{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:18:47Z","timestamp":1740158327116,"version":"3.37.3"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603395","U1433103","U1533017"],"award-info":[{"award-number":["61603395","U1433103","U1533017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007538","name":"Civil Aviation Administration of China","doi-asserted-by":"publisher","award":["61603395","U1433103","U1533017"],"award-info":[{"award-number":["61603395","U1433103","U1533017"]}],"id":[{"id":"10.13039\/501100007538","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007538","name":"Civil Aviation Administration of China","doi-asserted-by":"publisher","award":["61603395","U1433103","U1533017"],"award-info":[{"award-number":["61603395","U1433103","U1533017"]}],"id":[{"id":"10.13039\/501100007538","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA) method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>K<\/mml:mi><\/mml:mrow><\/mml:math>-fold cross-validation algorithm and the parameter in the radial basis function (RBF) is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times) faster than that of sequential CPU task execution. The OpenMP-based <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>K<\/mml:mi><\/mml:mrow><\/mml:math>-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times) faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.<\/jats:p>","DOI":"10.1155\/2017\/4890921","type":"journal-article","created":{"date-parts":[[2017,3,16]],"date-time":"2017-03-16T17:04:20Z","timestamp":1489683860000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5933-1977","authenticated-orcid":true,"given":"Xiaoyu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-5641","authenticated-orcid":true,"given":"Jiusheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, China"}]},{"given":"Quan","family":"Gan","sequence":"additional","affiliation":[{"name":"College of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, China"}]}],"member":"311","reference":[{"key":"2","doi-asserted-by":"publisher","DOI":"10.2514\/1.54964"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2012.2188391"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2004.08.007"},{"key":"7","first-page":"288","volume":"59","year":"2009","journal-title":"World Academy of Science, Engineering and Technology"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2011.2136441"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2010.10.008"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2003.09.012"},{"year":"2012","key":"12"},{"year":"2002","key":"13"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1002\/dac.2993"},{"first-page":"177","volume-title":"Large-scale machine learning with stochastic gradient descent","year":"2010","key":"16"},{"issue":"1","key":"19","first-page":"801","volume":"14","year":"2013","journal-title":"The Journal of Machine Learning Research"},{"year":"2015","key":"20"},{"first-page":"582","volume-title":"Support vector method for novelty detection","year":"1999","key":"21"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2017\/4890921.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2017\/4890921.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2017\/4890921.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,3,16]],"date-time":"2017-03-16T17:04:26Z","timestamp":1489683866000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2017\/4890921\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":16,"alternative-id":["4890921","4890921"],"URL":"https:\/\/doi.org\/10.1155\/2017\/4890921","relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"type":"print","value":"2090-0147"},{"type":"electronic","value":"2090-0155"}],"subject":[],"published":{"date-parts":[[2017]]}}}