首页|Investigators at Tsinghua University Report Findings in Machine Learning (Physic ally Interpretable Wavelet-guided Networks With Dynamic Frequency Decomposition for Machine Intelligence Fault Prediction)
Investigators at Tsinghua University Report Findings in Machine Learning (Physic ally Interpretable Wavelet-guided Networks With Dynamic Frequency Decomposition for Machine Intelligence Fault Prediction)
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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 have been published. According to news reporting originating from Beijing, P eople's Republic of China, by NewsRx correspondents, research stated, "Machine i ntelligence fault prediction (MIFP) is crucial for ensuring complex systems' saf e and reliable operation. While deep learning has become the mainstream tool for MIFP due to its excellent learning abilities, its interpretability is limited, and it struggles to learn frequencies, making it challenging to understand the p hysical knowledge of signals at the frequency level." Financial support for this research came from Beijing Municipal Natural Science Foundation-Rail Transit Joint Research Program.
BeijingPeople's Republic of ChinaAsi aEmerging TechnologiesMachine IntelligenceMachine LearningTsinghua Unive rsity
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