We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchic...We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.展开更多
本文讨论Y^(3+)掺杂对锂离子电池正极材料LiNi_(0.85)Co_(0.075)Mn_(0.075)O_(2)的影响.首先采用共沉淀法合成LiNi_(0.85)Co_(0.075)Mn_(0.075)(OH)_(2)前驱体,然后在一定的氧气气氛下,采用固态氧化法制备目标产物LiNi_(0.85)Co_(0.075)...本文讨论Y^(3+)掺杂对锂离子电池正极材料LiNi_(0.85)Co_(0.075)Mn_(0.075)O_(2)的影响.首先采用共沉淀法合成LiNi_(0.85)Co_(0.075)Mn_(0.075)(OH)_(2)前驱体,然后在一定的氧气气氛下,采用固态氧化法制备目标产物LiNi_(0.85)Co_(0.075)Mn_(0.075)O_(2)、Li(Ni_(0.85)Co_(0.075)Mn_(0.075))_(0.98)Y_(0.02)O_(2)正极材料.采用X射线衍射(X-ray diffraction,XRD)、扫描电子显微镜(scanning electron microscope,SEM)、恒流充放电、循环伏安法对材料的微观结构、颗粒形貌和电化学性能进行分析.结果表明,Y^(3+)的掺杂扩大了锂离子的扩散通道,抑制了样品与电解质之间的副反应,提高了样品的循环性能.在0.2C时,经过100次循环,掺杂样品的放电比容量为173 mAh·g^(-1),容量保持率为96.64%,电化学性能良好.展开更多
基金supported by the National High Technology Research and Development Program of China under Grant No.2012AA011104the Fundamental Research Funds for the Center Universities
文摘We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.
文摘本文讨论Y^(3+)掺杂对锂离子电池正极材料LiNi_(0.85)Co_(0.075)Mn_(0.075)O_(2)的影响.首先采用共沉淀法合成LiNi_(0.85)Co_(0.075)Mn_(0.075)(OH)_(2)前驱体,然后在一定的氧气气氛下,采用固态氧化法制备目标产物LiNi_(0.85)Co_(0.075)Mn_(0.075)O_(2)、Li(Ni_(0.85)Co_(0.075)Mn_(0.075))_(0.98)Y_(0.02)O_(2)正极材料.采用X射线衍射(X-ray diffraction,XRD)、扫描电子显微镜(scanning electron microscope,SEM)、恒流充放电、循环伏安法对材料的微观结构、颗粒形貌和电化学性能进行分析.结果表明,Y^(3+)的掺杂扩大了锂离子的扩散通道,抑制了样品与电解质之间的副反应,提高了样品的循环性能.在0.2C时,经过100次循环,掺杂样品的放电比容量为173 mAh·g^(-1),容量保持率为96.64%,电化学性能良好.
