首页|New Findings on Machine Learning from Otto-von-Guericke- University Summarized (P rediction of Rod-like Particle Mixing In Rotary Drums By Three Machine Learning Methods Based On Dem Simulation Data)
New Findings on Machine Learning from Otto-von-Guericke- University Summarized (P rediction of Rod-like Particle Mixing In Rotary Drums By Three Machine Learning Methods Based On Dem Simulation Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingin Magdeburg, Germany, by New sRx journalists, research stated, “The mixing of non-sphericalparticles in rota ry drums exhibits significant complexity, particularly when density segregation and sizesegregation occur simultaneously. Three machine learning models: artifi cial neural network (ANN), extremelyrandomized trees (ERT), and particle swarm optimized support vector regression (PSO-SVR) weredeveloped to predict the mixi ng time and mixing degree at the steady mixing state of rodlike particles inrot ary drums.”