抢占阈值调度算法的分析与研究

本文详细论述了应用于静态优先级实时系统的抢占阈值调度算法。描述了算法实现和任务集合可调度性判定公式的推导,分析了算法的性能特点,阐述了抢占阈值调度是静态优先级嵌入式实时系统开发中调度算法的合适选择。     

辫识时变随机系统遗忘因子算法的收敛性分析

文「１」给出了辨识时变随机系统遗忘因子算法的收敛性分子，本文指出了文「１」中的一些证明和结论上的错误，并给出了相应的改正和完善。     

A novel approach to ontology classification

Ontology classification–the computation of the subsumption hierarchies for classes and properties–is a core reasoning service provided by all OWL reasoners known to us. A popular algorithm for computing the class hierarchy is the so-called Enhanced Traversal (ET) algorithm. In this paper, we present a new classification algorithm that attempts to address certain shortcomings of ET and improve its performance. Apart from classification of classes, we also consider object and data property classification. Using several simple examples, we show that the algorithms commonly used to implement these tasks are incomplete even for relatively weak ontology languages. Furthermore, we show that property classification can be reduced to class classification, which allows us to classify properties using our optimised algorithm. We implemented all our algorithms in the OWL reasoner HermiT. The results of our performance evaluation show significant performance improvements on several well-known ontologies.     

A New Convergence Proof of Fuzzy c-Means

In this letter, we give a new, more direct derivation of the convergence properties of the fuzzy c-means (FCM) algorithm, using the equivalence between the original and reduced FCM criterion. From the point of view of the reduced criterion, the FCM algorithm is simply a steepest descent algorithm with variable steplength. We prove that steplength adjustment follows from the majorization principle for steplength. By applying the majorization principle we give a straightforward proof of global convergence. Further convergence properties follow immediately using known results of optimization theory     

时间自动机可达性分析算法的改进

模型检测时,实时系统的大多数安全属性和部分活性都可以通过可达性分析算法来验证。本文介绍了时间自动机和可达性分析算法,并对可达性分析算法中的后继算法进行了改进。     

A structured low-rank wavelet solver for the Ornstein-Zernike integral equation

In this article, we present a new structured wavelet algorithm to solve the Ornstein-Zernike integral equation for simple liquids. This algorithm is based on the discrete wavelet transform of radial distribution functions and different low-rank approximations of the obtained convolution matrices. The fundamental properties of wavelet bases such as the interpolation properties and orthogonality are employed to improve the convergence and speed of the algorithm. In order to solve the integral equation we have applied a combined scheme in which the coarse part of the solution is calculated by the use of wavelets and Newton-Raphson algorithm, while the fine part is solved by the direct iteration. Tests have indicated that the proposed procedure is more effective than the conventional method based on hybrid algorithms.     

An automatic and general least-squares projection procedure for sweep meshing

In this paper, we present a new and automatic node projection algorithm to generate hexahedral meshes in extrusion geometries. It is designed to preserve the shape of the cap surfaces in the inner layers of a sweeping mesh. The algorithm is based on least-squares approximation of affine mappings. We report that the functionals that have been traditionally used to compute the affine mapping generate four undesired effects on the inner layers of nodes. Namely, the flattening, skewing, offset scaling, and flipping of the inner layer meshes. To overcome these drawbacks we first analyze several properties of a new functional that depends on two vector parameters. Second, we introduce the concept of the pseudo-area and pseudo-normal vectors defined by a loop of nodes. In addition, we prove several geometrical properties of these vectors. Third, based on the properties of the new functional and on the definition of the pseudo-normal vector, we detail a new projection algorithm that automatically selects the functional vector parameters. The aim of this paper is to provide the implementation details to developers, although we also present the background of the algorithm. Finally, several mesh examples are discussed to assess the properties of the proposed algorithm.     

对象依赖和关键字的性质的初步研究

本文讨论了对象依赖集合和关键字的一些性质，首先给出有关对象依赖（OD）和关键字等的基本概念，然后讨论对象依赖的一些性质，最后给出并证明获取一个OD集合的所有关键字的JINGSI算法。     

Efficient implementation and application of the artificial bee colony algorithm to low-dimensional optimization problems

We adapt a swarm-intelligence-based optimization method (the artificial bee colony algorithm, ABC) to enhance its parallel scaling properties and to improve the escaping behavior from deep local minima. Specifically, we apply the approach to the geometry optimization of Lennard-Jones clusters. We illustrate the performance and the scaling properties of the parallelization scheme for several system sizes (5–20 particles). Our main findings are specific recommendations for ranges of the parameters of the ABC algorithm which yield maximal performance for Lennard-Jones clusters and Morse clusters. The suggested parameter ranges for these different interaction potentials turn out to be very similar; thus, we believe that our reported values are fairly general for the ABC algorithm applied to chemical optimization problems.     

Thick restarting the weighted harmonic Arnoldi algorithm for large interior eigenproblems

We propose a thick-restarted weighted harmonic Arnoldi algorithm for computing interior eigenpairs of large sparse matrices. Moreover, we consider how to determine the weighting matrix efficiently in practice. Some properties of the new algorithm are discussed. Numerical experiments demonstrate the numerical behaviour of our new algorithm.     

Lateness minimization with Tabu search for job shop scheduling problem with sequence dependent setup times

We tackle the job shop scheduling problem with sequence dependent setup times and maximum lateness minimization by means of a tabu search algorithm. We start by defining a disjunctive model for this problem, which allows us to study some properties of the problem. Using these properties we define a new local search neighborhood structure, which is then incorporated into the proposed tabu search algorithm. To assess the performance of this algorithm, we present the results of an extensive experimental study, including an analysis of the tabu search algorithm under different running conditions and a comparison with the state-of-the-art algorithms. The experiments are performed across two sets of conventional benchmarks with 960 and 17 instances respectively. The results demonstrate that the proposed tabu search algorithm is superior to the state-of-the-art methods both in quality and stability. In particular, our algorithm establishes new best solutions for 817 of the 960 instances of the first set and reaches the best known solutions in 16 of the 17 instances of the second set.     

A full-step interior-point algorithm for second-order cone optimization based on a simple locally kernel function

In this paper, we investigate the properties of a simple locally kernel function. As an application, we present a full-step interior-point algorithm for second-order cone optimization (SOCO). The algorithm uses the simple locally kernel function to determine the search direction and define the neighbourhood of central path. The full step used in the algorithm has local quadratic convergence property according to the proximity function which is constructed by the simple locally kernel function. We derive the iteration complexity for the algorithm and obtain the best-known iteration bound for SOCO.     

MD5报文摘要算法的各圈函数碰撞分析

本文通过分析ＭＤ５报文摘在要算法的四个非线性函数的特点，讨论了ＭＤ５的每个圈函数的许多碰撞及这些碰撞发生的概率，本文的分析结果有助于了解ＭＤ５各圈函数的 特点及ＭＤ５方案的安全性。     

广义系统观控性及正则束条件的数值判定

本文提出了一种用于判定广义系统观控性及正则束条件的算法.该算法具有误差小、数 值稳定的特点.     

A memetic algorithm for minimizing the total weighted completion time on a single machine under step-deterioration

In this paper, we consider minimizing total weighted completion time criteria on a single machine. Jobs processing times are step function of its starting time and all jobs have a common due date. First, we present some new lemmas and dominance properties for this NP-hard problem, and then a memetic algorithm using these properties is developed. We compare the solutions of the memetic algorithm with optimal solutions obtained from complete enumeration. The results show that the average percentage error of the proposed algorithm from optimal solutions is about 2% and as the variance of processing time increase, the percentage errors decrease.     

Tracking randomly varying parameters: Analysis of a standard algorithm

In linear stochastic system identification, when the unknown parameters are randomly time varying and can be represented by a Markov model, a natural estimation algorithm to use is the Kalman filter. In seeking an understanding of the properties of this algorithm, existing Kalman-filter theory yields useful results only for the case where the noises are gaussian with covariances precisely known. In other cases, the stochastic and unbounded nature of the regression vector (which is regarded as the output gain matrix in state-space terminology) precludes application of standard theory. Here we develop asymptotic properties of the algorithm. In particular, we establish the tracking error bounds for the unknown randomly varying parameters, and some results on sample path deviations of the estimates.     

Parameter Identification and Intersample Output Estimation for Dual-Rate Systems

In this paper, we derive a mathematical model for dual-rate systems and present a stochastic gradient identification algorithm to estimate the model parameters and an output estimation algorithm to compute the intersample outputs based on the dual-rate input-output data directly. Moreover, we investigate convergence properties of the parameter and intersample estimation, and we test the proposed algorithms with example systems, including an experimental water-level system.     

基于Bezier曲线求交的曲线裁剪算法

本文研究了三次Ｂｅｚｉｅｒ曲线的裁剪问题。在给出了三次Ｂｅｚｉｅｒ曲线的一种求交算法后，讨论了Ｂｅｚｉｅｒ曲线在一点处的局部性质，给出了三次Ｂｅｚｉｅｒ曲线的裁剪算法。这些算法已被应用到我们自己开发的软件中，取得了良好的效果。     

单层神经网络的快速学习算法研究

该文提出一种适用于单层神经网络(SNN)训练的新颖的广义误差函数,给出了 SNN新的快速学习算法(FLA).进一步提出了一种广义系统辨识模型,对FLA的收敛性进 行了理论分析.实验表明:文中给出的新FLA比Karayiannis的LFA具有更快的收敛速度.     

Rijndael算法代数性质及其七轮攻击

2000年被选为AES的Rijndael算法是SPN(SubstitutionPermutationNetwork)结构的分组密码。由于此算法依托于代数学理论的加密算法,所以本文介绍了它的基本函数的一些代数性质并提出了轮变换的一些差分特征。然后根据其中的一些代数性质和差分特征对Rijndael-128-192算法进行了七轮攻击,使得这种攻击方法比穷尽攻击更有效。