Noise Mitigation Analysis of a Pi-Filter for an Automotive Control Module

This paper has been reproduced on " InCompliance" magazine, May issue http://www.incompliancemag.com/ then "Issue Archive

"Good-but-Imperfect" electromagnetic reverberation in a VIRC

Novel theoretical probability density functions (PDF) of electromagnetic fields inside reverberation chambers operating in a “good-but-imperfect‿ regime have been recently reported. The present work reports on the application and assessment of these PDFs using a non-conventional type of reverberation chamber, namely the Vibrating Intrinsic Reverberation Chamber (VIRC). A vector network analyzer was used in order to measure the complex field components. An electrically short dipole antenna was used as a receiving antenna. Five thousand frequency points were taken ranging from 200MHz (undermoded regime) to 4 GHz (overmoded regime), so one measurement every 760 kHz was performed. For each frequency, 200 samples of the real and imaginary part of the field were measured. Measurements confirm the fact that the novel PDFs are able to describe the occurrence of anomalous statistics in the VIRC

Machine Learning Regression Techniques for the Modeling of Complex Systems: An Overview

Recently, machine learning (ML) techniques have gained widespread diffusion, since they have been successfully applied in several research fields. This paper investigates the effectiveness of advanced ML regressions in two EMC applications. Specifically, support vector machine, least-squares support vector machine and Gaussian process regressions are adopted to construct accurate and fast-to-evaluate surrogate models able to predict the output variable of interest as a function of the system parameters. The resulting surrogates, built from a limited set of training samples, can be suitably adopted for both uncertainty quantification and optimization purposes. The accuracy and the key features of each of the considered machine learning techniques are investigated by comparing their predictions with the ones provided by either circuital simulations or measurements

Efficient Statistical Extraction of the Per-Unit-Length Capacitance and Inductance Matrices of Cables with Random Parameters

Cable bundles often exhibit random parameter variations due to uncertain or uncontrollable physical properties and wire positioning. Efficient tools, based on the so-called polynomial chaos, exist to rapidly assess the impact of such variations on the per-unit-length capacitance and inductance matrices, and on the pertinent cable response. Nevertheless, the state-of-the-art method for the statistical extraction of the per-unit-length capacitance and inductance matrices of cables suffers from several inefficiencies that hinder its applicability to large problems, in terms of number of random parameters and/or conductors. This paper presents an improved methodology that overcomes the aforementioned limitations by exploiting a recently-published, alternative approach to generate the pertinent polynomial chaos system of equations. A sparse and decoupled system is obtained that provides remarkable benefits in terms of speed, memory consumption and problem size that can be dealt with. The technique is thoroughly validated through the statistical analysis of two canonical structures, i.e. a ribbon cable and a shielded cable with random geometry and position

Application of Different Learning Methods for the Modelling of Microstrip Characteristics

In this paper, the performance of four machine learning regressions like Support Vector Machine (SVM), Least Square-Support Vector Machine (LS-SVM), Gaussian Process Regression (GPR) and Random Forest method (RF) are investigated by means of an illustrative example referring to the characteristic impedance of a microstrip line in terms of electrical and geometrical parameters. The required dataset for training is obtained from a set of parametric electromagnetic simulations. The performance comparison of the four methods is done in the presence and absence of numerical noise and inaccuracies affecting the training samples. The results of our comparison provide a guidance for the proper method selection to model the electromagnetic characteristics of interconnects for high-speed signals: advantages and drawbacks of each of the proposed techniques clearly emerge from this analysis

SVM and LS-SVM for the Uncertainty Quantification of Complex Systems

This paper investigates the application of the support vector machine and the least-squares support vector machine regressions to the uncertainty quantification of complex systems. The feasibility and the accuracy of the above techniques are demonstrated by predicting the efficiency of an integrated voltage regulator with 8 stochastic parameter

Modeling of the Maximum Induced Currents in Automotive Radiated Immunity Tests via Thevenin-based Metamodels

This paper presents three different metamodels for the prediction of the maximum current induced on key vehicle electronic units during an automotive radiated immunity test. The proposed modeling approach is based on a Thevenin circuital interpretation of the test setup which is estimated from a small set of measurements or simulations. The FFT-based trigonometric regression, the support vector machine and the Gaussian process regression are then applied to provide three different metamodels able of predicting the spectrum of the induced currents for any value of the incidence angle of the external EM field. The accuracy and the convergence of the proposed alternatives are investigated by comparing model predictions with the results obtained by means of a parametric full-wave electromagnetic simulation

Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by Means of a Machine Learning Model Coupled with Kriging Regression

This paper presents a preliminary version of a probabilistic model for the uncertainty quantification of complex electronic systems resulting from the combination of the leastsquares support vector machine (LS-SVM) and the Gaussian process (GP) regression. The proposed model, trained with a limited set of training pairs provided by a set of full-wave expensive simulations, is adopted for the prediction of the efficiency of an integrated voltage regulator (IVR) with 8 uniformly distributed random parameters. The accuracy and the feasibility of the proposed model have been investigated by comparing the model predictions and its confidence intervals with the results of a Monte Carlo (MC) full-wave simulation of the device