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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

481
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...
481
Reducing Line Loss01:18

Reducing Line Loss

168
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
168
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Trimmed Mean01:10

Trimmed Mean

2.9K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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轻量级图像稳定分析与区块wise修剪.

Eungi Hong1, KyungTae Lim2, Tae-Woo Oh3

  • 1Department of Computer Engineering, Hanbat National University, Daejeon, Republic of Korea.

Scientific reports
|September 26, 2023
PubMed
概括

本研究介绍了一种轻量级的模型设计,用于通过逐步从深度学习网络中移除块来进行图像阶段分析. 这种策略可以减少模型大小和计算成本,而不会牺牲检测准确度,从而实现实际的现实应用.

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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
  • 网络安全 网络安全

背景情况:

  • 用于图像级分析的深度学习模型实现了高检测性能,但在计算上昂贵.
  • 现有的模型往往太重和昂贵,无法在现实应用中实际部署.
  • 需要轻量级和高效的稳定分析模型.

研究的目的:

  • 开发一种有效的模型设计策略,用于轻量化图像级分析.
  • 为了降低深度稳定分析模型的计算成本和尺寸.
  • 通过简化模型架构来保持高检测性能.

主要方法:

  • 建议在steganalysis中为深度分类网络提供一个删除区块的策略.
  • 从更深层逐渐去除卷积神经网络块.
  • 在BOSSBase和ALASKA#2数据集上评估模型性能.

主要成果:

  • 拟议的块移除策略显著减少了模型大小和FLOP (每秒浮点操作).
  • 与基线相比,EfficientNet-B0变体实现了9.58%更小的尺寸和2.16%更少的FLOP.
  • 检测准确度仍然很高,与基线相提并论,在各自数据集上为90.73%和82.40%.
  • 分析表明,网络的早期层对于有效的steganalysis至关重要.

结论:

  • 一个简单的块移除策略可以创建轻量级但有效的图像级分析模型.
  • 由于计算要求降低,开发的模型适合实际部署.
  • 早期的网络层包含了强大的图像阶段分析的基本特征.