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Communication Dans Un Congrès Année : 2022

Time Series Clustering of High Gamma Dose Rate Incidents

Résumé

In this paper, we proposed an unsupervised machine-learning-based framework to automate the process of extracting suspicious gamma dose rate incidents from the real unlabeled raw historical data measured in the German Radiation Early Warning Network and identify the underlying events behind each. This raised the research problem of clustering unlabeled time series data with varying lengths and scales. Based on the many evaluations, we demonstrated that the state-of-the-art’s most popular time series clustering models were not suitable to perform this task. This motivated us to introduce our own approach. Through this approach we were able to perform online classification for gamma dose rate incidents of varying lengths and scales.

Dates et versions

hal-04122422 , version 1 (08-06-2023)

Identifiants

Citer

Mohammed Al Saleh, Béatrice Finance, Yehia Taher, Ali Jaber, Roger Luff. Time Series Clustering of High Gamma Dose Rate Incidents. The 8th International Conference on Time Series and Forecasting, Jun 2022, Gran Canaria, Spain, France. pp.24, ⟨10.3390/engproc2022018024⟩. ⟨hal-04122422⟩
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