首页|Study Findings from Tsinghua University Provide New Insights into Machine Learning (Prediction of Friction Coefficient of Polymer Surface Using Variational Mode Decomposition and Machine Learning Algorithm Based On Noise Features)
Study Findings from Tsinghua University Provide New Insights into Machine Learning (Prediction of Friction Coefficient of Polymer Surface Using Variational Mode Decomposition and Machine Learning Algorithm Based On Noise Features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learning. According to news reporting originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Establishing a correlation between the friction coefficient and friction noise of metal-friction polymer interfaces is a challenging task in various environments. To address this issue, our study utilizes machine learning algo-rithms to construct a friction data-based model, elucidating the relationship between noise and friction coeffi-cient." Financial supporters for this research include Major National R & D Projects of China, National Natural Science Foundation of China (NSFC), Independent Research Project of State Key Laboratory of Tribology in Advanced Equipment. Our news editors obtained a quote from the research from Tsinghua University, "We propose the variational mode decomposition (VMD) along with five machine learning algorithms, each capturing unique data characteristics. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods."
BeijingPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningTsinghua University
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