有限域GF（2^m）模逆算法的改进与实现

在椭圆曲线密码体制中,有限域GF(2m)中模逆运算是最重要的运算之一。在分析一种通用有限域GF(2m)模逆算法的基础上,提出改进算法。改进算法减少了原算法快速实现时的缺点,能够有效地提高算法效率。基于FPGA分别实现了GF(283)和GF(2233)中模逆算法模块,并与2种已有实现结果进行了对比。结果表明,选取有限域GF(283)和GF(2233)时,改进算法效率提高率分别约为72.9%和59.5%。

椭圆曲线加密体制的有限域求模逆算法的改进

本文在整数的扩展欧几里德算法基础上,对椭圆曲线加密体制的有限域求模逆算法作出改进,不仅有效提高了运算速度,使之同时兼容二进制域和素数域,同时也利于硬件实现.

一种双域Montgomery求逆算法与硬件实现

有限域上的求逆运算是椭圆曲线密码算法的关键运算之一。分别对GF(p)和GF(2n)域上的Montgomery模逆算法进行分析,并将GF(2n)域上的Montgomery模逆算法中对变量阶数的比较进行了改进,这样不仅利于GF(p)和GF(2n)域上的模逆运算在统一的硬件结构上实现,也解决了数据位数较大时进行阶数比较延迟较大的问题,在此基础上提出一种基于GF(p)和GF(2n)双域上统一的模逆算法,并根据算法,采用双域可伸缩运算单元,实现了一种可扩展的统一Montgomery模逆硬件结构。设计采用Verilog-HDL语言进行硬件描述,并基于0.18μm工艺标准单元库进行了综合,结果表明该设计与其他设计相比具有灵活性好、性能高的特点。

基于自适应模型非线性系统容错控制的研究

针对未知的多变量非线性动态系统,提出了一种新型的自适应故障容错控制方法。该方法首先通过设计一个自适应RBF神经网络模型建立未知过程的动态模型,并利用扩展卡尔曼滤波算法在线调节RBF网络权值学习系统的时变参数和故障动态,然后设计基于此模型的自适应迭代逆模控制算法实现相应的容错控制策略。将容错方法成功应用到一个连续的多变量三水箱过程。

Genetic Algorithm-Based Estimation of Nonlinear Transducer

This paper describes an innovative, genetic algorithm based inverse model of nonlinear transducer. In the inverse modeling, using a genetic algorithm, the unknown coefficients of the model are estimated accurately. The simulation results indicate that this technique provides greater flexibility and suitability than the existing methods. It is very easy to modify the nonlinear transducer on line. Thus the method improves the transducer's accuracy. With the help of genetic algorithm (GA), the model coefficients' training are less likely to be trapped in local minima than traditional gradient based search algorithms.

Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

An integer multi-objective optimization model and an enhanced non-dominated sorting genetic algorithm for contraflow scheduling problem

To determine the onset and duration of contraflow evacuation, a multi-objective optimization(MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algorithm that enhances the popular evolutionary algorithm NSGA-II is proposed to solve the model. The algorithm incorporates preliminary results as prior information and includes a meta-model as an alternative to evaluation by simulation. Numerical analysis of a case study suggests that the proposed formulation and solution algorithm are valid, and the enhanced NSGA-II outperforms the original algorithm in both convergence to the true Pareto-optimal set and solution diversity.

Magnetic Flux Leakage Signal Inversion of Corrosive Flaws Based on Modified Genetic Local Search Algorithm

In this paper,a modified genetic local search algorithm(MGLSA) is proposed.The proposed algorithm is resulted from employing the simulated annealing technique to regulate the variance of the Gaussian mutation of the genetic local search algorithm(GLSA).Then,an MGLSA-based inverse algorithm is proposed for magnetic flux leakage(MFL) signal inversion of corrosive flaws,in which the MGLSA is used to solve the optimization problem in the MFL inverse problem.Experimental results demonstrate that the MGLSA-based inverse algorithm is more robust than GLSA-based inverse algorithm in the presence of noise in the measured MFL signals.