ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR

Alberto A. M. Albuquerque; Arthur P. S.  Braga; Bismark C. Torrico; Otacílio M. Almeida

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Paper

ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR

Topics: Detection and Identification; Neural Networks

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 0BIOSTEC, 276-281, 2012 , Vilamoura, Algarve, Portugal

Authors: Alberto A. M. Albuquerque ; Arthur P. S. Braga ; Bismark C. Torrico and Otacílio M. Almeida

Affiliation: Federal University of Ceara, Brazil

Keyword(s): Artificial Neural Network, Neonatal Incubator, Temperature and Humidity Estimation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper seeks to estimate through Artificial Neural Networks the future behavior of temperature and humidity inside an incubator. This goal is motivated by the observation that the model-based predictive control is an interesting alternative for the generation of control signals of a neonatal incubator since: (i) it seeks to optimize a performance criterion that considers the future behavior of this controller, and (ii) restrictions may be imposed on future control signals. These two features can make more safe and comfortable the microclimate inside the device for the newborn: variables such as temperature and humidity can be better kept within the limits of technical standards such as the NBR IEC 601-2-19 and its amendment No. 1, NBR IEC 60601-2-19-2000. However, one predictive model of the process to be controlled must first be obtained. The obtained neural model has accuracy in predicting the incubator behavior one time step forward compatible with the technical standard, and it is ready to be applied in a predictive control structure. (More)

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Paper citation in several formats:

A. M. Albuquerque, A.; P. S. Braga, A.; C. Torrico, B. and M. Almeida, O. (2012). ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS; ISBN 978-989-8425-89-8; ISSN 2184-4305, SciTePress, pages 276-281. DOI: 10.5220/0003791502760281

@conference{biosignals12,
author={Alberto {A. M. Albuquerque}. and Arthur {P. S. Braga}. and Bismark {C. Torrico}. and Otacílio {M. Almeida}.},
title={ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS},
year={2012},
pages={276-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003791502760281},
isbn={978-989-8425-89-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012) - BIOSIGNALS
TI - ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR
SN - 978-989-8425-89-8
IS - 2184-4305
AU - A. M. Albuquerque, A.
AU - P. S. Braga, A.
AU - C. Torrico, B.
AU - M. Almeida, O.
PY - 2012
SP - 276
EP - 281
DO - 10.5220/0003791502760281
PB - SciTePress