Convergence of gradient of solutions to elliptic equations with nonuniform growth

In this paper, we study elliptic partial differential equations with nonuniform growth, and obtain the strong convergence of the gradient of truncations of solutions and the strong convergence of the gradient of solutions in the setting of some Musielak-Orlicz spaces. Our results are generalization of the corresponding results for elliptic partial differential equations with power growth.

Detection of text in images using SUSAN edge detector

Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a pyramid structure is used to represent an image and the features of the text are extracted using SUSAN edge detector. Text regions at each level of the pyramid are identified according to the autocorrelation analysis. New techniques are introduced to split the text regions into basic ones and merge them into text lines. By evaluating the method on a set of images, we obtain a very good performance of text detection.

Study on geometric models of non-numerically controlled machining revolving cutter with constant pitch

This paper presents a new approach of designing the revolving cutter with constant pitch, and provides geometric models. The corresponding models in the non-numerically controlled manufacturing, such as designing the helical groove, grinding wheel, relative feeding motion, and calculating the helical angle of the cutting edge, are introduced. The examples are given to testify that the design approach is simple and readily realized in machining the revolving cutter with constant pitch. The effective design and manufacture method provides general references for non-NC machining revolving cutter with constant pitch and reducing the equipments input.

Study and application of reinforcement learning based on DAI in cooperative strategy of robot soccer

A dynamic cooperation model of multi-agent is established by combining reinforcement learning with distributed artificial intelligence(DAI),in which the concept of individual optimization loses its meaning because of the dependence of repayment on each agent itself and the choice of other agents.Utilizing the idea of DAI,the intellectual unit of each robot and the change of task and environment,each agent can make decisions independently and finish various complicated tasks by communication and reciprocation between each other.The method is superior to other reinforcement learning methods commonly used in the multi-agent system.It can improve the convergence velocity of reinforcement learning,decrease requirements of computer memory,and enhance the capability of computing and logical ratiocinating for agent.The result of a simulated robot soccer match proves that the proposed cooperative strategy is valid.

A NEW INVERSION METHOD OF TIME-LAPSE SEISMIC

Time-Lapse Seismic improves oil recovery ratio by dynamic reservoir monitoring. Because of the large number of seismic explorations in the process of time-lapse seismic inversion, traditional methods need plenty of inversion calculations which cost high computational works. The method is therefore inefficient. In this paper, in order to reduce the repeating computations in traditional, a new time-lapse seismic inversion method is put forward. Firstly a homotopy-regularization method is proposed for the first time inversion. Secondly, with the first time inversion results as the initial value of following model, a model of the second time inversion is rebuilt by analyzing the characters of time-lapse seismic and localized inversion method is designed by using the model. Finally, through simulation, the comparison between traditional method and the new scheme is given. Our simulation results show that the new scheme could save the algorithm computations greatly.

Establishment of control equations and adjoint equations using block-pulse functions for optimal control of linear systems with time delays

Control equation and adjoint equation are established by using block pulse functions, which transforms the linear time varying systems with time delays into a system of algebraic equations and the optimal control problems are transformed into an optimization problem of multivariate functions thereby achieving the optimal control of linear systems with time delays.

Mean square stability of recurrent neural networks with random delay and Markovian switching

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.

AN ADAPTIVE MULTI-SCALE CONJUGATE GRADIENT METHOD FOR DISTRIBUTED PARAMETER ESTIMATION OF 2-D WAVE EQUATION

An adaptive multi-scale conjugate gradient method for distributed parameter estimations (or inverse problems) of wave equation is presented. The identification of the coefficients of wave equations in two dimensions is considered. First, the conjugate gradient method for optimization is adopted to solve the inverse problems. Second,the idea of multi-scale inversion and the necessary conditions that the optimal solution should be the fixed point of multi-scale inversion method isconsidered. An adaptive multi-scale inversion method for the inverse problem is developed in conjunction with the conjugate gradient method. Finally, some numerical results are shown to indicate the robustness and effectiveness of our method.

A new method of constructing measure

By using a well known result in combinatorics, named Knig Lemma, this paper generalized the method of constructing measure by repeated subdivision, which was a basic tool for fractal geometry. A more general method was presented to construct measure, which was an essential improvement to the existing result. The proof employed a skill similar to that for Knig Lemma, which helped to avoid using the compactness in Euclidean space. Two conditions of the existing method were found not necessary.

Estimation of scale parameters of logistic distribution by linear functions of sample quantiles

The large sample estimation of standard deviation of logistic distribution employs the asymptotically best linear unbiased estimators based on sample quantiles. The sample quantiles are established from a pair of single spacing. Finally, a table of the variances and efficiencies of the estimator for 5≤n≤65 is provided and comparison is made with other linear estimators.

H-stability of the Runge-Kutta methods with general variable stepsize for system of pantograph equations with two delay terms

This paper deals with H-stability of the Runge-Kutta methods with a general variable stepsize for the system of pantograph equations with two delay terms. It is shown that the Runge-Kutta methods with a regular matrix A are H-stable if and only if the modulus of the stability function at infinity is less than 1.

A construction of authentication codes with arbitration based on orthogonal spaces

In this paper we give a new construction of authentication codes with arbitration using orthogonal spaces. Some parameters and the probabilities of successful attacks are computed.

Decomposition of Jordan automorphism of two-order upper triangular matrix algebra over certain semilocal rings

Let T(R) be a two-order upper matrix algebra over the semilocal ring R which is determined by R=F×F where F is a field such that CharF=0. In this paper, we prove that any Jordan automorphism of T(R) can be decomposed into a product of involutive, inner and diagonal automorphisms.

An involutive Jordan automorphism of triangular matrix algebra over commutative rings

Let T n+1(R)be upper matrix algebra of order n+1over a 2-torsion free commutative ring Rwith identity. In this paper, we find an automorphism, which is fixed by all orthogonal idempotents and is not an R-algebraautomorphism, of T n+1(R). Furthermore we prove that this automorphism is an involutive Jordan automorphism of T n+1(R).

Integral estimates for Jacobians and components of Quasiconformal mappings

We obtain the global weighted estimates for Jacobian J(x,f)and its subdeterminants, as well as the components of K-quasiconformalmappings in L^s(μ)-averagingdomains. To develop these estimates, both local and global weighted Ponincaré inequalities for differential forms are established.

Remarks on the lower bounds for sums of the BDH type

This paper will correct some gaps existing in the proof of a theorem written in an earlier paper by the author on the lower bounds for sums of BDH type published in 1993. Some improvements upon the previous version of that theorem will be obtained.

Clustering based segmentation of text in complex color images

We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.

Comments and further improvements on “LMI approach for stochastic stability of Markovian jumping Hopfield neural networks with Wiener Process”

In a very rencent paper,Lou and Cui investigated the stochastic stability of Markovian jumping Hopfield neural networks with Wiener process by LMI approach. Unfortunately,the main results derived by them are somewhat errors. In this note we point out that global Lipschitz condition on the activation functions should be revised. Moreover,we present some improved sufficient conditions which are less conservative than those in the above paper in term of linear matrix inequality(LMI). An numerical example is given to illustrate the theory.

Dynamic analysis of stochastic delayed cellular neural networks

The stability of stochastic delayed cellular neural networks(DCNNs) is investigated in this paper. Under the help of Lyapunov functional and the semimartingale convergence theorem, some sufficient criteria were obtained to check the almost sure exponential stability of the DCNNs.

Geometric representation for numerical stability region of linear multistep methods

Studies the numerical stability region of linear multistep(LM) methods applied to linear test equation of the form y′(t)=ay(t)+by(t-1), t>0, y(t)=g(t)-1≤t≤0, a,b∈R, proves through delay dependent stability analysis that the intersection of stability regions of the equation and the method is not empty, in addition to approaches to the boundary of the delay differential equation(DDEs) in the limiting case of step size boundary of the stability region of linear multistep methods.