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Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
313
Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

Updated: Jan 17, 2026

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

Published on: July 5, 2024

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图形卷积网络与自适应分组聚合策略.

Ruixiang Wang1, Chunxia Zhang2, Chunhong Pan3

  • 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural networks : the official journal of the International Neural Network Society
|September 15, 2025
PubMed
概括
此摘要是机器生成的。

图形卷积网络 (GCNs) 与天真聚合作斗争. 我们的自适应分组聚合 (AGA) 策略增强了节点信息保留和特征歧视,改善了GCN的性能.

关键词:
适应性分组是一种适应性分组.深度学习是一种深度学习.图表 卷积网络 卷积网络节点信息聚合 节点信息聚合

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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相关实验视频

Last Updated: Jan 17, 2026

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

Published on: July 5, 2024

736
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 图形卷积网络 (GCNs) 由于天真节点聚合函数而面临性能瓶,限制了它们的理论表达力和实际应用.
  • 现有的基于学习的聚合策略缺乏对表达力和标准化实验评估的关注.
  • 纯粹的聚合函数无法保留足够的节点信息,导致较少的区分特征和性能差距.

研究的目的:

  • 解决GCN中天真聚合函数的局限性.
  • 提出一种新的聚合策略,增强节点信息保留和特征歧视.
  • 提高GCN的理论表达力和实际性能.

主要方法:

  • 引入了自适应分组聚合 (AGA),灵感来自韦斯菲勒-莱曼 (WL) 测试的标签直方图.
  • 在节点特征和可学习组标签之间使用修改的学生t分布开发了一个分组机制.
  • 实现了AGA战略作为一个端到端可训练的管道,使用Gumbel Softmax进行无集成到GCN架构中.

主要成果:

  • 通过保留更全面的节点信息,AGA策略显著增强了特征歧视.
  • 在多个基准上的实验表明,与其他聚合策略相比,所有对照组的绩效都得到了持续的改善.
  • 在大多数实验环境中,包括大规模基准,AGA取得了最先进的结果.

结论:

  • 拟议的自适应分组聚合 (AGA) 有效地克服了GCN中天真聚合函数的局限性.
  • AGA提供了一个强大而灵活的插件模块,可以明显提高GCN的性能和表现力.
  • 该方法的优越性通过广泛的实验和与现有最先进的方法进行比较来验证.