| 1. |
- Song, Kai, et al.
(författare)
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Constant Current Charging and Maximum System Efficiency Tracking for Wireless Charging Systems Employing Dual-Side Control
- 2020
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Ingår i: IEEE Transactions on Industry Applications. - 0093-9994 .- 1939-9367. ; 56:1, s. 622-634
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Tidskriftsartikel (refereegranskat)abstract
- Wireless power transfer (WPT) systems have attracted much attention because of their safety, convenience, and environmental friendliness. For wireless supercapacitor charging, the system efficiency and charging current are highly dependent on the load that varies over a wide range. In this article, a simultaneous maximum system efficiency (MSE) tracking and constant current (CC) charging control scheme for a supercapacitor is proposed. For CC charging, a double-sided LCC topology is chosen due to its characteristic of providing a load-independent output current. Furthermore, the impact of the coil internal resistance on the system characteristics (especially the charging current) is investigated, so a semiactive rectifier is introduced on the secondary side to achieve accurate CC charging and improve the system robustness. Based on the variable-step perturbation and observation algorithm, the MSE is tracked by searching for the minimum system input dc current using a primary-side buck converter on the premise that the charging current reaches its target value. The abovementioned two control loops are independent, and mutual communication is unnecessary when they cooperate; thus, the system is simplified. The simulation and experimental results show great consistency with the theoretical analysis. The experimental system maintains the MSE of 86% during charging the supercapacitor from 20 to 50 V with 2 A.
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| 2. |
- Teng, Yiran, et al.
(författare)
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Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial]
- 2024
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 16:7, s. C76-C87
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Tidskriftsartikel (refereegranskat)abstract
- The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements.
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| 3. |
- Teng, Yiran, et al.
(författare)
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DRL-based RMSCA for SDM Networks with Core Switching in Multi-Core Fibres
- 2023
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Ingår i: Proceedings of the 2023 Photonics in Switching and Computing. - 9798350323702
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Konferensbidrag (refereegranskat)abstract
- We designed a DRL-based routing, modulation, spectrum, and core assignment algorithm to fully exploit the core switching capabilities of space division multiplexing networks with multi-core fibres. The proposed method shows up to 53\% better blocking probability performance compared to RMSCA benchmarks in different traffic loads.
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