首页|Reports from Xi’an University of Technology Advance Knowledge in Support Vector Machines (Establishment of Critical Non-depositing Velocity Prediction Model for Sediment In Drip Irrigation Laterals Based On Pso-svm)
Reports from Xi’an University of Technology Advance Knowledge in Support Vector Machines (Establishment of Critical Non-depositing Velocity Prediction Model for Sediment In Drip Irrigation Laterals Based On Pso-svm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing - Support Vector Machines have been published. According to news originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stat ed, “Accurately determining the critical non-depositing velocity for sediment (C NDVS) in drip irrigation laterals is essential to address issues of sediment dep osition and clogging in drip irrigation pipes caused by the use of muddy water.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), China Postdoctoral Science Foundation. Our news journalists obtained a quote from the research from the Xi’an Universit y of Technology, “This paper focuses on three main factors: the percentage of in termediate-sized sediment particles (P), pipe diameter (D), and sediment concent ration (S), all of which significantly influence the CNDVS in drip irrigation la terals.”
Xi’anPeople’s Republic of ChinaAsiaMachine LearningSupport Vector MachinesXi’an University of Technology
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