Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented

Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

info:eu-repo/semantics/acceptedVersio

Experimental Comparison of Preferential vs. Common Delta Connections for the Star-Delta Starting of Induction Motors

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Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

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