数千上万核上模拟结果的并行可视化

在科学与工程计算中,在数千上万核上,模拟包含上亿网格单元的模型通常输出TB量级的时变数据集。这些数据集包含多个时刻的数据,每个时刻的数据分布存储在数千个文件,单时刻的数据量达到几GB甚至几十GB。为了并行地可视化这种时变数据集,本文设计了基于网格片的层次化数据结构,基于该数据结构改进和优化了并行可视化流程和数据通信算法。在集成到可视化软件后,实现了两个应用的模拟结果可视化。实测数据表明,对于单时刻5GB和32GB的数据,在数十上百个处理器核上,从数据读入到获得体绘制结果的时间分别为19秒和80秒,交互操作为2秒到10秒;三维面绘制在数秒钟内,切片分析在2秒以内。     

一种基于风险传播的信息系统风险评估方法

传统信息系统的风险评估方法未考虑节点的状态变化和风险的传播方向,且评估结果的准确性受专家主观性的影响,对此,提出了一种基于风险传播的信息系统风险评估方法.首先,确定节点的初始状态转移概率矩阵,并根据攻击属性对矩阵进行修正,得到节点状态转移概率;其次,基于系统风险传播网络拓扑图和节点属性值计算节点在各方向的传播概率;然后,利用三参数区间数方法获取节点威胁事件的量化值;最后,根据风险评估方法计算各节点的风险值.实验结果表明,基于风险传播方法的评估流程更客观、合理,可提高信息系统风险评估的整体性和准确性.     

虚拟企业伙伴选择中模糊因素的定量评估

针对虚拟企业伙伴选择时存在不一致区间判断的复杂评估问题,组建战略联盟决策群并确定着眼于候选伙伴实现机遇的互补竞争力评价指标体系,提出在层次分析法(AHP)框架下,运用有下限和上限的区间数表达候选伙伴之间不一致、不确定的比较比例,获得区间比较矩阵,引入模糊集的隶属度函数表示对选择方案的满意程度,构造模糊约束集合,利用线性规划方法求解具有最大满意程度的优先权矢量,计算出候选者的总体优先顺序.结果表明,该方法能够从可行域为空的不一致区间比较矩阵导出模糊优先权矢量.列举的虚拟企业伙伴选择实例验证了应用这个方法从模糊判断矩阵计算出确定的优先权重的有效性,这是其他方法难以做到的.     

ESTIMATION OF THE PREDICTION ERROR VARIANCE AND AN R2 MEASURE BY AUTOREGRESSIVE MODEL FITTING

Abstract. For predicting the future values of a stationary process { x_t } ( t = 0, pL 1, pL 2,…) on the basis of its past, two key parameters are the variance V ( h ), h ≥ 1, of the h -step prediction error and Z ( h ) ={ R (0) - V ( h )}/ R (0), the corresponding measure, in an R ² sense, of the predictability of the process from its past, where R (0) denotes the process variance. The estimation of V ( h ) and Z ( h ) from a realization of T consecutive observations of { x_t } is considered, without requiring that the process follows a finite parameter model. Three different autoregressive estimators are examined and are shown to be asymptotically equivalent in the sense that as T ∝ they have the same asymptotic normal distribution. The question of bias in estimating these parameters is also examined and a bias correction is proposed. Finite sample behaviour is investigated by a simulation study.     

无约束非线性lp问题的区间极大熵方法

针对信号处理、系统识别等领域中涉及到的无约束非线性lp问题,为减小由于二进制编码的舍入误差对该问题计算结果的影响,对求解该问题的极大熵方法进行了区间扩张.证明了区间扩张后的极大熵函数至少具有二阶收敛性,并设计了具有多项式时间复杂度的区间算法进行求解,举例进行了数值计算.数值计算结果显示,该区间算法可靠,计算结果与区间扩张前相比,结果更加精确.     

TVBPS：一种基于Parallel Sets的具有度量属性的多变元时态数据可视化方法

针对现有“具有度量属性的多变元时态数据”可视化方法不足,提出Parallel Sets分类值排列顺序优化算法ACLEARCR、基于相关度的Parallel Sets变元轴配置算法（VABC）、深度信息Parallel Sets（DCPS）共同组成基于Parallel Sets的具有度量属性的多变元时态数据可视化方法TVBPS。使用具体数据集对提出的可视化方法进行实验,获得的视图能够挖掘数据集中的隐含知识,证明了该方法的有效性。TVBPS可视化方法为分析多变元时态数据集提供了有效手段,具有较高的适用性和易用性。     

Reliability analysis of a repairable duplex system

We analyse the survival time of a repairable duplex system characterised by cold standby and by a pre-emptive priority rule. We allow general probability distributions for failure and repair. Moreover, an important realistic feature of the system is the general assumption that the non-priority unit has a memory. This combination of features has not been analysed in the previous literature. Our (new) methodology is based on a concatenation of a Cauchy-type integral representation of the modified Heaviside unit-step function and a two-sided stochastic inequality. Finally, we introduce a security interval related to a security level and a suitable risk-criterion based on the survival function of the system. As a practical application, we analyse some particular cases of the survival function jointly with the security interval corresponding to a security level of 90.     

A parametric frequency response method for non-linear time-varying systems

A new parametric frequency response algorithm is introduced to investigate linear and non-linear dynamic systems with time-varying parameters. In the new algorithm the time-varying parameters are regarded as additional inputs of the systems and the non-linear generalised frequency response functions for multi-input-single-output systems are then employed to obtain Zadeh's system functions from a differential equation representation. The parametric frequency response method reveals how the time-varying parameters affect the behaviour of the systems through a time-varying term. The new method can be applied to both linear and non-linear time-varying systems.