id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ff365428v6p	Monica Arul Jayachandran	Application of Shapelet Transform to Time Series Classification of Earthquake, Wind and Wave Data	2022		.txt	text/plain	230	7	25	Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.	cache/ff365428v6p.txt	txt/ff365428v6p.txt