An iterative algorithm for magnetic induction modulus inversion

An iterative algorithm is proposed for magnetic induction modulus inversion. The iterative process is based on a recurrence relation for the squared induction modulus, which can be represented at each iteration step by a system of linear algebraic equations for components of the magnetization vector. Formulas for the calculation of matrix elements of this system and the determination of the magnetization at the next iteration step are given. Model and practical examples illustrating the reconstruction of magnetization and anomalous field components are presented.     

Curvelet阈值迭代法地震随机噪声压制(英文)

本文将近些年发展起来的多尺度分析技术——Curvelet变换与求解优化反演问题的阈值迭代法相结合,研究了基于Curvelet变换的阈值迭代法在地震数据随机噪声衰减中的应用。充分利用了Curvelet变换对地震数据表示的稀疏性,提出将地震数据随机噪声压制问题转化为基于Curvelet稀疏变换的L1范数最优化问题,并采用前人提出的阈值迭代法求解。通过与常规的中值滤波、FX反褶积和小波阈值法去噪方法对比,理论合成数据和实际数据试算表明,Curvelet阈值迭代法去噪法具有优势,该法不仅能够获得较高的信噪比,而且对有效信号的损失较小。为充分利甩Curvelet的多尺度、多方向特性,提出了在Curvelet阈值迭代法去噪结果的基础上再进行方向控制,进一步提高了数据信噪比。     

一种迭代算法在结构物理参数识别中的应用

本文在利用复合反演方法进行剪切型结构物理参数识别过程中,引入一种迭代算法来求解关于参数的病态方程组,探讨了几种比例阻尼形式的比例系数识别的处理方法。以10层剪切型框架结构为例,利用该迭代算法研究了无阻尼、三种比例阻尼形式以及噪信比为5％、10％的高斯白噪声等情形下的参数识别问题。结果表明,该迭代算法能很好地求解参数的病态方程组,且对四种阻尼形式和两种噪信比的噪声有很好的适应性。     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.     

一维溶质运移源(汇)项系数反演的迭代正则化算法

对于多孔介质中发生物理化学反应的溶质运移现象,可以用带有非线性源（汇）项作用的一维对流弥散-反应扩散方程来描述,但方程中反映溶质吸附/解吸附能力的源(汇)项系数往往是未知的. 本文讨论了基于出流端浓度观测数据的源项系数反演问题. 根据Tikhonov正则化和矩阵的奇异值分解系统,建立了一种离散的迭代正则化算法,给出了算法实现步骤.数值模拟结果表明该算法不仅精度高,而且对于数据的随机扰动具有稳定性.最后应用建立的算法反演计算了一个具体的土柱试验源项系数,数值结果也表明文中所构造算法的有效性.     

A parallel dynamic programming algorithm for multi-reservoir system optimization

This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.     

On the colocation iterative solution model and algorithm for gravity anomaly

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A new epsilon-dominance hierarchical Bayesian optimization algorithm for large multiobjective monitoring network design problems

This study focuses on the development of a next generation multiobjective evolutionary algorithm (MOEA) that can learn and exploit complex interdependencies and/or correlations between decision variables in monitoring design applications to provide more robust performance for large problems (defined in terms of both the number of objectives and decision variables). The proposed MOEA is termed the epsilon-dominance hierarchical Bayesian optimization algorithm (ε$\epsilon$-hBOA), which is representative of a new class of probabilistic model building evolutionary algorithms. The ε$\epsilon$-hBOA has been tested relative to a top-performing traditional MOEA, the epsilon-dominance nondominated sorted genetic algorithm II (ε$\epsilon$-NSGAII) for solving a four-objective LTM design problem. A comprehensive performance assessment of the ε$\epsilon$-NSGAII and various configurations of the ε$\epsilon$-hBOA have been performed for both a 25 well LTM design test case (representing a relatively small problem with over 33 million possible designs), and a 58 point LTM design test case (with over 2.88×10¹⁷$2.88\times {10}^{17}$ possible designs). The results from this comparison indicate that the model building capability of the ε$\epsilon$-hBOA greatly enhances its performance relative to the ε$\epsilon$-NSGAII, especially for large monitoring design problems. This work also indicates that decision variable interdependencies appear to have a significant impact on the overall mathematical difficulty of the monitoring network design problem.     

随机振动功率谱复现迭代算法的研究

采用功率谱复现迭代算法,依据系统的频率特性,对驱动信号进行迭代补偿,使得系统的响应谱能够高精度地复现期望谱。基于对影响随机振动功率谱复现精度因素的分析,提出在不同频段采用不同迭代步长的驱动谱迭代算法,给出了算法的程序框图,并通过试验验证了算法的有效性。     

An interactive multi-objective optimization framework for groundwater inverse modeling

The groundwater inverse problem of estimating heterogeneous groundwater model parameters (hydraulic conductivity in this case) given measurements of aquifer response (such as hydraulic heads) is known to be an ill-posed problem, with multiple parameter values giving similar fits to the aquifer response measurements. This problem is further exacerbated due to the lack of extensive data, typical of most real-world problems. In such cases, it is desirable to incorporate expert knowledge in the estimation process to generate more reasonable estimates. This work presents a novel interactive framework, called the ‘Interactive Multi-Objective Genetic Algorithm’ (IMOGA), to solve the groundwater inverse problem considering different sources of quantitative data as well as qualitative expert knowledge about the site. The IMOGA is unique in that it looks at groundwater model calibration as a multi-objective problem consisting of quantitative objectives – calibration error and regularization – and a ‘qualitative’ objective based on the preference of the geological expert for different spatial characteristics of the conductivity field. All these objectives are then included within a multi-objective genetic algorithm to find multiple solutions that represent the best combination of all quantitative and qualitative objectives. A hypothetical aquifer case-study (based on the test case presented by Freyberg [Freyberg DL. An exercise in ground-water model calibration and prediction. Ground Water 1988;26(3)], for which the ‘true’ parameter values are known, is used as a test case to demonstrate the applicability of this method. It is shown that using automated calibration techniques without using expert interaction leads to parameter values that are not consistent with site-knowledge. Adding expert interaction is shown to not only improve the plausibility of the estimated conductivity fields but also the predictive accuracy of the calibrated model.     

An interactive biplot implementation in R for modeling genotype-by-environment interaction

Classical and GGE biplot methods are graphical procedures that allow multivariate data to be analyzed. In particular, the GGE biplot displays the genotype main effect (G) and the genotype by environment interaction (GE) in two-way data. The GGE biplot originates from data graphical analysis of multi-environment trials (MET). Thus, agronomists, crop scientists and geneticists are potential users of this method. However, it can also be used to visualize and analyze other types of data. In this paper, we propose a new interactive computational implementation in R language to perform the main functions of the classical and GGE biplot methods, so it is also useful for MET data visual analysis. This implementation is organized in an R package named GGEBiplotGUI . This package is the only interactive, noncommercial and open source software that currently exists, offering a free alternative to available commercial software. In addition, it can be used without to practically have knowledge of the R programming language. Here, we present and discuss the capabilities and features of the GGEBiplotGUI package and illustrate them by using real data. The GGEBiplotGUI package graphically addresses the questions that a researcher likely asks. This R package is not only a tool for visual data analysis of multi-environment trials, useful for plant breeders and geneticists, in order to study yields from genotypes and interactions between genotype and environment, but also data from other areas can be analyzed by the GGEBiplotGUI package.     

位场向下延拓的改进迭代维纳滤波法

根据维纳滤波理论导出的位场向下延拓滤波器为最佳下延滤波器,但因其实现需要已知待求位场和噪声的功率谱而在实际应用中受到限制.针对该问题,本文首先提出一种基于位场径向平均功率谱的位场噪声水平估计方法,进而利用偏差准则求取正则化参数,实现位场正则化向下延拓;然后将位场正则化下延结果的功率谱作为待求位场功率谱的估计初值,采用带修正项的迭代维纳滤波方法来更新对待求位场功率谱的估计,最后提出本文的位场向下延拓改进迭代维纳滤波方法.基于理论重力模型数据及航磁实测数据进行了向下延拓对比试验,结果表明,改进迭代法具有较好的收敛性,且下延精度优于Tikhonov正则化法和递增型维纳滤波法.     

An inexact inventory-theory-based chance-constrained programming model for solid waste management

In this study, an inexact inventory-theory-based chance-constrained programming (IICP) model is proposed for planning waste management systems. The IICP model is derived through introducing inventory theory model into a general inexact chance-constrained programming framework. It can not only tackle uncertainties presented as both probability distributions and discrete intervals, but also reflect the influence of inventory problem in decision-making problems. The developed method is applied to a case study of long-term municipal solid waste (MSW) management planning. Solutions of total waste allocation, waste allocation batch and waste transferring period associated different risk levels of constraint violation are obtained. The results can be used to identify inventory-based MSW management planning with minimum system cost under various constraint-violation risks. Compared with the ICP model, the developed IICP model can more actually reflect the complexity of MSW management systems and provide more useful information for decision makers.     

随机分布多导体圆柱目标散射的快速计算 及其成像模拟

用二维迭代方法快速地解析计算多导体圆柱目标的散射场,其中各圆柱目标自身的一次散射以各自中心为原点的柱面波函数叠加,匹配各圆柱表面的边界条件获得展开系数;各圆柱间的散射相互作用由坐标变换实现,具体体现在圆柱中心偏移产生的初始相位上.为考虑各个柱目标间的二次散射,将每个柱目标自身的一次散射看成另一柱目标的激励场,利用柱面波函数的加法定理和边界条件匹配来计算各柱目标间的二次散射贡献.类似的处理可获得三次或更高次的散射计算.与矩量法(MoM)的数值结果比较,验证了该方法的有效性,明显提高了后向散射的计算速度.对于随机生成的多个圆柱目标模型,解析计算一定频带f和360°方位角θ范围内均匀离散样点的后向散射场.为抑制数据截断产生的振铃效应,对频率向的散射数据用Hamming窗实现平滑过渡.为充分利用快速Fourier变换(FFT)的高效计算,对f－θ平面上的离散散射数据用二维的三次样条插值处理,得到X－Y平面的均匀离散数据.最后,用FFT方法快速实现逆合成孔径雷达(ISAR)成像模拟,很好地重构了各圆柱体目标的位置和大小尺寸.     

An inexact programming method for agricultural irrigation systems under parameter uncertainty

Rapid population growth and economy development have led to increasing reliance on water resources. It is even aggravated for agricultural irrigation systems where more water is necessary to support the increasing population. In this study, an inexact programming method based on two-stage stochastic programming and interval-parameter programming is developed to obtain optimal water-allocation strategies for agricultural irrigation systems. It is capable of handling such problems where two-stage decisions need to be suggested under random- and interval-parameter inputs. An interactive solving procedure derived from conventional interval-parameter programming makes it possible for the impact of lower and upper bounds of interval inputs to be well reflected in the resulting solutions. An agricultural irrigation management problem is then provided to demonstrate the applicability, and reasonable solutions are obtained. Compared to the solutions from a representative interval-parameter programming model where only one decision-stage exists, the interval of optimized objective-function value is narrow, indicating more alternatives could be provided when water-allocation targets are rather high. However, chances of obtaining more benefits exist in association with a risk of paying more penalties; such a relationship becomes apparent when the variation of water availability is much intensive.     

正弦标定自动处理算法研究

由于种种原因, 地震计的周期、 阻尼、 灵敏度等参数在仪器运行的过程中会发生变化, 这些参数的变化直接影响到地震计的性能和观测数据的质量。 为了掌握地震计参数的变化, 通常采用定期标定的方法, 对运行中仪器的参数进行跟踪监测, 通过分析标定数据得到仪器的运行状态。 目前, 对于标定数据的处理通常采用人工分析的办法。 随着台站数量的增加, 对标定数据的处理工作变得日益繁重。 该文介绍一种正弦标定自动处理算法, 利用这种算法编写的自动处理程序, 在标定数据处理过程中不需要人工干预, 计算机可以自动提取有效的波形数据进行测量计算, 并可以对效果不好的数据进行修复, 最终给出仪器的频率响应特性。     

An alternative single-step algorithm for dynamic problems

In this paper we consider a one-step form of the weighted residual method for solving equations of motion for discrete structural problems. The resulting time integration algorithm is equivalent to the Newmark method for a restricted but important range of the parameters. The weighted residual treatment of the forcing function permits use of time increments based solely on the expected periods of the structural response, instead of the usual additional restrictions based on the periods of the forcing function. A numerical example is included to illustrate the behaviour of the algorithm for a forced ground motion with both low- and high-frequency components.     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.