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Abstract: Gong et al. (2010) and Xiao et al. (2010) have proposed the notion of bijective soft set and exclusive disjunctive soft set, respectively, which is a subtype of soft set. On the basis of their work, this paper extends these notions to fuzzy environments, and formulates the concept of bijective fuzzy soft set, which can deal with more uncertain problems. Moreover, this paper proposes two parameters reduction algorithms: one (Algorithm 1) is based on bijective fuzzy soft system, and the other (Algorithm 2) takes weight of an element into consideration. Since the threshold plays an important role in these algorithms, we proposed an algorithm (Algorithm 3) to decide the optimal value of threshold specially. Afterwards, an example analysis of the two parameters reduction algorithms is given and the result shows that the two algorithms lead to the same parameters reduction of a bijective fuzzy soft system. Since Algorithm 2 considers the detail weights of elements, thus it can be used in more uncertain problems, such as time series analysis problems, than Algorithm 1. [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of observer design for a class of implicit nonlinear systems of index one. To solve this problem, an implicit observer which is given by an implicit system is usually used. The implementation of this kind of observer requires an optimization algorithm to solve at each time the algebraic constraints. Alternatively, an explicit observer which is described by an ordinary differential equation can be used. The advantage of this approach is that no optimization algorithm is required. Moreover the observer initialization may be done outside the constraints. In this paper it is shown that, under some conditions, the existence of an implicit observer implies the existence of an explicit one. Then, an explicit observer is given for implicit state affine systems whose dynamic involves an additional nonlinear term depending on the input, the output and the implicit variable. [Copyright &y& Elsevier]

Abstract: In this paper, a methodology for limnimeter and rain-gauge fault detection and isolation (FDI) in sewer networks is presented. The proposed model based FDI approach uses interval parity equations for fault detection in order to enhance robustness against modelling errors and noise. They both are assumed unknown but bounded, following the so-called interval (or set-membership) approach. On the other hand, fault isolation relies on an algorithm that reasons using several fault signature matrices that store additional information to the typical binary one used in standard FDI approaches. More precisely, the considered fault signature matrices contain information about residual fault sign/sensitivity and time/order of activation. The paper also proposes an identification procedure to obtain the interval models used in fault detection that delivers the nominal model plus parameter uncertainty is proposed. To exemplify the proposed FDI methodology, a case study based on the Barcelona sewer network is used. [Copyright &y& Elsevier]

This paper proposes two measures for the evaluation of permutation techniques used in image encryption. First, a general mathematical framework for describing the permutation phase used in image encryption is presented. Using this framework, six different permutation techniques, based on chaotic and non-chaotic generators, are described. The two new measures are, then, introduced to evaluate the effectiveness of permutation techniques. These measures are (1) Percentage of Adjacent Pixels Count (PAPC) and (2) Distance Between Adjacent Pixels (DBAP). The proposed measures are used to evaluate and compare the six permutation techniques in different scenarios. The permutation techniques are applied on several standard images and the resulting scrambled images are analyzed. Moreover, the new measures are used to compare the permutation algorithms on different matrix sizes irrespective of the actual parameters used in each algorithm. The analysis results show that the proposed measures are good indicators of the effectiveness of the permutation technique. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of combining multi-agent preference orderings of different alternatives into a single fused ordering, when the agents’ importance is expressed through a rank-ordering and not a set of weights. An enhanced version of the algorithm proposed by Yager (2001) is presented. The main advantages of the new algorithm are that: (i) it better reflects the multi-agent preference orderings and (ii) it is more versatile, since it admits preference orderings with omitted or incomparable alternatives. The description of the new algorithm is supported by a realistic example. [ABSTRACT FROM AUTHOR]

Clustering real-world data is a challenging task, since many real-data collections are characterized by an inherent sparseness and variable distribution. An appealing domain that generates such data collections is the medical care scenario where collected data include a large cardinality of patient records and a variety of medical treatments usually adopted for a given disease pathology. This paper proposes a two-phase data mining methodology ( M L C ) to iteratively analyze different dataset portions and locally identify groups of objects with common properties. Discovered cohesive clusters are then analyzed using sequential patterns to characterize temporal relationships among data features. To support an automatic classification of new data objects within one of the discovered groups, a classification model is created starting from the computed cluster set. A mobile application has been also designed and developed to visualize and update data under analysis as well as categorizing new unlabeled data objects. The experimental evaluation conducted on real datasets in the medical care scenario showed the effectiveness of M L C to discover interesting knowledge items and to easily exploit them through a mobile application. Results have been also discussed from a medical perspective. [ABSTRACT FROM AUTHOR]

With the advent of Big Data, data is being collected at an unprecedented fast pace, and it needs to be processed in a short time. To deal with data streams that flow continuously, classical batch learning algorithms cannot be applied and it is necessary to employ online approaches. Online learning consists of continuously revising and refining a model by incorporating new data as they arrive, and it allows important problems such as concept drift or management of extremely high-dimensional datasets to be solved. In this paper, we present a unified pipeline for online learning which covers online discretization, feature selection and classification. Three classical methods—the k -means discretizer, the χ 2 filter and a one-layer artificial neural network—have been reimplemented to be able to tackle online data, showing promising results on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses. The PRPD is a two dimensional representation of pulses that enables visual inspection but lacks discriminative power in common scenarios found in industrial environments, such as many simultaneous PD sources and low magnitude events that can be hidden below noise. The literature shows several works that complement PRPD with machine learning detectors (neural networks and support vector machines) and with more sophisticated signal representations, like statistics captured in several modalities, wavelets and other transforms, etc. These methods improve the classification accuracy but obscure the interpretation of the results. In this paper, the use of a support vector machine (SVM) operating on the power spectrum density of signals is proposed to identify different pulses what could be used in an online tool in the maintenance decision-making of the utility. Particularly, the approach is based on an SVM endowed with a special kernel that operates in the frequency domain. The SVM is previously trained with pulses of different PD types (internal, surface and corona) and noise that are obtained with several test objects in the laboratory. The experimental results demonstrate that this technique is highly effective in identifying PD for cases where several sources are active or when the noise level is high. Thus, the early identification of critical events with this approach during normal operation of the equipment will help in the decision of decommissioning the asset with reduced costs and low impact to the grid reliability. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapur's method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithm provides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem. [ABSTRACT FROM AUTHOR]

Reaction network model is central to ethylene cracking process simulation. Studying an ethylene cracking reaction (ECR) network, which involves hundreds of components and thousands of reactions, becomes a difficult task. To facilitate a rapid and comprehensive reaction network analysis and improve ECR network, this paper introduced a ranking algorithm called network flow analysis algorithm (NFAA) to a reaction network analysis procedure. NFAA analyses the reaction network with comprehensive information such as network topological structure, reaction mechanism, and process model data. Following NFAA, reactions and species are ranked based on their significances. According to the ranking of reactions, unimportant reactions (lower ranking) in ECR network are removed to reduce ECR network complexity and decrease computational scale without loss of prediction accuracy. On the other hand, rankings provide guidance on adjusting parameters of ECR network. The application of NFAA makes a progress in improvement of ECR reaction network in an industrial case. [ABSTRACT FROM AUTHOR]

In this paper the reconstruction of non-bandlimited sampling is discussed and a regularized sampling algorithm for non-bandlimited signals is presented. The error of the regularized sampling algorithm is presented and compared with the previous algorithm based on Shannon’s sampling theorem and compared with the Tikhonov regularization method in the noisy case. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the full lattice representation of continuous piecewise affine (PWA) functions and give a formal proof of its representation ability. Based on this, we derive the irredundant lattice PWA representations through removal of redundant terms and literals. Necessary and sufficient conditions for irredundancy are proposed. Besides, we explain how to remove terms and literals in order to ensure irredundancy. An algorithm is given to obtain an irredundant lattice PWA representation. In the worked examples, the irredundant lattice PWA representations are used to express the optimal solution of explicit model predictive control problems, and the results turn out to be much more compact than those given by a state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses a problem of state and disturbance estimation for an open-channel hydraulic system. Particularly, a cascade of n canal reaches, joined by gates, is considered. The underlying Saint-Venant system of PDEs is managed by means of a collocation-based finite-dimensional approximation. The resulting nonlinear systems'' dynamics are linearized, and an estimation algorithm is designed by combining a conventional linear unknown-input observer (UIO) and a nonlinear disturbance observer (DO) based on the sliding-mode approach. By using measurements of the water level in three points per reach, the suggested algorithm is capable of estimating, both, the time varying infiltration and the discharge variables in the middle point of the reaches. The UIO and DO design procedures are constructively illustrated throughout the paper, and simulation results are discussed to verify their effectiveness. [Copyright &y& Elsevier]

Abstract: This paper is to illustrate that the main result of the paper [R.U. Verma, Generalized over-relaxed proximal algorithm based on -maximal monotonicity framework and applications to inclusion problems, Mathematical and Computer Modelling 49 (2009) 1587–1594] is incorrect. The convergence rate of the over-relaxed proximal point algorithm should be greater than 1. Moreover, the strong convergence and the unique solution may not be proved accordingly in the paper by Verma. [Copyright &y& Elsevier]

Abstract: Given a Probabilistic Boolean Network (PBN), an important problem is to study its steady-state probability distribution for network analysis. In this paper, we present a new perturbation bound of the steady-state probability distribution of PBNs with gene perturbation. The main contribution of our results is that this new bound is established without additional condition required by the existing method. The other contribution of this paper is to propose a fast algorithm based on the special structure of a transition probability matrix of PBNs with gene perturbation to compute its steady-state probability distribution. Experimental results are given to demonstrate the effectiveness of the new bound, and the efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: Recursive stochastic algorithms have various applications. In the literature, it is assumed that the true value lies in a connected domain. But, in many cases, it is known that the true value is contained in the union of a finite number of pairwise disjoint sets instead of a connected domain. In these situations, the existing algorithms may be not applicable. To cope with this problem, this paper proposes recursive stochastic algorithms with (event-triggered) Markovian jumps and presents sufficient conditions for almost sure convergence of the proposed algorithms. As an example of applications, this paper significantly improves an existing adaptive algorithm with the proposed method for consistent estimation of non-minimum phase zeros. [Copyright &y& Elsevier]

Abstract: In some recent papers we have introduced a notion of continuity in digital spaces which extends the usual notion of digital continuity. Our approach, which uses multivalued maps, provides a better framework to define topological notions, like retractions, in a far more realistic way than by using just single-valued digitally continuous functions. In particular, we have characterized the deletion of simple points and some well known parallel thinning algorithm as a particular type of retractions, called -retractions. In this paper we give a full characterization of the thinning algorithms that can be modeled as -retractions. This class agrees, with minor modifications, with the class of thinning algorithms satisfying Ronse sufficient conditions for preservation of topology. [Copyright &y& Elsevier]

Abstract: This paper describes an algorithm designed for the automatic coarsening of three-dimensional unstructured simplicial meshes. This algorithm can handle very anisotropic meshes like the ones typically used to capture the boundary layers in CFD with Low Reynolds turbulence modeling that can have aspect ratio as high as 104. It is based on the concept of mesh generation governed by metrics and on the use of a natural metric mapping the initial (fine) mesh into an equilateral one. The paper discusses and compares several ways to define node based metric from element based metric. Then the semi-coarsening algorithm is described. Several application examples are presented, including a full three-dimensional complex model of an aircraft with extremely high anisotropy. [Copyright &y& Elsevier]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

Abstract: Over the last years, various fast algorithms for computing the determinant of a pentadiagonal Toeplitz matrices were developed. In this paper, we give a new kind of elementary algorithm requiring operations, where is an integer that needs to be chosen freely at the beginning of the algorithm. For example, we can compute in and operations if we choose as and , respectively. For various applications, it will be enough to test if the determinant of a pentadiagonal Toeplitz matrix is zero or not. As in another result of this paper, we used modular arithmetic to give a fast algorithm determining when determinants of such matrices are non-zero. This second algorithm works only for Toeplitz matrices with rational entries. [Copyright &y& Elsevier]

Abstract: This paper is the first in this series to develop a numerical homogenization method for heterogeneous media and integrate it with goal-oriented finite element mesh adaptivity. We describe the physical application, Step and Flash Imprint Lithography, in brief and present the mathematical ideas and numerical verification. The method requires the Moore–Penrose pseudoinverse of element stiffness matrices. Algorithms for efficiently computing the pseudoinverse of sparse matrices will be presented in the second paper. The purpose of numerical homogenization is to reduce the number of degrees of freedom, find locally optimal effective material properties, and perform goal-oriented mesh refinement. Traditionally, a finite element mesh is designed after obtaining material properties in different regions. The mesh has to resolve material discontinuities and rapid variations in the solution. In our approach, however, we generate a sequence of coarse meshes (possibly 1-irregular), and homogenize material properties on each coarse mesh element using a locally posed constrained convex quadratic optimization problem. This upscaling is done using the Moore–Penrose pseudoinverse of the linearized fine-scale element stiffness matrices, and a material-independent interpolation operator. Numerical verification is done using a two dimensional conductivity problem with known analytical limit. Finally, we present results for two and three dimensional geometries. The results show that this method uses orders of magnitude fewer degrees of freedom to give fast and approximate solutions of the original fine-scale problem. [Copyright &y& Elsevier]

Abstract: The mesh density has an important affect on finite element analysis of engineering problems. This paper studies the adaptive and local refinement techniques of hexahedral element meshes. A set of refinement templates and its converting rules of refinement fields are established. The refinement field propagation problem is solved using two corner templates and an isolated node template. A relative curvature criterion for constructing refinement source point fields is proposed by introducing the relative area of STL (stereo lithography) facets into the curvature criterion. The adaptive refinement of meshes can be easily realized using the constructed refinement source point fields. Examples show that the relative curvature criterion can more accurately capture the curvature features of solid models than the conventional curvature refinement criterion. To facilitate users and obtain the effective local refinement, this paper proposes a local refinement technique. The levels of local refinement can be self-controlled by users. The geometric features can be easily identified before each level of refinement. The effective refinement of nodes, elements, element-edges, element-surfaces, mesh-boundaries, mesh-faces and local regions can be realized. The accuracy and robustness of the adaptive and local refinement algorithms presented in this paper are demonstrated using several examples. The mesh refinement quality and flexibility are improved significantly. [Copyright &y& Elsevier]

Abstract: In this paper, we study the numerical behavior of the semismooth and smoothing Newton methods for solving Pareto eigenvalue problem of the formwhere (A, B) is a pair of possibly asymmetric matrices of order n. Such an eigenvalue problem arises in mechanics and in other areas of applied mathematics. By using the (smoothing) Fischer-Burmeister NCP function and the normalization condition e T x =1, the Pareto eigenvalue problem can be converted into a equivalent semismooth (or smoothing) system of equations, where e =(1,…,1) T . Then a semismooth (or smoothing) Newton algorithm is designed to solve such a semismooth (or smoothing) system of equations. Some numerical results are reported in the paper, which indicates that the proposed algorithms are very effective. [Copyright &y& Elsevier]

Abstract: The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms. [Copyright &y& Elsevier]

Abstract: This paper describes algorithms to compute Voronoi diagrams, shortest path maps, the Hausdorff distance, and the Fréchet distance in the plane with polygonal obstacles. The underlying distance measures for these algorithms are either shortest path distances or link distances. The link distance between a pair of points is the minimum number of edges needed to connect the two points with a polygonal path that avoids a set of obstacles. The motivation for minimizing the number of edges on a path comes from robotic motions and wireless communications because turns are more difficult in these settings than straight movements. Link-based Voronoi diagrams are different from traditional Voronoi diagrams because a query point in the interior of a Voronoi face can have multiple nearest sites. Our site-based Voronoi diagram ensures that all points in a face have the same set of nearest sites. Our distance-based Voronoi diagram ensures that all points in a face have the same distance to a nearest site. The shortest path maps in this paper support queries from any source point on a fixed line segment. This is a middle-ground approach because traditional shortest path maps typically support queries from either a fixed point or from all possible points in the plane. The Hausdorff distance and Fréchet distance are fundamental similarity metrics for shape matching. This paper shows how to compute new variations of these metrics using shortest paths or link-based paths that avoid polygonal obstacles in the plane. [Copyright &y& Elsevier]

Abstract: This paper continues the theme of the recent work Chen et al. (2008) , in which fast collocation methods are introduced for solving ill-posed Fredholm integral equations of the first kind. We develop in this paper multilevel augmentation algorithms, which lead to fast solutions of the discrete equations resulting from fast collocation methods. Regularization parameter choice strategies are given for proposed methods. The theoretical analysis and numerical experiments illustrate the accuracy and efficiency of the algorithm. [Copyright &y& Elsevier]

Abstract: Faugère’s algorithm is one of the fastest algorithms to compute Gröbner bases. It uses two criteria namely the criterion and the IsRewritten criterion to detect the useless critical pairs (see Faugère (2002) ). The IsRewritten criterion has been used in the algorithm, but it has not been explicitly declared in the related paper. In this paper, we give first a complete proof for the IsRewritten criterion and then using a signature structure on Buchberger’s algorithm, we apply this criterion on Buchberger’s algorithm. We have implemented a new algorithm (based on the above results) in Maple to compute a Gröbner basis of a general ideal and we evaluate its performance via some examples. [Copyright &y& Elsevier]

Abstract: Fault-tolerant broadcasting and secure message distribution are important issues for network applications. It is a common idea to design multiple spanning trees with a specific property in the underlying graph of a network to serve as a broadcasting scheme or a distribution protocol for receiving high levels of fault-tolerance and security. An -dimensional folded hypercube, denoted by , is a strengthening variation of hypercube by adding additional links between nodes that have the furthest Hamming distance. In, , Ho(1990) proposed an algorithm for constructing edge-disjoint spanning trees each with a height twice the diameter of . Yang et al. (2009), recently proved that Ho’s spanning trees are indeed independent, i.e., any two spanning trees have the same root, say , and for any other node , the two different paths from to , one path in each tree, are internally node-disjoint. In this paper, we provide another construction scheme to produce independent spanning trees of , where the height of each tree is equal to the diameter of plus one. As a result, the heights of independent spanning trees constructed in this paper are shown to be optimal. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of computing the Bézier representation for a triangular sub-patch on a triangular Bézier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular Bézier patch. Based on de Casteljau recursions and shifting operators, previous methods express the control points of the triangular sub-patch as linear combinations of the construction points that are constructed from the control points of the triangular Bézier surface. The construction points contain too many redundancies. This paper derives a simple explicit formula that computes the composite triangular sub-patch in terms of the blossoming points that correspond to distinct construction points and then an efficient algorithm is presented to calculate the control points of the sub-patch. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration is fixed and the learning rate is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals. [Copyright &y& Elsevier]

Abstract: This paper investigates the economic order quantity (EOQ) — based inventory model for a retailer under two levels of trade credit to reflect the supply chain management situation in the fuzzy sense. It is assumed that the retailer maintains a powerful position and can obtain the full trade credit offered by the supplier yet the retailer just offers a partial trade credit to customers. The demand rate, holding cost, ordering cost, purchasing cost and selling price are taken as fuzzy numbers. Under these conditions, the retailer can obtain the most benefits. Study also investigates the retailer’s inventory policy for deteriorating items in a supply chain management situation as a cost minimization problem in the fuzzy sense. The annual total variable cost for the retailer in fuzzy sense is defuzzified using Graded Mean Integration Representation method. Then the present study shows that the defuzzified annual total variable cost for the retailer is convex, that is, a unique solution exists. Mathematical theorems and algorithms are developed to efficiently determine the optimal inventory policy for the retailer. Numerical examples are given to illustrate the theorems and the algorithms. Finally, the results in this paper generalize some already published results in the crisp sense. [Copyright &y& Elsevier]

Abstract: In a recent paper by the current authors a new methodology called the Extended-Domain-Eigenfunction-Method (EDEM) was proposed for solving elliptic boundary value problems on annular-like domains. In this paper we present and investigate one possible numerical algorithm to implement the EDEM. This algorithm is used to solve modified Helmholtz BVPs on annular-like domains. Two examples of annular-like domains are studied. The results and performance are compared with those of the well-known boundary element method (BEM). The high accuracy of the EDEM solutions and the superior efficiency of the EDEM over the BEM, make EDEM an excellent alternate candidate to use in the animation industry, where speed is a predominant requirement, and by the scientific community where accuracy is the paramount objective. [ABSTRACT FROM AUTHOR]

Abstract: Recently, block-sparse signals, whose nonzero coefficients appearing in blocks, have received much attention. A corresponding block-based orthogonal greedy algorithm (OGA) was proved by Eldar to successfully recover ideal noiseless block-sparse signals under a certain condition on block-coherence. In this paper, the stability problem of block OGA used to recover the noisy block-sparse signals is studied and the corresponding approximation bounds are derived. The theoretical bounds presented in this paper are more general and are proven to include those reported by Donoho and Tseng. Numerical experimental results are presented to support the validity and correctness of theoretical derivation. The simulation results also show that in the noisy case, the block OGA can be proved to achieve better reconstruction performance than the OGA when the conventional sparse signals are represented in block-sparse forms. [Copyright &y& Elsevier]

Abstract: This paper presents a study of extension of the Direct Torque Control approach (DTC) developed by Takahashi for a double star induction motor (DSIM) and a new DTC-Space vector modulation (DTC-SVM) strategy around two dead-beat controllers. The suggested control is performed by using a sliding mode stator flux observer (SMSFO). This last allow to estimating the mechanical speed and the electromagnetic torque. To adapt in real time the rotor resistance variations according to temperature effect, the sensorless method is performed by using an adaptive algorithm which is based on the Lyapunov stability theory. Moreover, this paper treats the study of the system stability under the new suggested control. The simulation results for various scenarios operation show the high performances of the proposed control in terms of piloting effectiveness, precision, rapidity and stability for the high powers DSIM operating at variable speeds. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the problem of underdetermined blind source separation (UBSS), i.e., there are more sources than mixtures. By exploiting the time–frequency (TF) sparsity of the source signals, some TF–UBSS algorithms have recently been proposed in the literature. These algorithms require that the number of active sources at any TF point should not exceed one or be strictly less than the number of mixtures. In this paper, we show that if the number of sources is greater than the number of mixtures by one, the sparsity assumption can be further relaxed. Especially, it is allowed to have as many sources as mixtures at any TF point in this case. Then we propose a new TF–UBSS method to recover the sources. The relaxation on the maximum number of active sources at TF points ensures that the TF-based methods can be used in a wider range of applications. [Copyright &y& Elsevier]

Abstract: This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT–PCNN in terms of objective criteria and visual appearance. [Copyright &y& Elsevier]

Abstract: The focus of this paper is to propose a hybrid neural network model for associative recall of analog and digital patterns. This hybrid model consists of self-feedback neural network structures (SFNN) in parallel with generalized regression neural networks (GRNN). Using a new one-shot learning algorithm developed in the paper, pattern representations are first stored as the asymptotically stable fixed points of the SFNN. Then in the retrieving process, each pattern is applied to the GRNN to make the corresponding initial condition and to initiate the dynamical equations of the SFNN that should in turn output the corresponding representation. In this way, the corresponding stored patterns are retrieved even under high noise degradation. Moreover, contrary to many associative memories, the proposed hybrid model is without any spurious attractors and can store both binary and real-value patterns without any preprocessing. Several simulations confirm the theoretical analyses of the model. Results indicate that the performance of the hybrid model is better than that of recurrent associative memory and competitive with other classes of networks. [Copyright &y& Elsevier]

Abstract: The usual assumptions for the average case analysis of binary search trees (BSTs) are random insertions and random deletions. If a BST is built by n random insertions the expected number of key comparisons necessary to access a node is 2ln n+O(1). This well-known result is already contained in the first papers on such ‘random’ BSTs. However, if random insertions are intermixed with random deletions the analysis of the resulting BST seems to become more intricate. At least this is the impression one gets from the related publications since 1962, and it is quite appropriate to speak of a story of errors in this context, as will be seen in the present survey paper, giving an overview on this story. [Copyright &y& Elsevier]

Abstract: In this paper, based on the homotopy analysis method (HAM), a powerful algorithm is developed for the solution of nonlinear ordinary differential equations of fractional order. The proposed algorithm presents the procedure of constructing the set of base functions and gives the high-order deformation equation in a simple form. Different from all other analytic methods, it provides us with a simple way to adjust and control the convergence region of solution series by introducing an auxiliary parameter . The analysis is accompanied by numerical examples. The algorithm described in this paper is expected to be further employed to solve similar nonlinear problems in fractional calculus. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of clustering a set of n finite point-sets in d-dimensional Euclidean space. Different from the traditional clustering problem (called points clustering problem where the to-be-clustered objects are points), the point-sets clustering problem requires that all points in a single point-set be clustered into the same cluster. This requirement disturbs the metric property of the underlying distance function among point-sets and complicates the clustering problem dramatically. In this paper, we use a number of interesting observations and techniques to overcome this difficulty. For the k-center clustering problem on point-sets, we give an -time 3-approximation algorithm and an -time -approximation algorithm, where m is the total number of input points and k is the number of clusters. When k is a small constant, the performance ratio of our algorithm reduces to for any . For the k-median problem on point-sets, we present a polynomial time -approximation algorithm. Our approaches are rather general and can be easily implemented for practical purpose. [Copyright &y& Elsevier]

Abstract: LP can be seen as a logic of knowledge with justifications. See [S. Artemov, The logic of justification, The Review of Symbolic Logic 1 (4) (2008) 477–513] for a recent comprehensive survey of justification logics generally. Artemov’s Realization Theorem says justifications can be extracted from validities in the more conventional Hintikka-style logic of knowledge S4, in which they are not explicitly present. Justifications, however, are far from unique. There are many ways of realizing each theorem of S4 in the logic LP. If the machinery of justifications is to be applied to artificial intelligence, or better yet, to everyday reasoning, we will need to work with whatever justifications we may have at hand—one version may not be interchangeable with another, even though they realize the same S4 formula. In this paper we begin the process of providing tools for reasoning about justifications directly. The tools are somewhat complex, but in retrospect this should not be surprising. Among other things, we provide machinery for combining two realizations of the same formula, and for replacing subformulas by equivalent subformulas. (The second of these is actually weaker than just stated, but this is not the place for a detailed formulation.) The results are algorithmic in nature—semantics for LP plays no role. We apply our results to provide a new algorithmic proof of Artemov’s Realization Theorem itself. This paper is a much extended version of [M.C. Fitting, Realizations and LP, in: S. Artemov, A. Nerode (Eds.), Logical Foundations of Computer Science—New York ’07, in: Lecture Notes in Computer Science, vol. 4514, Springer-Verlag, 2007, pp. 212–223]. [Copyright &y& Elsevier]

Abstract: This paper describes non-manifold offsetting operations that add or remove a uniform thickness from a given non-manifold topological model. The mathematical definitions and properties of the non-manifold offsetting operations are investigated first, and then an offset algorithm based on the definitions is proposed and implemented using the non-manifold Euler operators proposed in this paper. In this algorithm, the offset elements of minimal size for the vertices, edges and faces of a given non-manifold model are generated first. Then, they are united into a single body using the non-manifold Boolean operations. In order to reduce computation time and numerical errors, the intersections between the offset elements are calculated considering the origins of the topological entities during union. Finally, all topological entities that are within the offset distance are detected and removed in turn. In addition to the original offset algorithm based on mathematical definitions, some variant offset algorithms, called sheet thickening and solid shelling, are proposed and implemented for more practical and efficient solid modeling of thin-walled plastic or sheet metal parts. In virtue of the proposed non-manifold offset operation and its variations, different offsetting operations for wireframes, sheets and solids can be integrated into one and applied to a wide range of applications with a great potential usefulness. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the evacuation problem in a network which consists of a directed graph with capacities and transit times on its arcs. This problem can be solved by the algorithm of Hoppe and Tardos [B. Hoppe, É. Tardos, The quickest transshipment problem, Math. Oper. Res. 25(1) (2000) 36–62] in polynomial time. However their running time is high-order polynomial, and hence is not practical in general. Thus, it is necessary to devise a faster algorithm for a tractable and practically useful subclass of this problem. In this paper, we consider a network with a sink such that (i) for each vertex the sum of the transit times of arcs on any path from to takes the same value, and (ii) for each vertex the minimum - cut is determined by the arcs incident to whose tails are reachable from . This class of networks is a generalization of grid networks studied in the paper [N. Kamiyama, N. Katoh, A. Takizawa, An efficient algorithm for evacuation problem in dynamic network flows with uniform arc capacity, IEICE Trans. Infrom. Syst. E89-D (8) (2006) 2372–2379]. We propose an efficient algorithm for this network problem. [Copyright &y& Elsevier]

Abstract: In this paper, the penalty function method is used to study duality in nonconvex mathematical programming problems. In particular, we prove the zero duality gap between optimization problems involving invex functions with respect to the same function η and their Lagrangian dual problems. The results proved in the paper are illustrated by suitable examples of nonconvex optimization problems. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to propose several modified hybrid projection algorithms and prove several strong convergence theorems for common fixed points of a family of Lipschitz quasi-pseudo-contractions in the framework of Hilbert spaces. The results presented in this paper improve and extend the corresponding ones announced by many others. [Copyright &y& Elsevier]

Abstract: Multivariate Birkhoff interpolation is the most complex polynomial interpolation problem and people know little about it so far. In this paper, we introduce a special new type of multivariate Birkhoff interpolation and present a Newton paradigm for it. Using the algorithms proposed in this paper, we can construct a Hermite system for any interpolation problem of this type and then obtain a Newton basis for the problem w.r.t. the Hermite system. [Copyright &y& Elsevier]

Abstract: Threshold group testing first proposed by Damaschke is a generalization of classic group testing. Specifically, a group test is positive (negative) if it contains at least (at most ) positives, and if the number of positives is between and , the test outcome is arbitrary. Although sequential group testing algorithms have been proposed, it is unknown whether an efficient nonadaptive algorithm exists. In this paper, we give an affirmative answer to this problem by providing efficient nonadaptive algorithms for the threshold model. The key observation is that disjunct matrices, a standard tool for group testing designs, also work in this threshold model. This paper improves and extends previous results in three ways: 1. The algorithms we propose work in one stage, which saves time for testing. 2. The test complexity is lower than previous results, at least for the number of elements which need to be tested is sufficiently large. 3. A limited number of erroneous test outcomes are allowed. [Copyright &y& Elsevier]

Abstract: The effectiveness of the first entry of the original series by GM(1, 1) is researched in this paper. The results show that the modelling values and forecasts are independent of the first entry of the original series. The grey prediction model presented in this paper is called first-entry GM(1, 1), abbreviated as FGM(1, 1), which is based on the existing GM(1, 1) but modelled with data including the first-entry’s messages of the original series. A proof concerning this subject has been presented by other authors. However, the algorithm of their direct proof is too complicated. A more compact algorithm is presented in this paper to prove the first entry of the original series ineffective to the modelling values and forecasts by GM(1, 1). Then, an arbitrary number can be inserted in the front of the original series to extract the messages from its first entry. Only a few data (usually fewer than ten) are used for model building. This paper deals with the effectiveness of the first entry of the original series by GM(1, 1). [Copyright &y& Elsevier]

Abstract: In this paper, a method to estimate the domain of attraction of a class of discrete-time Lur’e systems is presented. A new notion of invariance, denoted -invariance, is introduced. An algorithm to determinate the largest -invariant set for this class of systems is proposed. Moreover, it is proven that the -invariant sets provided by this algorithm are polyhedral convex sets and constitute an estimation of the domain of attraction of the non-linear system. It is shown that any contractive set for the Lur’e system is contained in the -invariant set obtained applying the results of this paper. Two illustrative examples are given. [Copyright &y& Elsevier]

Abstract: In this paper we analyze the convergence properties of the Multigrid Method applied to the Black–Scholes differential equation arising in mathematical finance. We prove, for the discretized single-asset Black–Scholes equation, that the multigrid -cycle possesses optimal convergence properties. Furthermore, through a series of numerical experiments we test the performance of the method for single-asset option problems. Throughout the paper we focus on models of European options. [Copyright &y& Elsevier]