{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T11:09:47Z","timestamp":1707563387204},"reference-count":3,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2011,10]]},"abstract":"<jats:p> This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data. <\/jats:p>","DOI":"10.1142\/s179353691100091x","type":"journal-article","created":{"date-parts":[[2012,4,9]],"date-time":"2012-04-09T21:24:02Z","timestamp":1334006642000},"page":"483-491","source":"Crossref","is-referenced-by-count":12,"title":["ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION"],"prefix":"10.1142","volume":"03","author":[{"given":"BRADLEY LEE","family":"BARNHART","sequence":"first","affiliation":[{"name":"IIHR-Hydroscience &amp; Engineering, The University of Iowa, 130 W. Harrison St., Iowa City, Iowa 52242, USA"}]},{"given":"HONDA KAHINDO WA","family":"NANDAGE","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Iowa, 702 18th Ave #4, Coralville, Iowa 52241, USA"}]},{"given":"WILLIAM","family":"EICHINGER","sequence":"additional","affiliation":[{"name":"IIHR-Hydroscience &amp; Engineering, The University of Iowa, 130 W. Harrison St., Iowa City, Iowa 52242, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,4,11]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"rf2","volume":"1","author":"Kim D.","journal-title":"The R J."},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.2001.0247"}],"container-title":["Advances in Adaptive Data Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S179353691100091X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T21:19:38Z","timestamp":1565126378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S179353691100091X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,10]]},"references-count":3,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,4,11]]},"published-print":{"date-parts":[[2011,10]]}},"alternative-id":["10.1142\/S179353691100091X"],"URL":"https:\/\/doi.org\/10.1142\/s179353691100091x","relation":{},"ISSN":["1793-5369","1793-7175"],"issn-type":[{"value":"1793-5369","type":"print"},{"value":"1793-7175","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,10]]}}}