Effect of the Reference Line on Main Roughness Parameters

In the present paper the influence of the reference system with regard to the characterization of the surface finishing is analyzed. The effect of the reference system’s choice on the most representative surface finishing parameters (e.g. roughness average Ra and root mean square values Rq) is studied. The study can also be applied to their equivalent parameters in waviness and primary profiles. Based on ISO and ASME standards, three different types of regression lines (center, mean and orthogonal) are theoretically and experimentally analyzed, identifying the validity and applicability fields of each one depending on profile’s geometry.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.