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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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结构信息化影子清除网络

Yuhao Liu, Qing Guo, Lan Fu

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    此摘要是机器生成的。

    这项研究引入了一种新的深度学习方法来消除阴影,StructNet,它通过专注于图像结构来解决持久的阴影残余. 这种基于结构的方法显著提高了影子去除性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 图像处理 图像处理

    背景情况:

    • 当前的深度学习影子去除技术往往会留下残余的影子,特别是在低强度的同质区域.
    • 由于其微妙的性质,这些遗迹很难用传统的图像对图像映射来解决.
    • 阴影主要影响结构层面的图像感知,影响感知到的物体形状和颜色连续性.

    研究的目的:

    • 开发一个新的深度学习框架来消除影子,解决影子残留的问题.
    • 利用图像结构信息有效指导影子移除过程.
    • 提高数字图像中影子清除的质量和完整性.

    主要方法:

    • 提出了一个基于结构的影子去除网络 (StructNet),该网络在图像结构层面运行.
    • 引入了面具导向无阴影提取 (MSFE) 模块来提取结构特征.
    • 实施了多尺度特征和残余聚合 (MFRA) 模块,以利用无阴影结构先验来规范特征.
    • 将框架扩展到MStructNet,以使用多层结构信息提高性能.

    主要成果:

    • 结构网成功地重建了无阴影的结构信息,并使用它来指导图像级别的阴影去除.
    • 拟议的MSFE和MFRA模块有效地提取和利用结构特征,减轻阴影残余.
    • 三个基准的实验表明,StructNet的性能优于现有的影子清除方法.
    • 扩展的MStructNet进一步提高了性能,计算增加最小.

    结论:

    • 通过在结构层面解决图像退化问题,可以显著改善影子去除.
    • 在基于深度学习的影子清除中,StructNet为持久的影子残留问题提供了有效的解决方案.
    • 提出的方法具有多功能性,可以与现有技术集成,以提高其性能.