首页|Findings from National Institute of Scientific Research Broaden Understanding of Machine Learning (Characterizing Seismic Activity From a Rock Cliff With Unsupe rvised Learning)
Findings from National Institute of Scientific Research Broaden Understanding of Machine Learning (Characterizing Seismic Activity From a Rock Cliff With Unsupe rvised Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating in Quebec City, Canada , by NewsRx journalists, research stated, “Passive seismic monitoring (PSM) is e merging as a tool for detecting rockfall events and pre-failure seismicity. In t his paper, the potential of PSM for rockfall monitoring is assessed through a ca se study carried out in Gros-Morne, Eastern Qu & eacute;bec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are foun d on the main regional road each year.” Financial supporters for this research include Ministere des Transports du Quebe c, CGIAR, Fonds de recherche du Quebec (FRQ).
Quebec CityCanadaNorth and Central A mericaCyborgsEmerging TechnologiesMachine LearningUnsupervised LearningNational Institute of Scientific Research
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