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This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to derive cubature formulas using bivariate degree-graded alternative shifted Jacobi polynomials and present some of their applications through a method for fast and explicit construction of operational integration matrices in this polynomial basis. First, we take the non-degree-graded alternative shifted Jacobi polynomial basis over the interval [ 0 , 1 ] and introduce a corresponding degree-graded polynomial basis. We then construct sparse and well-structured operational integration matrices in that basis. The main advantage of the proposed idea is the low computational complexity which is derived through vector–matrix multiplications without change of bases. As an application, a fast and accurate numerical algorithm based on the obtained cubature formula is developed for the approximate solution of nonlinear multi-dimensional integral equations which arise in the theory of nonlinear parabolic boundary value problems as well as the mathematical modeling of the spatio-temporal development of an epidemic. It is shown that such a matrix representation of cubature formula in terms of the bivariate degree-graded basis gives an approximate solution with a higher order accuracy. The experimental results also illustrate that smaller size and lower orders of the operational matrix and basis functions can obtain a favorable approximate solution. [ABSTRACT FROM AUTHOR]

• The overall contour clarity of the image mainly depends on the edge distribution of the image. In our approach, the Laplace operator is used to sharpen the image, which can improve the quality of the image. • In the process of image style transfer, in order to reduce the iterative time. The image is first down-sampled to reduce the resolution, and then the generated image is super-resolution reconstructed. • Large number of evaluation experiments are conducted to show the effect of our proposed method. In deep learning, multi-robot systems intelligently cooperate to perform a complex task, and improve the performance of a single robot through communication, collaboration and sharing of information. Intelligent collaborative work makes the research and application of multi-robot systems develop rapidly. The traditional image style transfer algorithm mainly uses mathematical modeling to characterize the texture of the picture, and combines the content image with the style image to achieve the effect of style transfer. This style transfer algorithm ignores the edge distribution of the image, which makes the contour of the generated image blurred, and it takes a long time in the iterative process of style transfer, and the effect is poor. Therefore, this paper proposes a ceramic decoration pattern style migration algorithm based on the ESPCN model. The algorithm uses the Laplace operator to sharpen the image to highlight the edge distribution, and then uses downsampling to generate low-resolution images to reduce the iteration time of image style transfer. The final generated image uses ESPCN super-resolution reconstruction to reconstruct the low-resolution image. Converted to high-resolution images. The experimental results show that multiple image evaluation indicators show that the edge distribution of the generated image is clear, the iteration time is shortened, and the image quality and definition are improved. [ABSTRACT FROM AUTHOR]

The performance of the existing frequency control strategies for MT-HVDC systems relies on the accuracy of the mathematical models. However, it is hard to obtain the exact mathematical models of MT-HVDC systems. To deal with this challenge, this paper presents a distributed cooperative frequency control strategy for MT-HVDC systems by using a reinforcement learning method, i.e., value iteration algorithm. Specifically, the proposed control strategy is data driven and hence model free. Besides, the proposed control strategy is distributed in the sense that each AC area only requires the local and neighboring information, rather than the information of all the AC areas. Moreover, the proposed control strategy makes the connected AC areas compensate load disturbances together by sharing their power reserves via HVDC grids, which greatly decreases the operation costs of MT-HVDC systems. The performance of the proposed control strategy is validated by cases study. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS-type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS-type solvers used for linear, nonlinear, and hybrid MPC. [ABSTRACT FROM PUBLISHER]

The principle of justifiable granularity (PoJG) balances coverage and specificity to optimize information granularity. Although numerous studies have successfully formed information granules (IGs) using this principle, the stability of these IGs rarely receives attention. This paper analyzes the stability of such an IG's modal, performance and upper or lower bound. First, we define some concepts to quantify the stability. Then, by the use of the binomial distribution, the central limit theorem and the union bound, we prove some theorems, which rely on several reasonable hypotheses and reveal the relations between the data size and the stability of the IG's modal, performance and upper or lower bound. Furthermore, we put forward an algorithm built on the theorems to generate stable IGs by building data that have the proper size. Finally, we analyze its time complexity, applications and limitations. Experiments indicate the reliability of this algorithm when it is applied to several probability distributions and real datasets with a large scale of evidence. [ABSTRACT FROM AUTHOR]

Purpose In this paper, a new structure for the permanent magnet hysteresis synchronous (PMHS) motor is introduced. Moreover, this paper aims to presents a new analytical method for modeling of a disc-type PMHS motor.Design/methodology/approach Because the hysteresis and permanent magnet motors have unique characteristics, a motor (the PMHS motor) with excellent performance features can be achieved by combining them. Choosing a disc-type slotless structure causes the major advantages of both motors to be preserved in the new motor. To analyze PMHS motor, mathematical equations are obtained by using Ampere’s circuital law, flux continuity law and Faraday’s law. Then the air-gap voltage and exciting current of the motor can be calculated. To implement this method, a new iterative algorithm is proposed. This algorithm consists of one-iteration loop for each input voltage to find the maximum flux density of the operational hysteresis loop of the motor.Findings Validity of the analytical approach is confirmed by experimental results. A reasonably close agreement between the two is shown and some outstanding performances of the PMHS motor are demonstrated.Originality/value A new structure for PMHS motor and also a new analytical method for performance prediction of this motor is presented. [ABSTRACT FROM AUTHOR]

In order to solve the problem that previous relation researches in operational task-resource matching are hard to flexible computing for generating a new matching scheme from the existing scheme under the condition of operational tasks or resources adjusted in uncertainty cases, role-based approaches to operational task-resource flexible matching model and algorithm are proposed in the paper for flexible matching operational tasks with resources. Firstly, through introducing the concept of roles, the role-based calculation framework of operational task-resource flexible matching is constructed. Secondly, two aspect indexes about resource utilization ratio (RUR) and time utilization ratio (TUR) are given to reflect operational task-resource matching quality (TRMQ), which can be regarded as the objective function for modeling role-based operational task-resource flexible matching mathematical formulation. On the basis of this, the role-based artificial bee colony (RABC) algorithm, including five specific calculating operators, is put forward for solving the flexible matching problem with double encoding on operational tasks and resources by roles. Finally, via comparing with the previous methods in application cases, it can be validated that role-based model and proposed algorithm are more effective, which can be used to obtain a new incremental scheme from the existing matching scheme for adopting the operational tasks or resources adjustment. Moreover, there are flexible computation advantages of the approaches proposed in this paper to solve the problem about operational task-resource matching in large-scale. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to develop a new non-boundary fitted (NBF) Cartesian grid method to compute flows in a complex geometry such as that found in a human aorta. The algorithm is based on the volume of fluid (VOF) colour function in a regular Cartesian grid through the open source OpenFOAM platform. The solid was treated as a fluid with very high viscosity to theoretically reduce its deformability, and subsequently satisfy a no-slip condition at the boundary. We also introduced the artificial term of the colour function into an algebraic system of momentum equations, which had a significant impact on the non deformability of solid domain and originality of this study. The developed solver, NBF-VOF, is then thoroughly validated using a variety of numerical and experimental results available in the literature Finally, we demonstrated the applicability of our code on flows in a complex geometry based on patient-specific image data robustly without the need for complex meshing generation procedures that conventional boundary fitted (BF) methods do. Highlights • Use VOF colour function to distinguish between solid and fluid. • Non movable solid is treated with high viscous fluid and modification to the systems of linear algebraic equations. • The developed method was validated through CFD canonical test on varied Reynolds Number. • The developed method was applied to real human aorta. • Free open Source software OpenFOAM is used as solver platform. [ABSTRACT FROM AUTHOR]

Abstract Neighborhood rough sets are widely used as an effective tool to deal with numerical data. However, most of the existing neighborhood granulation models cannot well describe the neighborhoods of category-mixed samples when they are used to characterize the classification ability of a subset of attributes. In this paper, we propose a new neighborhood rough set model called k -nearest neighborhood rough sets. This model combines the advantages of both δ -neighborhood and k -nearest neighbor, and has a better ability to deal with this type of heterogeneous data than the existing models. We employ an iterative strategy to define rough approximations of a decision and discuss their monotonicity. Furthermore, an attribute reduction algorithm based on this model is designed. Experimental analysis shows that the proposed algorithm has better performance than some existing algorithms, especially the δ -neighborhood rough set model and k -nearest neighbor rough set model. [ABSTRACT FROM AUTHOR]

This paper considers the problem of a leader that seeks to optimally influence the opinions of agents in a directed network through connecting with a limited number of the agents (“direct followers”), possibly in the presence of a fixed competing leader. The settings involving a single leader and two competing leaders are unified into a general combinatoric optimization problem, for which two heuristic approaches are developed. The first approach is based on a convex relaxation scheme, possibly in combination with the $\ell _1$ -norm regularization technique, and the second is based on a greedy selection strategy. The main technical novelties of this work are in the establishment of supermodularity of the objective function and convexity of its continuous relaxation. The greedy approach is guaranteed to have a lower bound on the approximation ratio sharper than $(1-1/e)$ , while the convex approach can benefit from efficient (customized) numerical solvers to have practically comparable solutions possibly with faster computation times. The two approaches can be combined to provide improved results. In numerical examples, the approximation ratio can be made to reach ${\text{90}\%}$ or higher depending on the number of direct followers. [ABSTRACT FROM AUTHOR]

Abstract This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy. [ABSTRACT FROM AUTHOR]

The classic Luria–Delbrück model can be interpreted as a Poisson compound (number of mutations) of exponential mixtures (developing time of mutant clones) of geometric distributions (size of a clone in a given time). This “three-ingredients” approach is generalized in this paper to the case where the split instant distributions of cells are not i.i.d. : the lifetime of each cell is assumed to depend on its birth date. This model takes also into account cell deaths and non-exponentially distributed lifetimes. Previous results on the convergence of the distribution of mutant counts are recovered. The particular case where the instantaneous division rates of normal and mutant cells are proportional is studied. The classic Luria–Delbrück and Haldane models are recovered. Probability computations and simulation algorithms are provided. Robust estimation methods developed for the classic mutation models are adapted to the new model: their properties of consistency and asymptotic normality remain true; their asymptotic variances are computed. Finally, the estimation biases induced by considering classic mutation models instead of an inhomogeneous model are studied with simulation experiments. [ABSTRACT FROM AUTHOR]

Association rule mining is a popular data mining task, which has important in many domains. Because the task of association rule mining is very time consuming, evolutionary and swarm based algorithms have been designed to find approximate solutions. However, these approaches still have long execution times, especially when applied on dense and big databases, or when low minsup and minconf threshold values are used. Moreover, these approaches suffer from the lack of diversity in the rules presented to the user. To address these drawbacks of previous algorithms, this paper proposes an efficient parallel algorithm named CGPUGA. It is a genetic algorithm that runs on clusters of GPUs to efficiently discover diversified association rules. It benefits from cluster computing to generate rules. Then, to evaluate rules, which is the most time consuming task, the designed algorithm relies on the massively parallel GPU threads. Furthermore, to deal with the issue of rule quality, the search space of rules is partitioned into several regions assigned to different workers, and rules found by each workers are the merged to ensure diversification. The designed approach has been empirically compared with state-of-the-art algorithms using small, medium, large and big datasets. Results reveal that CGPUGA is 600 times faster than the sequential version of the algorithm for big datasets. Moreover, it outperforms state-of-the-art high performance computing based association rule mining algorithms for real big datasets such as Pokec, Webdocs and Wikilinks. In terms of rule quality, results show that the designed CGPUGA algorithm provides rules of higher quality compared to the state-of-the-art NIGGAR, MSP-MPSO and MPGA algorithms for diversified association rule mining. [ABSTRACT FROM AUTHOR]

Improvements to the exhaustive search method of best-match file searching have previously been achieved by doing a preprocessing step involving the calculation of distances from a reference point. This paper discusses the proper choice of reference points and extends the previous algorithm to use more than one reference point. It is shown that reference points should be located outside of data clusters. The results of computer simulations are presented which show that large improvements can be achieved by the proper choice and location of multiple reference points. [ABSTRACT FROM AUTHOR]

This paper presents a new theory that addresses the issue of passivity in macromodels of electrical interconnects constructed based on the method of characteristics (MoC). The proposed approach develops a new algebraic test to check for passivity in macromodels generated using MoC. The theory behind the developed test is based on deriving the necessary and sufficient conditions for the loss of positive-realness in the admittance matrix of the developed macromodel. An algorithmic procedure is proposed to verify passivity of general macromodels derived from the MoC. The results presented in this paper can be employed to test for positive-realness in dynamical systems described by algebraic delay-differential equations with discrete commensurate delays. [ABSTRACT FROM AUTHOR]

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided. [ABSTRACT FROM AUTHOR]

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. [ABSTRACT FROM AUTHOR]

Purpose – Variable density flows play an important role in many technological devices and natural phenomena. The purpose of this paper is to develop a robust and accurate method for low Mach number flows with large density and temperature variations. Design/methodology/approach – Low Mach number approximation approach is used in the paper combined with a predictor-corrector method and accurate compact scheme of fourth and sixth order. A novel algorithm is formulated for the projection method in which the boundary conditions for the pressure are implemented in such a way that the continuity equation is fulfilled everywhere in the computational domain, including the boundary nodes. Findings – It is shown that proposed implementation of the boundary conditions considerably improves a solution accuracy. Assessment of the accuracy was performed based on the constant density Burggraf flow and for two benchmark cases for the natural convection problems: steady flow in a square cavity and unsteady flow in a tall cavity. In all the cases the results agree very well with exemplary solutions. Originality/value – A staggered or half-staggered grid arrangement is usually used for the projection method for both constant and low Mach number flows. The staggered approach ensures stability and strong pressure-velocity coupling. In the paper a high-order compact method has been implemented in the framework of low Mach number approximation on collocated meshes. The resulting algorithm is accurate, robust for large density variations and is almost free from the pressure oscillations. [ABSTRACT FROM AUTHOR]

The convergence of the waveform relaxation (WR) method is demonstrated for a class of circuits: Chains of identical and symmetrical passive subcircuits. The WR algorithm uses resistive coupling to implement the iteration. Every part is modeled as a symmetric and reciprocal linear two-port network. The iteration matrices of the WR operator are constructed for the Gauss-Jacobi and Gauss-Seidel relaxations in the Fourier domain. An upperbound estimate of the spectral radius of the WR operator is presented. It demonstrates the convergence of the method independently of the number of cascaded parts in the chain and the coupling resistance. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of iterative learning control algorithm for a class of multi-agent systems with distributed parameter models. And the considered distributed parameter models are governed by the parabolic or hyperbolic partial differential equations. Based on the framework of network topologies, a consensus-based iterative learning control protocol is proposed by using the nearest neighbor knowledge. When the iterative learning control law is applied to the systems, the consensus errors between any two agents on L 2 space are bounded, and furthermore, the consensus errors on L 2 space can converge to zero as the iteration index tends to infinity in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present complexity certification results for a distributed augmented Lagrangian (AL) algorithm used to solve convex optimization problems involving globally coupled linear constraints. Our method relies on the accelerated distributed AL (ADAL) algorithm, which can handle the coupled linear constraints in a distributed manner based on local estimates of the AL. We show that the theoretical complexity of ADAL to reach an $\epsilon$-optimal solution both in terms of suboptimality and infeasibility is O(\frac{1}{\epsilon }) iterations. Moreover, we provide a valid upper bound for the optimal dual multiplier, which enables us to explicitly specify these complexity bounds. We also show how to choose the step-size parameter to minimize the bounds on the convergence rates. Finally, we discuss a motivating example, a model predictive control problem, involving a finite number of subsystems, which interact with each other via a general network. [ABSTRACT FROM PUBLISHER]

This paper generalizes the well-known covariance intersection algorithm for distributed estimation and information fusion of random vectors. Our focus will be on partially correlated random vectors. This is motivated by the restriction of the standard covariance intersection algorithm, which treats all random vectors with arbitrary cross correlations and the restriction of the classical Kalman filter, which requires complete knowledge of the cross correlations. We first give a result to characterize the conservatism of the standard covariance intersection algorithm. We then generalize the covariance intersection algorithm to two random vectors with a given correlation coefficient bound and show in what sense the resulting covariance bound is tight. Finally, we generalize the notion of correlation coefficient bound to multiple random vectors and provide a covariance intersection algorithm for this general case. Our results will make the already popular covariance intersection more applicable and more accurate for distributed estimation and information fusion problems. [ABSTRACT FROM PUBLISHER]

In this paper, source localization by a network of primitive binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented. Imperfections include sensor failures of two types, drifting, uncertainty, and heterogeneity in binary sensor trigger thresholds, presence of noise, and nonradial symmetry of sensing ranges. Theoretical results, including asymptotical convergence, are established, in particular in the presence of substantial outliers due to sensor failure and large noise. Efficient numerical algorithms are proposed and simulated supporting the theoretical analysis. [ABSTRACT FROM PUBLISHER]

Abstract: Formal models can be useful in computer graphics as a conceptual framework supporting representation systems. This allows to formally derive properties and algorithms and proof their correctness and validity. This paper describes a formal model based on a geometric algebra. This algebra has been used to obtain specific representation systems and study their equivalence. The representation systems derived in a natural way from this model are based on simplicial coverings and can be applied to non-manifold solids and to solids with holes. Representations have been developed for polyhedral and free-form solids. Algorithms described and proved include boolean operations and representation conversion. The paper covers the three abstraction levels: theoretical model, representations and derived algorithms. As a practical application an experimental modeller for free-form solid has been developed (ESC-MOD system: “Extended Simplicial Chains MOdeller”). [Copyright &y& Elsevier]

Purpose – The novel bearingless switched reluctance motor (BSRM) is proposed recently, which is different from the conventional BSRM in the stator structure and suspension winding arrangement. The reduced number of suspension windings makes the novel BSRM much simpler, so that the control circuit and algorithm are greatly simplified when compared to those of the conventional BSRM. This paper for the first time proposes the novel BSRM's analytic model, including the mathematical relationships among the winding currents, rotor angle, radial forces, and motor torque, to further achieve the suspending forces and torque control. The paper aims to discuss these issues. Design/methodology/approach – The magnetic equivalent circuit method is employed to obtain the self-inductances and mutual-inductances of the motor torque windings (main windings) and suspension windings (control windings). The straight flux paths are combined with the elliptical fringing flux paths to calculate the air-gap permeances, and the stored magnetic energy. Then, the mathematical expressions of radial forces and torque are derived. A novel BSRM prototype is analyzed through using the proposed analytical model and the finite element model. The results of both methods are compared to verify the proposed mathematical model. Findings – The proposed mathematical model of the novel BSRM considering unsaturated magnetic circuits is verified by finite-element analysis results. Research limitations/implications – The mathematical model represents the situation of magnetic circuit unsaturated and is not suitable for the magnetic circuit saturation. It cannot be used to control the motor which is working in the deep magnetic circuit saturation region. Practical implications – Building mathematical model is a necessary step for the motor's suspension and rotating control. The built model provides the fundamental for the preliminary control algorithm and experimental study of this novel BSRM. Originality/value – For the first time, the novel BSRM's mathematical model is proposed. It provides necessary fundamental for the motor's further analysis, design, and suspending and rotating controls. [ABSTRACT FROM AUTHOR]

This paper describes the development of an energy-efficient amplify-and-forward distributed estimation scheme using realistic amplifier models. Specifically, a novel algorithm is presented that enables distributed estimation in the presence of amplifier compression resulting from the energy-efficient but non-linear class AB operation. In this system, a digital predistortion scheme is utilized to fit the amplifier at each sensor to a mathematically tractable, soft compression function that roughly mimics the compression region of the amplifier. It is shown both analytically and via simulation that using this scheme has two benefits over linear amplifier operation: improved transmitter efficiency by operating the amplifier in compression, and reduced sensitivity to heavy-tailed distributions due to the soft saturation. [ABSTRACT FROM AUTHOR]

Abstract: Researchers in the past always laid more emphasis on constructing models, assessing model risks and developing algorithms to solve various models, paying little attention to the research on the value ranges of risks for portfolio models. This paper attempts to fill this gap by presenting some practical approaches to obtain new and accurate results on value ranges of risks for the traditional mean–variance portfolio models. The accurate upper and lower bounds are identified for the minimizing risk portfolio models with or without short selling. A rigorous mathematical proof is utilized to derive necessary and sufficient conditions of the equal weight portfolio model that is equivalent to the minimizing risk portfolio model. Two numerical examples are given to verify the effectiveness and correctness of the theorems and corollaries discussed in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, by adopting a coherence-based probabilistic approach to default reasoning, we focus the study on the logical operation of quasi conjunction and the Goodman–Nguyen inclusion relation for conditional events. We recall that quasi conjunction is a basic notion for defining consistency of conditional knowledge bases. By deepening some results given in a previous paper we show that, given any finite family of conditional events and any nonempty subset of , the family p-entails the quasi conjunction ; then, given any conditional event , we analyze the equivalence between p-entailment of from and p-entailment of from , where is some nonempty subset of We also illustrate some alternative theorems related with p-consistency and p-entailment. Finally, we deepen the study of the connections between the notions of p-entailment and inclusion relation by introducing for a pair the (possibly empty) class of the subsets of such that implies . We show that the class satisfies many properties; in particular is additive and has a greatest element which can be determined by applying a suitable algorithm. [Copyright &y& Elsevier]

This paper proposes an estimator of mutual information for both discrete and continuous variables and applies it to the Chow–Liu algorithm to find a forest that expresses probabilistic relations among them. The state-of-the-art assumes that the continuous variables are Gaussian and that the graphical model under discrete and continuous variables is ANOVA. Consequently, it is difficult to obtain the maximum likelihood of three connected variables such that the center is Gaussian and the other two are discrete, and thus, the state-of-the-art restricts the class to the forest such that there is no Gaussian node between discrete variables. The proposed method executes in a general setting without any assumptions, preparing several meshes, computing the mutual information values, and selecting the maximum value. We prove that the number of meshes to be prepared is at most O ( log 2 ⁡ n ) and that the estimated mutual information is no larger than zero if and only if the variables are independent for large n . Finally, we apply the proposed method to the problems of gene differential analysis and relation discovery between gene expression and SNPs (single nucleotide polymorphisms). In particular, for the latter experiment, we demonstrate that the proposed method successfully captures the relation among them but that the state-of-the-art fails because of the merits and demerits of the proposed and existing methods. [ABSTRACT FROM AUTHOR]

The conditional independencies from a joint probability distribution constitute a model which is closed under the semi-graphoid properties of independency. These models typically are exponentially large in size and cannot be feasibly enumerated. For describing a semi-graphoid model therefore, researchers have proposed a more concise representation. This representation is composed of a representative subset of the independencies involved, called a basis, and lets all other independencies be implicitly defined by the semi-graphoid properties. An algorithm is available for computing such a basis for a semi-graphoid independency model. In this paper, we identify some new properties of a basis in general which can be exploited for arriving at an even more concise representation of a semi-graphoid model. Based upon these properties, we present an enhanced algorithm for basis construction which never returns a larger basis for a given independency model than currently existing algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an adaptive algorithm is proposed for synchronization of chaotic systems with different orders. A modular adaptive control strategy is applied to make states of the slave system track those of the master, despite the unknown parameters. One of the most advantages of the modularity approach, which is applied for the first time in chaos synchronization, is its flexibility in choosing identification and control modules and designing them completely independently. In this paper, a modified recursive least square algorithm is used to identify the unknown parameters of the slave system, and the control module is designed by means of two different algorithms. First it is designed based on active control method, and then, in order to synchronize with a lower energy, we design an optimal controller. The two methods are applied on a practical case study, and the results are compared. Two different dimensional neuron models, the HR neuron model and the cable model of cylindrical cell, are considered as the master and slave systems, respectively. Simulation results confirm the effectiveness of the proposed method. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to attempt to realize a distribution network optimization in supply chain using grey systems theory for uncertain information. Design/methodology/approach – There is much uncertain information in the distribution network optimization of supply chain, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in supply chain, however grey information of the supply chain has not been covered. In the distribution problem of supply chain, grey demands are taken into account. Then, a mathematics model with grey demands has been constructed, and it can be transformed into a grey chance-constrained programming model, grey simulation and a proposed hybrid particle swarm optimization are combined to resolve it. An example is also computed in the last part of the paper. Findings – The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about distribution of supply chain, but grey chance-constrained programming, grey simulation and particle swarm optimization can be combined to resolve the grey model. Practical implications – The method exposed in the paper can be used to deal with distribution problems with grey information in the supply chain, and network optimization results with a grey uncertain factor could be helpful for supply chain efficiency and practicability. Originality/value – The paper succeeds in realising both a constructed model of the distribution of supply chain with grey demands and a solution algorithm of the grey mathematics model by using one of the newest developed theories: grey systems theory. [ABSTRACT FROM AUTHOR]

Classical methods for estimating additive faults are based on state-space models, e.g., moving horizon estimation (MHE) and unknown input observers (UIOs). This paper contributes new direct design methods from closed-loop I/O data for systems with stable inversion, which do not require building a state-space model by first principles, nor require identifying it. Inspired by subspace identification, we use the input and output (I/O) relationship of a plant in a Vector ARX (VARX) form to parameterize least-squares (LS) problems for estimating faults. We prove that with the order of the VARX descriptions tending to infinity, the fault estimates are unbiased. Under lower relative degrees, we prove that our new methods are equivalent to system-inversion-based estimation for both LTI and LTV systems. We will show more general unbiased estimation conditions for higher relative degrees. These require that the underlying inverted system from faults to outputs is stable. Algorithms of identifying unbiased fault estimation filters from data will be developed in this paper based on single LS. Moreover, covariance of the fault estimates can also be extracted from data. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present some results of the development of parametrical regulation theory elements for computable general equilibrium models (CGE models), taking into consideration their peculiarities. Design/methodology/approach – Theoretical results have been obtained by means of applying geometrical methods for variational problems and methods of the theory of discrete dynamic systems. These results have been used for solving concrete practical problems. Findings – The authors proved a statement on the existence of solution for variational calculus problem on the choice of the optimal laws of parametrical regulation within the given finite set of algorithms for discrete dynamic systems. A statement has been proved on sufficient conditions for the existence of an extremal's bifurcation point of variational calculus problem on the choice of the optimum laws of parametrical regulation within the given finite set of algorithms for discrete dynamic systems. Optimal laws of parametrical regulation (on the level of one and two parameters) of economic system evolution on the basis of the examined mathematical model have been defined. The bifurcation line was constructed for the given area of uncontrolled parameter values. Practical implications – The research results can be applied for the choice and realization of an effective state budget and tax policy. Originality/value – The paper elaborates the elements of parametrical regulation theory of economic system development on the basis of CGE models and shows the effectiveness of parametrical regulation theory application on the example of one CGE model. [ABSTRACT FROM AUTHOR]

Abstract: The variable precision rough set model and graded rough set model are 2 important extended rough set models. This paper aims to make a comparative study of the 2 models. Rough set regions, primitive notions, are proposed first for the 2 models, which classify the universe more precisely. Then, both of their logical meanings related to quantitative indexes and their basic structure are investigated, and their precise descriptions are obtained as well. Furthermore, in the graded rough set model, macroscopic and microscopic algorithms are proposed and analyzed to calculate rough set regions; then, the conclusion is drawn that macroscopic and microscopic algorithms have advantages in time and space complexities, respectively, and a medical example is provided to illustrate the rough set regions and the 2 algorithms. In addition, 3 new properties of the 2 models are investigated, which are the results of extending the classical rough set model, i.e. the relationships between approximations and the basic set, the power actions of approximation operators, and the modifications of approximation operator actions on set operations. Finally, the classical rough set model is used to obtain many corresponding results, and moreover, the relationship and transformation between the 2 models is investigated. The study results of this paper have extended and enriched rough set theory from both operator-oriented and set-oriented points of view. [Copyright &y& Elsevier]

Abstract: The generalized difference methods (GDM) have been successfully used in numerical simulations of unsaturated soil water flow problems. The object of this paper is to extend applications of the GDM, that is, apply GDM to numerically solve one-dimensional coupled transport models of unsaturated soil water flow and solute. The semi-discrete and fully discrete generalized difference schemes are established, and the detailed algorithm of numerical approximation schemes is provided. Moreover, some numerical examples illustrate that the generalized difference methods are feasible and efficient to find the numerical solutions of one-dimensional coupled transport models in this paper. Finally, satisfactory results and some other significant and valuable conclusions are obtained from studies. [Copyright &y& Elsevier]

Abstract: In this paper we describe the design of a control algorithm for MISO systems, which can be modelled as hybrid fuzzy models. Hybrid fuzzy models present a convenient approach to modelling nonlinear hybrid systems. We discuss the formulation of a hybrid fuzzy model, its structure and the identification procedure. This is followed by a derivation of the inverse model and its implementation in the control algorithm. The control scheme we are discussing splits the control algorithm in two parts: the feedforward part and the feedback part. In the paper, we deal with the feedforward part of the control algorithm, which is based on an inverse of a hybrid fuzzy model. Next, a batch-reactor process is introduced. The modelling of the batch reactor is tackled and the results of the simulation experiments using the proposed control algorithm are presented. The experiments involved controlling the temperature of a batch reactor using two on/off input valves and a continuous mixing valve. The main advantage of the proposed approach is that the feedforward part of the control algorithm can bring the system close to the desired adjusted feasible trajectory, which avoids the need for a very complex feedback part of the algorithm. Therefore, the control algorithm presents a low computational burden, particularly comparing to the standard model predictive control algorithms. These usually require a considerable computational effort, which often thwarts their implementation on real industrial systems. Nevertheless, we show that using the proposed control approach the hybrid fuzzy model framework presents a convenient option for modelling complex systems for control purposes in practice. [Copyright &y& Elsevier]

Abstract: It has long been established that when a small-displacement problem involving contact between two or more bodies is solved, conforming algorithm approaches based on BEM are reliable and robust. Nevertheless, the authors have shown in a previous paper that a relative sliding between the bodies in contact, even if small, can originate a fictitious corner in the numerical model, which may alter the stress and the displacement predictions. This problem disappears if a non-conforming algorithm is employed. In this paper a new problem with different features but also presenting artificial alteration of the results is addressed. The compression of a rounded punch on a foundation is analysed for many fillet radii. Conforming and non-conforming algorithms are used. Comparison of results shows that effects of an artificial ‘numerical corner’ may alter even the traction distribution relatively far from it. Consequently, the assumed connection between small displacements and initially conforming discretizations with the use of a conforming approach disappears. An adequate knowledge of the contact problem under consideration is then essential to determine whether the singularity is inherent to the problem or is artificial, a non-conforming algorithm then being recommended in this second case. [Copyright &y& Elsevier]

Abstract: In this paper a novel method for simulating unsteady incompressible viscous flow over a moving boundary is described. The numerical model is based on a 2D Navier–Stokes incompressible flow in artificial compressibility formulation with Arbitrary Lagrangian Eulerian approach for moving grid and dual time stepping approach for time accurate discretization. A higher order unstructured finite volume scheme, based on a Harten Lax and van Leer with Contact (HLLC) type Riemann solver for convective fluxes, developed for steady incompressible flow in artificial compressibility formulation by Mandal and Iyer (AIAA paper 2009-3541), is extended to solve unsteady flows over moving boundary. Viscous fluxes are discretized in a central differencing manner based on Coirier’s diamond path. An algorithm based on interpolation with radial basis functions is used for grid movements. The present numerical scheme is validated for an unsteady channel flow with a moving indentation. The present numerical results are found to agree well with experimental results reported in literature. [Copyright &y& Elsevier]

In this paper, we present adaptive and distributed algorithms for motion coordination of a group of m vehicles. The vehicles must service demands whose time of arrival, spatial location, and service requirement are stochastic; the objective is to minimize the average time demands spend in the system. The general problem is known as the m-vehicle Dynamic Traveling Repairman Problem (m-DTRP). The best previously known control algorithms rely on centralized task assignment and are not robust against changes in the environment. In this paper, we first devise new control policies for the 1-DTRP that: i) are provably optimal both in light-load conditions (i.e., when the arrival rate for the demands is small) and in heavy-load conditions (i.e., when the arrival rate for the demands is large), and ii) are adaptive, in particular, they are robust against changes in load conditions. Then, we show that specific partitioning policies, whereby the environment is partitioned among the vehicles and each vehicle follows a certain set of rules within its own region, are optimal in heavy-load conditions. Building upon the previous results, we finally design control policies for the m-DTRP that i) are adaptive and distributed, and ii) have strong performance guarantees in heavy-load conditions and stabilize the system in any load condition. [ABSTRACT FROM AUTHOR]

This paper studies the statistical behavior of the normalized subband adaptive filtering (NSAF) algorithm. An accurate statistical model of the NSAF algorithm is obtained. In the derivation, we focus on Gaussian correlated input signals. By assuming that the analysis filter bank is paraunitary and taking into account the full band adaptation mechanism of the NSAF, expressions for the first and the second moments of the adaptive filter weights are derived without invoking the slow adaptation assumption. In the derivations, several hyperelliptic integrals appear. To tackle those integrals induced by Gaussian correlated inputs, we first give a solution by resorting to the adaptive Lobatto quadrature. By invoking the averaging principle, two other approximation methods, the chi-square method and the partial fraction expansion method, are presented to approximate the statistical model as well. Monte Carlo (MC) simulation results corroborate our predictions. The Lobatto quadrature method achieves a good agreement with the MC simulation results, even for a relatively large step size. Compared with the chi-square method and the partial fraction expansion method, the Lobatto quadrature method gives better performance in terms of predicting the mean square error when the length of the adaptive filters is small to medium. The chi-square approximation method and the partial fraction expansion method give a satisfactory performance with a relatively low computational complexity when the filter length is large. [ABSTRACT FROM PUBLISHER]

Purpose - The aim of the paper is to present the theory and algorithms based on the methods of systems optimal control for a numerical solution of a defined mathematical model of a system as well as that of a mathematical model of game theory. Design/methodology/approach - The paper brings a formulation of the mathematical model of a problem of systems optimal control with distributed parameters in Hilbert space. The mathematical model of the optimal control problem includes equations that also occur in the defined mathematical model of the theory of a two player zero-sum game. Optimization problems of game theory have been defined for the purpose of finding a saddle point of a functional satisfying task constraints ?>0. Findings - In order to find a saddle point of a functional and that one of a functional with a limitation, a designed algorithm of an iterative gradient method is presented. Furthermore, the paper contains a concept of algorithms designing that can be applied to a numerical solution of the defined problem of game theory. These algorithms can be realized on the basis of the methods of systems optimal control. After an adjoint state of the system is defined, a saddle point of a functional will be characterized by equations and inequalities. Originality/value - The contribution of the paper lies in the formulation of the theorems which express the necessary and sufficient conditions of optimality for saddle points of a functional. Furthermore, it has been proved that algorithms of methods of systems optimal control with distributed parameters can be used for the solution of a mathematical model of game theory. The paper contains original results achieved by the authors within scientific projects. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In realizing PC-PMC mainly two problems should be solved. The first problem is that PMC method requires invertible modules in the front–end of the system while CMS normalization is not an invertible process. Moreover, when the recognition system is exposed to noisy speech, the adaptation of the PCA transform is required; therefore a framework is to be designed for adaptation of the PCA transform in the presence of noise. The method presented in this paper provides solutions to these problems. Our evaluations are done on the four different real noisy tasks using Nevisa HMM-based, Persian continuous speech recognition system. Experimental results demonstrate significant reduction in the system word error rate using PC-PMC. In addition, the effects of covariance matrix compensation, dynamic features adaptation and the effect of gain parameter in the PMC method are also studied and experimented in the real acoustic conditions. Besides, we have investigated the using of maximum likelihood linear regression (MLLR) and maximum a posteriori (MAP) adaptation techniques in the combination with the introduced PC-PMC method. Finally, a comprehensive discussion on the capabilities of the studied robustness methods is presented. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we provide numerical evidence, via graphics generated with the help of computer simulations, that switching the control parameter of a dynamical system belonging to a class of fractional-order systems in a deterministic way, one obtains an attractor which belongs to the class of all admissible attractors of the considered system. For this purpose, while a multistep numerical method for fractional-order differential equations approximates the solution to the mathematical model, the control parameter is switched periodically every few integration steps. The switch is made inside of a considered set of admissible parameter values. Moreover, the synthesized attractor matches the attractor obtained with the control parameter replaced with the averaged switched parameter values. The results are verified in this paper on a representative system, the fractional-order Lü system. In this way we were able to extend the applicability of the algorithm presented in earlier papers using a numerical method for fractional differential equations. [Copyright &y& Elsevier]

Purpose - The aim of this paper is to access performance of existing computational techniques to model strongly non-linear field diffusion problems. Design/methodology/approach - Multidimensional application of a finite volume front-fixing method to various front-type problems with moving boundaries and non-linear material properties is discussed. Advantages and implementation problems of the technique are highlighted by comparing the front-fixing method with computations using fixed grids. Particular attention is focused on conservation properties of the algorithm and accurate solutions close to the moving boundaries. The algorithm is tested using analytical solutions of diffusion problems with cylindrical symmetry with both spatial and temporal accuracy analysed. Findings - Several advantages are identified in using a front-fixing method for modelling of impulse phenomena in high-temperature superconductors (HTS), namely high accuracy can be obtained with a small number of grid points, and standard numerical methods for convection problems with diffusion can be utilised. Approximately, first order of spatial accuracy is found for all methods (stationary or mobile grids) for 2D problems with impulse events. Nevertheless, errors resulting from a front-fixing technique are much smaller in comparison with fixed grids. Fractional steps method is proved to be an effective algorithm for solving the equations obtained. A symmetrisation procedure has to be introduced to eliminate a directional bias for a standard asymmetric split in diffusion processes. Originality/value - This paper for the first time compares in detail advantages and implementation complications of a front-fixing method when applied to the front-type field diffusion problems common to HTS. Particular attention is paid to accurate solutions in the region close to the moving front where rapid changes in material properties are responsible for large computational errors. [ABSTRACT FROM AUTHOR]

Abstract: In the literature several authors describe methods to construct simplified models of networks. These methods are motivated by the need to gain insight into the main properties of medium sized or large networks. The present paper contributes to this research by setting focus on weighted networks, the geographical component of networks and introducing a class of functions to model how the weights propagate from one level of abstraction to the next. Hierarchies of network models can be constructed from reordering of the adjacency matrix of the network; this is how “hypernodes” are derived in the present paper. The hypernode algorithm is explored and it is shown how it can be formulated to handle weighted networks. Weighted networks enable handling the uncertainty or the strength of the components which make up the network. The hypernode algorithm can be run in an iterative manner so that a hierarchy of simplified models of the network can be derived. Some case studies demonstrate the hypernode algorithm. In the first case the algorithm is compared with a similar implementation described in the literature. In the second case an airline dataset is analysed. This study shows that when networks are embedded in the geographical space hypernodes may relate to clusters in the spatial domain. The selection of the visual variables to illustrate the strength of the edges and nodes in a weighted network is discussed. [Copyright &y& Elsevier]

Abstract: In this paper we study logical properties of the operation chance discovery (CD) via structures based on special Kripke/Hintikka models. These models use as bases partially ordered sets of indexes (indexes of steps in a computation, or ones indicating time points in a time flow), and clusters of states associated to each index. The language chosen to build the logical formulas includes modal/temporal operations, operations for the agents’ knowledge, local and global operations for CD, operation of local common knowledge, and an operation for chance of discovery via agents’ interactions. We introduce and study a logic (of knowledge and discovery via interaction of agents), LDK a , which is defined by semantics, as the set of all formulas that are valid in all suggested models. The paper provides an algorithm to recognize logical laws (and satisfiable formulas) of LDK a . The algorithm replaces a formula with a rule in a special, so-called reduced normal form, and, then it verifies the validity of this rule in specific models of exponential size in the size of the rule. We show that the problem of computing the true logical laws of LDK a is decidable. [Copyright &y& Elsevier]

Abstract: In this paper, a suitable model of distributed supply-chains (DSCs) is presented with the aim of providing a tool for DSC decentralised optimisation. To cope with this challenge, in the first part of the paper, a general model for distributed supply-chain including suppliers, processing units, assemblers, and transportation systems is presented with the aim of keeping the framework as general as possible. In the second part of the paper, an optimisation algorithm is also discussed. [Copyright &y& Elsevier]