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(Uploaded by Plazi for the Bat Literature Project) No abstract provided.

(Uploaded by Plazi for the Bat Literature Project) No abstract provided.

Magnetodynamic wireless power transfer (WPT), also known as electrodynamic wireless power transfer, is a low-frequency WPT technology based on an electromechanical receiver comprising a permanent magnet moving in a coil. Because of the low-frequency field, this technology is safe around humans and enables transfer through conductive media. In this paper, we focus on the design and the optimization of the power receiver with advanced modelling methods. Two operating modes, namely a continuously rotating mode and a resonant mode, targeting high-power-density and mid-range power transfer respectively, are investigated and compared to previous versions. The fabricated 25-cm3 receiver presents substantial improvements in terms of transferred power (3.3W) and power density (x7) compared to previous receivers in the same conditions of fields.

Magnetodynamic wireless power transfer (WPT), also known as electrodynamic wireless power transfer, is a low-frequency WPT technology based on an electromechanical receiver comprising a permanent magnet moving in a coil. Because of the low-frequency field, this technology is safe around humans and enables transfer through conductive media. In this paper, we focus on the design and the optimization of the power receiver with advanced modelling methods. Two operating modes, namely a continuously rotating mode and a resonant mode, targeting high-power-density and mid-range power transfer respectively, are investigated and compared to previous versions. The fabricated 25-cm3 receiver presents substantial improvements in terms of transferred power (3.3W) and power density (x7) compared to previous receivers in the same conditions of fields.

This paper presents a comparison of a Reinforcement-Learning (RL) based wave energy conversion controller against standard reactive damping and model predictive control (MPC) approaches, in the presence of modeling errors. Wave energy converters (WECs) are under the influence of many non-linear hydrodynamic forces, yet for ease and expediency, it is common to formulate linear WEC models and control laws. Therefore it is expected that significant modeling errors may be present, which may degrade model-based control performance. Model-free RL approaches to control may offer a significant advantage in robustness to modeling errors, in that the model is learned by the controller by experience. It is shown that, for an annual average sea state, RL-based controllers can outperform model-based control – reactive control and MPC – by 19% and 16%, respectively, when significant modeling error is present. Furthermore, compared to similar studies of RL-based control, the proposed model can reduce the training time from 8.4 hr to 1.5 hr.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.

This paper presents a comparison of a Reinforcement-Learning (RL) based wave energy conversion controller against standard reactive damping and model predictive control (MPC) approaches, in the presence of modeling errors. Wave energy converters (WECs) are under the influence of many non-linear hydrodynamic forces, yet for ease and expediency, it is common to formulate linear WEC models and control laws. Therefore it is expected that significant modeling errors may be present, which may degrade model-based control performance. Model-free RL approaches to control may offer a significant advantage in robustness to modeling errors, in that the model is learned by the controller by experience. It is shown that, for an annual average sea state, RL-based controllers can outperform model-based control – reactive control and MPC – by 19% and 16%, respectively, when significant modeling error is present. Furthermore, compared to similar studies of RL-based control, the proposed model can reduce the training time from 8.4 hr to 1.5 hr.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.

A new millipede species, Dorypetalum helenae (Callipodida: Dorypetalidae), is described from the Kuru Dağ Mts, European Turkey. The species is morphologically close to D. bulgaricum from the Rhodope Mts, SE Bulgaria but is well distinguished by the particular, racket-shaped mesal coxosternal process of gonopods. All hitherto described species in the genus are reviewed, and new illustrations are provided for D. bosniense and D. trispiculigerum. A new key for identification of the species in the genus is made to accommodate the new findings.

A new millipede species, Dorypetalum helenae (Callipodida: Dorypetalidae), is described from the Kuru Dağ Mts, European Turkey. The species is morphologically close to D. bulgaricum from the Rhodope Mts, SE Bulgaria but is well distinguished by the particular, racket-shaped mesal coxosternal process of gonopods. All hitherto described species in the genus are reviewed, and new illustrations are provided for D. bosniense and D. trispiculigerum. A new key for identification of the species in the genus is made to accommodate the new findings.