基于Partial MAX-SAT求解法的RBAC授权查询方法

为保证系统的安全性并体现授权的有效性,结合部分最大可满足性问题(Partial MAX-SAT)的研究,提出一种基于Partial MAX-SAT求解法的授权查询方法。使用转换规则将静态授权逻辑和动态互斥角色约束转化为严格子句,采用子句更新算法将满足不同匹配的请求权限转化为松弛子句,并利用子句编码及递归算法寻求真值指派,以满足所有严格子句和尽可能多的松弛子句。实验结果表明,该方法搜索的角色组合能够保证系统的安全性,并满足最小权限分配要求,且最大、精确匹配请求的查询效率优于MAX-SAT求解法。     

一种多变量决策树方法研究

单变量的决策树算法造成树的规模庞大,规则复杂,不易理解.本文结合粗糙集原理中的相对核及加权粗糙度的方法,提出了一种新的多变量决策树算法.通过实例表明,本文的多变量决策树方法产生的决策树比传统的ID3算法构造的决策树更简单,具有较好的分类效果.     

基于变量分离和加权最小二乘法的图像复原*

为提高图像复原的质量和速度,提出一种新的图像复原算法。首先基于变量分离技术,加入新的约束条件,建立解决图像复原问题的目标函数;然后利用交替最小化方法,将目标函数的优化分解为两个交替迭代的过程,以获得图像复原问题的全局最优解。在求解分离得到的新变量的过程中,引入迭代重加权最小二乘法(IRLS)处理L1范式的不可微分问题。实验结果表明,提出的算法有效地解决了图像复原问题;与同类的一些算法相比,该算法在复原速度和复原效果方面均具有优势。     

加权约束满足问题的改进RDS符号代数决策图求解算法*

加权约束满足问题(WCSP)是一类约束最优化问题.文中基于RDS思想,从减少RDS分解的子问题个数及提高各个子问题的求解效率入手,提出WCSP的改进RDS符号代数决策图(ADD)求解算法.通过改进最多约束变量的变量选择法,引入RDS变量引导原问题的子问题分解,进而减少RDS中分解的子问题个数.利用变量的后向度,进一步改进子问题的分解方法.为提高各个子问题的求解效率,利用桶消元算法并结合ADD操作消去子问题中的非RDS变量,进而减少子问题中的变量个数,提高深度优先分支界定法的下界.在大量随机生成的测试用例上的实验证明文中算法的优越性.     

整数规划的新算法

整数规划是NP困难的经典问题之一，将传统的二分搜索方法推广应用到整数规划的解空间中，提出一种求解整数规划的新算法。当问题变量数固定时，算法的时间复杂性为0(Llog^L)，其中L为问题实例的输入规模，理论分析和实验结果表明：新算法不仅初步解决了目前求解系数呈指数增长的整数规划问题时存在的实质性困难，可直接用于此类大规模问题的求解，同时由于其特剐适合并行处理的算法结构，可望为一般大规模整数规划问题的精确求解提供新的途径。     

基于加权可能性均值的直觉梯形模糊集矩阵博弈求解方法

针对支付值为直觉梯形模糊数（ITFN）的矩阵博弈求解问题,提出了一种基于加权可能性均值的求解方法.定义了ITFN新的运算法则,并引入ITFN的下、上加权可能性均值和加权可能性均值的概念,根据加权可能性均值给出了ITFN新的排序方法;运用新的排序方法,将求解局中人最优策略问题转化为求解双目标线性规划问题.实例分析验证了所提出方法的可行性和有效性.     

复杂流程工业系统的优化操作

ＳＱＰ方法在中小规模非线性规划中已成为主流算法,但在求解大规模优化问题时存在Ｈｅｓｓｉａｎ矩阵规模过大,存储、计算困难,以及计算量随不等式约束数量呈指数上升等缺点,简约空间ＳＱＰ法将变量分解为独立变量和非猖变量两部分。优化时只考虑独立变量,从而大大降低了变量维数,减小了Ｈｅｓｓｉａｎ矩阵规模。内点法、修改障碍函数法在求解不等式约束问题时都具有迭代次数几乎不受不等式约束规模影响的特点,因此可以将它们集成入简约空间ＳＱＰ法,使之可以更有效地对大规模优化问题求解。     

一种基于精英策略的改进蚁群算法及应用

针对基本蚁群算法存在求解速度慢,容易出现"早熟",导致搜索停滞的缺点,将遗传算法中排序的概念扩展到精英机制当中,以一种新的加权方法进行信息素更新,建立了改进蚁群算法模型.对30城市物流配送问题仿真结果表明:改进算法的求解速度和求解精确度都明显优于基本蚁群算法.     

一种新的基于粗糙集构造决策树的方法

单变量决策树算法生成的决策树具有规模庞大、规则复杂且不易理解的不足.采用粗糙集相对核、加权粗糙度的概念和类别因子相结合的方法,提出一种新的决策树生成算法.对于即将生长的节点,若节点样本的类别因子大于给定阈值,则停止生长该节点,如此就有效地避免了划分过细的问题.通过实验说明,该算法比传统的ID3算法生成的决策树更简单、更易于理解、抗噪声能力更强.     

结合启发式算子的单变量边缘分布算法求解SAT问题

单变量边缘分布算法(UMDA)是一种新的进化算法,是求解复杂问题的一种有效算法.根据SAT问题的特点,本文提出了一种求解SAT问题的改进单变量边缘分布算法(HeUMDASAT),该算法结合SAT问题本身固有的结构信息与当前群体的优秀解所提供的全局信息,构造了一个新的启发算子,并将此算子结合到单变量边缘分布算法中.此算子不同于随机搜索算子,由其产生的个体可以使得算法跳出局部最优并探索新的潜在区域,并且加快算法的收敛速度.用SATLIB库中的标准SAT问题对HeUMDASAT算法进行测试,实验结果表明该算法在求解速度和成功率方面都有明显的改善.     

一种面向网格计算的分布式匿名协作算法

基于TCG提出的可信计算技术为网格协作安全性提出一种匿名分组身份验证算法,该算法可以非常可靠地解决网格计算平台之间的身份匿名验证问题.算法使用一个硬件模块TPM解决远程的身份验证,并通过TPM机制可以提供可靠的匿名验证和平台认证功能.算法中所有涉及的验证过程都是基于匿名机制实现的,除了实现匿名验证机制以外,算法还提供一套完整标记恶意网络实体的方法.提出了网格计算中虚拟分组的匿名认证平台架构,并在此架构基础上分成5步实现匿名验证算法,然后说明了算法在一种对等计算平台的应用实例,与GT2,GT3,GT4以及信任管理进行安全性的比较,并设计一个实验评价其性能.     

计算网格中经济模型调度算法

任务调度是网格计算系统的一个重要组成部分。随着网格计算的出现,由于缺少对网格资源的直接管理,给网格任务调度带来了新的挑战。目前的任务调度机制大多数只考虑了任务调度的服务质量(QoS),而没有考虑任务调度的费用。为此,在研究了目前已有的适应启发式任务调度算法之后,提出了在同等费用前提下,将任务调度到能够提供较高QoS的资源中去的任务调度算法。     

A parallel algorithm for sparse symbolic Cholesky factorization on a multiprocessor

We develop an algorithm for computing the symbolic Cholesky factorization of a large sparse symmetric positive definite matrix. The algorithm is intended for a message-passing multiprocessor system, such as the hypercube, and is based on the concept of elimination forest. In addition, we provide an algorithm for computing these forests along with a discussion of the algorithm's complexity and a proof of its correctness.     

On developing theory of reservoir computing for sensing applications: the state weaving environment echo tracker (SWEET) algorithm

As a paradigm of computation, reservoir computing has gained an enormous momentum. In principle, any sufficiently complex dynamical system equipped with a readout layer can be used for any computation. This can be achieved by only adjusting the readout layer. Owning to this inherent flexibility of implementation, new applications of reservoir computing are being reported at a constant rate. However, relatively few studies focus on sensing, and in the ones that do, the reservoir is often exploited in a somewhat passive manner. The reservoir is used to post-process the signal from sensing elements that are placed separately, and the reservoir could be replaced by other information processing system without loss of functionality of the sensor (‘reservoir computing and sensing’). An entirely different novel class of sensing approaches is being suggested, to be referred to as ‘reservoir computing for sensing’, where the reservoir plays a central role. In the State Weaving Environment Echo Tracker (SWEET) sensing approach, the reservoir functions as the sensing element if the dynamical states of the reservoir and the environment one wishes to analyze are strongly interwoven. Some distinct characteristics of reservoir computing (in particular the separability and the echo state properties) are carefully exploited to achieve sensing functionality. The SWEET approach is formulated both as a generic device setup, and as an abstract mathematical algorithm. This algorithmic template could be used to develop a theory (or a class of theories) of ‘reservoir computing for sensing’, which could provide guidelines for engineering novel sensing applications. It could also provide ideas for a creative recycling of the existing sensing solutions. For example, the Horizon 2020 project RECORD-IT (Reservoir Computing with Real-time Data for future IT) exploits the SWEET sensing algorithm for ion detection. Accordingly, the terms SWEET sensing algorithm and the RECORD-IT sensing algorithm can be used interchangeably.     

光网络环境中计算任务与光路的联合调度算法

针对光网络环境下分布式计算系统的资源调度问题,提出了一种光网络计算任务和光路联合调度方案。该方案将光网络的特性加入到传统调度模型中,提出了计算任务与光路通信的联合调度模型,设计求解联合调度模型的扩展型列表算法。仿真实验验证了联合调度的有效性。     

Quantum novel genetic algorithm based on parallel subpopulation computing and its application

A quantum novel genetic algorithm based on subpopulation parallel computing is presented, where quantum coding and rotation angle are improved to inspire more efficient genetic computing methods. In the algorithm, each axis of the solution space is divided into k parts, the individual (or chromosome) from each different subspace being coded differently, and the probability amplitude of each quantum bit or Q-bit is regarded as two paratactic genes. The basic quantum computing theory and classical quantum genetic algorithm are briefly introduced before a novel algorithm is presented for the function optimum and PID problem. Through a comparison between the novel algorithm and the classical counterpart, it is shown that the quantum inspired genetic algorithm performs better on running speed and optimization capability.     

Analysis of a Left-Shift Binary GCD Algorithm

We introduce a new left-shift binary algorithm, LSBGCD, for computing the greatest common divisor of two integers, and we provide an analysis of the worst-case behavior of this algorithm. The analysis depends on a theorem of Ramharter about the extremal behavior of certain continuants.     

The Michaelis-Menten equation: computing substrate concentration as a function of time without restrictions on the initial conditions

We describe a novel algorithm for enzyme kinetics following the Michaelis-Menten equation, with the particular aim of computing the substrate concentration as a function of time without restrictions on the initial conditions. This algorithm, named 'tangent exponential' was demonstrated to converge for all initial conditions when the initial substrate concentration is positive. When the data are close to the solution, a quadratic convergence was demonstrated.     

A novel ANN-based service selection model for ubiquitous computing environments

How to choose an appropriate service from all the usable services regardless of user's location and heterogeneous architecture of underlying software and hardware infrastructure is the most important study content in ubiquitous computing domain. In order to overcome the shortcomings of blindness and randomicity in traditional and improved trust-mechanism-based service selection models, we propose a novel ANN-based (Artificial Neural Network) service selection model (called the ANNSS model). We adopt a novel method which according to the earlier information of the cooperation between the devices and the context information, an ANN-based evaluation standard for the service quality of service provider is given out so that user can acquire an effective guidance and choose the most appropriate service. At the same time, we improved the traditional BP algorithm based on three-term method (called the TTMBP algorithm) consisting of a learning rate (LR), a momentum factor (MF) and a proportional factor (PF) in order to satisfy the requirements of time issue in real-time system. The convergence speed and stability were enhanced by adding the proportional factor. The self-adjusting architecture method is adopted so that a moderate scale of neural network can be obtained. We have implemented the ANNSS algorithm in an actual power supply system for communication devices and fulfilled various simulations. The results of simulation show that the proposed service selection scheme is not only scalable but also efficient, and that the novel BP algorithm based on three-term has high convergence speed and good convergence stability. The novel service selection scheme superior to the traditional and improved trust-mechanism-based service selection scheme. The novel scheme can exactly choose a most appropriate service from many service providers and provide the most perfect service performance to users.