Verification and historical perspective are presented on the gyrokinetic particle simulations that discovered the device size scaling of turbulent transport and indentified the geometry model as the source of the long...Verification and historical perspective are presented on the gyrokinetic particle simulations that discovered the device size scaling of turbulent transport and indentified the geometry model as the source of the long-standing disagreement between gyrokinetic particle and continuum simulations.展开更多
提出一种基于广义后缀树的概念生成算法(generalized suffix tree based concept generation algorithm,GSTCG),将背景中所有对象的属性序列及其后缀建立为一棵广义后缀树,并根据广义后缀树产生候选概念;其次,合并具有相同对象集合的候...提出一种基于广义后缀树的概念生成算法(generalized suffix tree based concept generation algorithm,GSTCG),将背景中所有对象的属性序列及其后缀建立为一棵广义后缀树,并根据广义后缀树产生候选概念;其次,合并具有相同对象集合的候选概念,再根据规则对候选概念进行扩展;最后,删除冗余的候选概念后得到全部形式概念。在两类不同参数人工数据集上的实验结果表明,GSTCG算法与NextClosure算法在所有背景上得到的概念数量一致,且前者具有更优的时间性能。展开更多
基金supported by US DOE SciDAC projects, the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2011-0030459)
文摘Verification and historical perspective are presented on the gyrokinetic particle simulations that discovered the device size scaling of turbulent transport and indentified the geometry model as the source of the long-standing disagreement between gyrokinetic particle and continuum simulations.
文摘提出一种基于广义后缀树的概念生成算法(generalized suffix tree based concept generation algorithm,GSTCG),将背景中所有对象的属性序列及其后缀建立为一棵广义后缀树,并根据广义后缀树产生候选概念;其次,合并具有相同对象集合的候选概念,再根据规则对候选概念进行扩展;最后,删除冗余的候选概念后得到全部形式概念。在两类不同参数人工数据集上的实验结果表明,GSTCG算法与NextClosure算法在所有背景上得到的概念数量一致,且前者具有更优的时间性能。