Robust strict positive realness: new results for intervalplant-controller families

A frequency domain approach is used to derive several robust strict positive realness results for interval plants and interval plant plus controller families of transfer functions. Based on simple frequency domain properties of transfer functions, the approach provides a framework for obtaining new results and constructing easy proofs of several important existing results on robust strict positive realness. The main new result states that the minimum of the real part of the transfer functions belonging to an interval plant controller family is achieved on one of the 32 Kharitonov segments of the interval plant. The argument used in the proof is of wider interest and suggests easy ways of proving that robustness of other different frequency properties of interval plant plus controller families of transfer functions, such as robust stability of H_∞-norm computation, can be deduced from a fixed number of segments of transfer functions of the family     

Lifespace variables and managerial success.

Correlated lifespace measures with the discrepancies between original test battery forecasts (Miller Analogies Test, Nonverbal Reasoning Ability Test, Guilford-Zimmerman Temperment Survey, and Management Judgment Test) and subsequent managerial success on the job. Variables included the manager's perceived task challenge on his entry job (Modified Task Characteristics Questionnaire), his life change between test and criterion measurement (Schedule of Recent Experiences), his personality match with his first supervisor, and the success of his first supervisor. These served in combination for 140 Exxon managers (average age, 31 yrs) to account for an additional 22.7% of the variance in success after 5 to 7 yrs on the job when added to the validity of prediction (.63) by the Exxon Early Identification of Management Program battery of aptitude and personality assessments administered during the 1st 18 mo of employment. The equivalent of a multiple correlation of .79 was attained. (29 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An LMI approach to constrained optimization with homogeneous forms

This paper considers the problem of determining the minimum Euclidean distance of a point from a polynomial surface in . It is well known that this problem is in general non-convex. The main purpose of the paper is to investigate to what extent linear matrix inequality (LMI) techniques can be exploited for solving this problem. The first result of the paper shows that a lower bound to the global minimum can be achieved via the solution of a one-parameter family of linear matrix inequalities (LMIs). It is also pointed out that for some classes of problems the solution of a single LMI problem provides the lower bound. The second result concerns the tightness of the bound. It is shown that optimality of the lower bound amounts to solving a system of linear equations. An application example is finally presented to show the features of the approach.     

Qualitative analysis of mathematical arc models using Lyapunov theory

An approach is proposed to the study of the electric arc near current zero by means of mathematical models. The approach is based on Lyapunov's stability theory and allows a qualitative analysis of the nonlinear differential equations describing the phenomenon. The main results concern the determination of the set of conditions leading to arc extinction and their dependence on the physical parameters involved. The classical Mayr and Cassie models are studied using the method, and some numerical results are given.     

A linear programming approach to online set membership parameter estimation for linear regression models

This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown‐but‐bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a suitable linear program, whenever a new measurement leads to a reduction of the approximating orthotope. The key idea for preventing the size of the linear programs from steadily increasing is to propagate only the binding constraints of these optimization problems. Numerical studies show that the new approach outperforms existing recursive set approximation techniques, while keeping the required computational burden within the same order of magnitude. Copyright © 2016 John Wiley & Sons, Ltd.     

Conditional central algorithms for worst case set-membershipidentification and filtering

This paper deals with conditional central estimators in a set membership setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like worst case optimal identification and state filtering, in contexts in which disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The conditional Chebyshev center problem is solved for the case when energy norm-bounded disturbances are considered. A closed-form solution is obtained by finding the unique real root of a polynomial equation in a semi-infinite interval     

Twenty-First Century Gateways: Immigrant Incorporation in Suburban America

Problem: Children make little use of many neighborhood parks even though they have facilities for active recreation.

Purpose: We examined the factors that bring children to parks, and aimed to understand how park visitation patterns differ between boys and girls, among children of different races and ethnicities, and between inner-city and suburban children.

Methods: We surveyed children and their parents in 12 middle schools and analyzed the results. Our team also observed children at 50 inner-city and 50 suburban parks, and we used multiple regression models containing park- and neighborhood-level variables to relate them to the numbers of children using parks.

Results and conclusions: The study confirms that many middle-school children make little use of parks. This is primarily explained by their own lack of interest in the existing park activities and their households' lack of time and concerns about safety. Active recreation facilities and organized sport programs, natural features, and good levels of maintenance and cleanliness are the most significant factors attracting middle-school children to parks. Our survey found some significant gender, racial, and ethnic differences in preferences for park equipment, perceptions of park safety, and park visitation patterns. Additionally, our regression models confirmed that inner-city and suburban children were attracted to parks of different sizes and containing different facilities, and that the association between park safety and park use was also different in these two settings.

Takeaway for practice: Neighborhood parks provide the potential for active recreation, but they are often underutilized and attract only a subset of neighborhood children. Planners should take into account that different factors attract different groups of children to parks.

Research support: This research was supported by the John Randolph and Dora Haynes Foundation.     

Keeping features in the field of view in eye-in-hand visual servoing: a switching approach

A visual servoing strategy for keeping features in the field of view is proposed which consists of a switching among position-based control strategies and backward motion. In the absence of uncertainty on the extrinsic parameters, all features are kept in the field of view. Moreover, if the intrinsic parameters are also known, the trajectory length is minimized in the rotational space and, for some cases, also minimized in the translational space. Simulation results also show a certain degree of robustness against uncertainty on the intrinsic parameters.     

Estimating the fundamental matrix via constrained least-squares: aconvex approach

In this paper, a new method for the estimation of the fundamental matrix from point correspondences in stereo vision is presented. The minimization of the algebraic error is performed while taking explicitly into account the rank-two constraint on the fundamental matrix. It is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques. The obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion, where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero. This suggests that the proposed estimate can be used to initialize nonlinear criteria, such as the distance to epipolar lines and the gradient criterion, in order to obtain a more accurate estimate of the fundamental matrix