A novel time-marching scheme using numerical Green’s functions: A comparative study for the scalar wave equation

The present paper presents the formulation of a novel time-marching method based on the Explicit Green’s Approach (ExGA) to solve scalar wave propagation problems. By means of the weighted residual method in both time and space, the time integral expression concerning the ExGA is readily established. The arising ExGA time integral expression is spatially discretized in a finite element sense and a recursive scheme that employs time-domain numerical Green’s function matrices is adopted to evaluate the displacement and the velocity vectors. These Green’s matrices are computed by the time discontinuous Galerkin finite element method only at the first time step. The system of coupled equations originated from the time discontinuous Galerkin method is then solved by an iterative predictor–multicorrector algorithm. Once the Green’s matrices are computed, no iterative process is required to obtain the displacement and the velocity vectors at any time level. At the end of the paper, numerical examples are presented in order to compare the proposed approach with other approaches.     

Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: multinorm algorithms

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on multiple weighted norms to measure the distance between the feature vectors and their prototypes. Clustering and LVQ are formulated in this paper as the minimization of a reformulation function that employs distinct weighted norms to measure the distance between each of the prototypes and the feature vectors under a set of equality constraints imposed on the weight matrices. Fuzzy LVQ and clustering algorithms are obtained as special cases of the proposed formulation. The resulting clustering algorithm is evaluated and benchmarked on three data sets that differ in terms of the data structure and the dimensionality of the feature vectors. This experimental evaluation indicates that the proposed multinorm algorithm outperforms algorithms employing the Euclidean norm as well as existing clustering algorithms employing weighted norms.     

Dual margin approach on a Lagrangian support vector machine

In this paper, we propose a new support vector machine (SVM) called dual margin Lagrangian support vectors machine (DMLSVM). Unlike other SVMs which use only support vectors to determine the separating hyperplanes, DMLSVM utilizes all the available training data for training the classifier, thus producing robust performance. The training data are weighted differently depending on whether they are in a marginal region or surplus region. For fast training, DMLSVM borrows its training algorithm from Lagrangian SVM (LSVM) and tailors the algorithm to its formulation. The convergence of our training method is rigorously proven and its validity is tested on a synthetic test set and UCI dataset. The proposed method can be used in a variety of applications such as a recommender systems for web contents of IPTV services.     

Synthesis of individual best local priority vectors in AHP-group decision making

An assessment of the individual judgments and AHP-produced priority vectors for involved decision-makers indicates that the individual consistencies of decision makers may vary significantly, thus making the final group decision less reliable. In this paper, an approach is proposed as to how to combine decision makers’ local priority vectors in AHP synthesis and reduce so-called group inconsistency. Instead of aggregating individual judgments (AIJ), or aggregating individually derived final priorities (AIP), we propose to perform an AHP synthesis of the best local priority vectors taken from the most consistent decision makers. The approach and related algorithm we label as MGPS after the key terms ‘multicriteria group prioritization synthesis.’ The concept is analogous to the one proposed by Srdjevic [1] for individual AHP applications where the best local priority vectors are selected based on the consistency performance of several of the most popular prioritization methods. Here, decision makers are combined instead of prioritization methods, and group context is fully implemented. After completing an evaluation of the decision makers inconsistencies in each node of the hierarchy, the selected best local priority vectors are synthesized in a standard manner, and the final solution is declared to be an AHP-group decision. Two numerical examples indicate that the developed approach and algorithm generate the final priorities of alternatives with the lowest overall inconsistency (in the multicriteria sense).     

The Discrete Basis Problem

Matrix decomposition methods represent a data matrix as a product of two factor matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how the observed data can be expressed as combinations of the basis vectors. Decomposition methods have been studied extensively, but many methods return real-valued matrices. Interpreting real-valued factor matrices is hard if the original data is Boolean. In this paper, we describe a matrix decomposition formulation for Boolean data, the Discrete Basis Problem. The problem seeks for a Boolean decomposition of a binary matrix, thus allowing the user to easily interpret the basis vectors. We also describe a variation of the problem, the Discrete Basis Partitioning Problem. We show that both problems are NP-hard. For the Discrete Basis Problem, we give a simple greedy algorithm for solving it; for the Discrete Basis Partitioning Problem we show how it can be solved using existing methods. We present experimental results for the greedy algorithm and compare it against other, well known methods. Our algorithm gives intuitive basis vectors, but its reconstruction error is usually larger than with the real-valued methods. We discuss about the reasons for this behavior.     

基于多层模糊综合评判的入侵检测系统报警验证

提出了一种基于多层模糊综合评判的报警验证方法,此方法不但可以有效地减少误报,还可以对不同情况下的无关报警进行滤除和标记等不同的操作,很好地处理验证过程中的各种不确定因素。所得的报警相关度参数有利于制定细致、灵活的报警处理策略,从而对报警进行更深入的自动化处理。     

多项式核函数SVM快速分类算法

标准的SVM分类计算过程中有大量的支持向量参与了计算,导致了分类速度缓慢。该文为提高SVM的分类速度,提出了一种快速的多项式核函数SVM分类算法,即将使用多项式核的SVM分类决策函数展开为关于待分类向量各分量的多项式,分类时通过计算各个多项式的值而得到分类结果,使分类计算量和支持向量数量无关,又保留了全部支持向量的信息。当多项式核函数的阶数或待分类向量的维数较低而支持向量数量较多时,使用该算法可以使SVM 分类的速度得到极大的提高。针对实际数据集的实验表明了该算法的有效性。     

线性规划ν-支持向量机的牛顿法

基于线性规划的ν-支持向量机(ν-LPSVM)是在基于二次规划的ν-支持向量机(ν-QPSVM)的基础上提出的。ν-LPSVM和ν-QPSVM模型中的参数ν都可以控制支持向量的数目和误差,但ν-LPSVM的模型更为简单,应用前景更广。讨论了这种新型支持向量机的线性规划问题的最小2-范数解,在此基础上给出了一个快速、有限步终止的牛顿算法。数值实验表明,ν-LPSVM的牛顿算法快速而且有效。     

基于多传感器融合的老人跌倒检测

老年人跌倒受伤的问题已经随着人口老龄化显得非常突出,目前主流的跌倒检测算法既不实用也不准确。为此提出一种新的基于加速度计、陀螺仪和磁力计的跌倒检测算法。把人的活动姿势分平躺姿态和非平躺姿态,并假设在跌倒后检测到躺卧姿势。检测算法分三步:四元数卡尔曼滤波,姿态识别,活动强度分析。通过安装在腰部的九轴传感器采集数据,通过四元数卡尔曼滤波器,使系统可以获得在地面坐标系统中人体姿态矢量。人体的姿态矢量包括欧拉角、四元数、加速度。欧拉角用于确定平躺姿态和非平躺姿态,四元数和加速度用来分析平躺时的活动强度。该算法具有计算量小、实时性好并且检测精度高、检测方便的特点。     

Intrinsic spline curve with local control

A new spline algorithm that uses arc length as parameter and generates a curve in terms of the quantities ‘relative curvature’ and ‘relative torsion’ is described. The tangent and curvature vectors at each datapoint are estimated and the spline is built up span by span. This allows local changes without affecting the whole curve and facilitates the incorporation of derivative discontinuities. Unless discontinuities are specifically requested, the solution obtained is curvature continuous.     

An integer programming approach to scheduling the transshipment of products at cross-docks in less-than-truckload industries

This paper introduces an exact method to schedule the internal transshipment process at cross-docks in less-than-truckload industries. An integer programming formulation is presented to minimize the cost of double handling by synchronizing two types of decisions: (1) products’ internal transferring route, and (2) the order of processing trucks at the terminal doors. Several valid inequalities are introduced to strengthen the formulation and to increase the efficiency of the proposed algorithm. A tailored branch and bound algorithm is developed. Several structural properties and a heuristic method are implemented to enhance the algorithm. Computational experiments of up to 40 trucks demonstrate the efficiency of the proposed approach.     

An algorithm for programming electrostatic plotter type devices

Electrostatic plotter type devices, CRT or hardcopy, are basically printers which allow the ‘paper’ movement only in one direction. As a result, the entire display file has to be created before the process of generating the display can begin. This arrangement differs significantly from the X-Y plotters where the ‘plotting pencil’ and/or paper may be moved in any direction. An algorithm for generating a display on the electrostatic plotter type devices is described in this paper. This algorithm is based upon creating and maintaining a vector file consisting of end points of visible vectors, sorting these vectors, and then generating the plot—a strip at a time. Only the points that lie within the strip are calculated. The details of implementation and the data structure used are discussed. There is no restriction on the size of the display that can be generated; in fact, plots of size 100 in by 20 in have been produced using this algorithm. No a-priori knowledge of the extent of the plot is required. The algorithm has been designed to operate in a device independent computer plotting system and has worked very satisfactorily without imposing any restrictions on the users.     

修改D-W分解求具有需求时间窗和投机性成本的批量问题

研究多产品具有能力约束、需求时间窗、允许延期交货和投机性成本的批量问题.分析无能力约束凸包极点的特征,采用修正的Dantzig-Wolfe分解对原问题进行等价变换.使用列生成获得下界,同时采用启发式分支定界寻找近优解.对随机算例进行了测试与比较,计算结果表明上界与下界之间的间隙非常小;另外分析了当能力参数和订单规模变化时解的质量和计算时间.     

求解连续函数优化问题的合作协同进化布谷鸟搜索算法

为改善布谷鸟搜索算法求解连续函数优化问题的性能,提出合作协同进化的布谷鸟搜索算法.改进算法通过应用合作协同进化框架,将种群的解向量分解成若干子向量,并构成相应子群体.利用标准布谷鸟算法更新各子群体的解向量.各子群体为其它子群体提供最优个体,组合成问题解向量并完成子群体评价.经10个测试函数实验仿真,结果说明改进算法能有效改善求解连续函数优化问题的性能.同时,针对连续函数优化问题,该算法与其它算法相比是有竞争力的优化算法.     

Numerical lower bound shakedown analysis of engineering structures

We propose a numerical method for the computation of shakedown loads of engineering structures subjected to varying thermo-mechanical loading. The method is based on Melan’s lower bound shakedown theorem using the von Mises yield criterion. The resulting nonlinear convex optimization problem is presented in a generalized formulation and then solved by an interior-point algorithm, which is characterized by a problem-tailored solution strategy, particularly suitable for application to large-scale engineering structures.Theoretical and numerical issues of the algorithm are described. It’s efficiency is shown by application to thermo-mechanical problems from power plant engineering. The results are compared to those found in literature as well as to calculations with other optimization codes lancelot, ipopt and ipdca.     

板带材缺陷检测中的多特征优化组合方法研究

针对冷轧板带材常见表面缺陷图像识别的特点,提出了板带材表面缺陷多特征优化组合方法,该方法以直方图统计特征、小波变换特征、灰度共生矩阵特征、不变矩特征等4类特征共26维特征向量为基础,依据类间类内距离差的类别可分离性判据对特征进行优化,选出最优特征向量组合。对6类典型板带材表面缺陷进行实验,实验结果表明,采用多特征优化组合方法选择出的特征向量具有较好的分离效果,显著地提高了表面缺陷的识别率。     

点云模型的多分辨率简化算法

为了有效地多分辨率简化点云模型,首先,采用均匀栅格法建立点云模型的拓扑关系,计算每个数据点的k邻域;然后,通过建立点云模型中数据点的协方差矩阵求得这些点的法向量,并且进行法向重定向,使所有法向量的方向都指向点云模型的外部;最后,通过衡量数据点对Laplace-Beltrami算子特征值频谱的影响,得到与数据点k邻域及其法向量相关的量化该点重要性的度量公式,随后调节控制因子的取值,实现点云模型的多分辨率简化。实验结果表明,该算法具有简化率高、保留点云模型的微小细节特征信息、简化速度快、稳定性强的特点。     

Euler–Lagrange coupling with damping effects: Application to slamming problems

During a high velocity impact of a structure on a nearly incompressible fluid, impulse loads with high-pressure peaks occur. This physical phenomenon called ‘slamming’ is a concern in shipbuilding industry because of the possibility of hull damage. Shipbuilding companies have carried out several studies on slamming modeling using FEM software with added mass techniques to represent fluid effects. In the added mass method inertia effects of the fluid are not taken into account and are only valid when the deadrise angle is small. This paper presents the prediction of the local high pressure load on a rigid wedge impacting a free surface, where the fluid is represented by solving Navier–Stokes equations with an Eulerian or ALE formulation. The fluid–structure interaction is simulated using a coupling algorithm; the fluid is treated on a fixed or moving mesh using an ALE formulation and the structure on a deformable mesh using a Lagrangian formulation.A new coupling algorithm is developed in the paper. The coupling algorithm computes the coupling forces at the fluid–structure interface. These forces are added to the fluid and structure nodal forces, where fluid and structure are solved using an explicit finite element formulation. Predicting the local pressure peak on the structure requires an accurate fluid–structure interaction algorithm. The Euler–Lagrange coupling algorithm presented in this paper uses a penalty based formulation similar to penalty contact in Lagrangian analyses. Both penalty coupling and penalty contact can generate high frequency oscillations due to the nearly incompressible nature of the fluid. In this paper, a damping force based on the relative velocity of the fluid and the structure is introduced to smooth out non-physical high frequency oscillations induced by the penalty springs in the coupling algorithm.     

基于几何思想的快速支持向量机算法

为了快速地进行分类,根据几何思想来训练支持向量机,提出了一种快速而简单的支持向量机训练算法——几何快速算法。由于支持向量机的最优分类面只由支持向量决定,因此只要找出两类样本中所有支持向量,那么最优分类面就可以完全确定。该新的算法根据两类样本的几何分布,先从两类样本的最近点开始;然后通过不断地寻找违反KKT条件的样本点来找出支持向量;最后确定最优分类面。为了验证新算法的有效性,分别利用两个公共数据库,对新算法与SMO算法及DIRECTSVM算法进行了实验对比,实验结果显示,新算法的分类精度虽与其他两个方法相当,但新算法的运算速度明显比其他两个算法快。