Reinforcement Learning-Based Dynamic Power Management of a Battery-Powered System Supplying Multiple Active Modes

Abstract-This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating mode to the actual battery state of charge. It uses RL technique to accurately define the optimal battery voltage threshold value and use it to specify the system active mode. In addition, the PM automatically adjusts the power management policy by learning the optimal timeout value. Moreover, the SoC and latency tradeoffs can be precisely controlled based on a userdefined parameter. Experiments show that the proposed method outperforms existing methods by 35% in terms of saving battery service lifetime. Keywords-Dynamic power management; reinforcement learning, extending battery lifetime; battery-powered system design

Reinforcement Learning-Based Dynamic Power Management of a Battery-Powered System Supplying Multiple Active Modes

Abstract-This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating mode to the actual battery state of charge. It uses RL technique to accurately define the optimal battery voltage threshold value and use it to specify the system active mode. In addition, the PM automatically adjusts the power management policy by learning the optimal timeout value. Moreover, the SoC and latency tradeoffs can be precisely controlled based on a userdefined parameter. Experiments show that the proposed method outperforms existing methods by 35% in terms of saving battery service lifetime. Keywords-Dynamic power management; reinforcement learning, extending battery lifetime; battery-powered system design

Providencia entomophila sp. nov., a new bacterial species associated with major olive pests in Tunisia.

Bioprospection for potential microbial biocontrol agents associated with three major insect pests of economic relevance for olive cultivation in the Mediterranean area, namely the olive fly, Bactrocera oleae, the olive moth, Prays oleae, and the olive psyllid, Euphyllura olivina, led to the isolation of several strains of readily cultivable Gram-negative, rod-shaped bacteria from Tunisian olive orchards. Determination of 16S ribosomal RNA encoding sequences identified the bacteria as members of the taxonomic genus Providencia (Enterobacterales; Morganellaceae). A more detailed molecular taxonomic analysis based on a previously established set of protein-encoding marker genes together with DNA-DNA hybridization and metabolic profiling studies led to the conclusion that the new isolates should be organized in a new species within this genus. With reference to their original insect association, the designation "Providencia entomophila" is proposed here for this hypothetical new taxon