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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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Yen-Chun Wen and Wun-Hwa Chen
This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and val...
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