Performance evaluation of machine learning methods for anomaly detection in cubesat solar panels

HIGHLIGHTS

• who: Adolfo Javier Jara Cespedes et al. from the Laboratory of Lean Satellite Enterprises and In-Orbit Experiments (LaSEINE), Kyushu Institute of Technology, Kitakyushu, Japan have published the research work: Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels, in the Journal: (JOURNAL) of 14/03/2021
• what: This study investigates five ML algorithm candidates considering classification score execution time model size and power consumption in a constrained computational environment. As a representative case, the work presented by Wu J. et_al used Long Short-Term Memory (LSTM) and Ensembled One-Class . . .
  

   

  

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