Findings from National Institute of Standards and Technology (NIST) Reveals New Findings on Machine Learning (Generating Synthetic Sensor Data To Facilitate Machine Learning Paradigm for Prediction of Building Fire Hazard)

Robotics & Machine Learning Daily News2024，Issue(Feb.15) ：16-17.DOI:10.1007/s10694-020-01022-9

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NETL
NSTL
Springer Nature

Findings from National Institute of Standards and Technology (NIST) Reveals New Findings on Machine Learning (Generating Synthetic Sensor Data To Facilitate Machine Learning Paradigm for Prediction of Building Fire Hazard)

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Abstract

Current study results on Machine Learning have been published. According to news reporting from Gaithersburg, Maryland, by NewsRx journalists, research stated, “Using the zone fire model CFAST as the simulation engine, time series data for building sensors, such as heat detectors, smoke detectors, and other targets at any arbitrary locations in multi-room compartments with different geometric configurations, can be obtained. An automated process for creating inputs files and summarizing model results, CData, is being developed as a companion to CFAST.”

Key words

Gaithersburg/Maryland/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/National Institute of Standards and Technology (NIST)

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出版年

2024

Robotics & Machine Learning Daily News

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被引量7

参考文献量33

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