Showing 1 - 20 results of 1,535 for search '"pattern interference"', query time: 1.09s Refine Results
  1. 1
    Conference

    Source: 2025 IEEE International Ultrasonics Symposium (IUS) Ultrasonics Symposium (IUS), 2025 IEEE International. :1-4 Sep, 2025

    Relation: 2025 IEEE International Ultrasonics Symposium (IUS)

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    Conference

    Source: 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS) Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), 2024 IEEE. :1-5 Sep, 2024

    Relation: 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)

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    Conference

    Source: 2022 IEEE International Ultrasonics Symposium (IUS) Ultrasonics Symposium (IUS), 2022 IEEE International. :1-4 Oct, 2022

    Relation: 2022 IEEE International Ultrasonics Symposium (IUS)

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    Conference

    Source: 2020 IEEE International Ultrasonics Symposium (IUS) Ultrasonics Symposium (IUS),2020 IEEE International. :1-4 Sep, 2020

    Relation: 2020 IEEE International Ultrasonics Symposium (IUS)

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    Mixed Materials

    Additional Titles: Kernel-based learning methods such as Kernel Logistic Regression (KLR) can substantially increase the storage capacity of Hopfield networks, but the principles governing their performance and stability remain largely uncharacterized. This paper presents a comprehensive quantitative analysis of the attractor landscape in KLR-trained networks to establish a solid foundation for their design and application. Through extensive, statistically validated simulations, we address critical questions of generality, scalability, and robustness. Our comparative analysis shows that KLR and Kernel Ridge Regression (KRR) exhibit similarly high storage capacities and clean attractor landscapes under typical operating conditions, suggesting that this behavior is a general property of kernel regression methods, although KRR is computationally much faster. We identify a non-trivial, scale-dependent law for the kernel width $γ$, demonstrating that optimal capacity requires $γ$ to be scaled such that $γN$ increases with network size $N$. This finding implies that larger networks require more localized kernels, in which each pattern's influence is more spatially confined, to mitigate inter-pattern interference. Under this optimized scaling, we provide clear evidence that storage capacity scales linearly with network size~($P \propto N$). Furthermore, our sensitivity analysis shows that performance is remarkably robust with respect to the choice of the regularization parameter $λ$. Collectively, these findings provide a concise set of empirical principles for designing high-capacity and robust associative memories and clarify the mechanisms that enable kernel methods to overcome the classical limitations of Hopfield-type models.

    Authors: Tamamori, Akira

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    Conference

    Source: 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS) Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), 2024 IEEE. :1-4 Sep, 2024

    Relation: 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)

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    Periodical
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    Conference

    Source: 2021 13th International Conference on Advanced Infocomm Technology (ICAIT) Advanced Infocomm Technology (ICAIT), 2021 13th International Conference on. :68-71 Oct, 2021

    Relation: 2021 13th International Conference on Advanced Infocomm Technology (ICAIT)

  19. 19
    Mixed Materials

    Additional Titles: A mathematical model of a full scale gas stirred ladle was implemented in designed experiments to assess the influence of various design variables on mixing efficiency. The discrete phase (DPM) and volume of fluid (VOF) models were used in conjunction to describe the different phases and the standard k–ε approach was used for modelling turbulence. Ladle height was identified as the most influential factor in determining mixing efficiency and could be altered by either changing the aspect ratio or the overall scale of the ladle. Gas injection through multiple tuyeres was also identified as a promising strategy for improving mixing, but the success of this strategy depended greatly on the tuyere arrangement. Incorrect tuyere arrangements created significant flow pattern interference in the ladle, which had a detrimental effect on mixing. At high aspect ratios, it was shown that the cross sectional area of the ladle can become too narrow, causing additional flow pattern interference between the rising plumes and the downward recirculating flows.

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