Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm
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 predic...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4890921 |
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| author | Xiaoyu Zhang Jiusheng Chen Quan Gan |
| author_facet | Xiaoyu Zhang Jiusheng Chen Quan Gan |
| author_sort | Xiaoyu Zhang |
| collection | DOAJ |
| description | 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 K-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 K-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%. |
| format | Article |
| id | doaj-art-791da673ceb648a384f68f4abf67e985 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-791da673ceb648a384f68f4abf67e9852025-02-03T01:25:40ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/48909214890921Anomaly Detection for Aviation Safety Based on an Improved KPCA AlgorithmXiaoyu Zhang0Jiusheng Chen1Quan Gan2College of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronics, Information & Automation, Civil Aviation University of China, Tianjin 300300, ChinaThousands 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 K-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 K-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%.http://dx.doi.org/10.1155/2017/4890921 |
| spellingShingle | Xiaoyu Zhang Jiusheng Chen Quan Gan Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm Journal of Electrical and Computer Engineering |
| title | Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm |
| title_full | Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm |
| title_fullStr | Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm |
| title_full_unstemmed | Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm |
| title_short | Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm |
| title_sort | anomaly detection for aviation safety based on an improved kpca algorithm |
| url | http://dx.doi.org/10.1155/2017/4890921 |
| work_keys_str_mv | AT xiaoyuzhang anomalydetectionforaviationsafetybasedonanimprovedkpcaalgorithm AT jiushengchen anomalydetectionforaviationsafetybasedonanimprovedkpcaalgorithm AT quangan anomalydetectionforaviationsafetybasedonanimprovedkpcaalgorithm |