TMsim : an algorithmic tool for the parametric and worst-case simulation of systems with uncertainties

This paper presents a general purpose, algebraic tool—named TMsim—for the combined parametric and worst-case analysis of systems with bounded uncertain parameters.The tool is based on the theory of Taylor models and represents uncertain variables on a bounded domain in terms of a Taylor polynomial plus an interval remainder accounting for truncation and round-off errors.This representation is propagated from inputs to outputs by means of a suitable redefinition of the involved calculations, in both scalar and matrix form. The polynomial provides a parametric approximation of the variable, while the remainder gives a conservative bound of the associated error. The combination between the bound of the polynomial and the interval remainder provides an estimation of the overall (worst-case) bound of the variable. After a preliminary theoretical background, the tool (freely available online) is introduced step by step along with the necessary theoretical notions. As a validation, it is applied to illustrative examples as well as to real-life problems of relevance in electrical engineering applications, specifically a quarter-car model and a continuous time linear equalizer

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

Black-Box Modeling of the Maximum Currents Induced in Harnesses During Automotive Radiated Immunity Tests

This letter presents a black-box modeling approach for the prediction of the spectrum of the maximum currents induced on a generic linear load in an automotive radiated immunity test. The proposed approach relies on a parametric Thévenin-based circuit equivalent built from a limited set of measured or simulated data. The frequency-domain behavior of the equivalent voltage source is provided via a metamodel by combining the support vector machine (SVM) regression with a regularized Fourier kernel with a simple adaptive algorithm. The latter allows defining the minimum number of training samples needed to accurately predict the maximum values of the currents induced on a generic linear load for different azimuth directions of the excitation field. The accuracy and the strength of the proposed approach are demonstrated for an example, by comparing the model predictions with the results of a parametric full-wave electromagnetic simulation

Does chess instruction enhance mathematical ability in children? A three-group design to control for placebo effects

Pupils’ poor achievement in mathematics has recently been aconcern in many Western countries. In order to address this is-sue, it has been proposed to teach chess in schools. However,in spite of optimistic claims, no convincing evidence of the ac-ademic benefits of chess instruction has ever been provided,because no study has ever controlled for possible placebo ef-fects. In this experimental study, a three-group design (i.e., ex-perimental, placebo, and control groups) was implemented tocontrol for possible placebo effects. Measures of mathematicalability and metacognitive skills were taken before and after thetreatment. We were interested in metacognitive skills becausethey have been claimed to be boosted by chess instruction, inturn positively influencing the enhancement of mathematicalability. The results show that the experimental group (partici-pants attending a chess course) achieved better scores in math-ematics than the placebo group (participants attending a Gocourse) but not than the control group (participants attendingregular school lessons). With regard to metacognition, no dif-ferences were found between the three groups. These resultssuggest that some chess-related skills generalize to the mathe-matical domain, because the chess lessons compensated for thehours of school lessons lost, whereas the Go lessons did not.However, this transfer does not seem to be mediated by meta-cognitive skills, and thus appears to be too limited to offer ed-ucational advantages

EMI Prediction of Switching Converters

This paper addresses the simulation of the conducted electromagnetic interference produced by circuits with periodically switching elements. The proposed method allows for the computation of their steady-state responses by means of augmented linear time-invariant equivalents built from circuit inspection only, and standard tools for circuit analysis. The approach is demonstrated on a real dc-dc boost converter by comparing simulation results with real measurements

Worst-Case Optimization of a Digital Link for Wearable Electronics in a Stochastic Framework

This paper demonstrates an optimization strategy for systems affected by uncertainties in the case of a textile interconnect line. Rather than simply conducting stochastic analysis at the end of the design process, tolerances are accounted for from the early stages of the flow. An unsupervised approach, used to describe the stochastic behavior of the line, isintegrated within a heuristic optimization algorithm with the aim of selecting the optimal parameters of a passive equalizer

A Statistical Assessment of Blending Hydrogen into Gas Networks

The deployment of low-carbon hydrogen in gas grids comes with strategic benefits in terms of energy system integration and decarbonization. However, hydrogen thermophysical properties substantially differ from natural gas and pose concerns of technical and regulatory nature. The present study investigates the blending of hydrogen into distribution gas networks, focusing on the steady-state fluid dynamic response of the grids and gas quality compliance issues at increasing hydrogen admixture levels. Two blending strategies are analyzed, the first of which involves the supply of NG–H2 blends at the city gate, while the latter addresses the injection of pure hydrogen in internal grid locations. In contrast with traditional case-specific analyses, results are derived from simulations executed over a large number (i.e., one thousand) of synthetic models of gas networks. The responses of the grids are therefore analyzed in a statistical fashion. The results highlight that lower probabilities of violating fluid dynamic and quality restrictions are obtained when hydrogen injection occurs close to or in correspondence with the system city gate. When pure hydrogen is injected in internal grid locations, even very low volumes (1% vol of the total) may determine gas quality violations, while fluid dynamic issues arise only in rare cases of significant hydrogen injection volumes (30% vol of the total)

Synthetic gas networks for the statistical assessment of low-carbon distribution systems

Most of the simulation studies on energy networks, including gas grids, derive their results from a limited number of network models. The findings of these works are therefore affected by a substantial case-specificity, which partially limits their validity and prevents their generalisation. To overcome this limitation, the present work proposes a novel statistical-based approach for studying distribution gas networks, enabled by a generator of random gas grids with accurate technical designs and structural features. Ten thousand random and unique networks are produced in three different tests, where increasingly tight constraints are applied to the synthetisation process for a higher control over the generated grids. The experiments verify the accuracy of the tool and highlight that substantial variations can be found in the hydraulic behaviour (pressures and gas velocities) and structural properties (pipe diameters and network volumes) of real-world gas networks. The observed 10,000 gas grids evidence the information gain offered by statistical-based approaches with respect to traditional case-specific studies. The tool opens a broad range of applications which include, but are not limited to, statistical analyses on the distributed injection of alternative gases, like hydrogen, in integrated, low-carbon, energy systems

Machine learning for the performance assessment of high-speed links

This paper investigates the application of support vector machine to the modeling of high-speed interconnects with largely varying and/or highly uncertain design parameters. The proposed method relies on a robust and well-established mathematical framework, yielding accurate surrogates of complex dynamical systems. An identification procedure based on the observation of a small set of system responses allows generating compact parametric relations, which can be used for design optimization and/or stochastic analysis. The feasibility and strength of the method are demonstrated based on a benchmark function and on the statistical assessment of a realistic printed circuit board interconnect, highlighting the main features and benefits of this technique over state-of-the-art solutions. Emphasis is given to the effects of the initial sample size and of input noise on the model estimation

Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA

In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that IRS assisted NOMA based on our utilized DRL scheme achieves high sum rate compared to OMA based one, and as the transmit power increases, the capability of serving more users increases. Furthermore, results show that imperfect successive interference cancellation (SIC) has a deleterious impact on the data rate of users performing SIC. As the imperfection increases by ten times, the rate decreases by more than 10%