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Yuanjie Ren, Lanyong Zhang, Peng Shi and Ziqi Zhang
The propulsion systems of hybrid electric ship output and load demand have substantial volatility and uncertainty, so a hierarchical collaborative control energy management scheme of the ship propulsion system is proposed in this paper. In a layer of con...
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Matin Mortaheb, Cemil Vahapoglu and Sennur Ulukus
Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achi...
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Spyros Sioutas, Efrosini Sourla, Kostas Tsichlas, Gerasimos Vonitsanos and Christos Zaroliagis
In this work, we propose ??3
D
3
-Tree, a dynamic distributed deterministic structure for data management in decentralized networks, by engineering and extending an existing decentralized structure. Conducting an extensive experimental study, we verify t...
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Dugan Um, Prasad Nethala and Hocheol Shin
In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a ?Hierarchical Deep Deterministic Policy Gradient (HDDPG)? has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. Howe...
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Huan Wang, Shuguang Liu, Maolong Lv and Boyang Zhang
Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperati...
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