Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM

[1] Kullback S., Leibler R. A.. On information and sufficiency [J]. The Annals of Mathematical Statistics, 1951, 22(1): 79-86 Kullback S. Leibler R. A. On information and sufficiency [J]. The Annals of Mathematical Statistics , 1951 , 22 ( 1 ): 79 – 86 Search in Google Scholar

[2] Zhang L. W., Lin J., Karim R.. Adaptive kernel density-based anomaly detection for nonlinear systems [J]. Knowledge-Based Systems, 2018, 139: 50-63 Zhang L. W. Lin J. Karim R. Adaptive kernel density-based anomaly detection for nonlinear systems [J]. Knowledge-Based Systems , 2018 , 139 : 50 – 63 Search in Google Scholar

[3] Liu F, Qi JP, Yu YW, et al. Density-based top-n local anomaly fast detection algorithm[J]. Journal of Automation, 2019, 45(9): 1756-1771 Liu F Qi JP Yu YW Density-based top-n local anomaly fast detection algorithm [J]. Journal of Automation , 2019 , 45 ( 9 ): 1756 – 1771 Search in Google Scholar

[4] Shang W-L, Shi He, Zhao J-M, et al. An SAE-LSTM-based method for process data anomaly detection [J]. Journal of Electronics, 2021, 49(08): 1561-1568. Shang W-L Shi He Zhao J-M An SAE-LSTM-based method for process data anomaly detection [J]. Journal of Electronics , 2021 , 49 ( 08 ): 1561 – 1568 . Search in Google Scholar

[5] Liu Yongmei. Research on the anomaly detection method of satellite power parameters based on SDAE [D]. Harbin Institute of Technology, 2021. Liu Yongmei . Research on the anomaly detection method of satellite power parameters based on SDAE [D]. Harbin Institute of Technology , 2021 . Search in Google Scholar

[6] Guo Lei. Research on rolling bearing fault diagnosis based on SDAE and LSTM fusion [D]. Shenyang University of Architecture, 2020. Guo Lei . Research on rolling bearing fault diagnosis based on SDAE and LSTM fusion [D]. Shenyang University of Architecture , 2020 . Search in Google Scholar

[7] Zhang, R. L., Wang, W., Guan, X. H.. Hidden Markov model-based anomaly detection for program behavior [J]. Journal of Xi’an Jiaotong University, 2005, 39(10): 1056-1059. Zhang R. L. Wang W. Guan X. H. Hidden Markov model-based anomaly detection for program behavior [J]. Journal of Xi’an Jiaotong University , 2005 , 39 ( 10 ): 1056 – 1059 . Search in Google Scholar

[8] Cuan-Ying,Wu-Yue. Research on time series anomaly detection method based on Seq2Seq depth self-encoder [J]. Modern Electronics Technology, 2022, 45(02): 26-30. Cuan-Ying Wu-Yue. Research on time series anomaly detection method based on Seq2Seq depth self-encoder [J]. Modern Electronics Technology , 2022 , 45 ( 02 ): 26 – 30 . Search in Google Scholar

[9] Jin Yupeng. Research on key issues of air pollutant time-series prediction based on improved self-coding network [D]. Shanghai Normal University,2019. Jin Yupeng . Research on key issues of air pollutant time-series prediction based on improved self-coding network [D]. Shanghai Normal University , 2019 . Search in Google Scholar

[10] DU Xiaolei,CHEN Zhigang,XU Xu,et al. Research on bearing fault diagnosis based on wavelet convolutional self-encoder and LSTM network [J]. Mechatronics Engineering, 2019, 36(07): 663-668. DU Xiaolei CHEN Zhigang XU Xu , Research on bearing fault diagnosis based on wavelet convolutional self-encoder and LSTM network [J]. Mechatronics Engineering , 2019 , 36 ( 07 ): 663 – 668 . Search in Google Scholar

[11] Cao Lichang, Deng Dexin, Shen Jian, et al, Meng Haitao. Research on integrated monitoring technology for monitoring station data quality [C]. Proceedings of the 12th Annual China Satellite Navigation Conference - S06 Time Reference and Precision Timing. 2021:54–58. Cao Lichang Deng Dexin Shen Jian Meng Haitao Research on integrated monitoring technology for monitoring station data quality [C]. Proceedings of the 12th Annual China Satellite Navigation Conference - S06 Time Reference and Precision Timing . 2021 : 54 – 58 . Search in Google Scholar

[12] Chen, J.S., Li, J., Chen, W.G., et al. Wind turbine state anomaly detection method using sliding window and multiple additive noise ratio stacking noise reduction self-coding [J]. Journal of Electrical Engineering Technology, 2020, 35(02): 346-358. Chen J.S. Li J. Chen W.G. Wind turbine state anomaly detection method using sliding window and multiple additive noise ratio stacking noise reduction self-coding [J]. Journal of Electrical Engineering Technology , 2020 , 35 ( 02 ): 346 – 358 . Search in Google Scholar

[13] Chen J, Liu M, Xiong P, et al. Noise reduction of ECG signals based on convolutional self-coding neural networks [J]. Computer Engineering and Applications, 2020, 56(16): 148-155. Chen J Liu M Xiong P Noise reduction of ECG signals based on convolutional self-coding neural networks [J]. Computer Engineering and Applications , 2020 , 56 ( 16 ): 148 – 155 . Search in Google Scholar

[14] Yuanyuan Wei, Julian Jang-Jaccard, et al. LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data [J]. arXiv preprint arXiv: 2204. 06701v1, 2022. Yuanyuan Wei Julian Jang-Jaccard LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data [J]. arXiv preprint arXiv: 2204. 06701v1 , 2022 . Search in Google Scholar

[15] Victoria Bell, Divish Rengasamy, Benjamin Rothwell , Grazziela P Figueredo. Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding [J]. arXiv preprint arXiv: 2203. 04734, 2022. Victoria Bell Divish Rengasamy Benjamin Rothwell Grazziela P Figueredo Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding [J]. arXiv preprint arXiv: 2203. 04734 , 2022 . Search in Google Scholar