首页|New Data from Liaoning Technical University Illuminate Findings in Machine Learn ing (Bayesian Optimized Deep Q-network for Diagnosing Mine Ventilation Systems W indage Alteration Fault Targeting Imbalanced Data)
New Data from Liaoning Technical University Illuminate Findings in Machine Learn ing (Bayesian Optimized Deep Q-network for Diagnosing Mine Ventilation Systems W indage Alteration Fault Targeting Imbalanced Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news originatingfrom Huludao, People’s Republic of China, by NewsRx correspondents, research stated, “Fault diagnosis ofmine vent ilation system is of great significance for mine safety production. Traditional machine learningalgorithms have been widely applied in the field of mine ventil ation systems windage alteration faults(WAFs) diagnosis, but these algorithms h ave poor intelligence and weak generalization ability.”
HuludaoPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningLiaoning Technical Universi ty
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