on the comparisons of decorrelation approaches for non gaussian neutral vector variables as a typical non gaussian vector variable a neutral vector variable contains nonnegative elements only and its l norm equals one in addition its neutral properties make it significantly different from the commonly studied vector variables e g the gaussian vector variables due to the aforementioned properties the conventionally applied linear transformation approaches e g principal component analysis pca and independent component analysis ica are not suitable for neutral vector variables as pca cannot transform a neutral vector variable which is highly negatively correlated into a set of mutually independent scalar variables and ica cannot preserve the bounded property after transformation in recent work we proposed an efficient nonlinear transformation approach i e the parallel nonlinear transformation pnt for decorrelating neutral vector variables in this article we extensively compare pnt with pca and ica through both theoretical analysis and experimental evaluations the results of our investigations demonstrate the superiority of pnt for decorrelating the neutral vector variables

the supervised hierarchical dirichlet process we propose the supervised hierarchical dirichlet process shdp a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group we compare the shdp with another leading method for regression on grouped data the supervised latent dirichlet allocation slda model we evaluate our method on two real world classification problems and two real world regression problems bayesian nonparametric regression models based on the dirichlet process such as the dirichlet process generalised linear models dp glm have previously been explored these models allow flexibility in modelling nonlinear relationships however until now hierarchical dirichlet process hdp mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the hdp on the grouped data results in learnt clusters that are not predictive of the responses the shdp solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group

micromechanics of granular media part ii overall tangential moduli and localization model for periodic assemblies of circular disks abstract in a previous paper a mathematical model that captures the effects of microstructure on the macromechanical response of granular materials was presented the overall response of the particle assembly derives the response of a macroscopic point using the conventional hypothesis in cmputational plasticity i e a given overall displacement gradient determines the overall stress response numerical simulations with uniform as well as non uniform assemblies of two dimension circular disks capture at least qualitatively such mechanical responses as structural anisotropy pressure dependency and strain softening effects due to microstructural changes the present paper attempts to re analyze the problem from an opposite point of view i e the overall imposed stresses determine the overall strain response this formulation allows the model to capture naturally the volume change characteristics of granular materials such as compaction as well as dilation since the strains now result from the analysis rather than act as a prescribed force that drives the problem the new formulation also requires the derivation of the so called overall algorithmic tangential moduli from the consistent linearization of the elasto plastic constitutive equation the task becomes complicated because the linearization permeates down to the microstructural level thus necessitating the extraction of the so called algorithmic concentration tensor to circumvent this difficulty we approximate the algorithmic tensor of overall moduli with a secant approximation that retains the essential properties of the exact description we then use this approximation to solve the inverse problem as well as perform some stability analyses that could be useful in predicting the onset of localized deformation in granular materials

subspace learning from image gradient orientations we introduce the notion of subspace learning from image gradient orientations for appearance based object recognition as image data are typically noisy and noise is substantially different from gaussian traditional subspace learning from pixel intensities very often fails to estimate reliably the low dimensional subspace of a given data population we show that replacing pixel intensities with gradient orientations and the l2 norm with a cosine based distance measure offers to some extend a remedy to this problem within this framework which we coin image gradient orientations igo subspace learning we first formulate and study the properties of principal component analysis of image gradient orientations igo pca we then show its connection to previously proposed robust pca techniques both theoretically and experimentally finally we derive a number of other popular subspace learning techniques namely linear discriminant analysis lda locally linear embedding lle and laplacian eigenmaps le experimental results show that our algorithms significantly outperform popular methods such as gabor features and local binary patterns and achieve state of the art performance for difficult problems such as illumination and occlusion robust face recognition in addition to this the proposed igo methods require the eigendecomposition of simple covariance matrices and are as computationally efficient as their corresponding l2 norm intensity based counterparts matlab code for the methods presented in this paper can be found at http ibug doc ic ac uk resources

effective properties of composite materials with periodic microstructure a computational approach abstract this study reviews several problems which are specific of composites with periodic microstructure composed of linear or nonlinear constituents the theoretical background of the method is recalled first two different families of numerical methods are considered to solve the problem the first is based on the finite element method the concept of macroscopic degrees of freedom is presented the implementation of periodicity conditions is discussed a general framework permitting either a strain or stress control is proposed the second numerical method is based on fast fourier transforms it considers first the problem of a homogeneous linear reference material undergoing a nonhomogeneous periodic eigenstrain the solution of this problem is based on the explicit form of the periodic green s function of the reference medium the relative merits of the two methods are compared and several examples are discussed both methods give very comparable results on test examples and their domains of applications appear to be complementary

elastic plastic analysis of arbitrary heterogeneous materials with the voronoi cell finite element method abstract in this paper a voronoi cell finite element method is developed to solve small deformation elastic plasticity problems for arbitrary heterogenous materials dirichlet tessellation of microstructural representative materials results in a network of arbitrary sided polygons called voronoi cells each voronoi cell encompasses one second phase heterogeneity at most these are natural elements for the microstructure representing the basic structural elements of the material in this paper formulations are developed for directly considering voronoi cells as elements in a finite element model without any further dissection furthermore a composite voronoi cell finite element method is developed to account for the presence of the second phase within each polygonal element various numerical elastic plastic examples are executed for validating the effectiveness of this formulation finally studies are conducted to understand the effect of size shape and distribution of second phase on the averaged and true local responses of representative material elements

multigrid method for periodic heterogeneous media part 1 convergence studies for one dimensional case abstract a multi grid method for a periodic heterogeneous medium in 1 d is presented based on the homogenization theory special integrid transfer operators have been developed to simulate a low frequency response of the differential equations with oscillatory coefficients the proposed multi grid method has been proved to have a fast rate of convergence governed by the ratio q 4 q where 0

multi graph matching via affinity optimization with graduated consistency regularization this paper addresses the problem of matching common node correspondences among multiple graphs referring to an identical or related structure this multi graph matching problem involves two correlated components i the local pairwise matching affinity across pairs of graphs ii the global matching consistency that measures the uniqueness of the pairwise matchings by different composition orders previous studies typically either enforce the matching consistency constraints in the beginning of an iterative optimization which may propagate matching error both over iterations and across graph pairs or separate affinity optimization and consistency enforcement into two steps this paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function especially when the function is biased due to noises or inappropriate modeling we propose composition based multi graph matching methods to incorporate the two aspects by optimizing the affinity score meanwhile gradually infusing the consistency we also propose two mechanisms to elicit the common inliers against outliers compelling results on synthetic and real images show the competency of our algorithms

just in time control of timed event graphs update of reference input presence of uncontrollable input a linear system theory has been developed for the class of discrete event systems subject to synchronization this paper presents the just in time control of such systems when reference input is updated and or in the presence of uncontrollable input s the proposed controls are the solutions to an optimization problem under equality constraint

observability of place transition nets we discuss the problem of estimating the marking of a place transition p t net based on event observation we assume that the net structure is known while the initial marking is totally or partially unknown we give algorithms to compute a marking estimate that is a lower bound of the actual marking the special structure of petri nets allows us to use a simple linear algebraic formalism for estimate and error computation the error between actual marking and estimate is a monotonically nonincreasing function of the observed word length and words that lead to null error are said to be complete we define several observability properties related to the existence of complete words and show how they can be proved to prove some of them we also introduce a useful tool the observer coverability graph i e the usual coverability graph of a p t net augmented with a vector that keeps track of the estimation error on each place of the net finally we show how the estimate generated by the observer may be used to design a state feedback controller for forbidden marking specifications

a linear system theoretic view of discrete event processes and its use for performance evaluation in manufacturing a discrete event system is a system whose behavior can be described by means of a set of time consuming activities performed according to a prescribed ordering events correspond to starting or ending some activity an analogy between linear systems and a class of discrete event systems is developed following this analogy such discrete event systems can be viewed as linear in the sense of an appropriate algebra the periodical behavior of closed discrete event systems i e involving a set of repeatedly performed activities can be totally characterized by solving an eigenvalue and eigenvector equation in this algebra this problem is numerically solved by an efficient algorithm which basically consists of finding the shortest paths from one node to all other nodes in a graph the potentiality of this approach for the performance evaluation of flexible manufacturing systems is emphasized the case of a flowshop like production process is analyzed in detail

bayesian estimation of the von mises fisher mixture model with variational inference this paper addresses the bayesian estimation of the von mises fisher vmf mixture model with variational inference vi the learning task in vi consists of optimization of the variational posterior distribution however the exact solution by vi does not lead to an analytically tractable solution due to the evaluation of intractable moments involving functional forms of the bessel function in their arguments to derive a closed form solution we further lower bound the evidence lower bound where the bound is tight at one point in the parameter distribution while having the value of the bound guaranteed to increase during maximization we derive an analytically tractable approximation to the posterior distribution which has the same functional form as the assigned prior distribution the proposed algorithm requires no iterative numerical calculation in the re estimation procedure and it can potentially determine the model complexity and avoid the over fitting problem associated with conventional approaches based on the expectation maximization moreover we derive an analytically tractable approximation to the predictive density of the bayesian mixture model of vmf distributions the performance of the proposed approach is verified by experiments with both synthetic and real data

consistent tangent operators for rate independent elastoplasticity abstract it is shown that for problems involving rate constitutive equations such as rate independent elastoplasticity the notion of consistency between the tangent stiffness operator and the integration algorithm employed in the solution of the incremental problem plays a crucial role in preserving the quadratic rate of asymptotic convergence of iterative solution schemes based upon newton s method within the framework of closest point projection algorithms a methodology is presented whereby tangent operators consistent with this class of algorithms may be systematically developed to wit associative j 2 flow rules with general nonlinear kinematic and isotropic hardening rules as well as a class of non associative flow rules are considered the resulting iterative solution scheme preserves the asymptotic quadratic convergence characteristic of newton s method whereas use of the socalled elastoplastic tangent in conjunction with a radial return integration algorithm a procedure often employed results in newton type of algorithms with suboptimal rate of convergence application is made to a set of numerical examples which include saturation hardening laws of exponential type

iterative gaussianization from ica to random rotations most signal processing problems involve the challenging task of multidimensional probability density function pdf estimation in this paper we propose a solution to this problem by using a family of rotation based iterative gaussianization rbig transforms the general framework consists of the sequential application of a univariate marginal gaussianization transform followed by an orthonormal transform the proposed procedure looks for differentiable transforms to a known pdf so that the unknown pdf can be estimated at any point of the original domain in particular we aim at a zero mean unit covariance gaussian for convenience rbig is formally similar to classical iterative projection pursuit algorithms however we show that unlike in pp methods the particular class of rotations used has no special qualitative relevance in this context since looking for interestingness is not a critical issue for pdf estimation the key difference is that our approach focuses on the univariate part marginal gaussianization of the problem rather than on the multivariate part rotation this difference implies that one may select the most convenient rotation suited to each practical application the differentiability invertibility and convergence of rbig are theoretically and experimentally analyzed relation to other methods such as radial gaussianization one class support vector domain description and deep neural networks is also pointed out the practical performance of rbig is successfully illustrated in a number of multidimensional problems such as image synthesis classification denoising and multi information estimation

decorrelation of neutral vector variables theory and applications in this paper we propose novel strategies for neutral vector variable decorrelation two fundamental invertible transformations namely serial nonlinear transformation and parallel nonlinear transformation are proposed to carry out the decorrelation for a neutral vector variable which is not multivariate gaussian distributed the conventional principal component analysis cannot yield mutually independent scalar variables with the two proposed transformations a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom we also evaluate the decorrelation performances for the vectors generated from a single dirichlet distribution and a mixture of dirichlet distributions the mutual independence is verified with the distance correlation measurement the advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

a class of general algorithms for multi scale analyses of heterogeneous media abstract a class of computational algorithms for multi scale analyses is developed in this paper the two scale modeling scheme for the analysis of heterogeneous media with fine periodic microstructures is generalized by using relevant variational statements instead of the method of two scale asymptotic expansion the mathematical results on the generalized convergence are utilized in the two scale variational descriptions accordingly the global local type computational schemes can be unified in association with the homogenization procedure for general nonlinear problems after formulating the problem in linear elastostatics that with local contact condition and the elastoplastic problem we present representative numerical examples along with the computational algorithm consistent with our two scale modeling strategy as well as some direct approaches

consistency driven alternating optimization for multigraph matching a unified approach the problem of graph matching gm in general is nondeterministic polynomial complete and many approximate pairwise matching techniques have been proposed for a general setting in real applications it typically requires to find the consistent matching across a batch of graphs sequentially performing pairwise matching is prone to error propagation along the pairwise matching sequence and the sequences generated in different pairwise matching orders can lead to contradictory solutions motivated by devising a robust and consistent multiple gm model we propose a unified alternating optimization framework for multi gm in addition we define and use two metrics related to graphwise and pairwise consistencies the former is used to find an appropriate reference graph which induces a set of basis variables and launches the iteration procedure the latter defines the order in which the considered graphs in the iterations are manipulated we show two embodiments under the proposed framework that can cope with the nonfactorized and factorized affinity matrix respectively our multi gm model has two major characters 1 the affinity information across multiple graphs are explored in each iteration by fixing part of the matching variables via a consistency driven mechanism and 2 the framework is flexible to incorporate various existing pairwise gm solvers in an out of box fashion and also can proceed with the output of other multi gm methods the experimental results on both synthetic data and real images empirically show that the proposed framework performs competitively with the state of the art

duality between invariant spaces for max plus linear discrete event systems we extend the notions of conditioned and controlled invariant spaces to linear dynamical systems over the max plus or tropical semiring we establish a duality theorem relating both notions which we use to construct dynamic observers these are useful in situations in which some of the system coefficients may vary within certain intervals the results are illustrated by an application to a manufacturing system

multiple scale computational model for damage in composite materials abstract in this paper a hierarchical multiple scale computational model is proposed to concurrently predict evolution of variables at the structural and microstructural scales as well as to track the incidence and propagation of microstructural damage the microscopic analysis is conducted with the voronoi cell finite element model vcfem while a conventional displacement based fem code executes the macroscopic analysis adaptive schemes and mesh refinement strategies are developed to create a hierarchy of computational sub domains with varying resolution such hierarchy allow for differentiation between non critical and critical regions and help in increasing the efficiency of computations through preferential zoom in regions coupling between the scales for regions with periodic microstructure is accomplished through asymptotic homogenization whereas regions of nonuniformity and non periodicity are modeled by true microstructural analysis with vcfem various numerical examples are executed for validating the effectiveness of the model for different microstructural configurations

max plus a b invariant spaces and control of timed discrete event systems the concept of a b invariant subspace or controlled invariant of a linear dynamical system is extended to linear systems over the max plus semiring although this extension presents several difficulties which are similar to those encountered in the same kind of extension to linear dynamical systems over rings it appears capable of providing solutions to many control problems like in the cases of linear systems over fields or rings sufficient conditions are given for computing the maximal a b invariant subspace contained in a given space and the existence of linear state feedbacks is discussed an application to the study of transportation networks which evolve according to a timetable is considered

modeling and control of weight balanced timed event graphs in dioids the class of timed event graphs tegs has widely been studied thanks to an approach known as the theory of max plus linear systems in particular the modeling of tegs via formal power series in a dioid called minax has led to input output representations on which some model matching control problems have been solved our work attempts to extend the class of systems for which a similar control synthesis is possible to this end a subclass of timed petri nets that we call weight balanced timed event graphs wbtegs will be first defined they can model synchronization and delays wbtegs contain tegs and can also describe dynamic phenomena such as batching and event duplications unbatching their behavior is described by rational compositions sum product and kleene star of four elementary operators n t m and b on a dioid of formal power series denoted e the main feature is that the transfer series of wbtegs have a property of ultimate periodicity such as rational series in minax finally the existing results on control synthesis for max plus linear systems find a natural application in this framework

on the schein rank of matrices over linear lattices abstract we extend the classical concept of schein rank of a boolean matrix to the case of a matrix a defined on a linear lattice and we prove that it coincides with the smallest regular integer for a in other words we show that there exists a partition of the set of the elements of a into classes such that it is possible to find suitable cross vectors whose sum is a

a dirichlet process mixture of generalized dirichlet distributions for proportional data modeling in this paper we propose a clustering algorithm based on both dirichlet processes and generalized dirichlet distribution which has been shown to be very flexible for proportional data modeling our approach can be viewed as an extension of the finite generalized dirichlet mixture model to the infinite case the extension is based on nonparametric bayesian analysis this clustering algorithm does not require the specification of the number of mixture components to be given in advance and estimates it in a principled manner our approach is bayesian and relies on the estimation of the posterior distribution of clusterings using gibbs sampler through some applications involving real data classification and image databases categorization using visual words we show that clustering via infinite mixture models offers a more powerful and robust performance than classic finite mixtures

iterative quantization a procrustean approach to learning binary codes for large scale image retrieval this paper addresses the problem of learning similarity preserving binary codes for efficient similarity search in large scale image collections we formulate this problem in terms of finding a rotation of zero centered data so as to minimize the quantization error of mapping this data to the vertices of a zero centered binary hypercube and propose a simple and efficient alternating minimization algorithm to accomplish this task this algorithm dubbed iterative quantization itq has connections to multiclass spectral clustering and to the orthogonal procrustes problem and it can be used both with unsupervised data embeddings such as pca and supervised embeddings such as canonical correlation analysis cca the resulting binary codes significantly outperform several other state of the art methods we also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to pca or cca finally we demonstrate an application of itq to learning binary attributes or classemes on the imagenet data set

observer design for max linear systems this technical note deals with the state estimation for max plus linear systems this estimation is carried out following the ideas of the observer method for classical linear systems the system matrices are assumed to be known and the observation of the input and of the output is used to compute the estimated state the observer design is based on the residuation theory which is suitable to deal with linear mapping inversion in idempotent semiring

scalable feature matching by dual cascaded scalar quantization for image retrieval in this paper we investigate the problem of scalable visual feature matching in large scale image search and propose a novel cascaded scalar quantization scheme in dual resolution we formulate the visual feature matching as a range based neighbor search problem and approach it by identifying hyper cubes with a dual resolution scalar quantization strategy specifically for each dimension of the pca transformed feature scalar quantization is performed at both coarse and fine resolutions the scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database the scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super vector and stored into the index list for efficient verification the proposed cascaded scalar quantization csq method is free of the costly visual codebook training and thus is independent of any image descriptor training set the index structure of the csq is flexible enough to accommodate new image features and scalable to index large scale image database we evaluate our approach on the public benchmark datasets for large scale image retrieval experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization

binary independent component analysis with or mixtures independent component analysis ica is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non gaussian source signals the classical independent components analysis ica framework usually assumes linear combinations of independent sources over the field of real valued numbers r in this paper we investigate binary ica for or mixtures bica which can find applications in many domains including medical diagnosis multi cluster assignment internet tomography and network resource management we prove that bica is uniquely identifiable under the disjunctive generation model and propose a deterministic iterative algorithm to determine the distribution of the latent random variables and the mixing matrix the inverse problem to infer the values of latent variables is also considered for noisy measurements we conduct an extensive simulation study to verify the effectiveness of the propose algorithm and present examples of real world applications where bica can be applied

observer based controllers for max plus linear systems max plus algebra is a suitable algebraic setting to model discrete event systems involving synchronization and delay phenomena which are often found in transportation networks communications systems and manufacturing systems one way of controlling this kind of systems consists in choosing the dates of input events in order to achieve the desired performances e g to obtain output events in order to respect given dates this kind of control is optimal according to a just in time criterion if the input event dates are delayed as much as possible while ensuring the output events to occur before a desired reference date this paper presents the observer based controller for max plus linear systems where only estimations of system states are available for the controller as in the classical sense this is a state feedback control problem which is solved in two steps first an observer computes an estimation of the state by using the input and the output measurements then this estimated state is used to compute the state feedback control action as a main result it is shown that the optimal solution of this observer based control problem leads to a greater control input than the one obtained with the output feedback strategy a high throughput screening example in drug discovery illustrates this main result by showing that the scheduling obtained from the observer based controller is better than the scheduling obtained from the output feedback controller

bayesian estimation of beta mixture models with variational inference bayesian estimation of the parameters in beta mixture models bmm is analytically intractable the numerical solutions to simulate the posterior distribution are available but incur high computational cost in this paper we introduce an approximation to the prior posterior distribution of the parameters in the beta distribution and propose an analytically tractable closed form bayesian approach to the parameter estimation the approach is based on the variational inference vi framework following the principles of the vi framework and utilizing the relative convexity bound the extended factorized approximation method is applied to approximate the distribution of the parameters in bmm in a fully bayesian model where all of the parameters of the bmm are considered as variables and assigned proper distributions our approach can asymptotically find the optimal estimate of the parameters posterior distribution also the model complexity can be determined based on the data the closed form solution is proposed so that no iterative numerical calculation is required meanwhile our approach avoids the drawback of overfitting in the conventional expectation maximization algorithm the good performance of this approach is verified by experiments with both synthetic and real data

multiple scale analysis of heterogeneous elastic structures using homogenization theory and voronoi cell finite element method abstract this paper deals with the development of a multiple scale finite element method by combining the asymptotic homogenization theory with voronoi cell vcfem for microstructural modeling the voronoi cell finite element model originates from dirichlet tessellation of a representative material element or a base cell in the microstructure homogenized material coefficients for a global displacement finite element model are generated by vcfem analysis using periodic boundary conditions on the base cell following the macroscopic analysis the local vcfem analysis is implemented to depict the true evolution of microstructural stresses and strains various numerical examples are executed for validating the effectiveness of vcfem macro micro modeling of elastic materials the effect of size shape orientation and distribution of heterogeneities on the local and global response are examined

kernel reconstruction ica for sparse representation independent component analysis with soft reconstruction cost rica has been recently proposed to linearly learn sparse representation with an overcomplete basis and this technique exhibits promising performance even on unwhitened data however linear rica may not be effective for the majority of real world data because nonlinearly separable data structure pervasively exists in original data space meanwhile rica is essentially an unsupervised method and does not employ class information motivated by the success of the kernel trick that maps a nonlinearly separable data structure into a linearly separable case in a high dimensional feature space we propose a kernel rica krica model to nonlinearly capture sparse representation in feature space furthermore we extend the unsupervised krica to a supervised one by introducing a class driven discrimination constraint such that the data samples from the same class are well represented on the basis of the corresponding subset of basis vectors this discrimination constraint minimizes inhomogeneous representation energy and maximizes homogeneous representation energy simultaneously which is essentially equivalent to maximizing between class scatter and minimizing within class scatter at the same time in an implicit manner experimental results demonstrate that the proposed algorithm is more effective than other state of the art methods on several datasets

finite deformation plasticity for composite structures computational models and adaptive strategies abstract we develop computational models and adaptive modeling strategies for obtaining an approximate solution to a boundary value problem describing the finite deformation plasticity of heterogeneous structures a nearly optimal mathematical model consists of an averaging scheme based on approximating eigenstrains and elastic concentration factors in each micro phase by a constant in the portion of the macro domain where modeling errors are small whereas elsewhere a more detailed mathematical model based on a piecewise constant approximation of eigenstrains and elastic concentration factors is utilized the methodology is developed within the framework of statistically homogeneous composite material and local periodicity assumptions

computational damage mechanics for composite materials based on mathematical homogenization this paper is aimed at developing a non local theory for obtaining numerical approximation to a boundary value problem describing damage phenomena in a brittle composite material the mathematical homogenization method based on double scale asymptotic expansion is generalized to account for damage effects in heterogeneous media a closed form expression relating local fields to the overall strain and damage is derived non local damage theory is developed by introducing the concept of non local phase fields stress strain free energy density damage release rate etc in a manner analogous to that currently practiced in concrete 1 2 with the only exception being that the weight functions are taken to be c0 continuous over a single phase and zero elsewhere numerical results of our model were found to be in good agreement with experimental data of 4 point bend test conducted on composite beam made of blackglas nextel 5 harness satin weave copyright 1999 john wiley sons ltd

model selection for gaussian kernel pca denoising we propose kernel parallel analysis kpa for automatic kernel scale and model order selection in gaussian kernel principal component analysis kpca parallel analysis is based on a permutation test for covariance and has previously been applied for model order selection in linear pca we here augment the procedure to also tune the gaussian kernel scale of radial basis function based kpca we evaluate kpa for denoising of simulated data and the u s postal data set of handwritten digits we find that kpa outperforms other heuristics to choose the model order and kernel scale in terms of signal to noise ratio of the denoised data

support vector machine classifier with pinball loss traditionally the hinge loss is used to construct support vector machine svm classifiers the hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re sampling in contrast the pinball loss is related to the quantile distance and the result is less sensitive the pinball loss has been deeply studied and widely applied in regression but it has not been used for classification in this paper we propose a svm classifier with the pinball loss called pin svm and investigate its properties including noise insensitivity robustness and misclassification error besides insensitive zone is applied to the pin svm for a sparse model compared to the svm with the hinge loss the proposed pin svm has the same computational complexity and enjoys noise insensitivity and re sampling stability

face recognition using an enhanced independent component analysis approach this paper is concerned with an enhanced independent component analysis ica and its application to face recognition typically face representations obtained by ica involve unsupervised learning and high order statistics in this paper we develop an enhancement of the generic ica by augmenting this method by the fisher linear discriminant analysis lda hence its abbreviation fica the fica is systematically developed and presented along with its underlying architecture a comparative analysis explores four distance metrics as well as classification with support vector machines svms we demonstrate that the fica approach leads to the formation of well separated classes in low dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression the comprehensive experiments are completed for the facial recognition technology feret face database a comparative analysis demonstrates that fica comes with improved classification rates when compared with some other conventional approaches such as eigenface fisherface and the ica itself

max plus control design for temporal constraints meeting in timed event graphs the aim of the presented work is the control of timed event graph to meet tight temporal constraints the problem of temporal constraints is formulated in terms of control of linear max plus models first the synthesis of a control law that ensures the satisfaction of a single constraint for a single input system is presented then the single input multi constraints problem is tackled and finally the method is extended to the multi inputs multi constraints problem the proposed method is illustrated on the example of a simple production process

on max plus linear dynamical system theory a class of timed discrete event systems can be modeled by using timed event graphs a class of timed petri nets that can have its firing dynamic described by using an algebra called max plus algebra for this kind of systems it may be desirable to enforce some timing constraints in steady state in this paper this problem is called a max plus regulation problem in this context we show a necessary condition for solving these regulation problems and in addition that this condition is sufficient for a large class of problems the obtained controller is a simple linear static state feedback and can be computed using efficient pseudo polynomial algorithms simulation results will illustrate the applicability of the proposed methodology

homogenization and two scale simulations of granular materials for different microstructural constraints the paper presents a new method for quasi static homogenization of granular microstructures and its embedding into two scale simulations on the coarse scale a homogenized standard continuum is considered where the granular microstructures are locally attached at each point these representative volumes are defined by aggregates of discrete solid particles which may come into contact on the micromechanical side the interparticle contact mechanisms between particles is governed by a coulomb type frictional contact in particular elliptical shaped plane particles are investigated a consistent extension of classical stiff soft and periodic boundary conditions from continuous to granular microstructures induce new classes of micro to macro transitions for granular aggregates these include constraints not only for particle center displacements but also for particle rotations at a driving boundary frame the stiff and soft constraints at the driving frame of the particle aggregate induce upper and lower bounds of the particle aggregate s stiffness on the computational side we outline a unified implementation of the displacement and rotational constraints by penalty methods that proves to be convenient for straightforward integration into discrete element codes representative numerical examples with elliptical shaped particles are investigated where the macroscopic stress responses for different boundary constraints are comparatively discussed finally we embed the granular microstructures into a coarse graining discrete to finite element model where they govern the micromechanical behavior of a two scale simulation copyright 2010 john wiley sons ltd

a coupled fem dem approach for hierarchical multiscale modelling of granular media summary a hierarchical multiscale framework is proposed to model the mechanical behaviour of granular media the framework employs a rigorous hierarchical coupling between the fem and the discrete element method dem to solve a bvp the fem is used to discretise the macroscopic geometric domain into an fem mesh a dem assembly with memory of its loading history is embedded at each gauss integration point of the mesh to serve as the representative volume element rve the dem assembly receives the global deformation at its gauss point from the fem as input boundary conditions and is solved to derive the required constitutive relation at the specific material point to advance the fem computation the dem computation employs simple physically based contact laws in conjunction with coulomb s friction for interparticle contacts to capture the loading history dependence and highly nonlinear dissipative response of a granular material the hierarchical scheme helps to avoid the phenomenological assumptions on constitutive relation in conventional continuum modelling and retains the computational efficiency of fem in solving large scale bvps the hierarchical structure also makes it ideal for distributed parallel computing to fully unleash its predictive power importantly the framework offers rich information on the particle level with direct link to the macroscopic material response which helps to shed lights on cross scale understanding of granular media the developed framework is first benchmarked by a simulation of single element drained test and is then applied to the predictions of strain localisation for sand subject to monotonic biaxial compression as well as the liquefaction and cyclic mobility of sand in cyclic simple shear tests it is demonstrated that the proposed method may reproduce interesting experimental observations that are otherwise difficult to be captured by conventional fem or pure dem simulations such as the inception of shear band under smooth symmetric boundary conditions non coaxial granular response large dilation and rotation at the edges of shear band and critical state reached within the shear band copyright 2014 john wiley sons ltd

simulation of the multi scale convergence in computational homogenization approaches although the asymptotic homogenization is known to explicitly predict the thermo mechanical behaviors of an overall structure as well as the microstructures the current developments in engineering fields introduce some kinds of approximation about the microstructural geometry in order for the homogenization method for periodic media to apply for general heterogeneous ones the problems arising from mathematical modeling are examined in the framework of representative volume element rve analyses here the notion of homogenization convergence allows us to eliminate the geometrical periodicity requirement when the size of rve is sufficiently large then the numerical studies in this paper realize the multi scale nature of the convergence of overall material properties as the unit cell size is increased in addition to such dependency of the macroscopic field variables on the selected size of unit cells the convergence nature of microscopic stress values is also studied quantitatively via the computational homogenization method similar discussions are made for the elastoplastic mechanical responses in both macro and microscopic levels in these multi scale numerical analyses the specific effects of the microstructural morphology are reflected by using the digital image based dib finite element fe modeling technique which enables the construction of accurate microstructural models

global local analysis of granular media in quasi static equilibrium abstract a method of global local analysis is developed for quasi static equilibrium problems for granular media the two scale modeling based on mathematical homogenization theory enables us to formulate two separate boundary value problems in terms of macro and microscales the macroscale problem governs the equilibrium of a global structure composed of granular assemblies while the microscale one is posed for the particulate nature of a local structure with the friction contact mechanism between particles the local structure is identified with a periodic representative volume element or equivalently a unit cell over which averaging is performed the mechanical behavior of unit cells is analyzed by a discrete numerical model in which spring and friction devices connect rigid particles whereas the continuum based finite element method is used for the macroscopic one representative numerical examples are presented to demonstrate the capability of the proposed two scale analysis method for granular materials