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Browsing by Author Fan, J.

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  • A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

    • Authors: Fan, J.;  Advisor: -;  Participants: Liu, F.; Qu, J.; Li, R. (2019)

  • Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is i...
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  • Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems

    • Authors: Fan, J.;  Advisor: -;  Participants: Awan, A. S. (2019)

  • Identi cation of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband localization systems. In the identi cation, supervised machine learning is often used, but it requires exorbitant efforts to maintain and label the LOS and NLOS database. In this paper, we apply unsupervised machine learning approach called ``expectation maximization for Gaussian mixture models'' to classify LOS and NLOS components. The key advantage of applying unsupervised machine learning is that it does not require any rigorous and explicit labeling of the database at a certain location. The simulation results demonstrate that by using the proposed algorithm, L...