Millaray Curilem

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In this work, the applicability of the circular statistics to feature extraction on seismic signals is presented. The seismic signals are captured from Llaima Volcano, located in Southern Andes Volcanic Zone at 38◦40’S 71◦40’W. Typically, the seismic signals can be divided in longperiod, tremor, and volcano-tectonic earthquakes. The seismic signals are(More)
Semiautogenous (SAG) mills for ore grinding are large energy consumption equipments. The SAG energy consumption is strongly related to the fill level of the mill. Hence, on-line information of the mill fill level is a relevant state variable to monitor and drive in SAG operations. Unfortunately, due to the prevailing conditions in a SAG mill, it is(More)
A comparative study between NARMAX and NARX models developed with ANN and SVM when used to forecast cash demand for ATMs is conducted. A simple methodology for developing SVM-NARMAX models is proposed. The best results were obtained with NARX-ANN models. In addition no significant differences were found between NARX and NARMAX for both ANN and SVM. Hence it(More)
Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). Several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (ODEs). Some approximations using piecewise linear differential equations (PLDEs), have been proposed to simplify the(More)
Intracranial Pressure (ICP) measurements are of great importance for the diagnosis, monitoring and treatment of many vascular brain disturbances. The standard measurement of the ICP is performed invasively by the perforation of the cranial scalp in the presence of traumatic brain injury (TBI). Measuring the ICP in a noninvasive way is relevant for a great(More)
In this work NARX-ANN, NARMAX-ANN and NARX-SVM models are compared when acting as software sensors of a relevant state variable for a Solid-substrate cultivation (SSC) process. Results show that NARX-SVM outperforms the other models with an Index of Agreement close to 1.0 even under very noisy conditions thus confirming the claimed superiority of SVM over(More)
Gray-Box models (GBM) which combine a priori knowledge of a process -e.g. first principle equations- with a black-box modeling technique are useful when some parameters of the first-principle model -normally time-variant parameters cannot be easily determined. In this case the black-box part of the GBM can be used to model the influence of input and state(More)