Browsing by Author "Matus Acuña, Marcelo Enrique"

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    A fuzzy logic based model computes cardiac output from the radial arterial pressure waveform
    (IEEE, 1994) Urzua Urzua, Jorge; Guarini Hermann, Marcelo Walter; Cipriano, Aldo; Matus Acuña, Marcelo Enrique; Lee, J.; Salinas, C.; Olmedo Hidalgo, Juan Carlos; Lema F., Guillermo; Canessa, Roberto
    An interactive scheme for the development of a fuzzy logic based model has been implemented and applied to build a model that is able to determine cardiac output from radial arterial pressure. It was built using as input radial pressure waveforms from patients in whom cardiac output was simultaneously measured by thermodilution. The scheme was programmed in C language in an Apollo workstation under Unix. Input variables were area under the curve, ratio between systolic and diastolic pressure, and pulse frequency. To determine fuzzy sets and membership functions the authors used 149 pressure waveforms. To test the model, the authors used 6 waveforms not used in building the model. Correlation between predicted cardiac output with measured flow was 0.98.<>
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    A simple index for ventriculoarterial coupling
    (1993) Urzúa Urzúa, Jorge; Lema F., Guillermo; Guarini Hermann, Marcelo Walter; Cipriano, Aldo; Meneses Riquelme, Gladys Elena; Matus Acuña, Marcelo Enrique
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    Stochastic modeling of street lamps operation
    (1996) Rebolledo, Rolando; Ríos Marcuello, Sebastián; Trigo, Rodrigo; Matus Acuña, Marcelo Enrique
    This paper is based on the application of stochastic differential equations in simulating the active power consumption in street lamp operation, both in transient and steady state. The method worked as follows. Firstly, a collection of about 400 street lamps was measured in the laboratory. In addition, a theoretical model for the mean power consumption was derived. This model was expressed by means of a linear stochastic differential equations dependent on two parameter processes. The aforementioned parameters were then estimated from the experimental data and the equations solved numerically leading to a representation of the mean active power by means of a stochastic process.