To understand the solidification behavior of austenitic stainless steel in pulsed magnetic field, the solidification process is investigated by means of the self-made high voltage pulse power source and the solidifica...To understand the solidification behavior of austenitic stainless steel in pulsed magnetic field, the solidification process is investigated by means of the self-made high voltage pulse power source and the solidification tester. The results show that the solidification structure of austenitic stainless steel can be remarkably refined in pulsed magnetic field, yet the grains become coarse again when the magnetic intensity is exceedingly large, indicating that an optimal intensity range existed for structure refinement. The solidification temperature can be enhanced with an increase in the magnetic intensity. The solidification time is shortened obviously, but the shortening degree is reduced with the increase of the magnetic intensity.展开更多
如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on ...如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on spark,Fmafibs),利用位运算速度快的特点,设计了一种新颖的模式增长策略.该算法首先采用位串表达项集,利用位运算来快速生成候选项集;其次,针对超长位串计算效率低的问题,考虑将事务垂直分组处理,将同一事务不同组之间的频繁项集通过连接获得候选项集,最后进行聚合筛选得到最终频繁项集.算法在Spark环境下,以频繁项集挖掘领域基准数据集进行实验验证.实验结果表明所提方法在保证挖掘结果准确的同时,有效地提高了挖掘效率.展开更多
基金Item Sponsored by National Natural Science Foundation of China (50274050) and Shanghai Baoshan Iron and Steel Group
文摘To understand the solidification behavior of austenitic stainless steel in pulsed magnetic field, the solidification process is investigated by means of the self-made high voltage pulse power source and the solidification tester. The results show that the solidification structure of austenitic stainless steel can be remarkably refined in pulsed magnetic field, yet the grains become coarse again when the magnetic intensity is exceedingly large, indicating that an optimal intensity range existed for structure refinement. The solidification temperature can be enhanced with an increase in the magnetic intensity. The solidification time is shortened obviously, but the shortening degree is reduced with the increase of the magnetic intensity.
文摘如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on spark,Fmafibs),利用位运算速度快的特点,设计了一种新颖的模式增长策略.该算法首先采用位串表达项集,利用位运算来快速生成候选项集;其次,针对超长位串计算效率低的问题,考虑将事务垂直分组处理,将同一事务不同组之间的频繁项集通过连接获得候选项集,最后进行聚合筛选得到最终频繁项集.算法在Spark环境下,以频繁项集挖掘领域基准数据集进行实验验证.实验结果表明所提方法在保证挖掘结果准确的同时,有效地提高了挖掘效率.