首页|Researchers at Hong Kong University of Science and Technology Release New Data o n Machine Learning (Fault detection using machine learning based dynamic ICA-dis tributed CCA: Application to industrial chemical process)
Researchers at Hong Kong University of Science and Technology Release New Data o n Machine Learning (Fault detection using machine learning based dynamic ICA-dis tributed CCA: Application to industrial chemical process)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from Hong Kong University of Science and Technology by NewsRx journalists, research stated, “Unexpected acci dents and events in industrial chemical processes have resulted in a considerabl e number of causalities and property damage.” Our news journalists obtained a quote from the research from Hong Kong Universit y of Science and Technology: “Safety process management in industrial chemical p rocesses is critical to avoid and ensure casualties and property damage. However , due to the immense scope and high complexity of current industrial chemical pr ocesses, the traditional safety process management approaches cannot address the se challenges to attain adequate fault detection accuracy. To address this issue , an innovative machine learning-based distributed canonical correlation analysi s-dynamic independent component analysis (DICADCCA) approach is needed to impro ve the fault detection effectiveness of complicated systems. The (DICA-DCCA) mod el could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmar k study. The research findings present that the suggested (DICA-DCCA) approach i s more resilient and effective in detecting abnormalities and faults than the IC A and DICA approaches with FDR 100 % and FAR 0 %.”
Hong Kong University of Science and Tech nologyCyborgsEmerging TechnologiesMachine Learning
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