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相关概念视频

Transformers01:26

Transformers

1.0K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.0K
Energy Losses in Transformers01:21

Energy Losses in Transformers

811
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
811
Types Of Transformers01:16

Types Of Transformers

935
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
935
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

122
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...
122
Properties of Transition Metals02:58

Properties of Transition Metals

24.6K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
24.6K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.7K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.7K

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相关实验视频

Updated: May 15, 2025

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

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使用基于变压器的语言模型预测高合金性能.

Spyros Kamnis1,2, Konstantinos Delibasis3

  • 1Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100, Lamia, Greece. spyros.kamnis@gmail.com.

Scientific reports
|April 8, 2025
PubMed
概括

本研究使用变压器机器学习模型来预测高合金的性能,克服数据稀缺. 该模型准确地预测了强度和延长等机械特征,有助于材料的发现.

关键词:
设计设计设计设计设计设计.高合金是一种高合金.语言模型 语言模型机器学习是机器学习.材料 材料 材料

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 材料 信息学 信息学

背景情况:

  • 高合金 (HEAs) 由于其复杂的组成和有限的实验数据,对属性预测具有独特的挑战.
  • 传统的机器学习模型经常与HEAs固有的复杂元素相互作用作斗争.
  • 开发准确的预测模型对于加速发现和优化新型HEA材料至关重要.

研究的目的:

  • 引入基于语言变压器的机器学习模型,用于预测高合金的关键机械性能.
  • 解决高等教育机构数据稀缺和复杂元素相互作用的挑战.
  • 提高材料信息学属性预测的准确性和可解释性.

主要方法:

  • 一个语言转换器模型在合成材料数据上进行了预训练,并在特定的HEA数据集上进行了微调.
  • 变压器内的自我注意机制被用来捕捉复杂的元素相互作用.
  • 转移学习被用来缓解有限的实验数据引起的问题.
  • 注意力权重被可视化,以提高模型的解释性.

主要成果:

  • 与随机森林和高斯过程相比,变压器模型对延长和最终抗拉强度等属性的预测精度得到了提高.
  • 该模型有效地捕获了复杂的元素相互作用,与已建立的金原理保持一致.
  • 对注意力权重的可视化提供了对重要元素关系的见解.

结论:

  • 变压器模型显示出在材料信息学领域加速材料发现和优化的巨大潜力.
  • 开发的模型能够准确预测机械性能,克服传统方法的局限性.
  • 未来的工作应侧重于先进的预处理,现实的合成数据生成,以及为实际应用改进的代币化.