Robotics & Machine Learning Daily News2024,Issue(MAY.27) :62-62.

Reports Summarize Machine Learning Study Results from Faculty of Engineering and Architecture (Predicting compressor mass flow rate using various machine learni ng approaches)

Robotics & Machine Learning Daily News2024,Issue(MAY.27) :62-62.

Reports Summarize Machine Learning Study Results from Faculty of Engineering and Architecture (Predicting compressor mass flow rate using various machine learni ng approaches)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from the Faculty of En gineering and Architecture by NewsRx correspondents, research stated, "A major f ocus of the present study is to construct high-fidelity models for predicting co rrected mass flow rates based on the collected compressor map data."The news editors obtained a quote from the research from Faculty of Engineering and Architecture: "Both traditional machine learning research and modern deep le arning techniques have been employed to obtain well-fitted regression models of compressor mass flow rate. As traditional machine learning methods, Multiple Lin ear Regression and Random Forest, are conducted on compressor maps for predictio n of corrected mass flow rate. The time series-based deep learning models are ab le to capture the overall trend of a given input for specific map data."

Key words

Faculty of Engineering and Architecture/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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