The identification of building structural systems. II. The nonlinear case

This paper models a building structure as a nonlinear feedback system and studies the effects of such a system model on the structural response to strong ground shaking. Nonlinear kernels arising in the identification procedure have been investigated and an error analysis presented. Applications of the Weiner method in studying the response of a reinforced concrete structure to strong ground shaking have been illustrated. The nature of the second order kernels has been displayed and the nonlinear contribution to the response at the roof level, during strong ground shaking, has been determined

Nonparametric Modeling and Model-Based Control of the Insulin-Glucose System

Non

Inhomogeneous point-process entropy: an instantaneous measure of complexity in discrete systems

Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties

Spatial Distribution of Potential in a Flat Cell: Application to the Catfish Horizontal Cell Layers

An analytical solution is obtained for the three-dimensional spatial distribution of potential inside a flat cell, such as the layer of horizontal cells, as a function of its geometry and resistivity characteristics. It was found that, within a very large range of parameter values, the potential is given by [Formula: see text] where r = ρ/ρ(0), z̄ = z/ρ(0), ρ = (R(i)/R(m))·ρ(0), δ = h/ρ(0); K is a constant; J is the assumed synaptic current; ρ, z are cylindrical coordinates; ρ(0) is the radius of the synaptic area of excitation; h is the cell thickness; and R(i), R(m) are the intracellular and membrane resistivities, respectively. Formula A closely fits data for the spatial decay of potential which were obtained from the catfish internal and external horizontal cells. It predicts a decay which is exponential down to about 40% of the maximum potential but is much slower than exponential below that level, a characteristic also exhibited by the data. Such a feature in the decay mode allows signal integration over the large retinal areas which have been observed experimentally both at the horizontal and ganglion cell stages. The behavior of the potential distribution as a function of the flat cell parameters is investigated, and it is found that for the range of the horizontal cell thicknesses (10-50 μ) the decay rate depends solely on the ratio R(m)/R(i). Data obtained from both types of horizontal cells by varying the diameter of the stimulating spot and for three widely different intensity levels were closely fitted by equation A. In the case of the external horizontal cell, the fit for different intensities was obtained by varying the ratio R(m)/R(i); in the case of the internal horizontal cell it was found necessary, in order to fit the data for different intensities, to vary the assumed synaptic current J