Showing 921 - 940 results of 24,297 for search 'Modeling decision', query time: 0.22s Refine Results
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    Optimization model for wireless charging and power saving of smart canes for the visually impaired based on DRL by Zhaohua Ji, Cheng Xu, Jie Huang, Qinghui Zhou, Tao Yang, Diyi Zhang, Wuchao Zheng

    Published 2025-07-01
    “…Unlike conventional charging strategies that rely on static scheduling, our model dynamically optimizes charging decisions using a Deep Q-Network (DQN)-based algorithm, considering real-time environmental factors and user behavior. …”
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    A Real Options Model for CCUS Investment: CO<sub>2</sub> Hydrogenation to Methanol in a Chinese Integrated Refining–Chemical Plant by Ruirui Fang, Xianxiang Gan, Yubing Bai, Lianyong Feng

    Published 2025-06-01
    “…The existing research on CCUS investment decisions predominantly centers on coal-fired power plants, with the utilization pathways placing a primary emphasis on storage or enhanced oil recovery (EOR). …”
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    Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making by Messaouda Boumaaza, Ahmed Belaadi, Hassan Alshahrani, Ibrahim M. H. Alshaikh, Djamel Ghernaout

    Published 2025-12-01
    “…These improvements are attributed to better interfacial bonding and fiber-matrix adhesion. To model and optimize the mechanical behavior, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Multi-Criteria Decision-Making (MCDM) method based on TOPSIS were applied. …”
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    Research on intelligent computing offloading model based on reputation value in mobile edge computing by Jin QI, Hairong SUN, Kun GONG, Bin XU, Shunyi ZHANG, Yanfei SUN

    Published 2020-07-01
    “…Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.…”
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