Generic Regular Decompositions for Parametric Polynomial Systems

This paper presents a generalization of the authors＇ earlier work. In this paper, the two concepts, generic regular decomposition （GRD） and regular-decomposition-unstable （RDU） variety introduced in the authors＇ previous work for generic zero-dimensional systems, are extended to the case where the parametric systems are not necessarily zero-dimensional. An algorithm is provided to compute GRDs and the associated RDU varieties of parametric systems simultaneously on the basis of the algorithm for generic zero-dimensional systems proposed in the authors＇ previous work. Then the solutions of any parametric system can be represented by the solutions of finitely many regular systems and the decomposition is stable at any parameter value in the complement of the associated RDU variety of the parameter space. The related definitions and the results presented in the authors＇ previous work are also generalized and a further discussion on RDU varieties is given from an experimental point of view. The new algorithm has been implemented on the basis of DISCOVERER with Maple 16 and experimented with a number of benchmarks from the literature.

Image decomposition using adaptive regularization and div(BMO)

In order to avoid staircasing effect and preserve small scale texture information for the classical total variation regularization, a new minimization energy functional model for image decomposition is proposed. Firstly, an adaptive regularization based on the local feature of images is introduced to substitute total variational regularization. The oscillatory component containing texture and/or noise is modeled in generalized function space div （BMO）. And then, the existence and uniqueness of the minimizer for proposed model are proved. Finally, the gradient descent flow of the Euler-Lagrange equations for the new model is numerically implemented by using a finite difference method. Experiments show that the proposed model is very robust to noise, and the staircasing effect is avoided efficiently, while edges and textures are well remained.

Numerical Solutions of the Regularized Long-Wave (RLW) Equation Using New Modification of Laplace-Decomposition Method

In this paper the new modification of Laplace Adomian decomposition method (ADM) to obtain numerical solution of the regularized long-wave (RLW) equation is presented. The performance of the method is illustrated by solving two test examples of the problem. To see the accuracy of the method, L2 and L∞ error norms are calculated.

ON THE ASCENDING SUBGRAPH DECOMPOSITIONS OF REGULAR GRAPHS

The definition of the ascending subgraph decomposition was given by Alavi. It has been conjectured that every graph of positive size has an ascending subgraph decomposition. In this paper it is proved that the regular graphs under some conditions do have an ascending subgraph decomposition.

Optimal Boundary Control Method for Domain Decomposition Algorithm

To study the domain decomposition algorithms for the equations of elliptic type, the method of optimal boundary control was used to advance a new procedure for domain decomposition algorithms and regularization method to deal with the ill posedness of the control problem. The determination of the value of the solution of the partial differential equation on the interface——the key of the domain decomposition algorithms——was transformed into a boundary control problem and the ill posedness of the control problem was overcome by regularization. The convergence of the regularizing control solution was proven and the equations which characterize the optimal control were given therefore the value of the unknown solution on the interface of the domain would be obtained by solving a series of coupling equations. Using the boundary control method the domain decomposion algorithm can be carried out.

The Cordiality on the Union of 3-regular Connected Graph and Cycle

Diab proved the following graphs are Cordial;Pm K1,n if and only if(m,n) =(1,2);Cm K1,n;Pm Kn;Cm Kn for all m and n except m ≡ 2(mod 4).In this paper,we proved the Cordiality on the union of 3-regular connected graph K3 and cycle Cm.First we have the Lemma 2,if uv ∈ E(G),G is Cordial,we add 4 vertices x,y,z,w in sequence to the edge uv,obtain a new graph denoted by G*,then G* is still Cordial,by this lemma,we consider four cases on the union of 3-regular connected graph R3,and for every case we distinguish four subcases on the cycle Cm.

Meshless Method with Domain Decomposition for Submerged Porous Breakwaters in Waves

Based on the improved version of the meshless singular boundary method(ISBM)in multi domain(MD),a numerical method is proposed in this paper to study the interaction of submerged permeable breakwaters and regular waves at normal incidence.To account for fluid flow inside the porous breakwaters,the conventional model of Sollitt and Cross for porous media is adopted.Both single and dual trapezoidal breakwaters are examined.The physical problem is formulated in the context of the linear potential wave theory.The domain decomposition method(DDM)is employed,in which the full computational domain is decomposed into separate domains,that is,the fluid domain and the domains of the breakwaters.Respectively,appropriate mixed type boundary and continuity conditions are applied for each subdomain and at the interfaces between domains.The solution is approximated in each subdomain by the ISBM.The discretized algebraic equations are combined,resulting in an overdetermined full system that is solved using a least-square solution procedure.The numerical results are presented in terms of the hydrodynamic quantities of reflection,transmission,and wave-energy dissipation.The relevance of the results of the present numerical procedure is first validated against data of previous studies,and then selected computations are discussed for various structural conditions.The proposed method is demonstrated to be highly accurate and computationally efficient.

SPECTRAL DECOMPOSITRION FORMULA FOR REGULARIZATION SOLUTION OF ILL-POSED EQUATION

This paper discusses the regularization solution of ill posed equation with the help of its spectral decomposition formula. It shows that regularization can filter the influence of the high frequency errors which are very sensitive to the parameters to be estimated, and gives a complete derivation of the spectral decomposition formulae of least squares adjustment, rank deficient adjustment and the regularization solution of ill posed equation. It also shows the equivalence between the trace of the mean squares error and the expectation of the secondnorm of estimated parameter’s total error.

Regularized dynamic mode decomposition algorithm for time sequence predictions

Dynamic mode decomposition(DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions. We adopt a regularized form and propose a Regularized DMD(Re DMD) algorithm to determine the regularization parameter. This leverages stability and accuracy. Numerical tests for Burgers' equation demonstrate that Re DMD effectively stabilizes the DMD prediction while maintaining accuracy. Comparisons are made with the truncated DMD algorithm.

基于ACO-USK优化VMD参数的滚动轴承故障诊断研究

传统变分模态分解(VMD)技术需要人为主观预设模态分解个数K和二次惩罚因子α,由此可能导致信号的欠分解、过分解、模态混叠或信息丢失等问题,从而影响对滚动轴承早期故障信号的分解效果。本文根据峭度指标对滚动轴承早期故障异常敏感的特点,提出了一种以联合平方峭度(USK)指标为目标函数,结合蚁群优化(ACO)算法的ACO-USK优化方法,对VMD模态分解个数K和二次惩罚因子α进行自适应寻优。研究结果表明:对于滚动轴承早期故障信号,与以包络熵(EE)为目标函数的VMD优化方法对比,本文提出的方法既具有较好的包络谱信噪比(SNRES),又有在计算用时上的优越性,具有一定的工程应用价值。

AM-FM算子分解方法在滚动轴承故障诊断中的应用

基于算子的零空间追踪算法能够实现复杂信号的自适应分解,其关键在于信号模型的构造与求解。通过定义一种新的可完全消除调幅调频信号的调幅调频算子(AFO),进一步建立了一种基于AFO的信号分解新模型。为了提高参数对信号分解的鲁棒性,将非参数正则化(NPR)方法用于解决上述模型的约束优化问题,提出了一种基于NPR的自适应信号分解方法——NPR-AFO。论文将NPR-AFO方法引入到机械故障诊断中,并通过仿真和滚动轴承局部故障实测数据分析,与现有的其他分解方法进行了对比。结果表明:所提方法不仅可以有效的提取故障特征,而且状态故障特征更加明显。

稀疏分解和图拉普拉斯正则化的图像前景背景分割方法

针对现有图像前景背景分割方法的分割结果存在孤立像素点的问题,利用图信号处理理论和稀疏分解模型,提出新的图像前景背景分割方法.将图像的内在结构建模为图,通过图模型有效地刻画像素之间的内在关联性.将图像的像素强度建模为图信号,其中图像背景作为平滑分量,由一组图傅里叶变换基函数线性表示,叠加在背景上的前景为稀疏分量,前景像素间的连通性可由图拉普拉斯正则化项进行刻画.将图像前景背景分割问题归结为包含稀疏分解模型和图拉普拉斯正则化项的约束优化问题,采用交替方向乘子法对该优化问题进行求解.实验结果表明,与现有的其他方法相比,所提方法具有更好的分割效果.

求解一类平行四边形域上热传导反问题的无网格方法

基于基本解和径向基函数相结合的方法,对采用不同径向基作为近似解的基底,用平行四边形域反演一类热传导反问题的初值和热源项进行研究.因插值构造的方程系数矩阵是病态的,即系数矩阵的条件数较大,本文基于GCV准则的奇异值分解正则化方法求解线性方程组,选取不同噪声级,通过数值算例显示所给方法是稳定有效的.

奇异值分解下在线鲁棒正则化随机网络

在线鲁棒随机权神经网络(OR-RVFLN)具有较好的逼近性、较快的收敛速度、较高的鲁棒性能以及较小的存储空间.但是,OR-RVFLN算法计算过程中会产生矩阵的不适定问题,使得隐含层输出矩阵的精度较低.针对这个问题,本文提出了奇异值分解下在线鲁棒正则化随机网络(SVD-OR-RRVFLN).该算法在OR-RVFLN算法的基础上,将正则化项引入到权值的估计中,并且对隐含层输出矩阵进行奇异值分解;同时采用核密度估计(KDE)法,对整个SVD-OR-RRVFLN网络的权值矩阵进行更新,并分析了所提算法的必要性和收敛性.最后,将所提的方法应用于Benchmark数据集和磨矿粒度的指标预测中,实验结果证实了该算法不仅可以有效地提高模型的预测精度和鲁棒性能,而且具有更快的训练速度.

Application of Tikhonov Regularization Technique to the Kinetic Data of an Autocatalytic Reaction: Pyrolysis of N-Eicosane

A new technique based on Tikhonov regularization, for converting time-concentration data into concentration-reaction rate data, was applied to a novel pyrolysis investigation carried out by Susu and Kunugi [1]. The reaction which involves the thermal decomposition of n-eicosane using synthesis gas for K2CO3-catalyzed shift reaction was reported to be autocatalytic. This result was confirmed by applying Tikhonov regularization to the experimental data (conversion vs. time) presented by Susu and Kunugi [1]. Due to the ill-posed nature of the problem of obtaining reaction rates from experimental data, conventional methods will lead to noise amplification of the experimental data. Hence, Tikhonov regularization is preferably employed because it is entirely independent of reaction rate model and it also manages to keep noise amplification under control, thus, leading to more reliable results. This is shown by the agreement of the kinetic parameters obtained using the resulting conversion-reaction rate profile, with the Ostwald-type process for autocatalysis suggested by Susu and Kunugi [1].

SHUBIN REGULARITY FOR THE RADIALLY SYMMETRIC SPATIALLY HOMOGENEOUS BOLTZMANN EQUATION WITH DEBYE-YUKAWA POTENTIAL

In this work, we study the Cauchy problem for the radially symmetric spatially homogeneous Boltzmann equation with Debye-Yukawa potential. We prove that this Cauchy problem enjoys the same smoothing effect as the Cauchy problem defined by the evolution equation associated to a fractional logarithmic harmonic oscillator. To be specific, we can prove the solution of the Cauchy problem belongs to Shubin spaces.

Some Properties of a Recursive Procedure for High Dimensional Parameter Estimation in Linear Model with Regularization

Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed to overcome the difficulties with high dimension of the observation vector in computation of a statistical regularized estimator. As to deal with high dimension of the vector of unknown parameters, the regularization is introduced by specifying a priori non-negative covariance structure for the vector of estimated parameters. Numerical example with Monte-Carlo simulation for a low-dimensional system as well as the state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of the proposed approach.

基于CEEMDAN多尺度排列熵和SO-RELM的高压隔膜泵单向阀故障诊断

高压隔膜泵单向阀受负载、摩擦和冲击等因素的影响,运行产生的振动信号具有非平稳、非线性的特点,为了从振动信号中提取设备的非线性动力学特征,将多尺度排列熵(multi-scale permutation entropy, MPE)引入高压隔膜泵单向阀故障诊断研究。提取振动信号多尺度排列熵特征,用于建立结构优化正则化极限学习机(structure optimization regularized extreme learning machine, SO-RELM)故障诊断模型,模型利用K-means优化RELM结构,提高模型识别精确度及稳定性。首先采用自适应噪声完备经验模态分解(complementary ensemble empirical mode decomposition with adaptive noise, CEEMDAN)将高压隔膜泵单向阀振动信号自适应分解为多个固有模态分量(intrinsic mode function, IMF),以相关系数为指标,优选包含故障特征信息丰富的分量;然后,计算IMFs的多尺度排列熵值,提取信号的非线性动力学特征;最后,基于多尺度排列熵,建立基于SO-RELM的故障诊断模型。试验结果表明,CEEMDAN多尺度排列熵能够准确表征高压隔膜泵单向阀运行状态的非线性动力学特征,基于CEEMDAN多尺度排列熵建立的SO-RELM故障模型,能够有效识别高压隔膜泵单向阀工况类型,准确率达98.89%。

复杂电磁环境下基于球谐函数分解的磁场传感数据反演电流方法

通过对空间磁场信息进行反演,能够获取复杂电磁环境下的被测电流信息。针对电气装备中的电流测量需求,提出一种基于球谐函数分解的电流反演方法。该方法基于空间磁场数据,采用球谐函数对干扰磁场源进行解析分解,从而在干扰磁场源参数未知的情况下,实现被测线电流源及干扰磁场源的解耦。分别通过弹性网正则化及贝叶斯弹性网正则化方法进行电流反演,在干扰源的磁场强度占被测线电流产生磁场强度的60%时,得到最大不超过10%的线电流反演误差,并分析了球谐函数分解的最大分解次数选取原则。所提出的非介入式电流测量新方法克服了复杂电磁环境下的外部干扰问题,适用于电力物联网广泛的电流测量计算需求。

结合TSVD正则化和离散反卷积的电压行波精确检测方法

为解决行波传感器传变过程中的衰变及噪声干扰等因素影响而导致的电网一、二次侧行波波形不一致问题,提出一种结合截断奇异值分解(TSVD)正则化和离散反卷积的电网故障电压行波精确检测方法。该方法选择集中参数传递函数模型构建电压行波传感器的正演模型;然后利用反卷积原理建立行波反演模型;针对行波波形反演过程中产生的病态问题,提出结合TSVD正则化理论的波形反演方法,实现电网故障电压行波的精确检测。仿真分析表明,该检测方法能够实现二次行波信号的精确反演,反演一次行波信号波形特征可以反映真实的故障波形特征。