NARX neural network model for strong resolution improvement in a distributed temperature sensor
Autor: | Jean Carlos Cardozo da Silva, Luís C. B. Silva, Marcelo E. V. Segatto, Maria José Pontes, Jorge Leonid Aching Samatelo, Cicero Martelli, João Paulo Bazzo |
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Rok vydání: | 2018 |
Předmět: |
Signal processing
Computer simulation Artificial neural network business.industry Computer science 010401 analytical chemistry Pattern recognition 02 engineering and technology 01 natural sciences Signal Atomic and Molecular Physics and Optics 0104 chemical sciences Reduction (complexity) Nonlinear system 020210 optoelectronics & photonics Optics Autoregressive model Brillouin scattering 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering business Engineering (miscellaneous) Image resolution |
Zdroj: | Applied optics. 57(20) |
ISSN: | 1539-4522 |
Popis: | This paper proposes an approach to process the response of a distributed temperature sensor using a nonlinear autoregressive with external input neural network. The developed model is composed of three steps: extraction of characteristics, regression, and reconstruction of the signal. Such an approach is robust because it does not require knowledge of the characteristics of the signal; it has a reduction of data to be processed, resulting in a low processing time, besides the simultaneous improvement of spatial resolution and temperature. We obtain total correction of the temperature resolution and spatial resolution of 5 cm of the sensor. |
Databáze: | OpenAIRE |
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