Search

This paper describes the application of the complex variable meshless manifold method (CVMMM) to stress intensity factor analyses of structures containing interface cracks between dissimilar materials. A discontinuous function and the near-tip asymptotic displacement functions are added to the CVMMM approximation using the framework of complex variable moving least-squares (CVMLS) approximation. This enables the domain to be modeled by CVMMM without explicitly meshing the crack surfaces. The enriched crack-tip functions are chosen as those that span the asymptotic displacement fields for an interfacial crack. The complex stress intensity factors for bimaterial interfacial cracks were numerically evaluated using the method. Good agreement between the numerical results and the reference solutions for benchmark interfacial crack problems is realized.

The extended isogeometric analysis (X-IGA) is the combination of the extended finite element method (X-FEM) and the isogeometric analysis (IGA), so the X-IGA possesses the advantages of both methods. In this paper, the X-IGA is extended to investigate the dynamic stress intensity factors of cracked isotropic/orthotropic media under impact loading. For this purpose, a corresponding dynamic X-IGA model is developed, the Newmark time integration scheme is used to achieve a dynamic response, and the dynamic stress intensity factors are evaluated through the contour interaction integral technique. Numerical simulations show that the X-IGA results agree with other available reference solutions, and accurate results can be obtained by using the X-IGA with a relatively coarse mesh.

One of the most common defects in digital photography is motion blur caused by camera shake. Shift-invariant motion blur can be modeled as a convolution of the true latent image and a point spread function (PSF) with additive noise. The goal of image deconvolution is to reconstruct a latent image from a degraded image. However, ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of deconvolution problem. When the estimated PSF is erroneous to some extent or the PSF size is large, conventional regularization to reduce ringing would lead to loss of image details. This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts. The way is to control the regularization weight adaptively according to the image local characteristics. We adopt elaborated reference maps that indicate the edge strength so that textured and smooth regions can be distinguished. Then we impose an appropriate constraint on the optimization process. The experiments’ results on both synthesized and real images show that our method can restore latent image with much fewer ringing and favors the sharp edges.

Link prediction tries to infer the likelihood of the existence of a link between two nodes in a network. It has important theoretical and practical value. To date, many link prediction algorithms have been proposed. However, most of these studies assumed that links of network are undirected. In this paper, we focus on link prediction in directed networks. We provide an efficient and effective link prediction method, which consists of three steps as follows: (1) we locate the similar nodes of a target node; (2) we identify candidates that the similar nodes link to; and (3) we rank candidates using weighing schemes. We conduct experiments to evaluate the accuracy of our proposed method using real microblog data. The experimental results show that the proposed method is promising.

In loss distribution approach (LDA), the most popular approach in operational risk modeling, frequency dependence and loss distribution dependence across business lines are two dependences which banks should consider. In practice, mainly for simplicity, many banks only model frequency dependence although they think that the impact of frequency dependence is insignificant. In this study, two approaches, respectively, models frequency dependence and loss distribution dependence, are introduced. Both approaches are modeled by copula function, which is capable of capturing nonlinear correlation. Based on the most comprehensive operational risk dataset of Chinese banking as far as we know, the operational risk capital charge of the overall Chinese banking is calculated by the two approaches. The results show that there is an obvious distinction between the capital calculated by modeling frequency dependence and the capital calculated by modeling loss dependence. The approach with very limited attention exactly yields a much larger capital result. So it is advised in this paper that banks should not just rely on the approach to modeling frequency dependence for it is natural and easy to deal with. A safer and more effective way for banks is to comprehensively take the results of the two kinds of approach into consideration.

Sparsity-promoting prior along with Bayesian inference is an effective approach in solving sparse linear models (SLMs). In this paper, we first introduce a new sparsity-promoting prior coined as Double Lomax prior, which corresponds to a three-level hierarchical model, and then we derive a full variational Bayesian (VB) inference procedure. When noninformative hyperprior is assumed, we further show that the proposed method has one more latent variable than the canonical automatic relevance determination (ARD). This variable has a smoothing effect on the solution trajectories, thus providing improved convergence performance. The effectiveness of the proposed method is demonstrated by numerical simulations including autoregressive (AR) model identification and compressive sensing (CS) problems.

Mutual information (MI) has been widely used in multisensor image matching, but it may lead to mismatch among images with messy background. However, additional prior information can be of great help in improving the matching performance. In this paper, a robust Bayesian estimated mutual information, named as BMI, for multisensor image matching is proposed. This method has been implemented by utilizing the gradient prior information, in which the prior is estimated by the kernel density estimate (KDE) method, and the likelihood is modeled according to the distance of orientations. To further improve the robustness, we restrict the matching within the regions where the corresponding pixels of template image are salient enough. Experiments on several groups of multisensor images show that the proposed method outperforms the standard MI in robustness and accuracy and is similar with Pluim’s method. However, our computation is far more cost saving.

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

For nonconforming finite elements, it has been proved that the models whose convergence is controlled only by the weak form of patch tests will exhibit much better performance in complicated stress states than those which can pass the strict patch tests. However, just because the former cannot provide the exact solutions for the patch tests of constant stress states with a very coarse mesh (strict patch test), their usability is doubted by many researchers. In this paper, the non-conforming plane 4-node membrane element AGQ6-I, which was formulated by the quadrilateral area coordinate method and cannot pass the strict patch tests, was modified by three different techniques, including the special numerical integration scheme, the constant stress multiplier method, and the orthogonal condition of energy. Three resulting new elements, denoted by AGQ6M-I, AGQ6M-II, and AGQ6M, can pass the strict patch test. And among them, element AGQ6M is the best one. The original model AGQ6-I and the new model AGQ6M can be treated as the replacements of the well-known models Q6 and QM6, respectively.

Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP) is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

Texture enhancement for digital images is the most important technique in image processing. The purpose of this paper is to design a texture enhancement technique using fractional order Savitzky-Golay differentiator, which leads to generalizing the Savitzky-Golay filter in the sense of the Srivastava-Owa fractional operators. By employing this generalized fractional filter, texture enhancement is introduced. Consequently, it calculates the generalized fractional order derivative of the given image using the sliding weight window over the image. Experimental results show that the operator can extract more subtle information and make the edges more prominent. In general, the capability of the generalized fractional differential will be high because it is sensitive to the subtle fluctuations of values of pixels.

The Outlier Interval Detection is a crucial technique to analyze spacecraft fault, locate exception, and implement intelligent fault diagnosis system. The paper proposes two OID algorithms on astronautical Time Series Data, that is, variance based OID (VOID) and FFT andknearest Neighbour based OID (FKOID). The VOID algorithm divides TSD into many intervals and measures each interval’s outlier score according to its variance. This algorithm can detect the outlier intervals with great fluctuation in the time domain. It is a simple and fast algorithm with less time complexity, but it ignores the frequency information. The FKOID algorithm extracts the frequency information of each interval by means of Fast Fourier Transform, so as to calculate the distances between frequency features, and adopts the KNN method to measure the outlier score according to the sum of distances between the interval’s frequency vector and theKnearest frequency vectors. It detects the outlier intervals in a refined way at an appropriate expense of the time and is valid to detect the outlier intervals in both frequency and time domains.

The Forward Least-Squares Approximation (FLSA) SVM is a newly-emerged Least-Square SVM (LS-SVM) whose solution is extremely sparse. The algorithm uses the number of support vectors as the regularization parameter and ensures the linear independency of the support vectors which span the solution. This paper proposed a variant of the FLSA-SVM, namely, Reduced FLSA-SVM which is of reduced computational complexity and memory requirements. The strategy of “contexts inheritance” is introduced to improve the efficiency of tuning the regularization parameter for both the FLSA-SVM and the RFLSA-SVM algorithms. Experimental results on benchmark datasets showed that, compared to the SVM and a number of its variants, the RFLSA-SVM solutions contain a reduced number of support vectors, while maintaining competitive generalization abilities. With respect to the time cost for tuning of the regularize parameter, the RFLSA-SVM algorithm was empirically demonstrated fastest compared to FLSA-SVM, the LS-SVM, and the SVM algorithms.

This paper presents a novel low-rank affinity based local-driven algorithm to robustly propagate the multilabels from training images to test images. A graph is constructed over the segmented local image regions. The labels for vertices from the training data are derived based on the context among different training images, and the derived vertex labels are propagated to the unlabeled vertices via the graph. The multitask low-rank affinity, which jointly seeks the sparsity-consistent low-rank affinities from multiple feature matrices, is applied to compute the edge weights between graph vertices. The inference process of multitask low-rank affinity is formulated as a constrained nuclear norm andℓ2,1-norm minimization problem. The optimization is conducted efficiently with the augmented Lagrange multiplier method. Based on the learned local patch labels we can predict the multilabels for the test images. Experiments on multilabel image annotation demonstrate the encouraging results from the proposed framework.

Perfect nonlinear (PN) functions have been an interesting subject of study for a long time and have applications in coding theory, cryptography, combinatorial designs, and so on. In this paper, the planarity of the trinomialsxpk+1+ux2+vx2pkover GF(p2k) are presented. This class of PN functions are all EA-equivalent tox2.

Multiattribute decision making (MADM) is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF) set (IVIFS). We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decision-making matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method.

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

Information system (IS) project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs). All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM) method based on intuitionistic fuzzy theory and TOPSIS method.

Video panoramic image stitching is in general challenging because there is small overlapping between original images, and stitching processes are therefore extremely time consuming. We present a new algorithm in this paper. Our contribution can be summarized as a multiple-constraint corner matching process and the resultant faster image stitching. The traditional Random Sample Consensus (RANSAC) algorithm is inefficient, especially when stitching a large number of images and when these images have quite similar features. We first filter out many inappropriate corners according to their position information. An initial set of candidate matching-corner pairs is then generated based on grayscales of adjacent regions around each corner. Finally we apply multiple constraints, e.g., their midpoints, distances, and slopes, on every two candidate pairs to remove incorrectly matched pairs. Consequently, we are able to significantly reduce the number of iterations needed in RANSAC algorithm so that the panorama stitching can be performed in a much more efficient manner. Experimental results demonstrate that (i) our corner matching is three times faster than normalized cross-correlation function (NCC) rough match in traditional RANSAC algorithm and (ii) panoramas generated from our algorithm feature a smooth transition in overlapping image areas and satisfy human visual requirements.

According to the image reconstruction accuracy influenced by the “soft field” nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based on Landweber is proposed in the paper, which is based on the working principle of the electrical capacitance tomography system. The method uses the algorithm which is derived by regularization of solutions derived and derives closed solution by fast Fourier transform of the convolution kernel. So, it ensures the certainty of the solution and improves the stability and quality of image reconstruction results. Simulation results show that the imaging precision and real-time imaging of the algorithm are better than Landweber algorithm, and this algorithm proposes a new method for the electrical capacitance tomography image reconstruction algorithm.

The computational methods for solving buckling loads of thin-walled members with open sections are not unique when different concerns are emphasized. In this paper, the buckling loads of thin-walled members in linear-elastic, geometrically nonlinear-elastic, and nonlinear-inelastic behaviors are investigated from the views of mathematical formulation, experiment, and numerical solution. The differential equations and their solutions of linear-elastic and geometrically nonlinear-elastic buckling of thin-walled members with various constraints are derived. Taking structural angle as an example, numerical analysis of elastic and inelastic buckling is carried out via ANSYS. Elastic analyses for linearized buckling and nonlinear buckling are realized using finite elements of beam and shell and are compared with the theoretical results. The effect of modeling of constraints on numerical results is studied when shell element is applied. The factors that influence the inelastic buckling load in numerical solution, such as modeling of constraint, loading pattern, adding rib, scale factor of initial defect, and yield strength of material, are studied. The noteworthy problems and their solutions in numerically buckling analysis of thin-walled member with open section are pointed out.

Image quality assessment (IQA) is a method to evaluate the perceptual performance of image. Many objective IQA algorithms are developed from the objective comparison of image features, which are mainly trained and evaluated from the ground truth of subjective scores. Due to the inconsistent experiment conditions and cumbersome observing processes of subjective experiments, it is imperative to generate the ground truth for IQA research via objective computation methods. In this paper, we propose a subjective score predictor (SSP) aiming to provide the ground truth of IQA datasets. In perfect accord with distortion information, the distortion strength of distorted image is employed as a dependent parameter. To further be consistent with subjective opinion, on the one hand, the subjective score of source image is viewed as a quality base value, and, on the other hand, we integrate the distortion parameter and the quality base value into a human visual model function to obtain the final SSP value. Experimental results demonstrate the advantages of the proposed SSP in the following aspects: effective performance to reflect the distortion strength, competitive ground truth, and valid evaluation for objective IQA methods as well as subjective scores.

Red tides are caused by the combination effects of many marine elements. The complexity of the marine ecosystem makes it hard to find the relationship between marine elements and red tides. The algorithm of fuzzyc-means (FCM) can get clear classification of things and expresses the fuzzy state among different things. Therefore, a prediction algorithm of red tide based on improved FCM is proposed. In order to overcome the defect of FCM which is overdependent on the initial cluster centers and the objective function, this paper gains the initial cluster centers through the principle of regional minimum data density and the minimum mean distance. The feature weighted cluster center is added to the objective function. Finally, the improved FCM algorithm is applied in the prediction research of red tide, and the results show that the improved FCM algorithm has good denoising ability and high accuracy in the prediction of red tides.

There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzyc-means (FCM) clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.

Ensemble pruning is a technique to increase ensemble accuracy and reduce its size by choosing a subset of ensemble members to form a subensemble for prediction. Many ensemble pruning algorithms via directed hill climbing searching policy have been recently proposed. The key to the success of these algorithms is to construct an effective measure to supervise the search process. In this paper, we study the importance of individual classifiers with respect to an ensemble using margin theory proposed by Schapire et al. and obtain that ensemble pruning via directed hill climbing strategy should focus more on examples with small absolute margins as well as classifiers that correctly classify more examples. Based on this principle, we propose a novel measure called the margin-based measure to explicitly evaluate the importance of individual classifiers. Our experiments show that using the proposed measure to prune an ensemble leads to significantly better accuracy results compared to other state-of-the-art measures.

This paper proposes a new algorithm (DA3DED) for edge detection in 3D images. DA3DED is doubly adaptive because it is based on the adaptive algorithm EDAS-1 for detecting edges in functions of one variable and a second adaptive procedure based on the concept of projective complexity of a 3D image. DA3DED has been tested on 3D images that modelize real problems (composites and fractures). It has been much faster than the 1D edge detection algorithm for 3D images derived from EDAS-1.

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

The mixture of two Burr Type III distributions (MTBIIID) is investigated. First, the identifiability property of the MTBIIID is proved. Then, two different methods of estimation are used. Next, the estimates of the unknown five parameters and reliability function of the MTBIIID under Type II censoring are obtained. To study the performance of the estimation technique in the paper, a Monte Carlo simulation is presented. In addition, the numerical illustration requires solving nonlinear equations; therefore, the software international mathematical statistical library (IMSL) is used to assess these effects numerically. Finally, a real data set is applied to illustrate the methods proposed here.

We present a new method to estimate noise for a single-slice sinogram of low-dose CT based on the homogenous patches centered at a special pixel, called center point, which has the smallest variance among all sinogram pixels. The homogenous patch, composed by homogenous points, is formed by the points similar to the center point using similarity sorting, similarity decreasing searching, and variance analysis in a very large neighborhood (VLN) to avoid manual selection of parameter for similarity measures.Homogenous pixels in the VLN allow us find the largest number of samples, who have the highest similarities to the center point, for noise estimation, and the noise level can be estimated according to unbiased estimation.Experimental results show that for the simulated noisy sinograms, the method proposed in this paper can obtain satisfied noise estimation results, especially for sinograms with relatively serious noises.

The main purpose of the paper is to optimize state feedback parameters using intelligent method, GA, Hermite-Biehler, and chaos algorithm. GA is implemented for local search but it has some deficiencies such as trapping into a local minimum and slow convergence, so the combination of Hermite-Biehler and chaos algorithm has been added to GA to avoid its deficiencies. Dividing search space is usually done by distributed population genetic algorithm (DPGA). Moreover, using generalized Hermite-Biehler Theorem can find the domain of parameters. In order to speed up the convergence at the first step, Hermite-Biehler method finds some intervals for controller, in the next step the GA will be added, and, finally, chaos disturbance will help the algorithm to reach a global minimum. Therefore, the proposed method can optimize the parameters of the state feedback controller.

Published version of an article in the journal: Mathematical problems in engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2012/619162 Many linguistic aggregation methods have been proposed and applied in the linguistic decision- making problems. In practice, experts need to assess a number of values in a side of reference domain higher than in the other one; that is, experts use unbalanced linguistic values to express their evaluation for problems. In this paper, we propose a new linguistic aggregation operator to deal with unbalanced linguistic values in group decision making, we adopt 2-tuple representation model of linguistic values and linguistic hierarchies to express unbalanced linguistic values, and moreover,we present the unbalanced linguistic ordered weighted geometric operator to aggregate unbalanced linguistic evaluation values; a comparison example is given to show the advantage of ourmethod.

The extraction of affine invariant features plays an important role in many fields of image processing. In this paper, the original image is transformed into new images to extract more affine invariant features. To construct new images, the original image is cut in two areas by a closed curve, which is called general contour (GC). GC is obtained by performing projections along lines with different polar angles. New image is obtained by changing gray value of pixels in inside area. The traditional affine moment invariants (AMIs) method is applied to the new image. Consequently, cutting affine moment invariants (CAMIs) are derived. Several experiments have been conducted to evaluate the proposed method. Experimental results show that CAMIs can be used in object classification tasks.

This paper investigates how to change the disassortativity of the whole network by connecting nodes of different types in two communities. A model connecting two multi-center networks is studied to see if analytical results are achievable. There are three main methods to connect two multi-center subnetworks depending on whether the connecting nodes are centers: (1) connect the centers of one sub-network to noncenter nodes of the other sub-network, (2) connect the centers of the two sub-networks together, and (3) connect non-center nodes of the two sub-networks. The results show that the disassortative property of a single multicenter network can be maintained in scenarios (1) and (2) above, but the disassortativity is changed in (3). In conclusion, either assortativity or disassortativity is achievable by connecting nodes with different degree properties in an ideal network constructed from two communities with similar network topology.

The static behavior of self-anchored cable-stayed suspension bridge under vertical load is described with the continuum method. Based on the partition generalized variation principle, considering the compression-bending coupling effect of the main girder and the tower, the large displacement incomplete generalized potential energy functional of three-span self-anchored cable-stayed suspension bridge is established. Then, the basic differential equations of self-anchored cable-stayed suspension bridge are derived through constraint variation. Taking a self-anchored cable-stayed suspension bridge with main span 100 m, for example, the results by the proposed analytic method agree with that of numerical analysis. Therefore, the basic differential equations proposed in this paper could be applied to the preliminary analysis of self-anchored cable-stayed suspension bridge. The equations also provide a theoretical basis to describe the static behavior of this type of bridge.

In this paper, we have presented a new permutation-substitution image encryption architecture using chaotic maps and Tompkins-Paige algorithm. The proposed encryption system includes two major parts, chaotic pixels permutation and chaotic pixels substitution. A logistic map is used to generate a bit sequence, which is used to generate pseudorandom numbers in Tompkins-Paige algorithm, in 2D permutation phase. Pixel substitution phase includes two process, the tent pseudorandom image (TPRI) generator and modulo addition operation. All parts of the proposed chaotic encryption system are simulated. Uniformity of the histogram of the proposed encrypted image is justified using the chi-square test, which is less than 𝜒 2 (255, 0.05). The vertical, horizontal, and diagonal correlation coefficients, as well as their average and RMS values for the proposed encrypted image are calculated that is about 13% less than previous researches. To quantify the difference between the encrypted image and the corresponding plain-image, three measures are used. These are MAE, NPCR, and UACI, which are improved in our proposed system considerably. NPCR of our proposed system is exactly the ideal value of this criterion. The key space of our proposed method is large enough to protect the system against any Brute-force and statistical attacks.

We consider the problem of a two-dimensional rectangular cavity in a PEC half plane covered by layers of material with uniform thickness. The rectangular geometry allows for an application of Fourier methods to solve the problem. The paper will also discuss how to compute the far field scattering once the solution is found. The fast mode matching approximation technique developed by Morgan and Schwering will be applied to this layered cavity problem setting. Numerical results will show that using the Fourier solution on the layered problem combined with Morgan and Schwering's technique can produce good approximations while using much less computation time than would be required for the entire solution.

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples.

Since the image feature points are always gathered at the range with significant intensity change, such as textured portions or edges of an image, which can be detected by the state-of-the-art intensity based point-detectors, there is nearly no point in the areas of low textured detected by classical interest-point detectors. In this paper we describe a novel algorithm based on affine transform and graph cut for interest point detecting and matching from wide baseline image pairs with weakly textured object. The detection and matching mechanism can be separated into three steps: firstly, the information on the large textureless areas will be enhanced by adding textures through the proposed texture synthesis algorithm TSIQ. Secondly, the initial interest-point set is detected by classical interest-point detectors. Finally, graph cuts are used to find the globally optimal set of matching points on stereo pairs. The efficacy of the proposed algorithm is verified by three kinds of experiments, that is, the influence of point detecting from synthetic texture with different texture sample, the stability under the different geometric transformations, and the performance to improve the quasi-dense matching algorithm, respectively.

A new method for designing wind turbine airfoils is presented in this paper. As a main component in the design method, airfoil profiles are expressed in a trigonometric series form using conformal transformations and series of polynomial equations. The characteristics of the coefficient parameters in the trigonometric expression for airfoils profiles are first studied. As a direct consequence, three generic airfoil profiles are obtained from the expression. To validate and show the generality of the trigonometric expression, the profiles of the NACA 64418 and S809 airfoils are expressed by the present expression. Using the trigonometric expression for airfoil profiles, a so-called integrated design method is developed for designing wind turbine airfoils. As airfoil shapes are expressed with analytical functions, the airfoil surface can be kept smooth in a high degree. In the optimization step, drag and lift force coefficients are calculated using the XFOIL code. Three new airfoils CQ-A15, CQ-A18, and CQ-A21 with a thickness of 15%, 18%, and 21%, respectively, are designed with the new integrated design method.

The objective of image denoising is to retain useful details while removing as much noise as possible to recover an original image from its noisy version. This paper proposes a novel normal inverse Gaussian (NIG) model-based method that uses a Bayesian estimator to carry out image denoising in the nonsubsampled contourlet transform (NSCT) domain. In the proposed method, the NIG model is first used to describe the distributions of the image transform coefficients of each subband in the NSCT domain. Then, the corresponding threshold function is derived from the model using Bayesian maximuma posterioriprobability estimation theory. Finally, optimal linear interpolation thresholding algorithm (OLI-Shrink) is employed to guarantee a gentler thresholding effect. The results of comparative experiments conducted indicate that the denoising performance of our proposed method in terms of peak signal-to-noise ratio is superior to that of several state-of-the-art methods, including BLS-GSM, K-SVD, BivShrink, and BM3D. Further, the proposed method achieves structural similarity (SSIM) index values that are comparable to those of the block-matching 3D transformation (BM3D) method.

We present two collocation algorithms based on interpolating and approximating subdivision schemes for the solution of fourth order boundary value problems arising in the mathematical modeling of viscoelastic, and inelastic flows, deformation of beams, arches, and load bearing members like street lights and robotic arms in multipurpose engineering systems. Numerical examples are given to illustrate the algorithms. We conclude that approximating schemes based collocation algorithms give better solution than interpolating schemes based collocation algorithms. Main purpose of this paper is to explore and seek the applications of interpolating and approximating subdivision schemes in the field of boundary value problems along with intrinsic comparison of the results obtained by algorithms based on these schemes. A comparison with other approaches of this type of boundary value problems in order to see the advantages of the proposed methods is also given.

Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library. Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library. But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting endmembers will have a negative effect on the estimation of the abundances corresponding to the actual endmembers. This paper presents a variant of SOMP, termed backtracking-based SOMP (BSOMP), for sparse unmixing of hyperspectral data. As an extension of SOMP, BSOMP incorporates a backtracking technique to detect the previous chosen endmembers’ reliability and then deletes the unreliable endmembers. Through this modification, BSOMP can select the true endmembers more accurately than SOMP. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed algorithm.

The paper addresses a problem of detection and classification of rubber stamp instances in scanned documents. A variety of methods from the field of image processing, pattern recognition, and some heuristic are utilized. Presented method works on typical stamps of different colors and shapes. For color images, color space transformation is applied in order to find potential color stamps. Monochrome stamps are detected through shape specific algorithms. Following feature extraction stage, identified candidates are subjected to classification task using a set of shape descriptors. Selected elementary properties form an ensemble of features which is rotation, scale, and translation invariant; hence this approach is document size and orientation independent. We perform two-tier classification in order to discriminate between stamps and no-stamps and then classify stamps in terms of their shape. The experiments carried out on a considerable set of real documents gathered from the Internet showed high potential of the proposed method.

The assumption that the training and testing samples are drawn from the same distribution is violated under covariate shift setting, and most algorithms for the covariate shift setting try to first estimate distributions and then reweight samples based on the distributions estimated. Due to the difficulty of estimating a correct distribution, previous methods can not get good classification performance. In this paper, we firstly present two types of covariate shift problems. Rather than estimating the distributions, we then desire an effective method to select a maximum subset following the target testing distribution based on feature space split from the auxiliary set or the target training set. Finally, we prove that our subset selection method can consistently deal with both scenarios of covariate shift. Experimental results demonstrate that training a classifier with the selected maximum subset exhibits good generalization ability and running efficiency over those of traditional methods under covariate shift setting.

The traditional integer-order partial differential equations and gradient regularization based image denoising techniques often suffer from staircase effect, speckle artifacts, and the loss of image contrast and texture details. To address these issues, in this paper, a difference curvature driven fractional anisotropic diffusion for image noise removal is presented, which uses two new techniques, fractional calculus and difference curvature, to describe the intensity variations in images. The fractional-order derivatives information of an image can deal well with the textures of the image and achieve a good tradeoff between eliminating speckle artifacts and restraining staircase effect. The difference curvature constructed by the second order derivatives along the direction of gradient of an image and perpendicular to the gradient can effectively distinguish between ramps and edges. Fourier transform technique is also proposed to compute the fractional-order derivative. Experimental results demonstrate that the proposed denoising model can avoid speckle artifacts and staircase effect and preserve important features such as curvy edges, straight edges, ramps, corners, and textures. They are obviously superior to those of traditional integral based methods. The experimental results also reveal that our proposed model yields a good visual effect and better values of MSSIM and PSNR.

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.