Detailed knowledge of the possible anatomical and morphometric variations of the infraorbital foramen (IOF) is important for ensuring safe and successful regional anesthesia, and for avoiding iatrogenic nerve injuries during surgery on the middle third of the face. To conduct a morphometric study on the IOF, correlating this with sex and side of the cranium. Two hundred forty-two crania were us...

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and ac...

In this paper we present a new approach to modeling dynamic production systems with discrete flow. This method is based on the automatic knowledge domain, in order to build a mathematical model that accurately formalizes the behavior of the studied system. The approach adopted for this study is the parametric identification of nonlinear systems (Hammerstein-Wiener system and NARX model). The pr...

In this paper, a new nonlinear wavelet identification structure is proposed for high noise resistive soft sensors. This method uses proposedPolynomial Nonlinear Auto Regressive Exogenous Model, which can be solved with linear Gaussian Least Square Method, alongside the Averaging Wavelet Method (AWM) filter. AWM uses the approximation spaces for analyzing the signals and reduce the noise by a me...

We introduce GP-RLARX, a novel Gaussian Process (GP) model for robust system identification. Our approach draws inspiration from nonlinear autoregressive modeling with exogenous inputs (NARX) and it encapsulates a novel and powerful structure referred to as latent autoregression. This structure accounts for the feedback of uncertain values during training and provides a natural framework for fr...