Highly Imbalanced Railway Station Structural Damage Monitoring Based on Cluster-Based Undersampling and Siamese Artificial Neural Network
Author:
Funder
the Project of Humanities and Social Sciences Research of Hebei Provincial Department of Education
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-023-08258-x.pdf
Reference47 articles.
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2. Mayank, M.; Lourenço, P.B.; Ramana, G.V.: Structural health monitoring of civil engineering structures by using the internet of things: a review. J. Build. Eng. (2022). https://doi.org/10.1016/j.jobe.2021.103954
3. Daneshvar, M.H.; Gharighoran, A.; Zareei, S.A.; Karamodin, A.: Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge. Struct. Infrastruct. Eng. 17, 902–920 (2021). https://doi.org/10.1080/15732479.2020.1777572
4. Srivastava, V.; Gupta, S.; Chaudhary, D.-G.; Balodi, A.; Khari, M.; GarcíaDíaz, V.: An enhanced texture-based feature extraction approach for classification of biomedical images of CT-scan of lungs. Int. J. Interact. Multimed. Artif. Intell. (2020). https://doi.org/10.9781/ijimai.2020.11.003
5. Jain, V.; Pillai, M.S.; Chandra, L.; Kumar, R.; Khari, M.; Jain, A.: CamAspect: an intelligent automated real-time surveillance system with smartphone indexing. IEEE Sens. Lett. 4, 1–4 (2020). https://doi.org/10.1109/LSENS.2020.3019172
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