On continued fraction expansion of real roots of polynomial systems, complexity and condition numbers

We elaborate on a correspondence between the coefficients of a multivariate polynomial represented in the Bernstein basis and in a tensor-monomial basis, which leads to homography representations of polynomial functions that use only integer arithmetic (in contrast to the Bernstein basis) and are feasible over unbounded regions. Then, we study an algorithm to split this representation and obtain a subdivision scheme for the domain of multivariate polynomial functions. This implies a new algorithm for real root isolation, MCF, that generalizes the Continued Fraction (CF) algorithm of univariate polynomials.A partial extension of Vincent’s Theorem for multivariate polynomials is presented, which allows us to prove the termination of the algorithm. Bounding functions, projection and preconditioning are employed to speed up the scheme. The resulting isolation boxes have optimized rational coordinates, corresponding to the first terms of the continued fraction expansion of the real roots. Finally, we present new complexity bounds for a simplified version of the algorithm in the bit complexity model, and also bounds in the real RAM model for a family of subdivision algorithms in terms of the real condition number of the system. Examples computed with our C++ implementation illustrate the practical aspects of our method.     

On the exact asymptotics for the stationary sojourn time distribution in a tandem of queues with light-tailed service times

We study the asymptotics of the stationary sojourn time Z of a “typical customer” in a tandem of single-server queues. It is shown that in a certain “intermediate” region of light-tailed service time distributions, Z may take a large value mostly due to a large value of a single service time of one of the customers. Arguments used in the paper also allow us to obtain an elementary proof of the logarithmic asymptotics for the tail distribution of the stationary sojourn time in the whole class of light-tailed distributions.     

TARGET expert systems for analysis and trouble resolution of remote telecommunications equipment

Current trends in technology and operational procedures related to telecommunications equipment present opportunities in which expert systems can play an important role in meeting the needs and objectives of telephone operating companies. The TARGET (Truoble Analysis and Resolution Guided by Expert Techniques) Expert Systems are a set of expert systems that extend the span of knowledge of telephone company personnel in the areas of telecommunication equipment troubleshooting and maintenance. The expert systems upgrade troubleshooting and maintenance performance and allow personnel to perform troubleshooting and maintenance tasks that they otherwise would not have the expertise to perform or to perform well. The initial systems will provide expert knowledfe for troubleshooting remotely sited equipment.     

Steady state analysis and computation of the GI/M/1/L queue with multiple working vacations and partial batch rejection

This paper analyzes a finite-buffer bulk-arrival bulk-service queueing system with multiple working vacations and partial batch rejection in which the inter-arrival and service times are, respectively, arbitrarily and exponentially distributed. Using the supplementary variable and the embedded Markov chain techniques, we obtain the waiting queue-length distributions at pre-arrival and arbitrary epochs. We also present Laplace–Stiltjes transform of the actual waiting-time distribution in the queue. Finally, several performance measures and a variety of numerical results in the form of tables and graphs are discussed.     

Necessary and sufficient conditions on Q ( = C( I + PC)) for stabilization of the linear feedback system S(P, C)

We find the following necessary and sufficient conditions for Q (:=C(I+PC)⁻¹) to -stabilize the standard linear time-invariant unity feedback system S(P, C) where P has the l.c.f. (D_pl, N_pl) and the r.c.f. (N_pr, D_pr); and is a principal ideal domain. (i) Q must have elements in (ii) (resp. (iii)) Q must factorize in with D_pr, (resp. D_pl) as a left (resp. right) factor and (iv) (I – QP) must factor in with D_pr, as a left factor.     

A computational method for solving optimal control and parameter estimation of linear systems using Haar wavelets

In this article, a computational method based on Haar wavelet in time-domain for solving the problem of optimal control of the linear time invariant systems for any finite time interval is proposed. Haar wavelet integral operational matrix and the properties of Kronecker product are utilized to find the approximated optimal trajectory and optimal control law of the linear systems with respect to a quadratic cost function by solving only the linear algebraic equations. It is shown that parameter estimation of linear system can be done easily using the idea proposed. On the basis of Haar function properties, the results of the article, which include the time information, are illustrated in two examples.     

L1 analysis and design of sampled-data systems

The focus of this work is L₁-optimal control of sampled-data systems. A converging approximation procedure is derived to compute the L_∞-induced norm of closed-loop finite-dimensional linear time-invariant (LTI) sampled-data systems. An approximation method is developed to synthesize L₁-optimal sampled-data regulators. Finally, an example is provided that illustrates the L₁ analysis and design techniques presented     

On busy period and sojourn time distributions in the M/G/1-EPS queue with catastrophes

We derive the Laplace-Stiltjes transforms of busy period and sojourn time distributions in the M/G/1 queue with egalitarian processor sharing (EPS) and the possibility of (external) catastrophes. Each arrival of the catastrophes immediately removes all the positive jobs (and hence unfinished work) in this non work-conserving queueing system. One of the main results is obtained by means of the so-called method of decomposition into delay elements introduced by the first author.     

Generalization of rectangular element stiffness matrix and thermal load vector associated with a0 + a1x + a2y + a3xy type interpolation rule

Explicit expressions for the element stiffness matrix K and element load vector p for the rectangular plane-stress and plane-strain finite elements associated with Ψ(x, y) = a₀ + a₁x + a₂y + a₃xy type interpolation rule are given for the general anisotropic material in xy-planc subjected to non-uniform temperature increases. The expressions are optimized with respect to the numerical operations required for the computation of K and p, and they are valid for special cases of material properties and thermal loading.     

Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity

Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is covered by subsolutions that are represented by a set of predictive rules, termed classifiers. The genetic algorithm is designed to evolve classifier structures that together cover the whole problem space and represent a complete problem solution. An obvious challenge for such an online evolving, distributed knowledge representation is to continuously sustain all problem subsolutions covering all problem niches, that is, to ensure niche support. Effective niche support depends both on the probability of reproduction and on the probability of deletion of classifiers in a niche. In XCS, reproduction is occurrence-based whereas deletion is support-based. In combination, niche support is assured effectively. In this paper we present a Markov chain analysis of the niche support in XCS, which we validate experimentally. Evaluations in diverse Boolean function settings, which require non-overlapping and overlapping solution structures, support the theoretical derivations. We also consider the effects of mutation and crossover on niche support. With respect to computational complexity, the paper shows that XCS is able to maintain (partially overlapping) niches with a computational effort that is linear in the inverse of the niche occurrence frequency.

On the utility of content analysis in author attribution:The Federalist

In studies of author attribution, measurement of differential use of function words is the most common procedure, though lexical statistics are often used. Content analysis has seldom been employed. We compare the success of lexical statistics, content analysis, and function words in classifying the 12 disputedFederalist papers. Of course, Mosteller and Wallace (1964) have presented overwhelming evidence that all 12 were by James Madison rather than by Alexander Hamilton. Our purpose is not to challenge these attributions but rather to useThe Federalist as a test case. We found lexical statistics to be of no use in classifying the disputed papers. Using both classical canonical discriminant analysis and a neural-network approach, content analytic measures — the Harvard III Psychosociological Dictionary and semantic differential indices — were found to be successful at attributing most of the disputed papers to Madison. However, a function-word approach is more successful. We argue that content analysis can be useful in cases where the function-word approach does not yield compelling conclusions and, perhaps, in preliminary screening in cases where there are a large number of possible authors.Colin Martindale is Professor of Psychology at the University of Maine. He is author of a number of articles and books on content analysis, literary history, and other topics. A recent book isThe Clockwork Muse: The Predictability of Artistic Change (New York: Basic Books). He is Executive Editor ofEmpirical Studies of the Arts and serves on the editorial boards ofComputers and the Humanities andPoetics.Dean McKenzie is Professional Officer/Statistician for Psychological Medicine, Monash University, Melbourne, Australia. He is author of several articles concerned with machine learning and artificial intelligence.     

On the choice of importance distributions for unconstrained and constrained state estimation using particle filter

Recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used (Arulampalam et al. [1]). As pointed out by Daum [2], particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approach for generating the proposal distribution based on a constrained Extended Kalman filter (C-EKF), Constrained Unscented Kalman filter (C-UKF) and constrained Ensemble Kalman filter (C-EnkF) has been proposed. The efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (Rao et al. [10]). We also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with Unscented Kalman filter as a proposal and unconstrained Ensemble Kalman filter (EnKF) as a proposal. The efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. The simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters.     

On the analysis of vibro-acoustic systems in the mid-frequency range using a hybrid deterministic-statistical approach

At present, several numerical prediction techniques are available for the steady-state dynamic analysis of vibro-acoustic problems. Most of these techniques can be classified as either deterministic or statistical approaches. The use of the deterministic methods, like the Finite Element Method (FEM), is limited to the so-called low-frequency range due to the decreasing wavelengths and subsequent increasing model sizes and computational loads for increasing frequency. The underlying assumptions for the statistical methods, like the Statistical Energy Analysis (SEA), limit their use to the so-called high-frequency range. In-between the low- and high-frequency ranges there is a relatively wide mid-frequency-range, in which some of the subsystems fulfill the requirements for the statistical approach and some others do not (yet). Therefore, Shorter and Langley developed a deterministic-statistical framework combining FEM and SEA models. However, this method is limited in use due to the computational load of the FEM models. This paper proposes a framework for coupling Trefftz-based deterministic models with statistical SEA models. Instead of the FEM, the recently developed and computationally more efficient Wave Based Method (WBM) is being used. The presented method is verified in a numerical example.     

Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation

We present a methodology to perform the identification and validation of complex uncertain dynamical systems using experimental data, for which uncertainties are taken into account by using the nonparametric probabilistic approach. Such a probabilistic model of uncertainties allows both model uncertainties and parameter uncertainties to be addressed by using only a small number of unknown identification parameters. Consequently, the optimization problem which has to be solved in order to identify the unknown identification parameters from experiments is feasible. Two formulations are proposed. The first one is the mean-square method for which a usual differentiable objective function and an unusual non-differentiable objective function are proposed. The second one is the maximum likelihood method coupling with a statistical reduction which leads us to a considerable improvement of the method. Three applications with experimental validations are presented in the area of structural vibrations and vibroacoustics.     

On the role of geometrically exact and second-order theories in buckling and post-buckling analysis of three-dimensional beam structures

Several geometrically nonlinear beam models are evaluated with respect to their utility in the analysis of buckling and post-buckling behavior of three-dimensional beam structures. The first two models are based on the so-called geometrically exact beam theory capable of representing finite rotations and finite displacements. The principal difference between these models concerns only the chosen parameterization of finite rotations, with the orthogonal matrix used in the first and the rotation vector used in the second one. The third beam model based on the second-order approximation of finite rotations is also discussed along with its application to constructing a consistent formulation of the linear eigenvalue problem for computing an estimate of the critical load. Exact linearized forms, which are crucial for facilitating the buckling load computation and assuring a robust performance of a Newton-method-based continuation strategy, are presented for all three beam models. An elaborate set of numerical simulations of buckling and post-buckling analysis of beam structures is given in order to illustrate the performance of each of the presented models. Finally, some conclusions are drawn.