基于混沌映射密钥空间拓展的DES算法

提出一种基于混沌映射产生一种伪随机密钥流发生器，并结合shanon的“一次一密”思想，建立一种基于混沌映射的“分组密码密钥空间拓展”理论，很好地解决DES加密算法密钥空间小的问题，实现了一种混沌的DES变形密码算法。     

混沌光学系统的前向神经网络系统辨识研究

为解决混沌光学系统自适应控制所需之控制参考动力学模型问题，以布拉格声光混沌系统的系统辨识为例．研究了利用前向神经网络对混沌光学系统进行系统辩识的可行性。计算机仿真实验发现．在静态ＢＰ算法支持下一结构十分简单的三层ＢＰ前向神经网络（１：４：１）即可在一定的精度范围内完成对布拉格声光混沌系统的系统辨识．此结果表明．三层前向ＢＰ神经网络在静态ＢＰ算法的支持下确是一良好的混沌光学系统辨识器，因而可用来处理混沌光学时间序列以进行混沌光学系统的动力学重构。     

Application of Galerkin Method to Dynamical Behavior of Viscoelastic Timoshenko Beam with Finite Deformation

The motion equations governing the dynamical behavior of a viscoelasticTimoshenko beam with finite deformation are derived and simplified byGalerkin method. The viscoelastic material is assumed to obey thethree-dimensional fractional derivative constitutive relation. Thedynamical behaviors of the simplified systems with order 1 and order 2are numerically computed and compared by using the computational methodpresented by the authors. The dynamical behaviors of the systems areuniform qualitatively, but there is a little deviation quantitatively.And the truncated system with order 1 is safer than the one of order 2.It is also shown that the lower order system is reasonable. Theinfluences of the load parameter and the fractional derivative parameter(material parameter) on the deflection of the beam are consideredrespectively. The numerical methods in nonlinear dynamics, such as phasediagram, and Poincaré section, are applied to reveal dynamical behaviorsof the nonlinear viscoelastic Timoshenko beam. There are plenty ofdynamical behaviors, such as periodicity, bifurcation, quasi-periodicityand chaos in the dynamical system.     

瞬时吸收率检测与高频噪声的剔除

在高分辨率处理过程中，由于地震数据记录中存在着一类并不被人们十分注意的，但影响却十分严重的高频噪声，使得高分辨率处理结果陷入一种“混沌”状态。为此，本文仔细分析和研究了这类噪声产生的物理机制和原因，并提出瞬时吸收率检测与高频噪声剔除的方法。     

三次谐波系统混沌光场的光子统计特性

本文数值分析了三次谐波系统混沌光场的光子统计特性，从混沌态的遍历性出发计算了基波和谐波光场的Haken因子，得出混沌光场是一种大涨落的光场，其光子统计服从Super-Poisson分布，光场涨落随系统奇怪吸引子分维数增大而变化。     

Beyond Wiener–Askey Expansions: Handling Arbitrary PDFs

In this paper we present a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement generalized Polynomial Chaos (gPC) locally. Numerical examples show that ME-gPC exhibits both p- and h-convergence for arbitrary probability measures     

Transient aspects of inner wall temperatures at different spatial positions in fluidized bed evaporators

In this work, the instantaneous data of inner wall temperatures obtained at given axial positions but for different heated tubes or at various axial positions but at the same heated tube in a tube-bank fluidized bed evaporator with a vapor–liquid–solid external natural circulation flow were analyzed by employing linear and nonlinear time series analysis methods, including correlation dimension and Kolmogorov entropy analyses besides time domain, power spectrum and autocorrelation analyses. It is found that nonlinear characteristics of the wall temperature time series vary with spatial position in the evaporator at given operation conditions, and thus the multi-phase flow boiling behavior. However, the signals obtained at given axial position but different heated tubes in the evaporator exhibit more similar nonlinear evolution behavior, and those measured in the same heated tube but at different axial heights in the evaporator undergo more distinct nonlinear dynamic behavior. Multi-scale phenomena in different spatial positions are also found and discussed. These transient aspects of inner wall temperatures are closely related to the average holdup of solid particles and motion of vapor bubbles in vapor–liquid–solid flow. These results can provide some valuable information on the system modeling and controlling from the point of view of nonlinearity.     

基于小波除噪和神经网络理论的气固循环流化床颗粒浓度预测

提出了一种将混沌的相空间重构、小波包分析和神经网络相结合的新方法用于预测气-固循环流化床的颗粒浓度。首先利用小波包进行数据的消噪,然后用混沌方法重构相空间吸引子,用径向基神经网络拟合吸引子上的全局整体映射,构成混合预测模型。实验结果表明,将此混合模型用于预测气-固循环流化床的颗粒浓度,能达到较好的预测效果,预测精度比奇异值分解和傅里叶变换除噪高。     

256比特的混沌分组密码

由于密码分析研究的进展及DES自身的弱点,原64比特的DES将不能作为数据加密的标准算法而长期存在。在原来工作的基础上,本文提出了256比特的分组密码方案。密码算法由基于混沌映射的数字滤波器构造。