참고문헌 (28)

1. 국가항공안전프로그램 행정규칙, 국토교통부고시, 2015. 3. 9. 

2. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Comput. Surv. vol. 41, 2009, pp. 1-58. 

   상세보기

3. G. Pang et al., "Deep learning for anomaly detection: A review," ACM Comput. Surv. vol. 54, no. 2, article no. 38, 2021. 

   상세보기

4. P. Vincent et al., "Extracting and composing robust features with denoising autoencoders," in Proc. Int. Conf. Mach. Learn. (Helsinki, Finland), July 2008. 

5. M. Welling and P.D. Kingma, "An introduction to variational autoencoders," Found. Trends Mach. Learn. vol. 12, no. 4, 2019, pp. 307-392. 

6. T. Spinner et al. "Towards an interpretable latent space: An intuitive comparison of autoencoders with variational autoencoders," in Proc. Workshop Vis. AI Explainability, (Berlin, Germany), Oct. 2018. 

7. D.M. Federico et al., "A survey on GANs for anomaly detection," arXiv preprint, CoRR, 2019, arXiv: 1906.11632. 

8. J. Donahue, P. Krahenbuhl, and T. Darrell, "Adversarial feature learning," ICLR, Apr. 2017. 

9. T. Schlegl et al., "Unsupervised anomaly detection with generative adversarial networks to guide marker discovery," arXiv preprint, CoRR, 2017, arXiv: abs/1703.05921. 

10. H. Zenati et al., "Efficient GAN-based anomaly detection," arXiv preprint, CoRR, 2018, arXiv: abs/1802.06222. 

11. S. Akcay, A. Atapour-Abarghouei, and T.P. Breckon, "GANomaly: Semi-supervised anomaly detection via adversarial training," arXiv preprint, CoRR, 2018, arXiv: abs/1805.06725. 

12. B. Scholkopf et al., "Estimating the support of a high-dimentional distribution," Neural Comput. vol. 13, no. 7, 2001, pp. 1443-1471. 

    상세보기

13. R. Chalapathy and S. Chawla, "Deep learning for anomaly detection: A survey," arXiv preprint, CoRR, 2019, arXiv: 1901.03407. 

14. R. Chalapathy, A.K. Menon, and S. Chawla, "Anomaly detection using one-class neural networks," arXiv preprint, CoRR, 2018, arXiv: 1802.06360. 

15. L. Ruff et al., "Deep one-class classification," in Proc. Int. Conf. Mach. Learn. (Stockholm, Sweden), 2018, pp. 4390-4399. 

16. L. Basora, X. Olive, and T. Dubot, "Recent advances in anomaly detection methods applied to aviation," Aerosapce, vol. 6, no. 11, 2018. 

17. S. Das et al., "Multiple kernel learning for heterogeneous anomaly detection: Algorithm and aviation safety case study," in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (Washington, DC, USA), July 2010, pp. 47-56. 

18. L. Li et al., "Anomaly detection in onboard-recorded flight data using cluster analysis," in Proc. IEEE/AIAA Digit. Avionics Syst. Conf. (Seattle, WA, USA), Oct. 2011, pp. 4A41-4A411. 

19. S. Das, B.L. Matthews, and R. Lawrence, "Fleet level anomaly detection of aviation safety data," in Proc. IEEE Conf. Progn. Health Manage. (Denver, CO, USA), June 2011, pp. 1-10. 

20. G. Jia et al., "Verification method for chinese aviation radiotelephony readbacks based on LSTM-RNN," Electron. Lett. vol. 53, 2017, pp. 401-403. 

    상세보기

21. J. Guimin et al., "Intelligent checking model of Chinese radiotelephony read-backs in civil aviation air traffic control," Chin. J. Aeronaut. vol. 31, 2018, pp. 2280-2289. 

    상세보기

22. X. Olive et al., "Detecting controllers' actions in past mode S data by autoencoder-based anomaly detection," in Proc. SESAR Innov. Days (Salzburg, Austria), Dec. 2018. 

23. S. Akerman, E. Habler, and A. Shabtai, "VizADS-B: Analyzing sequences of ADS-B images using explainable convolutional LSTM encoder-decoder to detect cyber attacks," arXiv preprint, CoRR, 2019, arXiv: 1906.07921. 

24. A. Nanduri and L. Sherry, "Anomaly detection in aircraft data using recurrent Neural Networks (RNN)," in Proc. Integrated Commun. Navig. Surveill. (ICNS), (Herndon, VA, USA), Apr. 2016, pp. 5C2-1-5C2-8. 

25. K.K. Reddy et al., "Anomaly detection and fault disambiguation in large flight data: A multi-modal deep auto-encoder approach," in Proc. Annu. Conf. Progn. Health Manage. Soc. (Denver, CO, USA), Oct. 2016. 

26. X. Fu et al., "Aircraft engine fault detection based on grouped convolutional denoising autoencoders," Chin. J. Aeronaut. vol. 32, 2019, pp. 296-307. 

    상세보기

27. N. Gugulothu et al., "Predicting remaining useful life using time series embeddings based on recurrent neural networks," arXiv preprint, CoRR, 2018, arXiv: 1709.01073. 

28. R. Zhao et al., "Deep learning and its applications to machine health monitoring," Mech. Syst. Signal Process. vol. 115, 2019, pp. 213-237. 

    상세보기