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- W2765687671 abstract "The real-time operator’s mental workload (MWL) monitoring system is crucial for the design and development of adaptive operator-aiding/assistance systems. Although the data-driven approach has shown promising performance for MWL recognition, its major challenge lies in the difficulty in acquiring extensive labeled data. This paper attempts to apply the semi-supervised extreme learning machine (ELM) to the challenging problem of operator’s mental workload classification based only on a small number of labeled physiological data. The real data analysis results show that the semi-supervised ELM method can effectively improve the accuracy and computational efficiency of the MWL pattern classification." @default.
- W2765687671 created "2017-11-10" @default.
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- W2765687671 date "2017-01-01" @default.
- W2765687671 modified "2023-09-23" @default.
- W2765687671 title "Mental Workload Classification Based on Semi-Supervised Extreme Learning Machine" @default.
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- W2765687671 doi "https://doi.org/10.1007/978-3-319-68612-7_34" @default.
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