Self-supervised learning for time-series anomaly detection in industrial internet of things

HIGHLIGHTS

• who: Duc Hoang Tran and colleagues from the Department of Electronics Engineering, Kookmin University, Seoul, Korea have published the Article: Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things, in the Journal: Electronics 2022, 2146 of /2022/
• what: To alleviate the aforementioned challenges, the authors provide a novel solution for automatic time-series anomaly detection on edge devices. The authors evaluate the effectiveness of the proposed SSL framework using different datasets and demonstrate the enhancement of the detection precision. The aim of the SSL model is to learn useful representations of . . .
  

   

  

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