Compositing soft and hard materials is a promising method to decrease the coercivity of L10 FePt, which is considered to be a suitable material for bit-patterned media. This paper reports the simulation of three model...Compositing soft and hard materials is a promising method to decrease the coercivity of L10 FePt, which is considered to be a suitable material for bit-patterned media. This paper reports the simulation of three models of FeCo/L10 FePt exchange-coupled composite particles for bit patterned media by the OOMMF micromagnetic simulation software： the enclosed model, the side-enclosed model, and the top-covered model. All of them have the same volumes of the soft and hard parts but different shapes. Simulation results show that the switching fields for the three models can be reduced to about 10 kOe （1 Oe = 79.5775 A/m） and the factor gain can be improved to 1.4 when the interface exchange coefficient has a proper value. Compared to the other models, the enclosed model has a wider range of interface exchange coefficient values, in which a low switching field and high gain can be obtained. The dependence of the switching fields on the angle of the applied field shows that none of the three models are easily affected by the stray field of a magnetic head.展开更多
The soft/hard composite patterned media have potential to be the next generation of magnetic recording, but the composing modes of soft and hard materials have not been investigated systematically. L10 FePt-based soft...The soft/hard composite patterned media have potential to be the next generation of magnetic recording, but the composing modes of soft and hard materials have not been investigated systematically. L10 FePt-based soft/hard composite patterned media with an anisotropic constant distribution are studied by micromagnetic simulation. Square arrays and hexagonal arrays with various pitch sizes are simulated for two composing types： the soft layer that encloses the hard dots and the soft layer that covers the whole surface. It is found that the soft material can reduce the switching fields of bits effectively for all models. Compared with the first type, the second type of models possess low switching fields, narrow switching field distributions, and high gain factors due to the introduction of inter-bit exchange coupling. Furthermore, the readout waveforms of the second type are not deteriorated by the inter-bit soft layers. Since the recording density of hexagonal arrays are higher than that of square arrays with the same center-to-center distances, the readout waveforrns of hexagonal arrays are a little worse, although other simulation results are similar for these two arrays.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supported by the National Natural Science Foundation of China (Grant Nos. 61003041,51171086,and 61272076)the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2010-81)
文摘Compositing soft and hard materials is a promising method to decrease the coercivity of L10 FePt, which is considered to be a suitable material for bit-patterned media. This paper reports the simulation of three models of FeCo/L10 FePt exchange-coupled composite particles for bit patterned media by the OOMMF micromagnetic simulation software： the enclosed model, the side-enclosed model, and the top-covered model. All of them have the same volumes of the soft and hard parts but different shapes. Simulation results show that the switching fields for the three models can be reduced to about 10 kOe （1 Oe = 79.5775 A/m） and the factor gain can be improved to 1.4 when the interface exchange coefficient has a proper value. Compared to the other models, the enclosed model has a wider range of interface exchange coefficient values, in which a low switching field and high gain can be obtained. The dependence of the switching fields on the angle of the applied field shows that none of the three models are easily affected by the stray field of a magnetic head.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51171086 and 61272076)the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.61003041)
文摘The soft/hard composite patterned media have potential to be the next generation of magnetic recording, but the composing modes of soft and hard materials have not been investigated systematically. L10 FePt-based soft/hard composite patterned media with an anisotropic constant distribution are studied by micromagnetic simulation. Square arrays and hexagonal arrays with various pitch sizes are simulated for two composing types： the soft layer that encloses the hard dots and the soft layer that covers the whole surface. It is found that the soft material can reduce the switching fields of bits effectively for all models. Compared with the first type, the second type of models possess low switching fields, narrow switching field distributions, and high gain factors due to the introduction of inter-bit exchange coupling. Furthermore, the readout waveforms of the second type are not deteriorated by the inter-bit soft layers. Since the recording density of hexagonal arrays are higher than that of square arrays with the same center-to-center distances, the readout waveforrns of hexagonal arrays are a little worse, although other simulation results are similar for these two arrays.
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.
