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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

820
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...
820
Types Of Transformers01:16

Types Of Transformers

943
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...
943
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

132
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...
132
Downsampling01:20

Downsampling

126
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
126
The Ideal Transformer01:26

The Ideal Transformer

344
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...
344
Deconvolution01:20

Deconvolution

129
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
129

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Updated: May 28, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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DeepReducer:一个基于线性变压器的模型,用于MEG无声化.

Hui Xu1, Li Zheng2, Pan Liao3

  • 1McGovern Institute for Brain Research, Peking University, Beijing 100871, PR China; Center for MRl Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, PR China; Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, PR China.

NeuroImage
|February 10, 2025
PubMed
概括
此摘要是机器生成的。

深度降解器 (DeepReducer) 是一种新的深度学习模型,在磁脑摄影 (MEG) 中有效地消除与事件相关的磁场 (ERF). 这减少了对广泛数据收集的需求,提高了信号质量和参与者舒适度.

关键词:
深度学习是一种深度学习.一个Denoise,一个Denoise.欧洲农业基金 (ERF) 是一个基金.在MEG MEG中,我们可以使用MEG.变压器变压器变压器

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相关实验视频

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 信号处理 信号处理

背景情况:

  • 磁脑摄影 (MEG) 测量与事件相关的磁场 (ERF) 用于认知和感知研究.
  • 在单个试验中,ERF经常被噪音所掩盖,需要长时间的数据采集.
  • 有效地隔离ERF对于推进神经科学和临床应用至关重要.

研究的目的:

  • 引入DeepReducer,这是一个新的深度学习模型,用于消除与MEG事件相关的磁场.
  • 减少可靠ERF分析所需的试验数量.
  • 提高MEG数据采集的效率和实用性.

主要方法:

  • 开发了DeepReducer,这是一个基于线性变压器的深度学习模型.
  • 在有限试验和多试验平均ERF的组合上训练模型.
  • 使用平均平方误差作为损失函数来捕获MEG数据中的信号波动.

主要成果:

  • 与传统的试验平均化方法相比,DeepReducer表现出更高的性能.
  • 显著改善了与事件相关的磁场的信号噪声比 (SNR).
  • 在半合成和实验MEG数据中减少源定位错误.

结论:

  • DeepReducer提供了一种可靠和有效的方法,用于在MEG中拒绝ERF.
  • 该模型优化了MEG数据采集,减少了参与者负担和文物.
  • 促进更容易获得和更少的时间消耗的神经成像研究.