基于PAR的高可信装备保障算法推演

算法被誉为计算的“灵魂”（spirit of computing），算法程序的可靠性和正确性对软件系统的可信度起着至关重要的作用。动态规划、贪心、分支限界等传统的算法设计策略缺乏有效的选择标准，一些算法形式化开发方法和工具的应用水平和范围也很有限，因此算法的可靠性已成为高可信软件系统的一个主要瓶颈。 作为军事运筹学的一个重要分支，装备保障计算主要研究军事活动中装备领域的决策优化问题，保障计算软件特别是算法的可靠性在很大程度上决定了装备保障的效率乃至军事行动的成败。利用形式化软件工程的研究成果、特别是引入算法程序的形式化开发方法，对我军通用装备保障领域中的大量算法类问题进行有效求解，有利于优化保障结构、统筹保障资源、整合保障力量、提高保障时效，促进装备整体战斗力水平的提升。 本文以高可信软件开发方法PAR为基础，面向装备保障计算的领域需求，提出了一类离散最优化问题（discrete optimization problem, DOP）的结构模型和算法推演技术，并成功推演了一系列典型的装备保障算法。本论文的主要创新性贡献如下： （1）定义了DOP的结构模型，提出了基于单点结构（Singleton）的问题分划递推策略，进而通过PAR算法推演生成问题的高效求解算法，涵盖了多种传统算法设计策略，显著提高了算法程序设计的机械化水平。 （2）提出了描述DOP分划递推过程的问题简约图（problem reduction graph, PRG）模型，并针对典型DOP结构推演得到了一组PRG构造算法模式，其中每个算法模式都涵盖了满足特定代数结构性质的一大类具体问题。 （3）阐述了PAR算法推演的范畴模型，为算法设计和重用提供了抽象而有效的方法指导。 （4）对我军通用装备保障领域具有代表性的48个问题进行了形式化的算法推演，构建了领域算法库。 （5）在PAR平台的基础上设计了装备保障算法开发平台的原型COPALM，支持DOP算法推演和重用，进而提高了相关应用软件的开发效率。

基于椭圆曲线的代理多签名方案的安全性分析

在一个安全的代理签名方案中，只有指定的代理签名人能够代表原始签名人生成代理签名．基于椭圆曲线离散对数问题，纪家慧和李大兴提出了一个代理签名方案和一个代理多签名方案，陈泽雄等人给出了另外两个代理多签名方案．但是，在他们的方案中，原始签名人能够伪造代理签名私钥．为了抵抗原始签名人的伪造攻击，改进了代理签名密钥的生成过程，并对改进的方案进行了安全性分析．

Analysis of critical excavation depth for a jointed rock slope using a face-to-face discrete element method

The critical excavation depth of a jointed rock slope is an important problem in rock engineering. This paper studies the critical excavation depth for two idealized jointed rock slopes by employing a face-to-face discrete element method (DEM). The DEM is based on the discontinuity analysis which can consider anisotropic and discontinuous deformations due to joints and their orientations. It uses four lump-points at each surface of rock blocks to describe their interactions. The relationship between the critical excavation depth D-s and the natural slope angle alpha, the joint inclination angle theta as well as the strength parameters of the joints c(r) ,phi(r) is analyzed, and the critical excavation depth obtained with this DEM and the limit equilibrium method (LEM) is compared. Furthermore, effects of joints on the failure modes are compared between DEM simulations and experimental observations. It is found that the DEM predicts a lower critical excavation depth than the LEM if the joint structures in the rock mass are not ignored.

A smoothing technique for discrete delta functions with application to immersed boundary method in moving boundary simulations.

The effects of complex boundary conditions on flows are represented by a volume force in the immersed boundary methods. The problem with this representation is that the volume force exhibits non-physical oscillations in moving boundary simulations. A smoothing technique for discrete delta functions has been developed in this paper to suppress the non-physical oscillations in the volume forces. We have found that the non-physical oscillations are mainly due to the fact that the derivatives of the regular discrete delta functions do not satisfy certain moment conditions. It has been shown that the smoothed discrete delta functions constructed in this paper have one-order higher derivative than the regular ones. Moreover, not only the smoothed discrete delta functions satisfy the first two discrete moment conditions, but also their derivatives satisfy one-order higher moment condition than the regular ones. The smoothed discrete delta functions are tested by three test cases: a one-dimensional heat equation with a moving singular force, a two-dimensional flow past an oscillating cylinder, and the vortex-induced vibration of a cylinder. The numerical examples in these cases demonstrate that the smoothed discrete delta functions can effectively suppress the non-physical oscillations in the volume forces and improve the accuracy of the immersed boundary method with direct forcing in moving boundary simulations.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.