面向近邻泄露的数值型敏感属性隐私保护方法

提出一种面向近邻泄露的数值型敏感属性隐私保护方法,该方法首先在保护准标识符属性和数值型敏感属性内在关系的前提下,将数值型敏感属性进行离散化划分;然后,提出一种面向近邻泄露的隐私保护原则——(k, ε)- proximity;最后,设计了最大邻域优先算法MNF(maximal neighborhood first)来实现该原则。实验结果表明,提出的方法能在有效保护数值型敏感信息不泄露的同时保持较高的数据效用,并且保护了数据间的关系。     

高层框架-简体结构弹塑性分析方法的改进

采用杆系-层间模型,提出多阶变刚度框架-简体结构的连续-离散化方法,推导出框架-筒体结构的单元刚度矩阵和整体刚度矩阵.编制了考虑框架与筒体协同工作,并且能够分析每根杆件的结构动力时程分析程序,程序中考虑了简体空间作用、P-△效应等,该程序已应用于一些高层建筑的结构分析.     

一种新的多峰值函数优化算法

根据对一元多峰值函数的单调区间的分析,提出了一种新的多峰值函数优化算法——形态分析法。该算法根据给定的精度要求,用有穷个离散点确定一元多峰值函数的形态,进而确定其单调递增区间和单调递减区间,一次搜索就可找出函数的所有局部最优解和全局最优解。用不同的多峰值函数进行了仿真实验,并和相关算法进行了比较,结果表明所提出的算法计算量比其它算法小一个数量级,不存在震荡现象。     

船舶兴波问题面元法计算中自由面离散的稳定性分析

本文通过多变量的连续及离散Fourier分析，研究了船舶兴波问题中面元法计算的稳定性，对于计及流体域外奇点分布的自由面面元离散方式建立了满足稳定性要求的准则，给出了以网格弗氏数F△及网格纵横尺度比α为参数的稳定性限界线。     

Variable Resolution Discretization in Optimal Control

The problem of state abstraction is of central importance in optimal control, reinforcement learning and Markov decision processes. This paper studies the case of variable resolution state abstraction for continuous time and space, deterministic dynamic control problems in which near-optimal policies are required. We begin by defining a class of variable resolution policy and value function representations based on Kuhn triangulations embedded in a kd-trie. We then consider top-down approaches to choosing which cells to split in order to generate improved policies. The core of this paper is the introduction and evaluation of a wide variety of possible splitting criteria. We begin with local approaches based on value function and policy properties that use only features of individual cells in making split choices. Later, by introducing two new non-local measures, influence and variance, we derive splitting criteria that allow one cell to efficiently take into account its impact on other cells when deciding whether to split. Influence is an efficiently-calculable measure of the extent to which changes in some state effect the value function of some other states. Variance is an efficiently-calculable measure of how risky is some state in a Markov chain: a low variance state is one in which we would be very surprised if, during any one execution, the long-term reward attained from that state differed substantially from its expected value, given by the value function.The paper proceeds by graphically demonstrating the various approaches to splitting on the familiar, non-linear, non-minimum phase, and two dimensional problem of the Car on the hill. It then evaluates the performance of a variety of splitting criteria on many benchmark problems, paying careful attention to their number-of-cells versus closeness-to-optimality tradeoff curves.     

基于信息熵的粗糙集连续属性离散化算法

该文提出了一种新的粗糙集连续属性离散化算法．首先对每一个候选断点定义了信息熵，以此作为对断点重要性的量度，在此基础上给出了断点选择的粗糙集连续属性离散化算法．最后采用多组数据对此算法的性能进行了检验，并与其它算法做了对比实验．实验结果表明此算法是有效的，而且当候选断点个数增多时仍有很高的计算效率．     

二阶椭圆型偏微分方程奇导摄动问题的有限元解法

本文用有限元方法研究椭圆型偏微分方程奇异摄动问题。我们构造了一种特殊的有限元子空间,当网格参数取特定值时得到一致收敛的有限元离散化。     

Stable relaxations of stochastic stress-constrained weight minimization problems

The problem of finding a truss of minimal weight subject to stress constraints and stochastic loading conditions is considered. We demonstrate that this problem is ill-posed by showing that the optimal solutions change discontinuously as small changes in the modelling of uncertainty are introduced. We propose a relaxation of this problem that is stable with respect to such errors. We establish a classic -perturbation result for the relaxed problem, and propose a solution scheme based on discretizations of the probability measure. Using Chebyshevs inequality we give an a priori estimation of the probability of stress constraint violations in terms of the relaxation parameter. The convergence of the relaxed optimal designs towards the original (non-relaxed) optimal designs, as the relaxation parameter decreases to zero, is established.     

Computational uncertainty principle in nonlinear ordinary differential equations——Ⅱ.Theoretical analysis

The error propagation for general numerical method in ordinary differential equations ODEs is studied. Three kinds of convergence, theoretical, numerical and actual convergences, are presented. The various components of round-off error occurring in floating-point computation are fully detailed. By introducing a new kind of recurrent inequality, the classical error bounds for linear multi-step methods are essentially improved, and joining probabilistic theory the "normal" growth of accumulated round-off error is derived. Moreover, a unified estimate for the total error of general method is given. On the basis of these results, we rationally interpret the various phenomena found in the numerical experiments in part I of this paper and derive two universal relations which are independent of types of ODEs, initial values and numerical schemes and are consistent with the numerical results. Furthermore , we give the explicitly mathematical expression of the computational uncertainty principle and expound the i