FWT2D: A massively parallel program for frequency-domain full-waveform tomography of wide-aperture seismic data—Part 1: Algorithm

This is the first paper in a two-part series that describes a massively parallel code that performs 2D frequency-domain full-waveform inversion of wide-aperture seismic data for imaging complex structures. Full-waveform inversion methods, namely quantitative seismic imaging methods based on the resolution of the full wave equation, are computationally expensive. Therefore, designing efficient algorithms which take advantage of parallel computing facilities is critical for the appraisal of these approaches when applied to representative case studies and for further improvements. Full-waveform modelling requires the resolution of a large sparse system of linear equations which is performed with the massively parallel direct solver MUMPS for efficient multiple-shot simulations. Efficiency of the multiple-shot solution phase (forward/backward substitutions) is improved by using the BLAS3 library. The inverse problem relies on a classic local optimization approach implemented with a gradient method. The direct solver returns the multiple-shot wavefield solutions distributed over the processors according to a domain decomposition driven by the distribution of the LU factors. The domain decomposition of the wavefield solutions is used to compute in parallel the gradient of the objective function and the diagonal Hessian, this latter providing a suitable scaling of the gradient. The algorithm allows one to test different strategies for multiscale frequency inversion ranging from successive mono-frequency inversion to simultaneous multifrequency inversion. These different inversion strategies will be illustrated in the following companion paper. The parallel efficiency and the scalability of the code will also be quantified.     

Generating generalized inverse Gaussian random variates by fast inversion

The inversion method for generating non-uniformly distributed random variates is a crucial part in many applications of Monte Carlo techniques, e.g., when low discrepancy sequences or copula based models are used. Unfortunately, closed form expressions of quantile functions of important distributions are often not available. The (generalized) inverse Gaussian distribution is a prominent example. It is shown that algorithms that are based on polynomial approximation are well suited for this distribution. Their precision is close to machine precision and they are much faster than root finding methods like the bisection method that has been recently proposed.     

基于对数压缩的超声各向异性扩散去噪方法

针对当前超声图像去噪算法很难同时做到降噪和边缘保持的情况,在进行各向异性扩散模型研究的基础上,提出基于对数压缩的改进各向异性扩散算法（LCAD）去除超声散斑噪声。算法将图像对数压缩后进行噪声分布模型估计,然后构造基于广义伽马分布的扩散系数,在扩散过程中达到降噪和边缘保持效果。     

Modeling large strain anisotropic elasto-plasticity with logarithmic strain and stress measures

In this paper we present a model and a fully implicit algorithm for large strain anisotropic elasto-plasticity with mixed hardening in which the elastic anisotropy is taken into account. The formulation is developed using hyperelasticity in terms of logarithmic strains, the multiplicative decomposition of the deformation gradient into an elastic and a plastic part, and the exponential mapping. The novelty in the computational procedure is that it retains the conceptual simplicity of the large strain isotropic elasto-plastic algorithms based on the same ingredients. The plastic correction is performed using a standard small strain procedure in which the stresses are interpreted as generalized Kirchhoff stresses and the strains as logarithmic strains, and the large strain kinematics is reduced to a geometric pre- and post-processor. The procedure is independent of the specified yield function and type of hardening used, and for isotropic elasticity, the algorithm of Eterovi? and Bathe is automatically recovered as a special case. The results of some illustrative finite element solutions are given in order to demonstrate the capabilities of the algorithm.     

可预测期望航向的船舶航迹控制器设计

为了提高航迹控制器的跟踪性能,节省航程,本文推导了期望航向和航迹偏差的数学模型,设计了可预测期望航向的船舶航迹预测控制器,它不仅能预测系统的输出(船首向和航迹偏差),而且可以提前多步预测系统的参考输入(期望航向),以提前修正命令舵角.对货船的航迹控制计算机仿真结果表明,算法提高了控制器的跟踪速度和精度,缩短了船舶的实际航程.本文还给出了求解控制器中逆矩阵的递推算法,其计算量为通常算法的1/3.     

Sampling Archimedean copulas

The challenge of efficiently sampling exchangeable and nested Archimedean copulas is addressed. Specific focus is put on large dimensions, where methods involving generator derivatives are not applicable. Additionally, new conditions under which Archimedean copulas can be mixed to construct nested Archimedean copulas are presented. Moreover, for some Archimedean families, direct sampling algorithms are given. For other families, sampling algorithms based on numerical inversion of Laplace transforms are suggested. For this purpose, the Fixed Talbot, Gaver Stehfest, Gaver Wynn rho, and Laguerre series algorithm are compared in terms of precision and runtime. Examples are given, including both exchangeable and nested Archimedean copulas.     

基于广义积分平方误差谱选择的图像分割

谱聚类算法中并不是所有的顶层谱都含有聚类信息,对于实际含噪声数据的聚类,由于谱数据分布复杂,谱的选择是必要的。文中推广积分平方误差散度,验证所提出的广义积分平方误差散度可用来估计数据分布的模态,以及度量谱所含的聚类信息量,并提出一种基于谱选择的谱聚类算法。自然图像分割实验结果表明,提出的算法比以往的谱聚类算法更为简单有效。     

一般多边形窗口的线裁剪

已有的线裁剪算法都是针对矩形窗口或凸多边形窗口的。对于一般的多边形窗口(包括凹多边形)的线裁剪,目前尚无有效的算法。开发这种算法是很必要的,因为它在计算机图形学中有很广泛的应用,如物体的消隐处理等。因此,提出一个对于一般多边形窗口的线裁剪算法,并给出了最优实现。     

Application of finitized power series distributions to accelerated variate generation. Part II: the case of the logarithmic distribution

The method of aliasing vastly expands the palette of discrete random variate generation methodologies while providing excellent speed. However, its application is limited to finitely supported distributions. We demonstrate that an application of moment preserving finitization called the Negative Taylor Series Finitization (NTSF) method for the power series family of discrete distributions, when coupled with the method of aliasing, can greatly improve infinitely supported discrete random variate generation speed with certain limitations. We illustrate this with the logarithmic power series distribution, and we compare four published algorithms designed to generate random variates from a logarithmic distribution to the aliasing method of random variate generation from an NTSF version of the same distribution. We compare the accuracy and speed (user‐time) of these various methods for generating variates from a logarithmic distribution.     

NASA系列雪参数反演算法在单像元内的时间序列验证与分析

NASA系列算法(Chang,NASA96和Foster算法)是被动微波遥感反演雪深、雪水当量的简单、实用的经验算法,并经过了很多学者大范围的算法验证和改进。为了进一步评价NASA系列算法在东北地区的时空适用性,于长春净月潭区域选定了一个以农田和森林为主的10km×10km被动微波遥感混合像元,在时间上连续观测整个干雪期(2014年12月至次年2月)的积雪参数和气象数据,结合FY3B卫星搭载的微波成像仪(MWRI)亮温数据,对NASA系列算法精度进行了评价分析。结果表明:对于雪深的反演,Chang算法和NASA 96算法前期反演效果较好,后期随着时间的推进高估雪深的趋势愈加明显。由于考虑了森林覆盖率的影响,NASA 96算法的反演精度更高。两种算法最大高估值分别是24.46和14.62cm,这是因为期间雪性质不断变化,尤其是雪粒径不断增大的缘故。Foster算法也严重高估了雪水当量,可能是由于积雪类型的分类系统未必适合于东北地区的积雪特征。本文的积雪连续观测数据为认识东北地区的积雪特性奠定了基础,对算法的时间序列验证与分析为雪参数反演算法的进一步改进提供了可靠依据。     

On Two Techniques of Combining Branching and Treewidth

Branch & Reduce and dynamic programming on graphs of bounded treewidth are among the most common and powerful techniques used in the design of moderately exponential time exact algorithms for NP hard problems. In this paper we discuss the efficiency of simple algorithms based on combinations of these techniques. The idea behind these algorithms is very natural: If a parameter like the treewidth of a graph is small, algorithms based on dynamic programming perform well. On the other side, if the treewidth is large, then there must be vertices of high degree in the graph, which is good for branching algorithms. We give several examples of possible combinations of branching and programming which provide the fastest known algorithms for a number of NP hard problems. All our algorithms require non-trivial balancing of these two techniques. In the first approach the algorithm either performs fast branching, or if there is an obstacle for fast branching, this obstacle is used for the construction of a path decomposition of small width for the original graph. Using this approach we give the fastest known algorithms for Minimum Maximal Matching and for counting all 3-colorings of a graph. In the second approach the branching occurs until the algorithm reaches a subproblem with a small number of edges (and here the right choice of the size of subproblems is crucial) and then dynamic programming is applied on these subproblems of small width. We exemplify this approach by giving the fastest known algorithm to count all minimum weighted dominating sets of a graph. We also discuss how similar techniques can be used to design faster parameterized algorithms. A preliminary version of this paper appeared as Branching and Treewidth Based Exact Algorithms in the Proceedings of the 17th International Symposium on Algorithms and Computation (ISAAC 2006) [15]. Additional support by the Research Council of Norway.     

Combinatorial search versus genetic algorithms: A case study based on the generalized median graph problem

The computation of generalized median graphs (the graph with the smallest average edit distance to all graphs in a given set of graphs) is highly computationally complex. As a matter of fact, it is exponential in the number of nodes of the union of all graphs under consideration. Thus, the generalized median graph computation problem seems to be a suitable and challenging testbed for a comparison of combinatorial search and genetic algorithms. Two solutions are described in this paper. The first is an exact algorithm based on combinatorial search, while the second is a genetic algorithm. Both approaches are compared to each other in a series of experiments.     

基于分布估计算法的朴素贝叶斯分类问题研究

随着基础理论研究所取得的一系列进展,分布估计算法逐渐成为进化计算研究领域的一个新的研究方向,并成为当今国际进化算法研究的新热点。采用机器学习的方法分析数据、指导搜索已经成为设计新算法的趋势。将分布估计算法引入到朴素贝叶斯分类器系统中,设计基于基尼指数的适应度函数,从而进一步提高朴素贝叶斯分类器的性能。     

Nonstationary time series analysis by temporal clustering

The object of this paper is to present a model and a set of algorithms for estimating the parameters of a nonstationary time series generated by a continuous change in regime. We apply fuzzy clustering methods to the task of estimating the continuous drift in the time series distribution and interpret the resulting temporal membership matrix as weights in a time varying, mixture probability distribution function (PDF). We analyze the stopping conditions of the algorithm to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The algorithm performance is demonstrated with three different types of signals.     

Comparative Analysis on Three Land Surface Temperature Inversion Algorithm based on Landsat 8 Data over Sanheba Basin

The Land Surface Temperature (LST) of TIRS10 / Landsat 8 remote sensing data is studied and analyzed by combining the data and related parameters of Sanheba basin,and the LST inversion algorithm are used the Radiative Transfer Equation Method (RTE),Mono\|Window algorithm (MW) and Single\|Channel Method (SC).The parameters of the MW algorithm are corrected.The LST gray scale and density segmentation graphs,the histogram of LST and the cross validation flank are used to compare the results of the LST inversion algorithm.The results show that the three kinds of algorithms are similar to the linear fitting degree of LST,and the spatial distribution is consistent.The RTE and SC algorithm are close to each other,the average error of algorithm is 0~0.05 K.the LST of MW algorithm is higher than that of the other two algorithms,the average error of algorithm is 0~1.27 K.The LST of different land cover types in this basin is compared,and the inversion results can effectively reflect the details of the surface thermal field structure according to the different land cover types.The LST values obtained by these three algorithms are compared with the MODIS LST product values.The results show that there is a significant correlation between the LST values and the MODIS LST products.In this paper,3 kinds of the LST inversion algorithms are analyzed detailed accurate on TIRS10/Landsat 8 remote sensing data,provide a reference for other thermal infrared satellite data inversion LST algorithm,but also for the subsequent LST improve the accuracy of inversion basis.     

On scalarized calculation of the likelihood function in array square-root filtering algorithms

An efficient method of scalarized calculation of the logarithmic likelihood function based on the array square-root implementation methods for Kalman filtering formulas was proposed. The algorithms of this kind were shown to be more stable to the roundoff errors than the conventional Kalman filter. The measurement scalarization technique enables a substantial reduction in the computational complexity of the algorithm. Additionally, the new implementations are classified with the array filtering algorithms and thereby are oriented to the parallel calculations. Computational results corroborated effectiveness of the new algorithm.     

基于NSCT 的自适应乘性水印局部最优非线性盲检测算法

图像水印算法研究是多媒体技术领域中的重要议题。比较并结合当前两类主流的 图像水印算法,提出了一种基于非下采样Contourlet 变换的自适应乘性水印算法。借鉴Barni 的“pixel-wise masking”模型和冗余小波域掩盖效应建模的做法,建立非下采样Contourlet 变换域 掩盖效应计算模型。用广义高斯分布模型和Cauchy 分布模型描述非下采样Contourlet 变换系数 的统计特性,将水印的检测问题表述为一个复合假设检验。通过理论推导分别建立了乘性水印 的两种局部最优非线性盲检测器及检测门限的自适应确定方法。实验结果表明,非下采样 Contourlet 变换域掩盖效应计算模型使得水印嵌入算法具有良好的视觉不可见性,两种检测器在 无原始图像和自适应嵌入强度系数参与检测的情况下均能准确地检测到水印信息的存在。实验 结果同时显示,基于Cauchy 分布的盲检测器在检测效果和检测时间方面优于基于广义高斯分 布的盲检测器。     

一种基于取样的概念层次数据挖掘新算法

本文分析了几种传统属性归纳算法，针对它们的不足，提出了基于取样的概念层次挖掘算法，它不仅可以处理不平衡的概念层次，而且得到的泛化规则可以反映实际的数据分布。此外，这种算法具有最优的时间和空间复杂性。实验证明，本文算法是有效、可行的。     

基于覆盖的构造性学习算法SLA及在股票预测中的应用

覆盖算法是神经网络学习算法中的一个十分有效的方法，它克服了基于搜索机制的学习方法和规划学习方法计算复杂性高，难以用于处理海量数据的不足，为神经网络提供一个构造性的学习方法，但该方法是建立在所有训练样本都是精确的假设上的，未考虑到所讨论的数据具有不精确的情况，若直接将该方法应用于数据不精确情况，所得到效果不理想．主要讨论数据具有不精确情况下的时间序列的预测问题，为此将原有的覆盖算法进行改进，引入“覆盖强度”和“拒识样本”的概念，并结合这些新概念给出相应的覆盖学习算法(简称SLA)，最后将SLA算法，应用于金融股市的预测，具体应用到以上(海)证(券)综合指数构成的时间序列的预测，取得了较好的结果，这表明了SLA方法的可行性和应用前景。     

基于空域特征的无参考图像质量评价算法

针对现有无参考图像质量评价算法通用性差,且存在高计算复杂度的问题,提出一种基于图像空域统计特征的无参考图像质量评价算法。该算法引入图像对数统计特性,通过度量失真图像与自然图像统计特征之间的偏差,实现无参考图像质量评价。仿真测试结果表明,与现有无参考技术相比,提出算法简单有效,适用于多种失真类型,且与人类主观感知具有较好一致性。