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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

105
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
105
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

162
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
162

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相关实验视频

Updated: Jul 12, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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演变为变压器:从一种基于无训练检索的异常障碍细分方法.

Yongjian Fu, Dingli Gao, Ting Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |October 19, 2023
    PubMed
    概括

    本研究引入了一种基于检索的新方法,用于自动驾驶汽车中的异常障碍细分 (AOS). 该方法有效地将意外障碍物与道路结构区分开来,提高了安全性和感知系统的稳定性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 自主系统 自主系统
    • 机器学习 机器学习

    背景情况:

    • 异常障碍细分 (AOS) 对于自动驾驶汽车的安全至关重要,旨在检测出不可预见的障碍.
    • 现有的AOS方法通常需要广泛的再培训或图像再生,导致高计算成本和潜在的性能下降.
    • 目前的方法不充分利用驾驶场景的固有特征.

    研究的目的:

    • 为自动驾驶开发一种更高效,更有效的异常障碍细分 (AOS) 方法.
    • 为了减少计算负担,并保持语义感知模型的性能.
    • 提高感知系统在现实驾驶条件下对未知物体的耐受性.

    主要方法:

    • 提出了一种基于无训练检索的方法,利用外观特征的等号相似性来区分障碍物和道路纹理.
    • 该方法的重点是利用驾驶场景的先验来简化AOS任务.
    • 开发了一个新的变压器架构,灵感来自自我注意力机制和基于检索的方法.

    主要成果:

    • 拟议的基于检索的方法在同一类别的现有方法中显著优于大约20个百分点.
    • 开发的变压器模型,源自检索方法,进一步提高了性能.
    • 该方法在不需要图像再生或感知模型重新训练的情况下证明了有效性.

    更多相关视频

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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    结论:

    • 关注驾驶场景特征简化了异常障碍物细分.
    • 无培训检索方法为AOS提供了一个计算效率高和高性能解决方案.
    • 新的变压器架构显示了提高自动驾驶汽车感知能力的前景.