Xin K - Search Results

1

Bridging hydraulics and graph signal processing: A new perspective to estimate water distribution network pressures.

Zhou X, Liu S, Xu W, Xin K, Wu Y, Meng F. Zhou X, et al. Among authors: xin k. Water Res. 2022 Jun 15;217:118416. doi: 10.1016/j.watres.2022.118416. Epub 2022 Apr 5. Water Res. 2022. PMID: 35429881

2

Deep learning identifies accurate burst locations in water distribution networks.

Zhou X, Tang Z, Xu W, Meng F, Chu X, Xin K, Fu G. Zhou X, et al. Among authors: xin k. Water Res. 2019 Dec 1;166:115058. doi: 10.1016/j.watres.2019.115058. Epub 2019 Sep 6. Water Res. 2019. PMID: 31536886 Free article.

3

Contamination source identification in water distribution networks using convolutional neural network.

Sun L, Yan H, Xin K, Tao T. Sun L, et al. Among authors: xin k. Environ Sci Pollut Res Int. 2019 Dec;26(36):36786-36797. doi: 10.1007/s11356-019-06755-x. Epub 2019 Nov 19. Environ Sci Pollut Res Int. 2019. PMID: 31745764

4

Maintaining the long-term accuracy of water distribution models with data assimilation methods: A comparative study.

Zhou X, Guo S, Xin K, Xu W, Tao T, Yan H. Zhou X, et al. Among authors: xin k. Water Res. 2022 Nov 1;226:119268. doi: 10.1016/j.watres.2022.119268. Epub 2022 Oct 17. Water Res. 2022. PMID: 36302270

5

A convenient and stable graph-based pressure estimation methodology for water distribution networks: Development and field validation.

Zhou X, Zhang J, Guo S, Liu S, Xin K. Zhou X, et al. Among authors: xin k. Water Res. 2023 Apr 15;233:119747. doi: 10.1016/j.watres.2023.119747. Epub 2023 Feb 14. Water Res. 2023. PMID: 36841165

6

Improving the interpretability of deep reinforcement learning in urban drainage system operation.

Tian W, Fu G, Xin K, Zhang Z, Liao Z. Tian W, et al. Among authors: xin k. Water Res. 2024 Feb 1;249:120912. doi: 10.1016/j.watres.2023.120912. Epub 2023 Nov 23. Water Res. 2024. PMID: 38042066

7

Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich.

Tian W, Zhang Z, Bouffard D, Wu H, Xin K, Gu X, Liao Z. Tian W, et al. Among authors: xin k. Water Res. 2024 Feb 1;249:120996. doi: 10.1016/j.watres.2023.120996. Epub 2023 Dec 10. Water Res. 2024. PMID: 38103441

8

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks.

Guo S, Xin K, Tao T, Yan H. Guo S, et al. Among authors: xin k. Water Res. 2024 Nov 15;266:122318. doi: 10.1016/j.watres.2024.122318. Epub 2024 Aug 26. Water Res. 2024. PMID: 39236501

9

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning.

Wang S, Wang J, Xin K, Yan H, Li S, Tao T. Wang S, et al. Among authors: xin k. Water Res. 2024 Oct 11;268(Pt A):122614. doi: 10.1016/j.watres.2024.122614. Online ahead of print. Water Res. 2024. PMID: 39418800

10

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning.

Li S, Tian W, Yan H, Zeng W, Tao T, Xin K. Li S, et al. Among authors: xin k. Water Res X. 2024 Oct 15;25:100266. doi: 10.1016/j.wroa.2024.100266. eCollection 2024 Dec 1. Water Res X. 2024. PMID: 39497789 Free PMC article.

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