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Xu Feng, Mengyang He, Lei Zhuang, Yanrui Song and Rumeng Peng
SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneo...
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Jacopo Guadagnini, Gabriele De Zaiacomo and Michèle Lavagna
This paper focuses on the mission analysis of the return trajectory of a Vertical Landing Reusable Launch Vehicle, both for Return-to-Launch-Site (RTLS) and DownRange Landing (DRL) recovery strategies. The main objective is to assess the mission performa...
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Xiaoping Zhang, Yitong Wu, Huijiang Wang, Fumiya Iida and Li Wang
Animals have evolved to adapt to complex and uncertain environments, acquiring locomotion skills for diverse surroundings. To endow a robot?s animal-like locomotion ability, in this paper, we propose a learning algorithm for quadruped robots based on dee...
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Abiodun Abiola, Francisca Segura Manzano and José Manuel Andújar
Hydrogen provides a clean source of energy that can be produced with the aid of electrolysers. For electrolysers to operate cost-effectively and safely, it is necessary to define an appropriate maintenance strategy. Predictive maintenance is one of such ...
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Wongwan Jung and Daejun Chang
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane ...
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Xi Lyu, Yushan Sun, Lifeng Wang, Jiehui Tan and Liwen Zhang
This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-bas...
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Alamir Labib Awad, Saleh Mesbah Elkaffas and Mohammed Waleed Fakhr
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has int...
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Hao Wang, Jinan Zhu and Bao Gu
In the modern world, the extremely rapid growth of traffic demand has become a major problem for urban traffic development. Continuous optimization of signal control systems is an important way to relieve traffic pressure in cities. In recent years, with...
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Reinis Cimurs, Vilnis Turkovs, Martins Banis and Aleksandrs Korsunovs
For mobile cleaning robot navigation, it is crucial to not only base the motion decisions on the ego agent?s capabilities but also to take into account other agents in the shared environment. Therefore, in this paper, we propose a deep reinforcement lear...
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Raphael C. Engelhardt, Marc Oedingen, Moritz Lange, Laurenz Wiskott and Wolfgang Konen
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque d...
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