MRRI: Memory Retention Mechanism for Robust and Interpretable Time Series Forecasting
Li, X., Zheng, L., Shang, J., Liao, L., & Zhang, J. (2024). MRRI: Memory Retention Mechanism for Robust and Interpretable Time Series Forecasting.
CV
Education
- Ph.D in College of Computer Science, Chongqing University, 2022-now
- M.S. in College of Computer Science, Chongqing University, 2018-2021
- B.S. in College of Information Science and Technology, Nanjing Forestry University, 2014-2018
Publications
DUVET: Dual View Enhanced Transformer for Multivariate Flight Time series Anomaly Detection
Li, C., Li, X., Chen, H., Zheng, L., Sun, H., Shang, J. (2024). DUVET: Dual View Enhanced Transformer for MultivariateFlight Time series Anomaly Detection.
ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph
Liao, L., Zheng, L., Shang, J., Li, X., & Chen, F. (2024). ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph. IEEE Transactions on Knowledge and Data Engineering.
DGAN: Flight sensor data anomaly detection based on dual-view graph attention network
Yan, W., Li, X., Zheng, L., Shang, J., Wu, L., Lu, J., et al. (2024). DGAN: Flight sensor data anomaly detection based on dual-view graph attention network.
MDGNN: Multiple Flight Safety Incidents Prediction Model Based on Dynamic Graph Neural Networks
Song, L., Li, X., Liu, H., Wu, L., Sun, H., Zheng, L., Shang, J.(2024). MDGNN: Multiple Flight Safety Incidents Prediction Model Based on Dynamic Graph Neural Networks.
Decomposition with feature attention and graph convolution network for traffic forecasting
Liu, Y., Wu, X., Tang, Y., Li, X., Sun, D., & Zheng, L. (2024). Decomposition with feature attention and graph convolution network for traffic forecasting. Knowledge-Based Systems, 112193.
IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional Networks
Li, X., Shang, J., Zheng, L., Wang, Q., Liu, D., Liu, X., ... & Sun, H. (2024). IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional Networks. IEEE Transactions on Intelligent Transportation Systems.
SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction
Chen, H., Shang, J., Zheng, L., Li, X., Liu, X., Sun, H., ... & Yu, L. (2023). SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction. IEEE Transactions on Intelligent Transportation Systems, 24(9), 10211-10223.
PathSAGE: Spatial Graph Attention Neural Networks with Random Path Sampling
Ma, J., Li, J., Li, X., & Li, X. (2021). PathSAGE: Spatial Graph Attention Neural Networks with Random Path Sampling. In Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part II 28 (pp. 111-120). Springer International Publishing.
CurveCluster+: Curve Clustering for Hard Landing Pattern Recognition and Risk Evaluation Based on Flight Data
Li, X., Shang, J., Zheng, L., Wang, Q., Sun, H., & Qi, L. (2021). Curvecluster+: Curve clustering for hard landing pattern recognition and risk evaluation based on flight data. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12811-12821.
A Relation-Guided Attention Mechanism for Relational Triple Extraction
Yang, Y., Li, X., & Li, X. (2021, July). A relation-guided attention mechanism for relational triple extraction. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Active instance segmentation with fractional-order network and reinforcement learning
Li, X., Wu, G., Zhou, S., Lin, X., & Li, X. (2022). Active instance segmentation with fractional-order network and reinforcement learning. The Visual Computer, 1-14.
A Deep learning Method for Landing Pitch Prediction based on Flight Data
Chen, H., Shang, J., Zhao, X., Li, X., Zheng, L., & Chen, F. (2020, October). A deep learning method for landing pitch prediction based on flight data. In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT (pp. 199-204). IEEE.
CurveCluster: Automated Recognition of Hard Landing Patterns Based on QAR Curve Clustering
Li, X., Shang, J., Zheng, L., Liu, D., Qi, L., & Liu, L. (2019, August). CurveCluster: Automated recognition of hard landing patterns based on QAR curve clustering. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 602-609). IEEE.