A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | W. Li and B. Ning, “Autonomous recommendation of fault detection algorithms for spacecraft,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 273–275, Jan. 2024. doi: 10.1109/JAS.2023.123423
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X. Wang, J. Sun, G. Wang, et al., “Data-driven control of distributed event-triggered network systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 351–364, 2023.
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Y. Gao, T. Yang, M. Xu, and N. Xing, “An unsupervised anomaly detection approach for spacecraft based on normal behavior clustering,” in Proc. ICICTA, 2012, pp. 478–481.
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K. Li, Y. Wu, S. Song, et al., “A novel method for spacecraft electrical fault detection based on FCM clustering and wpsvm classification with PCA feature extraction,” in Proc. Institution of Mechanical Engineers, 2017, pp. 98–108.
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S. Bai, J. Kolter, V. Koltun, et al., “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv preprint arXiv: 1803.01271, 2018.
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D. Park, Y. Hoshi, and C. C. Kemp, “A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1544–1551, 2018.
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