首页|Chinese Academy of Sciences Reports Findings in Machine Learning (GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Cryst al Lubricants)
Chinese Academy of Sciences Reports Findings in Machine Learning (GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Cryst al Lubricants)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Shanghai, People's Rep ublic of China, by NewsRx correspondents, research stated, "The intricate develo pment of liquid-crystal lubricants necessitates the timely and accurate predicti on of their tribological performance in different environments and an assessment of the importance of relevant parameters. In this study, a classification model using Gaussian noise extreme gradient boosting (GNBoost) to predict tribologica l performance is proposed." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "Three additives, polysorbate-85, polysorbate-80, and graphene oxi de, were selected to fabricate liquidcrystal lubricants. The coefficients of fr iction of these lubricants were tested in the rotational mode using a universal mechanical tester. A model was designed to predict the coefficient of friction t hrough data augmentation of the initial data. The model parameters were optimize d using particle swarm optimization techniques." According to the news editors, the research concluded: "This study provides an e ffective example for lubricant performance evaluation and formulation optimizati on."
ShanghaiPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesLubricantsMachine Learning
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