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- W4207040929 abstract "Operational cycle control is an attractive field of research which can lead to improvements in the services offered by power-aware monitoring embedded IoT devices. Machine learning (ML) is an infrastructure for operational cycle control and provides many approaches which provide more energy-efficient operation. One subfield of ML is Q-learning (QL), which forms the basis of the data-driven self-learning (DDSL) controller. The DDSL algorithm dynamically sets operational duty cycles according to estimates of future collected data values, leading to effective operation of power-aware systems. However, QL performs very poorly in stochastic environments as a result of overestimation of action values. The double estimator implemented in QL therefore applies Double QL (DQL) and forms the basis for a novel Double DDSL (DDDSL). The results of testing a DDDSL controller on historical data showed 42–50 % greater performance than a controller with a fixed duty-cycle, and 2–12 % more performance than a DDSL controller." @default.
- W4207040929 created "2022-01-26" @default.
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- W4207040929 date "2021-12-05" @default.
- W4207040929 modified "2023-09-27" @default.
- W4207040929 title "Data-Driven Self-Learning Controller Design Approach for Power-Aware IoT Devices based on Double Q-Learning Strategy" @default.
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- W4207040929 doi "https://doi.org/10.1109/ssci50451.2021.9659989" @default.
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