Ocean observing time-series anomaly detection based on DTW-TRSAX method
Author:
Funder
National Natural Science Foundation of China
the Key Laboratory of Ocean Observation Technology, Ministry of Natural Resources
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11227-024-06183-w.pdf
Reference48 articles.
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2. Kocak D, Clark A (2013) Motivations and methods for modern maritime communications: a survey of recent and emerging trends. Mar Technol Soc J 47:7–18. https://doi.org/10.4031/MTSJ.47.5.7
3. Wu P, Harris CA, Salavasidis G et al (2021) Unsupervised anomaly detection for underwater gliders using generative adversarial networks. Eng Appl Artif Intell 104:1–12. https://doi.org/10.1016/j.engappai.2021.104379
4. Dong G, Lin M (2021) Model-based thermal anomaly detection for lithium-ion batteries using multiple-model residual generation. J Energy Storage 40:1–11. https://doi.org/10.1016/j.est.2021.102740
5. Chen Z, Xiong R, Tian J et al (2016) Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles. Appl Energy 184:365–374. https://doi.org/10.1016/j.apenergy.2016.10.026
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