个性化公开密钥系统的设计

论文提出利用用户提供的个性化信息来随机产生关联大素数,保证了素数选取的随机化、个性化,提高了RSA算法的安全性。     

板式塔中无伸缩型受液槽的改进

对板式塔中的受液槽进行了改进，将无伸缩型改性伸缩型，避免了漏液，提高了常压塔的分离效果。     

Computing surfaces invariant under subdivision

In this paper, we propose a general subdivision algorithm for generating surfaces. The algorithm has as motivation our earlier work on the design of free form curves where similar ideas were investigated. Here we describe some properties of uniform refinement algorithms for surface generation. A detail analysis of their properties will be given later by one of us.     

条件概率关系数据库模型

现实世界中大量存在着的不确定性信息，关系数据库模型仅视它们为空值，有必要增强其处理这类信息的能力，文章在总结前人工作的基础上推广关系数据库模型，创建有效处理随机型不确定性信息的条件概念关系数据库模型，该模型通过在关系模式中增加一个条件概率测度属性，为每条记录指定适当的条件概率的途径，来表示不确定性信息。文中以对象码为基本工具，创建了条件概率关系结构；以特征函数为基本工具，定义了一套基于该结构的代数运算规则。条件概率的语意比概率的语意广泛，灵活，因而该模型能有效克服概率关系模型的许多不足。     

Micromechanical approach to damage mechanics of composite materials with fabric tensors

The purpose of this study is to apply continuum damage mechanics – introduced through the concept of fabric tensors – to composite materials within the framework of the theory of elasticity. A directional data model of damage mechanics for composite materials will be developed using fabric tensors. The introduction of fabric tensors into the analysis of damage of composite materials will allow for an enhanced and better understood physical meaning of damage. The micromechanical approach will be used here to relate the damage effect through fabric tensors to the behavior of composite materials. In this approach, damage mechanics is introduced separately to the constituents of the composite material through different constituents’ damage effect tensors. The damaged properties of the composite system as a whole can then be obtained by proper homogenization of the damaged properties of the constituents.

The derivation of a generalized formulation of damage evolution will be shown here in a mathematically consistent manner that is based on sound thermodynamic principles. Numerical examples will be presented to show applicability. In addition, damage evolution for the one-dimensional tension case is also illustrated.     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

Inference and learning in hybrid probabilistic network

This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.     

Probabilistic analysis of uncertainties in seismic hazard assessment

Sources and current methods of analysis of uncertainty from randomness, fuzziness and ignorance or incomplete knowledge in seismic hazard assessment problem are briefly discussed at beginning; understandings of the authors are then presented in the following order. All three types of uncertainty come from incomplete knowledge. Probabilistic method can be applied to all of them, objective probability for random factors and subjective probability for the other two types of uncertain factors. Discrete subjective probability mass functions for incomplete and fuzzy factors can be obtained from logic-tree and membership functions respectively. Fractile curves may be used to show the scattering of any uncertainty factor, but a unified probabilistic treatment may be applied to any combination of all three types of uncertainty.     

基于邻域统计特性的概率神经网络及其在自动目标识别中的应用

在概率神经的一种改进模型－ＦＤＯ网络的基础上，提出在设计网络收敛域时进一步考虑每一像素点周围８邻域的影响，对网络的作用函数加以修，使改进后的网络具有稳定性好且收敛速度快的优点。通过实验对改进前后网络的识别性能加以比较，证明改进后的网络特别适用于噪声图像的识别。