Automatic generation of fast algorithms for matrix–vector multiplication

This paper describes the methods for finding fast algorithms for computing matrix–vector products including the procedures based on the block-structured matrices. The proposed methods involve an analysis of the structural properties of matrices. The presented approaches are based on the well-known optimization techniques: the simulated annealing and the hill-climbing algorithm along with its several extensions. The main idea of the proposed methods consists in finding a decomposition of the original matrix into a sparse matrix and a matrix corresponding to an appropriate block-structured pattern. The main criterion for optimizing is a reduction of the computational cost. The methods presented in this paper can be successfully implemented in many digital signal processing tasks.     

A Lyapunov–Razumikhin approach for stability analysis of logistics networks with time-delays

Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. Complexity of the system, time-delays and perturbations in a customer demand may cause unstable behaviour of the network. This leads to the loss of the customers and high inventory costs. Thus the investigation of the network on stability is desired during its design. In this article we consider local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. We are looking for verifiable conditions that guarantee stability of the network under consideration. Lyapunov–Razumikhin functions and the local small gain condition are utilised to obtain such conditions. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate the proposed approach.     

Rapid transit network design for optimal cost and origin–destination demand capture

This paper proposes a tractable model for the design of a rapid transit system. Travel cost is minimized and traffic capture is maximized. The problem is modeled on an undirected graph and cast as an integer linear program. The idea is to build segments within broad corridors to connect some vertex sets. These segments can then be assembled into lines, at a later stage. The model is solved by branch-and-cut within the CPLEX framework. Tests conducted on data from Concepción, Chile, confirm the effectiveness of the proposed methodology.     

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

This paper proposed a hybrid genetic based functional link artificial neural network (HFLANN) with simultaneous optimization of input features for the purpose of solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded of selected features, HFLANN overcomes the nonlinearity nature of problems, which is commonly encountered in single-layer neural networks. The features like simplicity of the architecture and low computational complexity of the network encourage us to use it in classification task of data mining. Further, the issue of statistical tests for comparison of algorithms on multiple datasets, which is even more essential to typical machine learning and data mining studies, has been all but ignored. In this work, we recommend a set of simple, yet safe and robust parametric and nonparametric tests for statistical comparisons of HFLANN with FLANN and RBF classifiers over multiple datasets by an extensive simulation studies.     

A feasibility evaluation approach for time-evolving multi-item production–distribution networks

Time-dependent multi-item problems arise frequently in management applications, communication systems, and production–distribution systems. Our problem belongs to the last category, where we wish to address the feasibility of such systems when all network parameters change over time and product. The objective is to determine whether it is possible to have a dynamic production–shipment circuit within a finite planning horizon. And, if there is no such a flow, the goal is to determine where and when the infeasibility occurs and the approximate magnitude of the infeasibility. This information may help the decision maker in their efforts to resolve the infeasibility of the system. The problem in the discrete-time settings is investigated and a hybrid of scaling approach and penalty function method together with network optimality condition is utilized to develop a network-based algorithm. This algorithm is analysed from theoretical and practical perspectives by means of instances corresponding to some electricity transmission-distribution networks and many random instances. Computational results illustrate the performance of the algorithm.     

A comparison of tools for strategic simulation and scenario generation with special emphasis on ‘soft factors’

This paper describes a comparison of different tools for strategic simulation and scenario generation. Techniques and tools, namely GAMMA, a causal network editor, a connectance modeller allowing linear analysis and CRIMP, a tool based on dynamic cross-impact modelling and simulation, are presented and applied to a sample company specialised in technological breakthroughs. The results of tools are compared to identify strengths and weaknesses of these approaches.     

SHAPE—an approach for self-healing and self-protection in complex distributed networks

Increasing complexity of large scale distributed systems is creating problem in managing faults and security attacks because of the manual style adopted for management. This paper proposes a novel approach called SHAPE to self-heal and self-protect the system from various kinds of faults and security attacks. It deals with hardware, software, and network faults and provides security against DDoS, R2L, U2L, and probing attacks. SHAPE is implemented and evaluated against various standard metrics. The results are provided to support the approach.     

Multilevel algorithms for Rannacher–Turek finite element approximation of 3D elliptic problems

Generalizing the approach of a previous work of the authors, dealing with two-dimensional (2D) problems, we present multilevel preconditioners for three-dimensional (3D) elliptic problems discretized by a family of Rannacher Turek non-conforming finite elements. Preconditioners based on various multilevel extensions of two-level finite element methods (FEM) lead to iterative methods which often have an optimal order computational complexity with respect to the number of degrees of freedom of the system. Such methods were first presented by Axelsson and Vassilevski in the late-1980s, and are based on (recursive) two-level splittings of the finite element space. An important point to make is that in the case of non-conforming elements the finite element spaces corresponding to two successive levels of mesh refinement are not nested in general. To handle this, a proper two-level basis is required to enable us to fit the general framework for the construction of two-level preconditioners for conforming finite elements and to generalize the method to the multilevel case. In the present paper new estimates of the constant γ in the strengthened Cauchy–Bunyakowski–Schwarz (CBS) inequality are derived that allow an efficient multilevel extension of the related two-level preconditioners. Representative numerical tests well illustrate the optimal complexity of the resulting iterative solver, also for the case of non-smooth coefficients. The second important achievement concerns the experimental study of AMLI solvers applied to the case of micro finite element (μFEM) simulation. Here the coefficient jumps are resolved on the finest mesh only and therefore the classical CBS inequality based convergence theory is not directly applicable. The obtained results, however, demonstrate the efficiency of the proposed algorithms in this case also, as is illustrated by an example of microstructure analysis of bones.      

A comparison of algorithms for Near‐Real Time water vapour retrieval from MODIS

When studying the Earth's surface from space it is important that the component of the signal measured by the satellite‐based sensor due to the atmosphere is accurately estimated and removed. Such atmospheric correction requires good knowledge of atmospheric parameters including precipitable water (PW), ozone concentration and aerosol optical depth. To make full use of the capabilities of satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) these parameters should be accurately estimated in Near‐Real Time (NRT) with complete global coverage approximately every two days. NRT retrieval of the required ancillary information facilitates the atmospheric correction of such direct broadcast data from the MODIS instrument in the operational environment. In this paper three Near Infrared (NIR) algorithms for PW retrieval from MODIS are compared to determine which is most suitable for use in an operational MODIS‐based process for the atmospheric correction of spectral reflectance data. Two of the algorithms estimate PW in NRT and gave RMS errors of approximately 0.48 g cm^?2 (23%) and 0.59 g cm^?2 (28%), respectively, when compared against radiosonde data and modelled PW fields over Western Australia. The third algorithm was the NIR PW product from MODIS (MOD05) archived by the Distributive Active Archive Centre (DAAC). For the same locations the MOD05 NIR PW dataset gave an RMS error of approximately 0.95 g cm^?2 (44%). In each of the cases the best results were obtained after optimal cloudmasking of the NIR data. In this paper, the accuracy and suitability of the three algorithms for use in the operational atmospheric correction of MODIS data are evaluated and the importance of an accurate cloudmask for atmospheric correction in NRT is discussed.     

A comparison of several methods for calculating the general functional matrix for linear systems†

Methods due to Undrill and to Burnett and Storey for calculating MacFarlane's generalized functional matrix are critically compared. A comparison is also made with a method of Bass and Webber for finding the optimal feedback control in the case of linear systems with higher than quadratic indices.     

Synchronization of delayed reaction–diffusion neural networks via an adaptive learning control approach

An adaptive learning control strategy is utilized to investigate the synchronization problem for delayed reaction–diffusion neural networks (RDNNs) with unknown time-varying coupling strengths. A novel adaptive synchronization approach is proposed, which is consisted of differential–difference type updating law and feedback control law. By constructing a Lyapunov–Krasovskii-like composite energy functional (CEF), based on the LaSalle invariant principle of functional differential equations, a sufficient condition for the adaptive synchronization of such a system is obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.     

Multi-degree reduction of NURBS curves based on their explicit matrix representation and polynomial approximation theory

NURBS curve is one of the most commonly used tools in CAD systems and geometric modeling for its various specialties, which means that its shape is locally adjustable as well as its continuity order, and it can represent a conic curve precisely. But how to do degree reduction of NURBS curves in a fast and efficient way still remains a puzzling problem. By applying the theory of the best uniform approximation of Chebyshev polynomials and the explicit matrix representation of NURBS curves, this paper gives the necessary and sufficient condition for degree reducible NURBS curves in an explicit form. And a new way of doing degree reduction of NURBS curves is also presented, including the multi-degree reduction of a NURBS curve on each knot span and the multi-degree reduction of a whole NURBS curve. This method is easy to carry out, and only involves simple calculations. It provides a new way of doing degree reduction of NURBS curves, which can be widely used in computer graphics and industrial design.     

A comparison of mix models for the Rayleigh–Taylor instability

Four mix models, implemented into an Arbitrary Lagrangian–Eulerian (ALE) multi-physics code, are compared on simulations of the Rayleigh–Taylor instability. The specific models of interest are a mass diffusion model, the $k\text{–}L$ turbulence model, the BHR turbulence model, and a multifluid interpenetration mix model. The bubble growth rates produced by the different models are compared to experimentally determined growth rates. The diffusion model reproduces the characteristic $t^{1/2}$ growth for diffusion processes and therefore does not reproduce instability growth rates, as expected. The $k\text{–}L$ and BHR turbulence models reproduce the nominal instability growth rates at multiple Atwood numbers with a single set of model parameters. The multifluid interpenetration model exhibits diffusion-like behavior and therefore does not reproduce instability growth rates. All four models exhibit Cauchy-like convergence in the mixing layer width with decreasing mesh size, although the multifluid model exhibits both a larger error for a given mesh size and a slower convergence rate than the turbulence models.     

Tire–road friction coefficient and tire cornering stiffness estimation based on longitudinal tire force difference generation

A sequential tire cornering stiffness coefficient and tire–road friction coefficient (TRFC) estimation method is proposed for some advanced vehicle architectures, such as the four-wheel independently-actuated (FWIA) electric vehicles, where longitudinal tire force difference between the left and right sides of the vehicle can be easily generated. Such a tire force difference can affect the vehicle yaw motion, and can be utilized to estimate the tire cornering stiffness coefficient and TRFC. The proposed tire cornering stiffness coefficient and TRFC identification method has the potential of estimating these parameters without affecting the vehicle desired motion control and trajectory tracking objectives. Simulation and experimental results with a FWIA electric vehicle show the effectiveness of the proposed estimation method.     

A comparative evaluation of analytical approximations for analyzing production–inventory networks

Since the early 1990s, there has been an increased interest in developing analytical models for production–inventory networks, which consider queueing and planned inventory issues in a unified manner. There appears to be considerable similarity among the analytical models and approximations developed by different researchers. The purpose of this article is to clearly identify the similarities and differences among the various approaches by using a tandem production–inventory network as a test-bed. In addition, numerical experiments were performed by varying different parameters to gain better insight into the performance prediction capability of the different approximations and to rank order the different approaches.