首页|Findings from Tsinghua University in Machine Learning Reported (Integrating Fric tion Noise for In-Situ Monitoring of Polymer Wear Performance: A Machine Learnin g Approach in Tribology)
Findings from Tsinghua University in Machine Learning Reported (Integrating Fric tion Noise for In-Situ Monitoring of Polymer Wear Performance: A Machine Learnin g Approach in Tribology)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on artificial intell igence are discussed in a new report. Accordingto news originating from Tsinghu a University by NewsRx editors, the research stated, "Friction and wearbetween mating surfaces significantly affect the efficiency and performance of mechanica l systems."Our news correspondents obtained a quote from the research from Tsinghua Univers ity: "Traditionaltribological research relies on post-observation methods, limi ting the understanding of dynamic frictionbehavior. In contrast, in-situ monito ring provides real-time insights into evolving friction dynamics. Thisstudy emp loys machine learning to monitor polymer wear performance through friction noise . Thepredictive accuracy of various machine learning methods, including Extreme ly Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared forwear type classification."
Tsinghua UniversityCyborgsEmerging T echnologiesMachine Learning
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