首页|Researchers from Virginia Polytechnic Institute and State University Report Rece nt Findings in Machine Learning (Tire mode shape categorization using Zernike an nular moment and machine learning classification)
Researchers from Virginia Polytechnic Institute and State University Report Rece nt Findings in Machine Learning (Tire mode shape categorization using Zernike an nular moment and machine learning classification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Virginia Po lytechnic Institute and State University by NewsRx editors, research stated, “Th is research proposes a framework for categorizing the radial tire mode shapes us ing machine learning (ML) based classification and feature recognition algorithm s, advancing the development of a digital twin for tire performance analysis.” Financial supporters for this research include Centire, Which Operates Under Nsf . Our news journalists obtained a quote from the research from Virginia Polytechni c Institute and State University: “Tire mode shape categorization is required to identify modal features in a specific frequency range to maximize driving perfo rmance and secure safety. However, the mode categorization work requires a lot o f manual effort to interpret modes. Therefore, this study suggests an ML-based c lassification tool to replace the conventional categorization process with two p rimary objectives: (1) create a database by categorizing the tire mode shapes ba sed on the identified features and (2) develop an ML-based surrogate model to cl assify the tire mode shapes without manual effort. The feature map of the tire m ode shape is built with the Zernike annular moment descriptor (ZAMD). The mode s hapes are categorized using the correlation value derived by the modal assurance criteria (MAC) with all ZAMD values for each tire mode shape and subsequently c reating the appropriate labels. The decision tree, random forests, and XGBoost, the representative supervised-learning algorithms for classification, are implem ented for surrogate model development.”
Virginia Polytechnic Institute and State University, Cyborgs, Emerging Technologies, Machine Learning
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