Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.

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Emails: luttiane@dca.ufrn.br 1 , jmjunior@dca.ufrn.br 2 and meneghet@dca.ufrn.br 3 .Abstract-This paper presents the identification of a simulated nonlinear system that represents the dynamical mechanism of the human lower limb. The study and application of this model may have a relevant importance in the research area of rehabilitation of patients suffering from any kind of paralysis of their lower limbs. Here, a Fuzzy Wavelet Neural Network (FWNN) is used to identify the lower limb model under study. In order to evaluate the FWNN model, it was validated in two distinct situations. Firstly it was considered that the original model does not suffer any modification in its parameters and, in the second case, the viscous coefficient was reduced. In this way, it was possible to analyze the FWNN model robustness in terms of this parameter change. The performance of the FWNN was also compared with other two neural network structures: Multilayer Perceptron (MLP) and Wavelet Neural Network (WNN).

In this work a study and the implementation of level control is presented, using the MPS-PA Festo industrial process module. This process has non-linear characteristics, and it was initially analysed in closed-loop, where, after watching its response, it was developed via MATLAB a fuzzy controller. To test the performance of the fuzzy controller, it was compared with a PI controller. The experimental results show a basis for comparation between the two control techniques. Resumo: Neste trabalhoé apresentado um estudo e implementação do controle de nível no modulo didático de processos industriais MPS-PA Festo. Este processo, que apresenta características não lineares foi analisado inicialmente em malha fechada, no qual, após um estudo das características de sua resposta, implementou-se, via MATLAB um controlador fuzzy. Para avaliar o desempenho do controlador fuzzy, será realizada uma comparação com o controlador PI. Os resultados obtidos experimentalmente apresentam uma base para a comparação entre as duas técnicas de controle.