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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

409
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
409
Aggregates Classification01:29

Aggregates Classification

301
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...
301
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

272
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
272
Force Classification01:22

Force Classification

1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

8.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Jun 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

463

使用VGG-19深度学习模型提取表格数据的表格提取.

Muhammad Zahid Iqbal1, Nitish Garg1, Saad Bin Ahmed1

  • 1Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种深度学习方法,用于从文档图像中提取表列和列,在Marmot数据集上获得最先进的结果. 该方法增强了表格结构识别和数据集注释.

关键词:
卷积神经网络是一种卷积神经网络.深度神经网络是一个神经网络.提取信息 提取信息表抽取模型的表抽取模型.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 目前用于表格式数据处理的现有方法与不同的表格布局,风格和噪音作斗争.
  • 特定任务的特性和模型架构限制了准确的表结构提取.

研究的目的:

  • 开发一个全面的深度学习方法,从包含表的文档图像中精确地提取行和列.
  • 改进表格结构识别和解决现有数据集中的局限性.

主要方法:

  • 一个结合表检测,结构识别和基于语义规则的行提取的深度学习模型.
  • 使用VGG-19的转移学习进行模型微调.
  • 通过额外的表格结构注释,包括列检测,增强了Marmot数据集.

主要成果:

  • 在Marmot数据集上实现了最先进的表格结构提取性能.
  • 证明了拟议的深度学习方法的有效性.
  • 成功扩展了"松鼠"数据集的注释范围.

结论:

  • 拟议的深度学习方法提供了一个强大的解决方案,用于从文档图像中准确地提取表列和列.
  • 增强的Marmot数据集为未来的表理解研究提供了宝贵的资源.
  • 转移学习进一步提高了模型在表结构识别方面的表现.