[1] Chen, J., Wu, X., Jiang, Z., Li, Q., Zhang, L., Chu, J., ... & Yang, L. (2025). Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect. Measurement, 248, 116857.
[2] Chen, J., Xing, H., Liu, M., Lv, X., Zhang, L., Chu, J., ... & Yang, L. (2025). An optimised variational mode decomposition method based on improved coati optimisation algorithm for blockage detection of natural gas pipelines. Nondestructive Testing and Evaluation, 1-39.
[3] Chen, J., Wu, X., Jiang, Z., Li, Q., Zhang, L., Chu, J., ... & Yang, L. (2025). A novel parameter-optimised LSTM model fused by convolutional block attention module for water pipeline leakage detection. Nondestructive Testing and Evaluation, 1-33.
[4] Chen, J., Xing, H., Liu, M., Lv, X., Zhang, L., Chu, J., ... & Liu, Z. (2026). Cascaded variable step LMS filters fused by wave velocity estimation model for leakage localization of water supply pipeline. Applied Acoustics, 241, 110981.
[5] Chen, J., Wu, X., Jiang, Z., Li, Q., Zhang, L., Chu, J., ... & Yang, L. (2025). CS-MI-PSVM: An Efficient Approach for Leak Identification in Water Supply Pipelines. Next Research, 100787.