Diseño de un sistema para la detección y caracterización de impactos sobre estructuras.

[ES]Este documento pretende describir de manera clara y concisa la creacion de un dispositivo detector de impactos para estructuras de avion durante su transporte de muy bajo consumo, desde su concepcion como idea hasta su montaje, programacion y posterior comprobacion. El objetivo sera la culminacion exitosa del producto para mas adelante exponerlo ante posibles compradores y/o investigadores. Este dispositivo esta compuesto de tres partes, una unidad de adquisicion y adecuacion de la señal, que ser a recogida por un piezoelectrico; la unidad de inteligencia, que sera un microcontrolador programado en ensamblador mediante la herramienta MPLAB y un chip de radio, que se encargara de transmitir los datos a una unidad mas potente, lease un PC, tablet, movil...

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Consumption and portfolio rules whit stochastic hyperbolic discounting

We extend the classic Merton (1969, 1971) problem that investigates the joint consumption-savings and portfolio-selection problem under capital risk by assuming sophisticated but time-inconsistent agents. We introduce stochastic hyperbolic preferences as in Harris and Laibson (2013) and find closed-form solutions for Merton's optimal consumption and portfolio selection problem in continuous time. We find that the portfolio rule remains identical to the time-consistent solution with power utility and no borrowing constraints. However,the marginal propensity to consume out of wealth is unambiguously greater than the time-consistent, exponential case and,importantly, it is also more responsive to changes in risk. These results suggest that hyperbolic discounting with sophisticated agents offers promise for contributing to explaining important aspects of asset market data.