[1] W WANG, H FAN, H ZHANG et al. Research on rocket control system architecture based on domain controller, 342-346(2024).

[10] L REN, H T WANG, Z JIA, L ZHANG. Time-varying gaussian encoder based adaptive sensor-weighted method for turbofan engine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 72, 1-11(2023).

[11] R B JIN, Z H CHEN, K Y WU et al. Bi-LSTM-based two-stream network for machine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 71, 3511110(2022).

[13] S LONGARI, D H VALCARCEL, M ZAGO et al. CANnolo：an anomaly detection system based on LSTM autoencoders for controller area network. IEEE Transactions on Network and Service Management, 18, 1913-1924(2021).

[15] Z WANG, H LU, Y H SHI et al. Lightweight CNN architecture design based on spatial-temporal tensor and its application in bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 73, 1-12(2024).

[16] J ZHONG, Y J ZHANG, J Y WANG et al. Unmanned aerial vehicle flight data anomaly detection and recovery prediction based on spatio-temporal correlation. IEEE Transactions on Reliability, 71, 457-468(2021).

[17] B K WANG, X Y PENG, M JIANG et al. Real-time fault detection for UAV based on model acceleration engine. IEEE Transactions on Instrumentation and Measurement, 69, 9505-9516(2020).

[19] G PANAGOPOULOUS, G NIKOLENTZOS, M VAZIGIANNIS. Transfer graph neural networks for pandemic forecasting, 35, 4838-4845(2021).

[21] X X MA, J WU, S XUE et al. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35, 12012-12038(2023).

[22] Z C LAI, D ZHANG, H LI et al. Lightcts：A lightweight framework for correlated time series forecasting. Proceedings of the ACM on Management of Data, 1, 1-26(2023).

[24] L ZHAO, Y J SONG, C ZHANG et al. T-GCN：A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21, 3848-3858(2020).

[25] A GARG, W ZHANG, J SAMARAN et al. An evaluation of anomaly detection and diagnosis in multivariate time series. IEEE Transactions on Neural Networks and Learning Systems, 33, 2508-2517(2021).

[26] H LU, Z WANG, Y H SHI. Unmanned aerial vehicle flight data anomaly detection based on multirate-aware LSTM. IEEE Transactions on Instrumentation and Measurement, 73, 1-13(2024).

[27] P P LI, H X ZHANG, X G HU et al. High-dimensional multi-label data stream classification with concept drifting detection. IEEE Transactions on Knowledge and Data Engineering, 35, 8085-8099(2023).

[28] Y HE, B SICK. CLeaR：An adaptive continual learning framework for regression tasks. AI Perspectives, 3, 1-15(2021).

[29] M A SUM, M PRATAMA, R SAVITHA et al. Unsupervised few-shot continual learning for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 62, 4707214(2024).

[30] W Z ZHAO, R PENG, Q WANG et al. Life-long learning with continual spectral-spatial feature distillation for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 5544214(60).

[31] S S TIAN, L LI, W J LI et al. A survey on few-shot class-incremental learning. Neural Networks, 169, 307-324(2024).

[32] K HE, D J YU, D WANG et al. Graph attention network-based fault detection for UAVs with multivariant time series flight data. IEEE Transactions on Instrumentation and Measurement, 71, 3530213(2022).

[33] D GUHA, R CHATTERJEE, B SIKDAR. Anomaly detection using LSTM-based variational autoencoder in unsupervised data in power grid. IEEE Systems Journal, 17, 4313-4323(2023).

[34] D PARK, Y HOSHI, C C KEMP. A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robotics and Automation Letters, 3, 1544-1551(2018).

[35] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).

[36] M BRONZ, E BASKAYA, D DELAHAYE et al. Real-time fault detection on small fixed-wing UAVs using machine learning, 1-10(2020).