首页|Data from Yellow River Conservancy Technical Institute Advance Knowledge in Mach ine Learning (Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series)
Data from Yellow River Conservancy Technical Institute Advance Knowledge in Mach ine Learning (Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting from Kaifeng, People ’s Republic of China, by NewsRx journalists, research stated, “On the basis of a nalyzing the natural frequency of the structure, the identification quantity of each process is constructed with modal parameters and input into the machine lea rning as characteristic parameters to realize the damage identification.” The news journalists obtained a quote from the research from Yellow River Conser vancy Technical Institute: “By extracting the median curve of vegetation index t ime series after 5G filtering in the damaged area of typical civil structures, a nd comparing it with the actual growth curve of crops in the area, the vegetatio n index time series monitoring model was constructed, and 10 was selected as the best threshold. The accuracy of the result is verified, and the iteration time is 0.18 hours. A damage detection method based on machine learning is proposed.”
Yellow River Conservancy Technical Insti tuteKaifengPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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