Showing 181 - 200 results of 6,567 for search 'computing continues', query time: 0.09s Refine Results
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    FInCH: Fast statistical inference for continuous‐time animal movement by Dominic Grainger, Paul G. Blackwell

    Published 2025-07-01
    “…Continuous‐time models of animal movement, which can be formulated in a scale‐invariant way, avoid these complications but typically lack computational efficiency. …”
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    Reducing power line interference with the continuous automatic cab signalling channel by R. R. Yusupov, A. S. Khokhrin

    Published 2024-04-01
    “…Interference in the continuous automatic cab signalling channel distorts transmitted signals and causes signalling malfunctions. …”
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    Small Language Model Agent for the Operations of Continuously Updating ICT Systems by Nobukazu Fukuda, Haruhisa Nozue, Haruo Oishi

    Published 2025-01-01
    “…We extensively evaluate the proposed method using both synthetic and real-world benchmarks and demonstrate that it significantly enhances the performance of the SLM agent with some computational overhead in comparison with the baselines.…”
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  14. 194

    Comparison and analysis of drilling gas flow based on continuous and dual media by Hao Yongjiang, Hu Jie

    Published 2025-07-01
    “…The physical model was then simplified, the computational grid and nodes were defined, and the finite difference method was employed for model discretization. …”
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  15. 195

    An energy-based numerical continuation approach for quasi-static mechanical manipulation by Lin Yang, Huu-Thiet Nguyen, Chen Lv, Domenico Campolo, Franco Cardin

    Published 2025-01-01
    “…In this work, building on a recently proposed framework for energy-based quasi-static manipulation, we propose an approach to manipulation planning by adapting a numerical continuation algorithm to compute the equilibrium manifold (EM), which is implicitly derived from physical laws. …”
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    Combined Activity Recognition Based on Continuous-Wave Radar and Vision Transformer by Junhao Zhou, Chao Sun, Youngok Kim

    Published 2024-01-01
    “…Our approach hinges on the micro-Doppler features derived from continuous-wave radar signals. We first process the human-generated radar echo signals to produce micro-Doppler spectrogram images. …”
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    Orthogonal Adversarial Deep Reinforcement Learning for Discrete- and Continuous-Action Problems by Kohei Ohashi, Kosuke Nakanishi, Nao Goto, Yuji Yasui, Shin Ishii

    Published 2024-01-01
    “…This approach allows for the use of relatively large adversarial perturbations during training, resulting in more robust policies without significant computational overhead. We evaluate our technique on benchmarks for both discrete and continuous action domains, including Atari 2600 and PyBullet, by integrating it into popular DRL methods such as deep Q-network (DQN), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC). …”
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