一种分布式实时系统的支撑平台

分布式实时系统支撑平台试图为应用开发者提供一个开发分布式实时应用的通用框架·该支撑平台由事件总线、数据访问总线和系统服务组成,具有开放的分布式实时系统结构·概述了事件总线和数据访问总线的组成、工作原理和工作过程等·重点讨论了实现实时QoS的描述、实时QoS的映射、实时资源的监视与分配等关键技术问题·该支撑平台已在True64UNIX系统上实现,并应用于电力实时控制系统·应用表明,该支撑平台可以满足实时应用的可预见性、可扩充性、高可用性等·     

分形模糊神经网络诊断方法研究和应用

从分形理论和神经网络原理出发,基于信号分形计算维数定义,以采样周期和分形计算维数两个坐标对信号特征进行合理地模糊化处理,提出分形维数隶属度特征量概念·以多时间尺度采样所获得的分形维数隶属度作为网络输入,单位矩阵为网络输出的分形模糊神经网络,建立了时域精确诊断新方法,通过对典型齿轮系统故障进行精确诊断,结果表明分形模糊神经网络诊断方法的有效性·     

基于遗传珊瑚礁优化算法的负载均衡问题

基于珊瑚礁优化算法, 通过在种群的每次进化过程中引入遗传算法中的交叉和变异算子, 提出一种遗传珊瑚礁优化算法, 并将改进的遗传珊瑚礁优化算法运用到负载均衡中， 有效解决了算法过早收敛的问题, 提升了算法的优化性能. 对比经典遗传算法、 珊瑚礁优化算法等群智能算法, 在CloudSim上仿真实验结果表明, 遗传珊瑚礁优化算法优化负载均衡策略取得了满意的结果, 提升了资源能耗利用率, 均衡了控制策略.     

云及其他颠覆性技术趋势对商业经济领域的影响

 技术革命带动社会变革,人类社会发展经历的3次浪潮--从农业革命到工业革命再到信息革命,无一不是当时的“颠覆性技术”带来的巨大变革。如今,从云计算到大数据,到人工智能等,这些颠覆性技术每年都给世界经济甚至世界格局带来巨大影响。世界主要国家对技术预见能力都高度重视,颠覆性技术发展核心技术能力成为各国战略布局的重点。本文阐述了颠覆性技术的8个趋势、2个环境和7层技术模型,并介绍了云计算、人工智能对数字化经济的重要变革作用。     

基于云计算的知识产权基础信息公共服务平台建设研究

针对知识产权基础信息公共服务平台面临的挑战进行分析和归类,发现存在着知识产权信息服务体系亟待完善、知识产权信息资源开放共享和深度开发利用偏低、知识产权高端信息服务供需严重不平衡等问题。在此基础之上,构建了基于云计算以专利信息为主的知识产权基础信息公共服务平台设计方案,提出了专利信息应用、专利分析预警咨询报告、专利文献翻译、专利数据加工、专利在线分析、中外专利数据库、行业专利专题数据库等服务功能模块分析,在方案中分析了专利的信息应用、预警咨询、在线分析、文献翻译、数据加工和中外、专题的专利数据库的服务功能模块,并阐述了知识产权基础信息公共服务平台的云架构方案和具体的业务流程。     

PDD算法在对称多处理器高性能计算机上的并行实现

使用基于MPI并行编程方法，对PDD算法进行了并行处理及fortran编程，给出了并行代码在上海大学计算机学院自强2000集群式高性能计算机上的运行结果，以及与串行程序相比较的并行加速比。     

摆动频率影响扑翼推进水动力的数值仿真研究

通过建立简谐运动耦合的二自由度扑翼运动模型,基于动网格技术对水下扑翼推进水动力特性展开数值仿真研究。通过数值仿真发现：扑翼推进力和最大升力与扑翼摆动频率的平方近似成正比;摆动频率影响前沿涡和尾涡的产生和脱落,从而影响推力     

Hybrid Decomposition Method in Parallel Molecular Dynamics Simulation Based on SMP Cluster Architecture

A hybrid decomposition method for molecular dynamics simulations was presented, using simultaneously spatial decomposition and force decomposition to fit the architecture of a cluster of symmetric multi-processor (SMP) nodes. The method distributes particles between nodes based on the spatial decom-position strategy to reduce inter-node communication costs. The method also partitions particle pairs within each node using the force decomposition strategy to improve the load balance for each node. Simulation results for a nucleation process with 4 000 000 particles show that the hybrid method achieves better parallel performance than either spatial or force decomposition alone, especially when applied to a large scale particle system with non-uniform spatial density.     

Multidomain implicit numerical scheme

A multidomain method for the solution of elliptic CFD problems with an ADI scheme is described. Two methods of treatment of internal boundary conditions for ADI functions are discussed, namely an explicit and a semi‒implicit method. Stability conditions for the proposed methods are derived theoretically. The semi‒implicit scheme is more stable than the explicit scheme, leading to improved numerical efficiency for multidomain computations. Numerical computations for a linear convection-diffusion equation, for buoyancy‒driven recirculating flow in a square cavity and for turbulent flow in a square duct confirmed the theoretical results. Computer runs of the multidomain code in a distributed memory multiprocessor system were successful and efficient and produced reliable results. © 1997 John Wiley & Sons, Ltd.     

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.