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相关概念视频

Types Of Transformers01:16

Types Of Transformers

951
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...
951
The Ideal Transformer01:26

The Ideal Transformer

359
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
359
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

141
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...
141
Associative Learning01:27

Associative Learning

308
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
308
Transformers01:26

Transformers

1.1K
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...
1.1K
Energy Losses in Transformers01:21

Energy Losses in Transformers

841
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...
841

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相关实验视频

Updated: Jun 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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超级学习用于现实世界级的增量学习:基于变压器的方法.

Sandeep Kumar1, Amit Sharma2, Vikrant Shokeen1

  • 1Maharaja Surajmal Institute of Technology, New Delhi, India.

Scientific reports
|October 4, 2024
PubMed
概括
此摘要是机器生成的。

本研究将元学习应用于类增量学习 (IL),使模型能够在不需要再培训的情况下对新数据进行分类. 与meta-learners一起的Few-shot学习 (FSL) 显示出对现实世界IL任务的强烈概括.

关键词:
深度学习是一种深度学习.有几次射击学习学习.增量学习是一种增量学习.超级学习 (Meta-learning) 是一种学习方式.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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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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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 自然语言处理 (NLP) 中的深度学习 (DL) 模型非常复杂,具有数百万个参数,需要大量的数据集进行训练.
  • 虽然预训练减少了数据需求,但微调仍然需要人类标记的数据集,这会产生很大的成本.
  • 短暂学习 (FSL) 技术,像元学习一样,旨在在较小的数据集上有效地训练模型.

研究的目的:

  • 将元学习应用于课堂增量学习 (IL),这是一个比标准FSL评估任务更相关的现实问题.
  • 为了使模型能够在初始培训后对新引入的类进行分类,而无需完全重新培训.
  • 为了有效地利用meta-learners的概括能力来进行IL课程.

主要方法:

  • 使用代理新类来模拟IL类,以允许超级学习者在不需要再培训的情况下进行适应.
  • 在meta-learner中开发基于变压器的聚合函数,以修改所有类的数据以进行预测.
  • 同时考虑整个支持和查询集,优先关注关键样本,以增强推理影响.

主要成果:

  • 拟议的超级学习方法超越了当前行业在课堂增量学习方面的基准.
  • 超学习者在IL类中表现出显著的概括能力,即使没有特定任务的培训.
  • 该研究为IL的基于变压器的聚合技术建立了一个高性能基线.

结论:

  • 超级学习为课堂增量学习挑战提供了实用和有效的解决方案.
  • 拟议的基于变压器的meta-learner为未来的IL进步提供了一个强大的框架.
  • 这些发现凸显了meta-learning在需要持续学习的现实世界NLP应用中的巨大潜力.