D-optimality is a well-known concept in experimental design that seeks to select an optimal set of points estimate the unknown parameters statistical model with minimum variance. In this paper, we focus on proving conjecture made by Ford, Torsney and Wu regarding existence class D-optimal designs for binary weighted linear regression models. Our concentration models one variable. The states tha...

This paper presents a versatile and robust algorithm for the optimal design of adjacent structural systems linked by passive energy dissipation devices. The algorithm works both with stationary and nonstationary force input. It is a two-stage design procedure that (i) determines an active control law, and then (ii) adds damper and stiffness coefficients that are computed based on the minimizati...

This paper is concerned with the problem of computing approximate D-optimal design for polynomial regression with analytic weight function on a interval [m0-a, mo+ a]. It is shown that the structure of the optimal design depends on a and weight function. Moreover, the optimal support points and weights are analytic functions of a at a = 0. We make use of a Taylor expansion to provide a recursiv...

Reduced-order models that are able to approximate output quantities of interest of high-fidelity computational models over a wide range of input parameters play an important role in making tractable large-scale optimal design, optimal control, and inverse problem applications. We consider the problem of determining a reduced model of an initial value problem that spans all important initial con...

induction heating is widely used in metal industry for melting or heating thin slabs in a continuous casting plant. the wide application is based on good efficiency, high production rate and non-polluting conditions. for designing a parallel resonant induction heating system, calculating the capacitance for the resonant circuit is important, because its value affects resonance frequency, output...

the integration of an approximate model and a clonal selection algorithm (csa) is considered to one of the most effective ways to solve complex optimization design problems in engineering. in this study, first, a process for developing an approximate model and the principles of clonal selection algorithms were presented. then, a variotherm temperature injection mold used to produce a large liqu...

In this note we consider the D optimal design problem for the heteroscedastic polynomial regression model Karlin and Studden a found explicit solutions for three types of e ciency functions We introduce two new functions to model the heteroscedastic structure for which the D optimal designs can also be found explicitly The optimal designs have equal masses at the roots of generalized Bessel pol...

D-optimal and c-optimal designs for binary response experiments with two design variables are considered. It is shown that with two design variables and a bounded design space, there exists a D-optimal design which has at most four or six symmetrically arranged support points, from which further reduction in the number of support points may sometimes be accomplished by using an asymmetric weigh...

Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D-optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied and explicit characterisations of the Bayesian D-optimal saturated designs for the Michaelis-M...

in zero acceptance number sampling plans, the sample items of an incoming lot are inspected one by one. the proposed method in this research follows these rules: if the number of nonconforming items in the first sample is equal to zero, the lot is accepted but if the number of nonconforming items is equal to one, then second sample is taken and the policy of zero acceptance number would be appl...