首页|New Machine Learning Data Have Been Reported by Researchers at Pontifical Univer sity (A Machine Learning Approach for Enhancing Permittivity Mixing Rules of Binary Liquids With a Gaussian Modification and a New Interaction Factor Estimation)
New Machine Learning Data Have Been Reported by Researchers at Pontifical Univer sity (A Machine Learning Approach for Enhancing Permittivity Mixing Rules of Binary Liquids With a Gaussian Modification and a New Interaction Factor Estimation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing have been published. According to newsreporting originating from Madrid, Sp ain, by NewsRx correspondents, research stated, “The microstructureand solvatio n mechanics of binary liquids are key for predicting mixture permittivity. Howev er, sincetraditional mixing rules do not consider this complexity, they must be modified to address the mixturecharacteristics through an interaction factor ( k(int)).”Our news editors obtained a quote from the research from Pontifical University, “This paper evaluatesthis parameter for several mixing rules, applying Support Vector Regressor models trained with glycerinwaterreflective signals acquired with a Dielectric Resonator sensor. The regression error of these modelsindicat es both the optimal interaction factor and the mixing rule that fits the most wi th experimentalpermittivity values. Kraszewski and Hashin-Shtrikman mixing rule s achieved the best performance withan RMSE of around 1. In addition, this pape r suggests that the interaction factor can be estimatedthrough the molar volume and the dielectric contrast between liquids (k(int) = 2.67) without acquiring experimental data. Moreover, after analyzing the physical limitations of a linear modification formula, thispaper proposes an alternative based on a Gaussian fu nction that avoids unrealistic volume fractions.”
MadridSpainEuropeCyborgsEmerging TechnologiesMachine LearningPontifical University
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