Harmonic balance surrogate-based immunity modeling of a nonlinear analog circuit

A novel harmonic balance surrogate-based technique to create fast and accurate behavioral models predicting, in the early design stage, the performance of nonlinear analog devices during immunity tests is presented. The obtained immunity model hides the real netlist, reduces the simulation time, and avoids expensive and time-consuming measurements after tape-out, while still providing high accuracy. The model can easily be integrated into a circuit simulator together with additional subcircuits, e.g., board and package models, as such allowing to efficiently reproduce complete immunity test setups during the early design stage and without disclosing any intellectual property. The novel method is validated by means of application to an industrial case study, being an automotive voltage regulator, clearly showing the technique's capabilities and practical advantages

Green Policymaking in Japanese Municipalities: An Empirical Study on External and Internal Contextual Factors

This article examines the establishment and publication of green plans and green public procurement (GPP) policies in Japanese municipalities. The purpose of the study was to investigate these green policymaking initiatives from a contingency theory perspective. The first research question examined contextual factors for green policymaking. The second research question focused on barriers and enablers. For RQ1, through hypothesis testing and a regression analysis (n = 1663), we found that green policymaking differs by organization location, organization size, and organizational green capabilities. More specifically, we identified prefectures where municipalities score relatively higher as well as lower. Second, we found that larger (vs. smaller) municipalities undertake more (vs. less) green policymaking initiatives. Third, we observed that organizations with more (vs. less) green capabilities develop more (vs. less) green initiatives. For RQ2, through a descriptive and cluster analysis, we identified dominant barriers and enablers to establishing a GPP policy. The dominant barriers include a lack of information, lack of staff, and cost concerns, whereas manuals and example forms are important enablers. These findings are highly relevant to understanding and supporting green policymaking in Japanese municipalities

Machine Learning-Based Hybrid Random-Fuzzy Modeling Framework for Antenna Design

A machine learning-based framework is proposed to evaluate the effect of design parameters, affected by both aleatory and epistemic uncertainty, on the performance of antennas. In particular, possibility theory is leveraged to define aleatory and epistemic uncertainty in a common framework. Then, a method combining Bayesian optimization and Polynomial Chaos expansion is applied to accurately and efficiently propagate both uncertainties throughout the system under study. A suitable application example validates the proposed method

Gaussian processes for history-matching : application to an unconventional gas reservoir

The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions