Chebyshev Pseudo-Spectral Method for Solving Fractional Advection-Dispersion Equation

Fractional differential equations have recently been applied in various areas of engineering, science, finance, applied mathematics, bio-engineering and others. However, many researchers remain unaware of this field. In this paper, an efficient numerical method for solving the fractional Advection-dispersion equation (ADE) is considered. The fractional derivative is described in the Caputo sense. The method is based on Chebyshev approximations. The properties of Chebyshev polynomials are used to reduce ADE to a system of ordinary differential equations, which are solved using the finite difference method (FDM). Moreover, the convergence analysis and an upper bound of the error for the derived formula are given. Numerical solutions of ADE are presented and the results are compared with the exact solution.

Comparison of finite difference and pseudo-spectral methods in forward modelling based on metal ore model of random media

With more applications of seismic exploration in metal ore exploration,forward modelling of seismic wave has become more important in metal ore. Finite difference method and pseudo-spectral method are two important methods of wave-field simulation. Results of previous studies show that both methods have distinct advantages and disadvantages: Finite difference method has high precision but its dispersion is serious; pseudospectral method considers both computational efficiency and precision but has less precision than finite-difference. The authors consider the complex structural characteristics of the metal ore,furthermore add random media in order to simulate the complex effects produced by metal ore for wave field. First,the study introduced the theories of random media and two forward modelling methods. Second,it compared the simulation results of two methods on fault model. Then the authors established a complex metal ore model,added random media and compared computational efficiency and precision. As a result,it is found that finite difference method is better than pseudo-spectral method in precision and boundary treatment,but the computational efficiency of pseudospectral method is slightly higher than the finite difference method.

An Accurate Numerical Solution for the Modified Equal Width Wave Equation Using the Fourier Pseudo-Spectral Method

In this study, the numerical solution for the Modified Equal Width Wave (MEW) equation is presented using Fourier spectral method that use to discretize the space variable and Leap-frog method scheme for time dependence. Test problems including the single soliton wave motion, interaction of two solitary waves and interaction of three solitary waves will use to validate the proposed method. The three invariants of the motion are evaluated to determine the conservation properties of the generated scheme. Finally, a Maxwellian initial condition pulse is then studied. The L2 and L∞ error norms are computed to study the accuracy and the simplicity of the presented method.

Pseudo-Spectral Method for Space Fractional Diffusion Equation

This paper presents a numerical scheme for space fractional diffusion equations (SFDEs) based on pseudo-spectral method. In this approach, using the Guass-Lobatto nodes, the unknown function is approximated by orthogonal polynomials or interpolation polynomials. Then, by using pseudo-spectral method, the SFDE is reduced to a system of ordinary differential equations for time variable t. The high order Runge-Kutta scheme can be used to solve the system. So, a high order numerical scheme is derived. Numerical examples illustrate that the results obtained by this method agree well with the analytical solutions.

Solving a Nonlinear Multi-Order Fractional Differential Equation Using Legendre Pseudo-Spectral Method

In this paper, we apply the Legendre spectral-collocation method to obtain approximate solutions of nonlinear multi-order fractional differential equations (M-FDEs). The fractional derivative is described in the Caputo sense. The study is conducted through illustrative example to demonstrate the validity and applicability of the presented method. The results reveal that the proposed method is very effective and simple. Moreover, only a small number of shifted Legendre polynomials are needed to obtain a satisfactory result.

基于格林函数法研究分数阶Bessel-Gaussian光束近场传输

为揭示FBG(fractional-order Bessel-Gaussian)光束在近场传输时的光强和相位演化情况,基于光传播的独立性和叠加性原理,通过引入虚光源点,并结合格林函数法,严格推导了FBG光束的非傍轴积分表达式,并进一步利用非傍轴下的各阶修正解,精确计算了FBG光束轴上的光强分布.计算结果表明:对于分数阶Bessel-Gaussian光束的远场传输,利用经典的傍轴近似理论可以得到准确的计算结果,但在近场传输时,傍轴近似理论的计算结果误差很大,只有利用非傍轴修正解才能精确研究FBG光束的近场光学传输.研究结果为将FBG光束更好地应用于近场光学奠定必要的理论基础.

Machine Learning Enhanced Boundary Element Method:Prediction of Gaussian Quadrature Points

This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods.A model based on the neural network multi-classification algorithmis constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy.The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected.The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model,and the accuracy of the model is about 90%.Finally,by incorporating the predicted Gaussian quadrature points into the boundary element analysis,we find that the numerical solution and the analytical solution are in good agreement,which verifies the robustness of the proposed method.

An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

Methods for Derivation of Density Matrix of Arbitrary Multi-Mode Gaussian States from Its Phase Space Representation

We present a method for derivation of the density matrix of an arbitrary multi-mode continuous variable Gaussian entangled state from its phase space representation.An explicit computer algorithm is given to reconstruct the density matrix from Gaussian covariance matrix and quadrature average values.As an example,we apply our method to the derivation of three-mode symmetric continuous variable entangled state.Our method can be used to analyze the entanglement and correlation in continuous variable quantum network with multi-mode quantum entanglement states.

Fast Remaining Capacity Estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

Three-dimensional acoustic radiation force of a eukaryotic cell arbitrarily positioned in a Gaussian beam

Expressions are derived for calculating the three-dimensional acoustic radiation force(ARF)on a multilayer microsphere positioned arbitrarily in a Gaussian beam.A theoretical model of a three-layer microsphere with a cell membrane,cytoplasm,and nucleus is established to study how particle geometry and position affect the three-dimensional ARF,and its results agree well with finite-element numerical results.The microsphere can be moved relative to the beam axis by changing its structure and position in the beam,and the axial ARF increases with increasing outer-shell thickness and core size.This study offers a theoretical foundation for selecting suitable parameters for manipulating a three-layer microsphere in a Gaussian beam.

Gaussian Weighted Trajectory Method Ⅳ： No Rainbow Effect in Practice

Gaussian 加权的轨道方法(GWTM ) 在随机的阶段近似是古典 S 矩阵理论(CSMT ) 的实际实现，是的 CSMT 第一并且分子的碰撞的最简单的半古典作品途径，在七十年代初发展了。很不过精神上关门到完全古典的描述，到为不同 degrees-of-freedom 的量子化的某程度的 GWTM 报道在这些过程包含了。当 CSMT 可以给分叉的期末考试时，说分布，在与散布理论的橡皮的彩虹效果的关系， GWTM 从来没导致过如此的一场数学大祸。现在的笔记的目标是解释这发现。

基于分裂阳极的非柱对称电弧电流密度分布测量方法

电弧是弧焊过程中熔池金属熔化的能量来源,分裂阳极法是电弧阳极能量密度直接测量的有效方法之一.常规分裂阳极法是建立在电弧为柱对称结构的基础上,不适用于非柱对称电弧的阳极电流密度测量.试验提出在两条相互垂直的路径上依次进行分裂阳极电流数据采集的新方法,引入表征电弧不对称度的特征参量,进行椭圆环分割,以计算电弧阳极电流密度分布,修正分裂阳极法.通过预设的二维高斯分布的电流密度模型,检验了修正的分裂阳极法的可行性.结果表明,采用修正的分裂阳极法可以有效计算出电弧阳极电流密度分布.利用修正后的分裂阳极法实现了非柱对称侧向压缩等离子弧阳极电流密度的测量,与x方向相比,侧向压缩的等离子弧在y方向压缩明显,呈现典型的非柱对称特性.

基于层次分解、主成分分析和高斯混合模型的火成岩岩性识别——以惠州26洼古潜山为例

火成岩油气成藏规律复杂,受到火山运动、强构造运动以及风化剥蚀等叠加影响,火成岩的化学成分和结构构造复杂多样,非均质性极强,采用常规岩性识别方法难以一次性将所有岩性准确识别。借鉴层次分解思路,以惠州26洼古潜山为例,提出了一种火成岩岩性测井识别分类方法:综合考虑火成岩地质分类原则和测井响应特征来确定岩性识别层级,基于这种层次性的分类原则,在每一层次定量优选岩性识别敏感参数,建立研究区岩性识别优选层级;在明确岩性识别优选层级的基础上,逐级逐次使用主成分分析(PCA)和高斯混合模型(GMM)对岩性进行判别并确定其计算函数,建立分级优选岩性识别模型,最终达到整体岩性区分的目的。研究结果表明,研究区辉绿岩和闪长岩识别正确率分别为87.31%和84.32%,未分级未优选辉绿岩和闪长岩识别正确率为60.45%和54.88%,分级未优选其岩性识别正确率为69.61%和67.04%,有效提高了研究区的复杂岩性识别精度。该方法的提出对提高火成岩岩性识别精度提供了一种思路,也为研究区古潜山火成岩岩性精确识别提供了参考依据。

水体透射光谱结合主成分分析(PCA)改进化学需氧量(COD)含量估算研究

为了解决传统的化学需氧量(Chemical Oxygen Demand,COD)测量方法耗时较长,不利于快速、实时地获取水体中COD的信息等问题。通过采集100组COD水体光谱信息,分别使用3种不同的高光谱数据预处理方法对光谱数据进行预处理,并基于不同的预处理方法分别建立高斯过程回归模型(Gaussian Process Regression,GPR)和BP神经网络模型,分析不同预处理方法对模型精度的影响,建立了基于透射光谱测量结合主成分分析(Principal Component Analysis,PCA)改进水体COD含量估算模型。对各模型结合PCA数据降维方法进行模型的改进,通过比较模型的精度选择最优模型进行水体COD含量的检测。结果显示:相比于原始光谱数据建立的GPR模型和BP神经网络模型,数据预处理后的模型精度明显提升;且结合PCA对预处理后的数据进一步降维处理后,模型精度得到了进一步的提升。其中,基于标准正态变量变换特征结合PCA改进BP神经网络模型R 2高达0.9940,均方根误差RMSE为0.022540。证明了基于PCA数据降维方法对预处理后的光谱数据进行降维处理,有利于去除光谱中的冗余信息,提取特征信息,可以实现COD含量估算模型的优化,从而为传统COD测量方法存在的问题提出了一种新的解决思路。

基于改进潜能模型的南康家具物流园区空间可达性分析

潜能模型作为测度空间可达性的重要模型,广泛应用于医疗教育、公园绿地等公共设施可达性分析中。以家具物流园为切入点,在物流园区占地面积影响因素的基础上加入物流企业数量因子,构建改进潜能模型测度其空间可达性。同时结合地理信息系统(GIS)插值方法进行可达性等级划分,并在出行时间测算中引入货车限行影响因子。以南康区为例进行家具物流园空间可达性验证分析。结果表明:南康家具物流园区潜能分布较为平衡,中部物流商贸城和红土地物流园作为成熟的物流园其潜能值最大,物流园区空间可达性分布差异不明显,大致呈现出中东部高、周边低的分布格局。

基于Gaussian模型的先期固结压力确定

针对现有拟合e-lgp曲线模型存在的不足,借助Matlab软件自主开发的集曲线拟合、绘图和先期固结压力求解的复合工具箱CFSPT,以太原市的粉质粘土分析为例,提出了基于Casagrande法确定先期固结压力的Gaussian模型。分析结果表明,该方法可行,Gauss1、Gauss2拟合程度高,Gauss1确定的先期固结压力与土工试验方法现行规范较吻合。

双自由曲面光束整形系统的虚拟面迭代设计法

双自由曲面光束整形系统可在不改变光束相位分布的情况下实现光束空间光强分布的按需调控,然而其需要设置虚拟平面来进行双自由曲面面形的求解。研究发现,传统单一虚拟面法在应用于结构紧凑和扩束倍率较大的系统时,存在设计不精准、整形效果差等缺陷,为此,提出了一种基于虚拟面迭代策略的双自由曲面光束整形系统设计方法。基于不同参数的同轴透射式和离轴反射式光束整形系统的数值仿真研究结果,表明该方法可有效规避传统虚拟面法的局限性。以将束腰半径为5 mm的高斯光束整形为半径30 mm的准直平顶光束的透射式双自由曲面光束整形系统为例,虚拟面迭代法所设计的光束整形系统将光强分布均匀性和能量利用率相对于单一虚拟面法而言分别提升了2.93%和8.93%。

基于改进加权核范数最小化的图像去噪算法研究

针对彩色图像的高斯噪声,在加权核范数最小化(WNNM)框架下,提出了一种对于彩色图像去噪的多信道(MC)优化模式。首先,选择多种类型噪声计算量,利用信道的冗余特性将RGB补丁连接起来,再引入权重矩阵以协调3种信道的图像保真性。把所提的MC-WNNM模型转换成线性等式约束现象,并采用交替位置乘子法(ADMM)解决。每个变量更新步骤都具有其封闭解,并能保证收敛性。基于真实用于无人机目标识别的彩色图像添加噪声进行仿真实验,实验结果说明,该算法相较现有的BM3D方法及WNNM方法具有明显优势。

基于最优R-Vine Gaussian Copula模型的服役大跨桥梁主梁失效概率分析

考虑到服役大跨桥梁主梁多个控制监测点失效模式的相关性,提出失效概率分析的最优regular-vine(R-vine)Gaussian copula信息融合新方法。利用极值应变信息,引入双变量pair-Gaussian-copula模型和最优R-vine模型,结合多个控制监测点的功能函数,进行失效模式相关性的最优R-vine Gaussian copula建模分析;融合一次二阶矩方法,进行失效模式相关的服役大跨桥梁主梁失效概率分析;通过在役桥梁监测数据对所提方法的合理性进行验证,并与其他分析方法进行比较。结果表明,考虑控制监测点失效模式相关性的大跨桥梁主梁失效概率分析的最优R-Vine Gaussian copula信息融合方法更为合理。