外和站内制氢2组目标函数,利用改进自适应遗传算法分别进行优化布局和容量配置。以沈海高速(浙江段)作为研究对象进行分析,提供了不同制氢方法下加氢站布局方案及相关设施的容量配置,算例结果与模糊综合评价结果相互验证了模型的有效性。     

More DOE awards for advanced hydrogen and fuel cell R&D projects

Another slice of the much larger pie of US fuel cell money promised by President Bush has been served up by Secretary of Energy Spencer Abraham, with the selection of 13 more organizations to receive $75 million in cost-shared awards to fund new research in advanced fuel cell technology for vehicles, buildings and other applications. Combined with recent awards to 11 firms and universities of $21m for hydrogen storage, production and sensor technologies, DOE has awarded a total of $96m to 24 organizations in new awards in support of President Bush’s FreedomCAR and Hydrogen Fuel Initiatives.This is a short news story only. Visit www.re-focus.net for the latest renewable energy industry news     

锥套定位的高精度点圆特征提取算法

光学标记辅助加油锥套识别定位系统中,快速高精度提取光学标记质心坐标是锥套定位的重要基础。 实际应用场景 下,工作距离越远,光学标记点特征的成像越弱;且复杂的背景干扰使得质心提取的准确性大大降低。 通过分析光学标记点、圆 特征的成像特性,结合锥套结构特点,对光学标记圆特征进行提取,精确分割锥套有效特征区域以代替全局搜索,剔除大量背景 干扰;进而提出了一种由类 FAST 阈值粗定位法与二维高斯残差重心拟合法相结合的二级高效点特征质心提取算法,通过设置 亮度阈值快速识别光斑特征并获取粗略位置,在此基础上精确框选光斑像素分布邻域,拟合计算提取高精度亚像素质心坐标。 实验表明,本文算法满足实时提取要求,与其他经典算法相比,本文算法在保持较高精度的同时,稳定性也能达到较高水平。     

The comparative evaluation of excellence for student awards

A recurring problem in the award of academic honors is the comparison of the highest grade achieved by students from different groups with different traditions, practices, and evaluation scales. Group averages are not useful because it is the position of the top student that is of interest, not the average; also, the normal distribution cannot be assumed for the upper tail of the distribution, which must be obtained from the data. We find that the upper grades of each group can be modeled very well, permitting the statistical theory of extremes to be used. From the model which we fit to each group separately, we obtain the probability for a top grade at least as high as the one observed, and from this, we obtain the return period which is the expected interval before the return of a similarly high grade. This return period is a common measure which enables top grades from several disparate groups to be compared on the basis of how rare or exceptional they are as members of their own group. The precision of the return period is estimated from the correlation coefficient of the regression model and used to indicate the effectiveness of the decision in a particular case. The method is applied to data from different disciplines at the University of Waterloo, including comparisons among Engineering Departments