为了提高电信大数据处理的性能,提出了一种Spark on Yarn模式的电信大数据处理平台SY-TPP(Spark on Yarn Telecommunication Big Data Processing Platform)。SY-TPP平台的实现采用Hadoop2.0的Yarn规范,运用了Spark分布式内存计算框架,...为了提高电信大数据处理的性能,提出了一种Spark on Yarn模式的电信大数据处理平台SY-TPP(Spark on Yarn Telecommunication Big Data Processing Platform)。SY-TPP平台的实现采用Hadoop2.0的Yarn规范,运用了Spark分布式内存计算框架,使SY-TPP平台数据集的处理尽量在内存中进行。以分级聚类算法为案例分析了SY-TPP平台的编程步骤;测试结果表明:电信运营商的上GB级的用户数据能够半个工作日内完成,32物理节点的SY-TPP平台比同等配置的MapReduce平台的加速比从9.5提升10.25。展开更多
The four-dimensional(4D) printing technology, as a combination of additive manufacturing and smart materials, has attracted increasing research interest in recent years. The bilayer structures printed with smart mater...The four-dimensional(4D) printing technology, as a combination of additive manufacturing and smart materials, has attracted increasing research interest in recent years. The bilayer structures printed with smart materials using this technology can realize complicated deformation under some special stimuli due to the material properties.The deformation prediction of bilayer structures can make the design process more rapid and thus is of great importance. However, the previous works on deformation prediction of bilayer structures rarely study the complicated deformations or the influence of the printing process on deformation. Thus, this paper proposes a new method to predict the complicated deformations of temperature-sensitive 4D printed bilayer structures,in particular to the bilayer structures based on temperature-driven shape-memory polymers(SMPs) and fabricated using the fused deposition modeling(FDM) technology. The programming process to the material during printing is revealed and considered in the simulation model. Simulation results are compared with experiments to verify the validity of the method. The advantages of this method are stable convergence and high efficiency,as the three-dimensional(3D) problem is converted to a two-dimensional(2D) problem.The simulation parameters in the model can be further associated with the printing parameters, which shows good application prospect in 4D printed bilayer structure design.展开更多
Graphene-based resistive random access memory （GRRAM） has grasped researchers＇ attention due to its merits com- pared with ordinary RRAM. In this paper, we briefly review different types of GRRAMs. These GRRAMs can...Graphene-based resistive random access memory （GRRAM） has grasped researchers＇ attention due to its merits com- pared with ordinary RRAM. In this paper, we briefly review different types of GRRAMs. These GRRAMs can be divided into two categories： graphene RRAM and graphene oxide （GO）/reduced graphene oxide （rGO） RRAM. Using graphene as the electrode, GRRAM can own many good characteristics, such as low power consumption, higher density, transparency, SET voltage modulation, high uniformity, and so on. Graphene flakes sandwiched between two dielectric layers can lower the SET voltage and achieve multilevel switching. Moreover, the GRRAM with rGO and GO as the dielectric or electrode can be simply fabricated. Flexible and high performance RRAM and GO film can be modified by adding other materials layer or making a composite with polymer, nanoparticle, and 2D materials to further improve the performance. Above all, GRRAM shows huge potential to become the next generation memory.展开更多
文摘为了提高电信大数据处理的性能,提出了一种Spark on Yarn模式的电信大数据处理平台SY-TPP(Spark on Yarn Telecommunication Big Data Processing Platform)。SY-TPP平台的实现采用Hadoop2.0的Yarn规范,运用了Spark分布式内存计算框架,使SY-TPP平台数据集的处理尽量在内存中进行。以分级聚类算法为案例分析了SY-TPP平台的编程步骤;测试结果表明:电信运营商的上GB级的用户数据能够半个工作日内完成,32物理节点的SY-TPP平台比同等配置的MapReduce平台的加速比从9.5提升10.25。
基金the National Natural Science Foundation of China(Nos.52130501 and 52075479)the National Key R&D Program of China(No.2018YFB1700804)。
文摘The four-dimensional(4D) printing technology, as a combination of additive manufacturing and smart materials, has attracted increasing research interest in recent years. The bilayer structures printed with smart materials using this technology can realize complicated deformation under some special stimuli due to the material properties.The deformation prediction of bilayer structures can make the design process more rapid and thus is of great importance. However, the previous works on deformation prediction of bilayer structures rarely study the complicated deformations or the influence of the printing process on deformation. Thus, this paper proposes a new method to predict the complicated deformations of temperature-sensitive 4D printed bilayer structures,in particular to the bilayer structures based on temperature-driven shape-memory polymers(SMPs) and fabricated using the fused deposition modeling(FDM) technology. The programming process to the material during printing is revealed and considered in the simulation model. Simulation results are compared with experiments to verify the validity of the method. The advantages of this method are stable convergence and high efficiency,as the three-dimensional(3D) problem is converted to a two-dimensional(2D) problem.The simulation parameters in the model can be further associated with the printing parameters, which shows good application prospect in 4D printed bilayer structure design.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61574083 and 61434001)the National Basic Research Program of China(Grant No.2015CB352101)+4 种基金the National Key Research and Development Program of China(Grant No.2016YFA0200404)the National Key Project of Science and Technology of China(Grant No.2011ZX02403-002)Special Fund for Agroscientic Research in the Public Interest of China(Grant No.201303107)the Independent Research Program of Tsinghua University,China(Grant No.2014Z01006)Advanced Sensor and Integrated System Lab of Tsinghua University Graduate School at Shenzhen,China(Grant No.ZDSYS20140509172959969)
文摘Graphene-based resistive random access memory （GRRAM） has grasped researchers＇ attention due to its merits com- pared with ordinary RRAM. In this paper, we briefly review different types of GRRAMs. These GRRAMs can be divided into two categories： graphene RRAM and graphene oxide （GO）/reduced graphene oxide （rGO） RRAM. Using graphene as the electrode, GRRAM can own many good characteristics, such as low power consumption, higher density, transparency, SET voltage modulation, high uniformity, and so on. Graphene flakes sandwiched between two dielectric layers can lower the SET voltage and achieve multilevel switching. Moreover, the GRRAM with rGO and GO as the dielectric or electrode can be simply fabricated. Flexible and high performance RRAM and GO film can be modified by adding other materials layer or making a composite with polymer, nanoparticle, and 2D materials to further improve the performance. Above all, GRRAM shows huge potential to become the next generation memory.
