首页|New Findings on Machine Learning from Nanjing Forestry University Summarized (Toward the Balance Between Computational Cost and Model Performance for the Void Detection of Concrete-filled Steel Tubular Structure Using One-dimensional …)
New Findings on Machine Learning from Nanjing Forestry University Summarized (Toward the Balance Between Computational Cost and Model Performance for the Void Detection of Concrete-filled Steel Tubular Structure Using One-dimensional …)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Machine Learning. According to news reportingout of Jiangsu, People’s Republic of China, by NewsRx editors, research stated, “Deep learning (DL)based percussion-acoustic methods have gained attention, but their multi-layer architectures and iterativeprocesses increase computational time and power. This paper proposes a lightweight concrete-filled steeltubular (CFST) void detection method using Mel-frequency cepstral coefficient (MFCC) algorithm andensemble machine learning.”
JiangsuPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNanjing Forestry University
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