56 research outputs found

    Predictive energy-efficient motion trajectory optimization of electric vehicles

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    This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

    Дослідження щодо застосування регіональної культури в міському парковому ландшафтному дизайні

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    Based on the meaning and composition of regional culture, this paper analyzes the role of regional culture in landscape design, and discusses its expression methods at this area of investigation. Taking Chongqing Liziba Park as an example, this paper studies the application of regional culture in urban park landscape design.Ґрунтуючись на значенні та складі регіональної культури, ця стаття аналізує роль регіональної культури в ландшафтному дизайні та обговорює методи її вираження в цій галузі дослідження. На прикладі парку Лізіба в Чунціні досліджується застосування регіональної культури в ландшафтному дизайні міських парків

    Learning to Coordinate with Anyone

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    In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability

    Voltage regulation strategy for alternating current microgrid under false data injection attacks

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    This study introduces a robust strategy for regulating output voltage in the presence of false data injection (FDI) attacks. Employing a hierarchical approach, we disentangle the distributed secondary control problem into two distinct facets: an observer-based resilient tracking control problem and a decentralized control problem tailored for real systems. Notably, our strategy eliminates the reliance on global information and effectively mitigates the impact of FDI attacks on directed communication networks. Ultimately, simulation results corroborate the efficacy of our approach, demonstrating successful voltage regulation within the system and proficient management of FDI attacks
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