Brescia, Italy
Brescia, Italy

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Ezzine L.,Sensor | Guerbaoui M.,Sensor | El Afou Y.,Sensor | Ed-Dahhak A.,Sensor | And 3 more authors.
4th Int. Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2010, Held at the International Mediterranean and Latin American Modeling Multiconference, I3M 2010 | Year: 2010

The goal of this paper is to carry out a statistical study whose objective is the identification of time series of the greenhouse climatic parameters, in order to optimize the expenditure in cost and time of the culture under greenhouse. In this study, we showed that the inside temperature is the most influential parameters on the greenhouse. However, the automatic climate control requires the development of appropriate control laws that are based on models representing linear and nonlinear system. We are therefore forced to make a study of the system to generate a model that faithfully reproduces the operating parameters of greenhouse climate. In order to achieve the maximum benefit it is important to exploit the available data and an obvious choice here are the machine learning methods such as artificial neural networks. The use of recurrent Radial Basis Function (RBF) models is justified by employing a nonlinear greenhouse system, and hence to give the possibility to identify and to control in the real time the inside temperature in the greenhouse, taking into accounts other climatic parameters within and outside the greenhouse. A comparison of the measured and simulated data proved that the found model can envisage correctly the inside greenhouse temperature.


Comini E.,Sensor | Zappa D.,Sensor | Cerqui C.,Sensor | Ponzoni A.,Sensor | And 2 more authors.
Sensor Letters | Year: 2014

Increasing concern on health threat due to pollution or terrorist attacks stimulated the research on gas sensing for real-time monitoring of all aspects of indoor and outdoor environments. Industrial requirements for a sensor include high sensitivity and selectivity together with good stability, as well as low fabrication costs. Conductometric gas sensors based on metal oxide semiconductors are, among all possible technological approaches, the most promising for the development of low cost and reliable sensors. Metal oxide nanostructures were synthetized using catalyst-assisted evaporation/condensation processes. Structural and compositional characterization has been performed in order to confirm the characteristics of material investigated. Results on the application of metal oxide sensors in security and safety have been presented. Copyright © 2014 American Scientific Publishers.

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